A steel pipe power transmission tower cast steel node, parameter optimization method, system, device and medium

Abstract

The application discloses a steel pipe power transmission tower cast steel node, a parameter optimization method, a system, equipment and a medium, and relates to the technical field of tower construction; the technical points are as follows: the ratio of the node structure weight to the structure bearing capacity is taken as a structure specific strength index, and the structure specific strength index is used to construct a fitness function; a dynamic evaluation function containing multiple physical field constraints is constructed to determine the optimization boundary condition, and the particle swarm is initialized; each particle in the current particle swarm is respectively subjected to multiple iteration updates, and the fitness value of the particle swarm is calculated by using the fitness function in each update until the particle swarm fitness change amount is less than a preset convergence threshold value, so that the updated particle swarm is obtained; a global optimal particle is selected from the updated particle swarm, and all solutions corresponding to the global optimal particle are determined as the optimal parameters of the node.

Description

Technical Field

[0001] This invention relates to the field of tower construction technology, specifically to a cast steel node for a steel pipe transmission tower, a parameter optimization method, a system, equipment, and a medium. Background Technology

[0002] Long-span transmission towers are characterized by their height, multiple circuits, large spans, thick conductor cross-sections, and high flexibility. Compared to conventional transmission towers, the loads borne by long-span transmission towers increase exponentially, making conventional structural designs difficult to apply. Therefore, steel pipe structures are often used in long-span transmission towers, placing higher demands on structural performance and design.

[0003] Long-span steel pipe transmission towers are characterized by high loads and multiple branch pipe intersections. For example, the connection between the crossarm and the main structure may involve as many as 10 or more steel pipe members. These features are completely different from those of ordinary civil building structures. Currently, the main types of joints used in long-span steel pipe transmission towers are stiffened intersecting welded joints, ribbed ball joints, and thick-walled cast steel joints. Among these, stiffened intersecting welded joints have problems such as overlapping components, unclear stress paths, and difficulties in calculating the load-bearing capacity caused by stiffening measures when dealing with multiple branch pipe intersections. Ribbed ball joints, compared to stiffened... While intersecting welded joints offer advantages such as uniform force transmission and avoidance of overlapping connecting branches, they also suffer from drawbacks including large size, demanding manufacturing processes, and uneven residual stress. Thick-walled cast steel joints, on the other hand, combine the advantages of both ball joints and intersecting welds, and their load-bearing capacity can be increased by adding wall thickness, making them more convenient than ball joints. However, there are few practical engineering applications of thick-walled cast steel joints, and they also suffer from low specific strength and high cost due to their high wall thickness. Furthermore, existing technologies do not offer specific optimization methods for addressing the low specific strength issue of thick-walled cast steel joints.

[0004] Therefore, there is a need to provide a cast steel node for steel pipe transmission towers, a parameter optimization method, a system, equipment, and a medium to solve the aforementioned problems. Summary of the Invention

[0005] The technical problem this invention aims to solve is the difficulty in optimizing the specific strength of thick-walled cast steel nodes using existing technologies. The goal is to provide a method, system, equipment, and medium for optimizing the parameters of cast steel nodes in steel pipe transmission towers. This invention uses three-dimensional numerical simulation analysis as the core evaluation index, takes manufacturing process requirements and structural requirements as boundary conditions, and maximizes specific strength as the optimization objective. It employs a particle swarm optimization algorithm to search for optimal parameters, and then uses the obtained optimal parameters for three-dimensional numerical simulation modeling. This model is then directly used to generate the three-dimensional model of the cast steel pipe node, thus achieving integrated design, optimization, and manufacturing. Simultaneously, a casting steel manufacturing process evaluation mechanism is introduced during the parameter optimization process. By real-time verification of the sensitivity of residual cooling stress to structural parameters, simultaneous optimization of mechanical properties and casting defect prevention is achieved.

[0006] This invention is achieved through the following technical solution:

[0007] A cast steel joint for a steel pipe transmission tower, the joint comprising:

[0008] Tower body main material connecting pipe, crossarm main material connecting pipe, crossarm cross member connecting pipe, front horizontal member connecting pipe, first front cross member connecting pipe, second front cross member connecting pipe, side horizontal member connecting pipe, first side cross member connecting pipe, second side cross member connecting pipe, galvanized holes and inner ring plate;

[0009] The main crossarm connecting pipe, the crossarm cross member connecting pipe, the front horizontal member connecting pipe, and the side horizontal member connecting pipe are located in the same cross section and are all perpendicularly installed on the outer side wall of the main crossarm connecting pipe; the crossarm cross member connecting pipe is located between the main crossarm connecting pipe and the side horizontal member connecting pipe.

[0010] The first front cross member connecting pipe, the second front cross member connecting pipe and the front horizontal member connecting pipe are located in the same longitudinal section and are respectively arranged on both sides of the front horizontal member connecting pipe;

[0011] The first side cross member connecting pipe, the second side cross member connecting pipe and the side horizontal member connecting pipe are located in the same longitudinal section and are respectively arranged on both sides of the side horizontal member connecting pipe;

[0012] The inner ring plate is located on the inner wall of the main tower material connecting pipe and is in the same cross-section as the side horizontal material connecting pipe; the galvanized holes are located on the inner walls of the main tower material connecting pipes on both sides of the inner ring plate.

[0013] This invention also provides a parameter optimization method for cast steel nodes of steel pipe transmission towers. This method is used in the aforementioned cast steel nodes of steel pipe transmission towers and includes:

[0014] The ratio of the nodal structure weight to the structural bearing capacity is used as the structural specific strength index, and the fitness function is constructed using the structural specific strength index.

[0015] A dynamic evaluation function with multiphysics constraints is constructed to determine the optimization boundary conditions, and the particle swarm is initialized.

[0016] Each particle in the current particle swarm is iteratively updated multiple times, and the fitness value of the particle swarm is calculated using the fitness function in each update until the change in the fitness of the particle swarm is less than the preset convergence threshold, so as to obtain the updated particle swarm.

[0017] The globally optimal particle is selected from the updated particle swarm, and all solutions corresponding to the globally optimal particle are determined as the node optimal parameters.

[0018] Furthermore, the ratio of the nodal structure weight to the structural bearing capacity is used as the structural specific strength index, specifically:

[0019] Obtain the node structure parameters; among which, the node structure parameters include the key quantity, pipe fitting position, number of inner ring plates, position of inner ring plates, size of inner ring plates, slope of cylindrical cavity, chamfer size, pipe fitting end size, and type of cast steel node material;

[0020] Based on the node structural parameters, the finite element analysis method is used to determine the node structural weight and structural bearing capacity, and the ratio of the node structural weight to the structural bearing capacity is used as the structural specific strength index.

[0021] Furthermore, each particle in the current particle swarm is iteratively updated multiple times, and the fitness value of the particle swarm is calculated using the fitness function in each update, until the change in the fitness of the particle swarm is less than a preset convergence threshold, to obtain the updated particle swarm, specifically:

[0022] In each iteration update process, the fitness value of each particle is calculated using the fitness function, and the fitness value of each particle is compared with its historical best fitness value. Based on the comparison result, the historical best fitness value of each particle is updated, and after the update, the position and velocity variables of each particle are updated using the position update function and the velocity update function.

[0023] The fitness value of each particle is compared with the historical best fitness value of the particle swarm, and the historical best fitness value of the particle swarm is updated based on the comparison results.

[0024] The historical best fitness values ​​before and after the particle swarm update are compared. If the change in the particle swarm fitness is less than the preset convergence threshold, the update is stopped to obtain the updated particle swarm.

[0025] Furthermore, the fitness value of each particle is calculated using the fitness function, specifically as follows: In the formula, Indicates the first The position variables of each particle Indicates the first The fitness value of each particle. Indicates structural weight. The following symbols represent structural bearing capacity, number of pipe fittings, location of pipe fittings, number of inner ring plates, location of inner ring plates, dimensions of inner ring plates, cylindrical cavity slope, chamfer dimensions, pipe fitting end dimensions, and material type of cast steel joint. This represents the penalty factor.

[0026] Furthermore, the position update function is specifically as follows: In the formula, Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The velocity variable of each particle.

[0027] Furthermore, the speed update function is specifically as follows: In the formula, Indicates the first During the iteration step The velocity variable of each particle Indicates the first During the iteration step The velocity variable of each particle Indicates inertia weight, , They represent learning factors, , All represent random numbers. Indicates the first During the iteration step The historical best fitness value of each particle. Indicates the first During the iteration step The position variables of each particle Indicates the first The population's historical best fitness value at the iteration step.

[0028] This invention also provides a parameter optimization system for steel pipe transmission tower nodes, which is used in the parameter optimization method for cast steel nodes of steel pipe transmission towers described in any one of the above claims, the system comprising:

[0029] The function construction module is used to take the ratio of nodal structural weight to structural bearing capacity as the structural specific strength index, and to construct a fitness function using the structural specific strength index;

[0030] The particle swarm generation module is used to construct a dynamic evaluation function with multiphysics constraints to determine the optimization boundary conditions and initialize the particle swarm.

[0031] The particle swarm update module is used to perform multiple iterations of updates on each particle in the current particle swarm, and to calculate the fitness value of the particle swarm using the fitness function in each update, until the change in the fitness of the particle swarm is less than a preset convergence threshold, so as to obtain the updated particle swarm.

[0032] The parameter acquisition module is used to select the globally optimal particle from the updated particle swarm and determine all solutions corresponding to the globally optimal particle as the node optimal parameters.

[0033] The present invention also provides a computer device, including a system memory and a processor, wherein the system memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0034] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0035] The present invention also provides a computer program product containing instructions that, when executed by a cluster of computer devices, cause the cluster of computer devices to perform the method described in any of the preceding claims.

[0036] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0037] In this invention, three-dimensional numerical simulation analysis is used as the core evaluation index. Manufacturing process requirements and structural requirements are used as boundary conditions, and the optimization objective is to maximize specific strength. The particle swarm optimization algorithm is used to search for optimal parameters. The obtained optimal parameters are used for three-dimensional numerical simulation modeling. The model is then directly used to generate the three-dimensional model of the cast steel pipe node, thereby realizing the integration of design, optimization and manufacturing. At the same time, a cast steel manufacturing process evaluation mechanism is introduced in the parameter optimization process. By checking the sensitivity of cooling residual stress to structural parameters in real time, the synchronous optimization of mechanical properties and casting defect prevention is achieved. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0039] Figure 1 This is a schematic diagram of the connection of a high specific strength thin-walled cast steel pipe transmission tower node in this embodiment;

[0040] Figure 2 This is a cross-sectional schematic diagram of a high specific strength thin-walled cast steel pipe transmission tower node in this embodiment;

[0041] Figure 3 This is a flowchart illustrating a parameter optimization method for cast steel nodes of a steel pipe transmission tower in this embodiment.

[0042] Figure 4 This is a schematic diagram of the module connection of a parameter optimization system for a steel pipe transmission tower node in this embodiment;

[0043] Figure 5 This is a schematic diagram of the structure of a computer device in this embodiment.

[0044] The attached diagram shows the markings and corresponding component names:

[0045] 1. Main tower body connecting pipe; 2. Main crossarm connecting pipe; 3. Crossarm cross member connecting pipe; 4. Front horizontal member connecting pipe; 5. First front cross member connecting pipe; 6. Second front cross member connecting pipe; 7. Side horizontal member connecting pipe; 8. First side cross member connecting pipe; 9. Second side cross member connecting pipe; 10. Galvanized hole; 11. Inner ring plate; 12. Chamfer. Detailed Implementation

[0046] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0047] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0048] The terminology used in the description of the various examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0049] Example 1

[0050] See Figure 1 , Figure 1 A schematic diagram of the connection of a high specific strength thin-walled cast steel pipe transmission tower node is shown. Figure 2 A cross-sectional schematic diagram of a high-strength thin-walled cast steel pipe transmission tower node is shown. The node includes: a main tower body connecting pipe 1, a crossarm main material connecting pipe 2, a crossarm crossing material connecting pipe 3, a front horizontal material connecting pipe 4, a first front crossing material connecting pipe 5, a second front crossing material connecting pipe 6, a side horizontal material connecting pipe 7, a first side crossing material connecting pipe 8, a second side crossing material connecting pipe 9, a galvanized hole 10, and an inner ring plate 11. A chamfer 12 is also provided at each connecting pipe connection.

[0051] Tower body main material connecting pipe 1, crossarm main material connecting pipe 2, crossarm cross member connecting pipe 3, front horizontal member connecting pipe 4, first front cross member connecting pipe 5, second front cross member connecting pipe 6, side horizontal member connecting pipe 7, first side cross member connecting pipe 8, second side cross member connecting pipe 9, galvanized hole 10 and inner ring plate 11.

[0052] The main crossarm connecting pipe 2, the crossarm cross member connecting pipe 3, the front horizontal member connecting pipe 4, and the side horizontal member connecting pipe 7 are located in the same cross section and are all vertically installed on the outer side wall of the main crossarm connecting pipe 1; the crossarm cross member connecting pipe 3 is located between the main crossarm connecting pipe 2 and the side horizontal member connecting pipe 7.

[0053] The first front cross member connecting pipe 5, the second front cross member connecting pipe 6 and the front horizontal member connecting pipe 4 are located in the same longitudinal section and are respectively arranged on both sides of the front horizontal member connecting pipe 4.

[0054] The first side cross member connecting pipe 8, the second side cross member connecting pipe 9 and the side horizontal member connecting pipe 7 are located in the same longitudinal section and are respectively arranged on both sides of the side horizontal member connecting pipe 7;

[0055] The inner ring plate 11 is located on the inner side wall of the main tower material connecting pipe 1 and is located in the same cross section as the side horizontal material connecting pipe 7; the galvanized holes 10 are located on the inner side walls of the main tower material connecting pipe 1 on both sides of the inner ring plate 11.

[0056] Among them, the central axes of the main crossarm connecting pipe 2, the first front horizontal connecting pipe 4, the first front cross connecting pipe 5, and the second front cross connecting pipe 6 constitute a first plane; the central axes of the crossarm cross connecting pipe 3, the front horizontal connecting pipe 4, and the side horizontal connecting pipe 7 constitute a second plane; the central axes of the side horizontal connecting pipe 7, the first side cross connecting pipe 8, and the second side cross connecting pipe 9 constitute a third plane; the first, second, and third planes can realize bolted connections with the steel pipe tower in three spatial directions;

[0057] Meanwhile, in this embodiment, the diameter of the main tower body connecting pipe 1 is 864mm, and the total length is 2500mm; the diameter of the crossarm main connecting pipe 2 is 630mm, and the distance from the pipe end to the center of the cast steel node is 1100mm; the diameter of the crossarm cross member connecting pipe 3 and the side horizontal member connecting pipe 7 is 457mm, and the distance from the pipe end to the center of the cast steel node is 1100mm and 1140mm respectively; the diameter of the front horizontal member connecting pipe 4, the second front cross member connecting pipe 6, and the second side cross member connecting pipe 9 is 559mm, and the distance from the pipe end to the center of the cast steel node is 1140mm, 1280mm, and 1280mm respectively; the diameter of the first front cross member connecting pipe 5 and the first side cross member connecting pipe 8 is 480mm, and the distance from the pipe end to the center of the cast steel node is 1250mm for both.

[0058] Based on this, this embodiment also provides a parameter optimization method for cast steel nodes of steel pipe transmission towers, see [link to relevant documentation]. Figure 3 , Figure 3 A flowchart illustrating a parameter optimization method for cast steel nodes of steel pipe transmission towers is shown, wherein the method includes:

[0059] S1: The ratio of the nodal structural weight to the structural bearing capacity is used as the structural specific strength index, and the fitness function is constructed using the structural specific strength index;

[0060] Specifically, the structural specific strength is first defined as an optimization index, and the node structural parameters are obtained. These parameters include the number of key components, pipe locations, number of inner ring plates, inner ring plate locations, inner ring plate dimensions, cylindrical cavity slope, chamfer dimensions, pipe end dimensions, and the type of cast steel node material. Based on these parameters, the finite element analysis method is used to determine the node structural weight and bearing capacity, and the ratio of the node structural weight to its bearing capacity is used as the structural specific strength index. Then, a fitness function is constructed using the structural specific strength index, specifically: In the formula, Indicates the first The position variables of each particle Indicates the first The fitness value of each particle. Indicates structural weight. The following symbols represent structural bearing capacity, number of pipe fittings, location of pipe fittings, number of inner ring plates, location of inner ring plates, dimensions of inner ring plates, cylindrical cavity slope, chamfer dimensions, pipe fitting end dimensions, and material type of cast steel joint. This represents the penalty factor.

[0061] It should be noted that, in this embodiment, in order to optimize the structural parameters of the steel pipe transmission tower node with a thin-walled cavity structure in the core stress area, parameters such as the position, quantity and size of the inner ring plate are incorporated into the structural specific strength index for optimization and quantification, thereby achieving targeted design for this type of node structure.

[0062] S2: Construct a dynamic evaluation function with multiphysics constraints to determine the optimization boundary conditions and initialize the particle swarm;

[0063] Specifically, firstly, key working conditions are screened, and the nodal internal forces under each condition are analyzed. Conditions with relatively small internal forces are excluded, and a smaller number of key working conditions are selected for analysis. Secondly, control working conditions are defined, and a parameterized three-dimensional numerical simulation finite element calculation model is established. Stress index analysis is performed on these key working conditions to determine the optimal control working conditions. Then, optimal boundary conditions are determined, and a dynamic evaluation function incorporating multiphysics constraints is constructed, specifically: In the formula, Indicates the structural strength index. This represents the dynamic penalty coefficient that increases with the number of iterations. This indicates the inclusion of stress intensity limits, casting cooling residual stress concentration factor limits based on thermo-mechanical coupling analysis, and geometric parameter robustness evaluation factors.

[0064] It should be noted that in this embodiment, different application scenarios have different control conditions. In order to effectively optimize for special scenarios and key application scenarios, the control conditions are screened and the nodal internal forces in the key control conditions are analyzed, including nodal axial forces, bending moments and shear forces. Then, control conditions with small internal forces are excluded to obtain the key control conditions.

[0065] Finally, the particle swarm is initialized as follows: Input the user-defined number of particles N (N≥10) and the maximum number of iterations T. max Given the determined control conditions and optimization boundary conditions, within a D-dimensional search space comprised of parameters such as the size of the annular plate E, the number of annular plates A, the linear variation rate of the wall thickness of each branch pipe K, and the chamfer size R, Latin hypercube sampling is used to divide the domain of each parameter into N intervals with equal probability, uniformly sample and randomly combine them to construct an initial population matrix. Subsequently, the Chebyshev chaotic mapping formula is introduced to generate a non-periodic perturbation sequence, which is superimposed on the initial population matrix to break the local search blind zone, and finally, the initial position vectors of N particles are generated, with the initial iteration number set to t=1.

[0066] It should be noted that the dynamic evaluation function is to assess the feasibility of the optimized parameter combination. If the constraints are not met, such as the parameters causing the local section modulus of the cast steel part to be too large, resulting in excessive cooling stress, a dynamic penalty term will be applied. The fitness is reduced in weight until the iterated parameters fully match the physical characteristics of stable casting and solidification of steel castings.

[0067] It should also be noted that in this embodiment, the number of particles is set to 30, but other particle numbers may be used in other embodiments.

[0068] S3: Perform multiple iterations to update each particle in the current particle swarm, and calculate the fitness value of the particle swarm using the fitness function in each update, until the change in the fitness of the particle swarm is less than the preset convergence threshold, so as to obtain the updated particle swarm.

[0069] Specifically, in each iteration, the fitness value of each particle is calculated using the fitness function. This fitness value is then compared to the historical best fitness value of the particle swarm. Based on the comparison results, the historical best fitness value of each particle is updated. After the update, the position and velocity variables of each particle are updated using the position and velocity update functions. The fitness value of each particle is then compared to the historical best fitness value of the particle swarm. Based on the comparison results, the historical best fitness value of the particle swarm is updated. Finally, the historical best fitness values ​​of the particle swarm before and after the update are compared. If the change in the particle swarm fitness is less than a preset convergence threshold, the update is stopped to obtain the updated particle swarm.

[0070] It should be noted that in this embodiment, for each particle, its current fitness value is compared with its historical best fitness value. If the current fitness value is better, its historical best position is updated to the current position. At the same time, the current fitness value of all particles is compared with the historical best fitness value of the group. If there is a better value, the historical best fitness value of the group is updated.

[0071] Meanwhile, for the velocity vector V of the next iteration step t+1 i (t+1) and position vector X i (t+1), using the current position X of each particle i i (t), Current velocity V i (t), Individual's historical best position and the group's historical best position By combining individual and collective memory, the parameter search direction and step size are determined, and the velocity V of the particle in the next iteration step t+1 is calculated. i (t+1) and position X i At (t+1), the new position vectors of all particles are obtained, serving as a new set of node structure parameters to be evaluated; each particle updates its state according to the position update function and the velocity update function; the position update function is specifically: In the formula, Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The velocity variable of each particle;

[0072] The speed update function is as follows: In the formula, Indicates the first During the iteration step The velocity variable of each particle Indicates the first During the iteration step The velocity variable of each particle Indicates inertia weight, , They represent learning factors, , All represent random numbers. Indicates the first During the iteration step The historical best fitness value of each particle. Indicates the first During the iteration step The position variables of each particle Indicates the first The historical best fitness value of the population at the iteration step;

[0073] Apply a Gaussian perturbation to the current historical best fitness value of the particle swarm. Compare the change between the historical best fitness value obtained in the current iteration step t and the historical best fitness value of the swarm in the previous iteration step. If the change is less than a preset convergence threshold, or if the current iteration number t has reached the maximum iteration number T, then... max If the iteration converges, then the particle evaluation and iteration will continue.

[0074] It should be noted that, in this embodiment, the learning factor , All are set to 1.5, inertia weight It is set to 0.8; in other embodiments, other parameters may also be used.

[0075] S4: Select the globally optimal particle from the updated particle swarm, and determine all solutions corresponding to the globally optimal particle as the node optimal parameters.

[0076] Specifically, after iterative convergence, all structural parameters contained in the finally determined group's historical optimal position are extracted to generate a three-dimensional numerical simulation model that satisfies the boundary conditions and makes the optimization index optimal. This model is then directly exported as a data file for generating three-dimensional molds and cores for cast steel, achieving a seamless connection from optimization design to manufacturing model.

[0077] Specifically, in this embodiment, three-dimensional numerical simulation analysis is used as the core evaluation index. Manufacturing process requirements and structural requirements are used as boundary conditions, and the optimization objective is to maximize specific strength. The particle swarm optimization algorithm is used to search for optimal parameters. The obtained optimal parameters are used for three-dimensional numerical simulation modeling. The model is then directly used to generate the three-dimensional model of the cast steel pipe node, thereby realizing the integration of design, optimization and manufacturing. At the same time, a cast steel manufacturing process evaluation mechanism is introduced in the parameter optimization process. By checking the sensitivity of cooling residual stress to structural parameters in real time, the synchronous optimization of mechanical properties and casting defect prevention is achieved.

[0078] Example 2

[0079] See Figure 4 The present invention also provides a parameter optimization system for steel pipe transmission tower nodes, which is used in the parameter optimization method for cast steel nodes of steel pipe transmission towers described in any one of the above claims, the system comprising:

[0080] The function construction module 100 is used to take the ratio of the nodal structure weight to the structural bearing capacity as the structural specific strength index, and to construct a fitness function using the structural specific strength index.

[0081] The particle swarm generation module 200 is used to construct a dynamic evaluation function with multiphysics constraints to determine the optimization boundary conditions and to initialize the particle swarm.

[0082] The particle swarm update module 300 is used to perform multiple iterations of updates on each particle in the current particle swarm, and to calculate the fitness value of the particle swarm using the fitness function in each update, until the change in the fitness of the particle swarm is less than a preset convergence threshold, so as to obtain the updated particle swarm.

[0083] The parameter acquisition module 400 is used to select the globally optimal particle from the updated particle swarm and determine all solutions corresponding to the globally optimal particle as the node optimal parameters.

[0084] It should be noted that the modules in the system of Embodiment 2 correspond to the steps in the method of Embodiment 1. The steps in the method of Embodiment 1 have been described in detail in Embodiment 1, and the module content in the system will not be described in detail in this Embodiment 2.

[0085] Example 3

[0086] See Figure 5 This embodiment also provides a computer device, including a system memory 1005 and a processor 1001. The system memory 1005 stores a computer program, and the processor 1001 executes the computer program to implement the steps of any of the methods described above.

[0087] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, when the processor 1001 executes the computer program, it implements the functions of each module / unit in the above system / device embodiments.

[0088] Specifically, in this embodiment, the computer program can be divided into one or more modules / units, which are stored in the system memory 1005 and executed by the processor 1001 to complete this application. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the terminal device.

[0089] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will understand that this does not constitute a limitation on the terminal device; it may include more or fewer components than shown in the figures, or a combination of certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0090] The processor 1001 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0091] System memory 1005 can be an internal storage unit of the terminal device, such as a hard drive or RAM. System memory 1005 can also be a storage device 1004 of the terminal device, such as an external hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or FlashCard. Furthermore, system memory 1005 can include both internal storage units and storage device 1004. System memory 1005 is used to store computer programs and other programs and data required by the terminal device. System memory 1005 can also be used to temporarily store data that has been output or will be output.

[0092] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0093] Example 4

[0094] This embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0095] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), registers, hard disks, optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof, or any other form of computer-readable storage medium in the art.

[0096] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside within an application-specific integrated circuit (ASIC). In embodiments of the invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.

[0097] Example 5

[0098] This embodiment also provides a computer program product containing instructions that, when executed by a cluster of computer devices, cause the cluster of computer devices to perform the method described in Embodiment 1.

[0099] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A cast steel joint for a steel pipe transmission tower, characterized in that the joint... include: Main connecting pipes for tower body, main connecting pipes for crossarm, connecting pipes for crossarm cross members, connecting pipes for front horizontal members, connecting pipes for first front cross members, connecting pipes for second front cross members, connecting pipes for side horizontal members, connecting pipes for first side cross members, connecting pipes for second side cross members, galvanized holes, and inner ring plates. The main crossarm connecting pipe, the crossarm cross member connecting pipe, the front horizontal member connecting pipe, and the side horizontal member connecting pipe are located in the same cross section and are all perpendicularly installed on the outer side wall of the main crossarm connecting pipe; the crossarm cross member connecting pipe is located between the main crossarm connecting pipe and the side horizontal member connecting pipe. The first front cross member connecting pipe, the second front cross member connecting pipe and the front horizontal member connecting pipe are located in the same longitudinal section and are respectively arranged on both sides of the front horizontal member connecting pipe; The first side cross member connecting pipe, the second side cross member connecting pipe and the side horizontal member connecting pipe are located in the same longitudinal section and are respectively arranged on both sides of the side horizontal member connecting pipe; The inner ring plate is located on the inner wall of the main tower material connecting pipe and is in the same cross-section as the side horizontal material connecting pipe; the galvanized holes are located on the inner walls of the main tower material connecting pipes on both sides of the inner ring plate.

2. A method for optimizing the parameters of cast steel nodes in steel pipe transmission towers, characterized in that, This method is used in the cast steel node of the steel pipe transmission tower as described in claim 1, and the method includes: The ratio of the nodal structure weight to the structural bearing capacity is used as the structural specific strength index, and the fitness function is constructed using the structural specific strength index. A dynamic evaluation function with multiphysics constraints is constructed to determine the optimization boundary conditions, and the particle swarm is initialized. Each particle in the current particle swarm is iteratively updated multiple times, and the fitness value of the particle swarm is calculated using the fitness function in each update until the change in the fitness of the particle swarm is less than the preset convergence threshold, so as to obtain the updated particle swarm. The globally optimal particle is selected from the updated particle swarm, and all solutions corresponding to the globally optimal particle are determined as the node optimal parameters.

3. The parameter optimization method for cast steel nodes of steel pipe transmission towers according to claim 1, characterized in that, The ratio of the weight of the nodal structure to its bearing capacity is used as the structural specific strength index, specifically: Obtain the node structure parameters; among which, the node structure parameters include the key quantity, pipe fitting position, number of inner ring plates, position of inner ring plates, inner ring plate size, cylindrical cavity slope, chamfer size, pipe fitting end size, and type of cast steel node material; Based on the node structural parameters, the finite element analysis method is used to determine the node structural weight and structural bearing capacity, and the ratio of the node structural weight to the structural bearing capacity is used as the structural specific strength index.

4. The parameter optimization method for cast steel nodes of steel pipe transmission towers according to claim 1, characterized in that, For each particle in the current particle swarm, multiple iterations are performed, and the fitness value of the particle swarm is calculated using the fitness function in each update, until the change in the fitness of the particle swarm is less than a preset convergence threshold, to obtain the updated particle swarm. Specifically: In each iteration update process, the fitness value of each particle is calculated using the fitness function, and the fitness value of each particle is compared with its historical best fitness value. Based on the comparison result, the historical best fitness value of each particle is updated, and after the update, the position update function and velocity update function are used to update the position variables and velocity variables of each particle. The fitness value of each particle is compared with the historical best fitness value of the particle swarm, and the historical best fitness value of the particle swarm is updated based on the comparison results. The historical best fitness values ​​before and after the particle swarm update are compared. If the change in the particle swarm fitness is less than the preset convergence threshold, the update is stopped to obtain the updated particle swarm.

5. The parameter optimization method for cast steel nodes of steel pipe transmission towers according to claim 4, characterized in that, The fitness value of each particle is calculated using the fitness function, specifically as follows: In the formula, Indicates the first The position variables of each particle Indicates the first The fitness value of each particle. Indicates structural weight. The following symbols represent structural bearing capacity, number of pipe fittings, location of pipe fittings, number of inner ring plates, location of inner ring plates, dimensions of inner ring plates, cylindrical cavity slope, chamfer dimensions, pipe fitting end dimensions, and cast steel joint material type. This represents the penalty factor.

6. The parameter optimization method for cast steel nodes of steel pipe transmission towers according to claim 4, characterized in that, The location update function is specifically as follows: In the formula, Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The position variables of each particle Indicates the first During the iteration step The velocity variable of each particle.

7. The parameter optimization method for cast steel nodes of steel pipe transmission towers according to claim 4, characterized in that, The speed update function is specifically as follows: In the formula, Indicates the first During the iteration step The velocity variable of each particle Indicates the first During the iteration step The velocity variable of each particle Indicates inertia weight, , They represent learning factors, , All represent random numbers. Indicates the first During the iteration step The historical best fitness value of each particle. Indicates the first During the iteration step The position variables of each particle Indicates the first The population's historical best fitness value at the iteration step.

8. A parameter optimization system for steel pipe transmission tower nodes, characterized in that, This system is used in the parameter optimization method for cast steel nodes of steel pipe transmission towers according to any one of claims 2-7, wherein the system comprises: The function construction module is used to take the ratio of nodal structural weight to structural bearing capacity as the structural specific strength index, and to construct a fitness function using the structural specific strength index; The particle swarm generation module is used to construct a dynamic evaluation function with multiphysics constraints to determine the optimization boundary conditions and initialize the particle swarm. The particle swarm update module is used to perform multiple iterations of updates on each particle in the current particle swarm, and to calculate the fitness value of the particle swarm using the fitness function in each update, until the change in the fitness of the particle swarm is less than a preset convergence threshold, so as to obtain the updated particle swarm. The parameter acquisition module is used to select the globally optimal particle from the updated particle swarm and determine all solutions corresponding to the globally optimal particle as the node optimal parameters.

9. A computer device comprising a system memory and a processor, wherein the system 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 2 to 7.

10. 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 described in any one of claims 2 to 7.

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