A computer-aided optimization design method for angle steel tower structure

Abstract

This invention relates to the field of transmission tower structural engineering and computer-aided design, and discloses a computer-aided optimization design method for angle steel tower structures, comprising the following steps: First, the angle steel tower is discretized into a sequence of logical functional segments, and a raw material inventory state matrix is ​​established. During the optimization process, a segment-by-segment evolution strategy is adopted, combining finite element analysis and a node spatial topological entropy model to quantitatively evaluate the mechanical performance of the structure and the construction complexity of the nodes. The core lies in introducing a dynamic inventory decay mechanism, performing one-dimensional nesting analysis in real time based on remaining resources, using inventory matching degree as a key optimization objective, and automatically triggering topological mutation when inventory deadlock is detected. Finally, the overall tower stability is verified through global nonlinear buckling analysis. This invention achieves dynamic coupling between structural design and supply chain management, significantly improves the utilization rate of stagnant inventory, reduces cutting waste, and ensures the installation feasibility of complex nodes.

Description

Technical Field

[0001] This invention relates to the field of transmission tower structural engineering and computer-aided design technology, specifically a computer-aided optimization design method for angle steel tower structures. Background Technology

[0002] Transmission towers, as core supporting facilities of power transmission systems, are constructed on a massive scale, and steel consumption accounts for a significant proportion of the power grid construction cost. For a long time, the optimal design of angle steel tower structures has primarily followed the "minimum weight criterion," which utilizes finite element analysis and mathematical programming algorithms to minimize the theoretical weight by adjusting the cross-sectional dimensions or topological arrangement of members, while satisfying structural strength, stiffness, and stability constraints. While this classic design paradigm has achieved significant results in reducing tower weight, its limitations are becoming increasingly apparent when facing complex engineering supply chain environments and actual construction constraints.

[0003] Existing technological systems commonly suffer from a severe "island effect," where the design and material supply stages are severely disconnected. Traditional structural optimization algorithms often model based on idealized material supply assumptions, assuming that raw materials can be arbitrarily customized or supplied in unlimited quantities according to design requirements, completely ignoring the actual inventory status in the company's warehouse. This "blind" design leads to an extremely awkward engineering situation: on the one hand, company warehouses accumulate large amounts of short-length materials left over from cutting in past projects or long-length materials of specific specifications, forming "stagnant inventory" that is difficult to digest; on the other hand, new project designs, because they do not consider utilizing these existing resources, often require the re-procurement of large quantities of standard-length materials. In addition, existing optimization processes usually treat nesting and layout as a post-processing step after the design is completed. This means that once the length of structural components is determined in the design stage, it is difficult to change. If this length does not match the standard length of the raw materials, a high rate of cutting scrap will inevitably occur. This lagging approach makes it difficult to guide the "standardization" of component lengths based on inventory at the design source, thus creating a bottleneck in improving material utilization.

[0004] On the other hand, at the topological evolution level of space truss structures, existing algorithms often overemphasize the optimization of macroscopic mechanical responses while neglecting the physical feasibility of microscopic node construction. In pursuit of maximizing overall stiffness, algorithms may generate complex topologies where multiple members converge at a single point with minimal included angles. While this structure performs well mathematically, in reality it can lead to excessively large node plates, blocked bolt installation spaces, and even geometric interference at member ends, resulting in a "false optimal solution" that is difficult to construct. The lack of a quantitative evaluation mechanism for node space congestion and structural complexity often forces design changes during detailed fabrication or on-site installation, severely impacting project progress and structural safety. Therefore, a novel optimization design method is urgently needed that deeply integrates inventory resource constraints, cutting and layout efficiency, and node microscopic construction requirements into the structural evolution process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a computer-aided optimization design method for angle steel tower structures. This method solves the technical problems of existing angle steel tower structure optimization design methods, such as stagnant inventory accumulation, high cutting waste rate, and construction and installation difficulties caused by neglecting the complexity of the microstructure of nodes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a computer-aided optimization design method for angle steel tower structures, comprising the following steps: Step S1, Model Discretization and Parameter Initialization: Establish a parametric model of the angle steel tower, discretizing the tower along its height into several logical functional segments. Simultaneously, establish an initial raw material inventory state matrix. This matrix is ​​a multidimensional data structure used to record the specifications, fixed lengths, and initial quantities of all available angle steel.

[0007] Step S2, Segment-by-Segment Evolutionary Optimization Based on Inventory Status: Each logical functional segment is iteratively optimized according to a preset order. This step is a dynamic evolutionary process; when optimizing the current logical functional segment, it is strictly subject to the updated remaining raw material inventory status matrix after the previous logical functional segment's optimization. Constraints.

[0008] The specific optimization process includes: a. Joint sampling of topology and cross-section: Sampling is performed in the preset topology configuration library and cross-section library to generate candidate design individuals for the current segment that include topological form and component cross-section dimensions.

[0009] b. Calculation of Node Spatial Topological Entropy: To quantify the construction complexity of nodes, node spatial topological entropy is introduced. For any node among the candidate individuals Its calculation formula is a weighted sum of three dimensions: in: Congested items at the intersection : To converge at the node The total number of links represents the basic complexity resulting from the increase in the number of connections.

[0010] Spatial angular potential energy This characterizes the degree of space constraint caused by excessively small included angles between structural members. It is calculated by traversing all pairs of adjacent members at the node and calculating the included angle. Through formula Calculate the potential energy. When the included angle is close to the minimum critical angle threshold for installation... When the absolute value of the difference approaches zero, the value of the potential energy function approaches infinity.

[0011] Overlapping potential energy of node plates This characterizes the projection interference of the component ends on a reference projection plane determined by the resultant vector of the identified main member vector and the converging diagonal member vector. It is obtained by projecting each component end onto the reference projection plane and calculating the ratio of the sum of the intersection areas of the projected profiles to the estimated total area of ​​the node plate.

[0012] c. Real-time nesting and inventory matching assessment: Perform real-time one-dimensional nesting analysis, mapping the component length list of candidate individuals to the current remaining raw material inventory status matrix. Define inventory matching index. The calculation formula is as follows: This indicator penalizes the consumption of raw materials with remaining stock below the safety stock threshold through the denominator term, and penalizes schemes with excessively high scrap rates through the exponential term. Subsequently, it is based on the inventory decay model. Update the inventory status. If the calculation result is negative, the plan is deemed infeasible.

[0013] d. Dynamic topology mutation mechanism: During the optimization process, if a topology mutation mechanism cannot be found that satisfies the mechanical constraints and has the appropriate inventory matching index based on the current remaining inventory state, the topology mutation mechanism will be activated. If a candidate individual falls below the preset matching tolerance threshold, i.e., an "inventory deadlock" occurs, the algorithm will automatically trigger a topological mutation, select a new topological configuration with different member length characteristics from the preset topological configuration library to replace the current logical function segment's topological configuration, so as to utilize the inventory of other length specifications, and re-sample and evolve the cross-section.

[0014] e. Objective Function Selection: Construct an objective function, which is a weighted sum of the theoretical structural weight of the current logical functional segment, the weight of cutting waste, the sum of the node space topological entropy, and the aforementioned inventory matching index. By minimizing this objective function, the optimal design scheme for the current segment is selected.

[0015] Step S3, Global Verification and Output: After all logical function segments are optimized, assemble the entire tower model and perform nonlinear buckling analysis. If the global verification fails, record the failure mode, adjust the stiffness weight coefficient in the objective function, reset the raw material inventory state matrix to its initial state, and return to step S2 to perform segment-by-segment evolution optimization again. The final output includes manufacturing-level data containing structural construction drawings and a pre-layout procurement list.

[0016] A second aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect above.

[0017] This invention provides a computer-aided method for optimizing the design of angle steel tower structures. It offers the following advantages: 1. This invention breaks down the "data silos" between structural design and material inventory, enabling reverse design based on the current supply chain. Traditional optimization often assumes an unlimited supply of raw materials, leading to inconsistent lengths of designed members and resulting in numerous non-standard length cuts during actual manufacturing. This invention reconstructs the design process into a resource-constrained time-series process by establishing a dynamic raw material inventory state matrix and an inventory decay model. This means that each material selection at the base of the tower changes the available resource pool at the top in real time, forcing the algorithm to "adapt to the available resources," like an experienced craftsman, actively seeking design solutions that perfectly match the remaining inventory lengths. This fundamentally eliminates the hidden procurement costs caused by specification mismatches at the design source.

[0018] 2. Successfully transforming vague construction experience into calculable digital constraints significantly improves the constructability of nodes. In angle steel tower design, overcrowding in node areas often leads to difficulties in bolt installation, and relying on manual experience for adjustments is extremely inefficient. This invention introduces the innovative indicator of node spatial topological entropy, accurately capturing the geometric congestion at the intersection of multiple members through a model of spatial angular potential energy and node plate overlap potential energy. This mechanism gives the algorithm a "construction sense," enabling it to not only focus on stress safety during iteration but also automatically avoid "high-entropy" schemes that, while theoretically lightweight, have excessively small actual included angles or severe node plate interference, directly generating high-quality drawings that can be constructed smoothly without secondary refinement.

[0019] 3. A direct penalty mechanism for "scrap rate" on "structural topology" was established, resolving the contradiction between theoretical weight reduction and actual cost reduction. Unlike traditional methods that solely focus on structural net weight, this invention embeds real-time one-dimensional nesting analysis and inventory matching indicators into the fitness evaluation. When a design scheme, while structurally lightweight, results in an extremely high cutting scrap rate, the algorithm treats it as a "defective gene" and eliminates or penalizes it. This approach of prioritizing manufacturing-end nesting efficiency in the design iteration cycle ensures that the final solution represents the "generalized lowest cost" after factoring in the cost of all scrap materials, rather than merely the "lightest theoretical weight" on the drawings.

[0020] 4. The structure's topology is endowed with adaptive mutation capabilities in response to resource shortages, enhancing the robustness of the design. Faced with extreme conditions where the inventory of specific profiles is exhausted, traditional algorithms often fall into infinite loops or provide highly uneconomical splicing solutions. The dynamic topology mutation mechanism of this invention can keenly detect this "inventory deadlock" and proactively force the tower's logical segments to change the arrangement of the web members, intelligently matching topologies with different member length characteristics, and utilizing other ample lengths of inventory to "resolve the situation." This ability of the structural form to dynamically evolve with resource conditions allows the design to maintain extremely high economic efficiency and executability even in the face of an unbalanced supply chain environment.

[0021] 5. A seamless data loop from design simulation to manufacturing procurement has been achieved, significantly shortening the project delivery cycle. Because this invention completes the virtual pre-layout of all tower components during the optimization phase, its output includes not only structural construction drawings but also a pre-layout procurement list that perfectly corresponds to the design scheme. This "what you see is what you get" data output mode eliminates the lengthy cycle of waiting for the factory to perform secondary drawing breakdown, layout, and material substitution after the design is completed in the traditional process. This allows the procurement department to place orders directly and accurately based on the optimization results, and the manufacturing workshop to directly retrieve the layout data for CNC machining, truly achieving zero-time-difference connection between design and manufacturing. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall process flow of the present invention. Figure 2 This is a schematic diagram illustrating the discretization of the logical functional segments and the definition of design variables for the angle steel tower of the present invention; Figure 3 This is a schematic diagram of the raw material inventory status matrix and its dynamic decay process according to the present invention; Figure 4 This is a schematic diagram illustrating the principle of node space topological entropy calculation in this invention; Figure 5 This is a schematic diagram of the inventory matching index and one-dimensional nesting analysis of the present invention; Figure 6This is a schematic diagram of the dynamic topological mutation mechanism based on inventory constraints of the present invention; Figure 7 This is a hardware architecture diagram of the computer system of the present invention. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please see the appendix Figures 1 to 7 This invention provides a computer-aided optimization design method for angle steel tower structures. The method incorporates raw material length constraints from the supply chain dimension and node construction complexity from the microstructural dimension into the structural design cycle, achieving a globally optimal design scheme in terms of manufacturing feasibility and economy. This method typically runs on a computer equipped with a high-performance processor and memory capable of storing large-scale matrix data and performing finite element operations.

[0025] Referring to the attached diagram, this optimization design method first performs a model discretization and parameter initialization phase. In this phase, the system first reads the foundation design parameters of the angle steel tower and establishes a parametric three-dimensional overall geometric model. This model is not merely a collection of geometric wireframes, but a mechanical model containing physical properties. To address the search space explosion problem caused by simultaneous optimization of the entire tower, and to simulate the hierarchical sequence of actual construction, the system employs a discretization strategy, dividing the angle steel tower along the height direction (Z-axis) into... A logical function segment, denoted as a sequence. This division is usually based on the geometric slope change points or transverse diaphragm locations of the tower body. For example, the tower legs, lower section of the tower body, upper section of the tower body, tower head, and ground wire support are defined as independent functional sections. .

[0026] For each logical functional segment The system defines its design variable vector. This vector Core data includes three dimensions: topological configuration variables. Component cross-sectional variables and geometric morphological variables Among them, topological configuration variables Used to refer to the arrangement of the web members in this section, such as K-type, X-type, single diagonal member, or star-shaped, etc.; member section variables. It is a set of indexes pointing to specific specifications in the standard angle steel profile library; geometric shape variables This controls the macroscopic dimensions of the segment, such as the root opening width and inter-segment height. This parametric definition method allows the physical structure of the tower to be completely mapped into a mathematical object that can be processed by a computer.

[0027] While establishing the physical model, this embodiment constructs a key digital twin object—the initial raw material inventory state matrix, denoted as... Unlike traditional methods that only call upon a static profile section library, this invention introduces an inventory status matrix, a dynamically evolving multidimensional data structure used to accurately describe the physical resources available in the enterprise's supply chain at any given moment. This matrix... Defined as Dimensional matrix: In this matrix, row index Corresponding to the cross-sectional specifications of angle steel, such as L100x8, L160x14, etc.; Column index The standard lengths corresponding to raw materials typically include industrial standard lengths such as 6 meters, 9 meters, and 12 meters; matrix elements This indicates a specific specification. and a specific length The matrix determines the available inventory of raw materials at the initial optimization moment. Its introduction provides a benchmark for inventory decay calculations in subsequent steps, enabling the algorithm to perceive "resource boundaries" and thus avoid designing component solutions that require extensive non-standard customization or result in extremely high cutting scrap rates.

[0028] To support the instantiation of the above variables, the system provides a pre-built topology configuration library. and standard profile library Topology configuration library It stores various engineering-proven web member arrangement templates, each containing its corresponding node connection logic and force transmission path characteristics. Standard profile library. The parameters of hot-rolled equal angle steel conforming to national standards were entered, including physical properties such as cross-sectional area, radius of gyration, distance between centers of gravity, and weight per meter.

[0029] After completing the discretization and initialization, the system applies the design load to the entire tower. The load calculation strictly follows relevant structural load specifications, generating various load case combinations by considering wind load, icing load, conductor and ground wire tension, and the structure's self-weight. At this point, although each logical function segment... The specific cross-section and topology have not yet been finalized, but the system has established a basic structure containing virtual elements, preparing the computational foundation for subsequent piecewise search and finite element analysis based on evolutionary algorithms. This rigorous initialization ensures that each subsequent design entity is generated based on real physical and resource constraints.

[0030] In this embodiment, to quantify the construction complexity of the angle steel tower node structure and establish a dynamic coupling relationship between structural design and raw material inventory, the system introduces two core mathematical calculation models: a node spatial topological entropy model and a matching degree evaluation model based on inventory decay. These two models constitute the evaluation benchmark for subsequent step-by-step evolutionary optimization.

[0031] In this invention, to address the issues of limited installation space and cumbersome node plate fabrication caused by the convergence of members in angle steel tower structures, a node spatial topological entropy index is constructed. This indicator transforms the previously vague concept of "ease of installation" that relied on engineers' experience into a precise numerical value that can be calculated by a computer. For any given logical function segment... Nodes within Its node space topological entropy It is defined as a weighted sum of congestion degree, spatial angular potential energy, and node plate overlap potential energy, and the specific calculation formula is as follows: In the formula, Representatives converge at the node The total number of members (including main members, diagonal members, and auxiliary members), this item is in logarithmic form. This characterizes the non-linear growth trend of the basic complexity of node connections as the number of connecting components increases; These are normalized weighting coefficients used to balance the impact of each component based on the actual engineering requirements.

[0032] Furthermore, in order to accurately describe the operable space of the bolt installation tool, this invention defines a spatial angular potential energy term. This method identifies "acute angle regions" that may cause installation difficulties by traversing the spatial vectors of all components at a node and calculating the angles between each pair of components. The specific calculation expression is as follows: In this formula, Indicates the first root member and the first The spatial angle between the members; This is a preset minimum critical angle threshold for installation, which is usually determined based on the physical dimensions of the electric wrench or hydraulic pliers. It is a very small correction value set to prevent the denominator from being zero; The penalty order is typically greater than or equal to 2. The physical meaning of this mathematical model is that when the angle between any two members approaches the installation threshold... When the function value rises sharply, it forms an extremely high "potential energy barrier", forcing the optimization algorithm to automatically avoid design schemes with such small component angles.

[0033] Furthermore, to address the issue of machining waste caused by the complex shape of the gusset plate, this embodiment also defines the overlapping potential energy of the gusset plate. The system first determines the nodes. A reference projection plane is used. To ensure the determinism of the computer calculations, this reference projection plane is defined as the plane determined by the resultant vector of the identified main member vector and all converging diagonal members at the node. The projected profiles of the ends of each member converging at the node are calculated on this plane. Subsequently, the area of ​​the intersection region between the projected profiles is solved using computational geometry algorithms. The value is proportional to the sum of the overlapping areas of the projected areas of all components. The larger the value of this index, the more irregular the shape of the gusset plate needs to be designed, or the higher the risk of physical interference between components.

[0034] In this embodiment, after addressing the quantification of microstructure, an evaluation mechanism for the macroscopic manufacturing dimension is further established. The system optimizes each logical functional segment. At that time, a real-time one-dimensional nesting analysis will be performed. This analysis process is not a simple summation of lengths, but rather an attempt to fit the length list of all components in the current candidate design scheme into the remaining raw material inventory state matrix updated at the previous time step. The system then performs sorting. Based on the sorting results, the system calculates the inventory matching index. This is used to measure how well the current design is compatible with supply chain resources. In the formula, the double summation symbol represents iterating through all specifications. and fixed length Raw materials; This indicates the quantity of specific raw materials required for the current design scheme; This represents the remaining inventory of the raw material at the current moment. To prevent tiny constants with a denominator of zero; The fixed length of the raw material; This is the length of the raw material that is effectively utilized, which is the sum of the lengths of the cut components; This is the inventory sensitivity coefficient.

[0035] The ingenious design of this formula lies in its inclusion of two penalty dimensions: the first part (fractional term) is the "scarcity penalty," which significantly increases if the design heavily utilizes materials that are already scarce in stock; the second part (exponential term) is the "scrap penalty," which increases if the component length does not match the standard length of the raw material, resulting in a large amount of leftover material (i.e., scrap rate penalty). (For larger values), the exponential function amplifies the penalty value. Therefore, The lower the value, the more the design not only saves materials but also fits well with the current inventory structure.

[0036] Finally, this embodiment employs a rigorous inventory decay model to update the system state. Once a certain logical function segment... Once the optimal design scheme is selected, the system immediately calculates the raw material consumption matrix determined by that scheme. And according to the formula Update the inventory status matrix. If any inventory item is found during the calculation... If the value is less than zero, the current path is deemed infeasible, triggering the algorithm's backtracking or penalty mechanism. This dynamic update based on time series ensures that the design decisions for the lower part of the tower actually change the available resource boundaries of the upper part of the tower, thereby achieving overall resource optimization across the entire tower.

[0037] In this embodiment, after completing the model initialization and basic evaluation index definition, the system enters the core segmented dynamic evolution optimization stage. This stage is designed as a discrete time-series decision-making process, sequentially optimizing each logical functional segment according to the installation order from the tower base to the tower head or the project-specified installation sequence. Solve the problem. For the current logical function segment... During the optimization process, the system must not only find the lightest design that meets mechanical performance requirements, but also ensure that the design can be updated in the previous stage's remaining raw material inventory state matrix. What it contains.

[0038] In this invention, for each logical functional segment The solution employs an improved co-evolutionary algorithm. The system is first based on a topology configuration library. and standard profile library Discrete sampling is performed to generate several candidate design individuals. The initial population. Each candidate design individual. All of them carry a clear gene code, which includes the topological form of the selected segment. and specific component section vectors Subsequently, the system implanted these candidate individuals into the base structure of the entire tower for finite element analysis (FEA). During this process, the system extracted the stress ratio of each component. The displacement response of nodes is considered. Any individual that violates the strength, stiffness, or slenderness ratio specifications will be marked as an infeasible solution and subject to a large penalty value, causing it to be naturally eliminated in subsequent evolution.

[0039] In this embodiment, for feasible individuals that meet the mechanical constraints, the system further calculates their overall fitness. This fitness evaluation no longer relies solely on structural weight, but rather on a multi-objective weighted function. This is used to characterize the generalized cost. The system aims to find the minimum value of this objective function, which is mathematically expressed as follows: In the formula, The theoretical structural net weight, representing the current design scheme, is a fundamental item for controlling costs; The weight of cutting waste is calculated based on real-time one-dimensional nesting analysis, which directly reflects the material loss cost. It is the sum of the spatial topological entropy of all nodes within this logical functional segment, representing the manpower and time costs of construction and installation; This refers to the inventory matching index defined above, which is introduced as a penalty to suppress the improper use of scarce inventory; The weighting coefficients are dimensionless, allowing users to flexibly configure them according to the proportion of material costs, processing costs, and installation costs in a specific project.

[0040] This invention incorporates a dynamic topology mutation mechanism to address potential deadlock issues under inventory constraints. During the optimization process, the following scenario may occur: due to the consumption of a large amount of raw materials of a specific length at the lower part of the tower, optimization may fail at a certain point in the middle of the tower. At this point, regardless of adjustments to the component cross-section, there is insufficient stock of matching length materials to meet the requirements of the current default topology. At this time, the stock matching index... It may rise abnormally or be deemed infeasible.

[0041] When the system detects the optimal solution for multiple consecutive generations in the population When the value exceeds the preset warning threshold, a topology mutation operation will be automatically triggered. The system will forcibly change the design variables of the current segment. The system retrieves alternative configurations from a topology library. For example, it might abruptly change a large-span K-type arrangement to a short-intersection cross-type arrangement with auxiliary materials, thus transforming the demand for long-length raw materials into a demand for short-length raw materials, or utilizing surplus non-mainstream specifications in inventory. This mechanism endows the algorithm with adaptive survivability in resource-constrained environments, ensuring the continuity and manufacturability of the design.

[0042] In this embodiment, the current logical function segment is selected through the above mechanism. Global optimal individual The system then solidifies this solution as the final design for that segment and performs a status update operation. The system again calls the nesting engine to accurately calculate the raw material consumption of this optimal solution and updates the inventory status matrix accordingly, obtaining... This new inventory status will serve as an irreversible boundary condition, passed to the next logical function segment. The optimization process is repeated until all segments of the entire tower are optimized, thus mathematically ensuring strict physical and logical consistency between the generated overall tower design and the pre-layout scheme.

[0043] In this embodiment, when all logical function segments of the angle steel tower After the segmented evolution optimization was completed and each segment selected its locally optimal topology and cross-section scheme, the system automatically entered the global verification and closed-loop feedback stage. Because the previous steps employed a segmented decoupling optimization strategy, although the mechanical properties and inventory matching of each local segment were ensured, the continuity of stiffness distribution in each segment and the overall stability of the entire tower under ultimate load still needed to be verified through higher-precision overall analysis. Therefore, the system first reassembled the design schemes locked in each logical functional segment, constructing a full-tower finite element model that included complete node connection stiffness and initial geometric imperfections.

[0044] In this invention, the verification of the full-tower model is no longer limited to linear elastic analysis, but instead performs geometric nonlinear buckling analysis considering large deformation effects. The system is subjected to preset control conditions, such as a combination of "strong winds + icing," and the arc-length method or the Newton-Raphson method is used to trace the equilibrium path of the structure until numerical divergence is achieved to determine the ultimate bearing capacity of the structure. The system defines the following global stability criterion: In the formula, The ultimate load of the structure is obtained from nonlinear buckling analysis; The design loads specified in the standard; This is the structural importance coefficient or safety reserve coefficient (usually ranging from 1.0 to 1.2). This verification process can effectively identify overall torsional instability or flexural-torsional instability modes caused by abrupt changes in stiffness in each segment, which are hidden dangers that cannot be detected by piecewise linear verification alone.

[0045] In this embodiment, a global backtracking mechanism with adaptive adjustment capabilities is constructed. If the results of the above nonlinear buckling analysis show... This means that the overall structural stability is insufficient. Instead of simply increasing the component cross-sections, the system triggers a correction cycle of optimization parameters. The system first identifies the key weak layer causing instability, typically the region with the largest modal displacement, and then adjusts the objective function. The weight coefficient vector in the text. Specifically, the system reduces the structural self-weight term. The weights are adjusted, and the preference weights for topological configurations that contribute significantly to stiffness are increased, or the slenderness ratio limits are tightened directly in the constraint set.

[0046] After parameter adjustments are complete, the most crucial step is to reset the raw material inventory status matrix. The system will force the inventory status matrix to roll back to its initial state. Starting from this point, and carrying new weight parameters, restart from the first logical function segment. The initial step-by-step evolution process. This "complete reset" operation is necessary because adjusting the stiffness of the superstructure may change the force transmission path of the substructure, and the new design scheme will lead to a completely different raw material consumption sequence. If the inventory is not reset, subsequent nesting calculations will be based on incorrect remaining quantities, thereby compromising the accuracy of the "design-inventory" coupling.

[0047] In this invention, after the full tower model successfully passes the global nonlinear buckling check and all structural rationality checks, the system executes the final manufacturing-level data output. Unlike traditional design software that only outputs geometric drawings, the data package output by this system contains two strictly corresponding parts: first, the construction drawings and digital model of the angle steel tower structure, which clearly defines the geometric dimensions and connection relationships of each component; second, a pre-layout procurement list.

[0048] This pre-designed procurement list details which specific specification and furnace number of raw material should be used for each member, and provides specific cutting diagrams. For example, the system explicitly instructs: "The diagonal member numbered L1-4 and the auxiliary member numbered L1-6 should be cut together from a 12-meter-long, L100x8 specification stock material, with a remaining length of 450mm." This output mode achieves "what you see is what you get" digital delivery, ensuring that the material utilization rate estimated in the design phase can be 100% reproduced in the actual manufacturing process, completely eliminating engineering waste caused by the disconnect between procurement and design drawings.

[0049] As a specific application example, this invention was applied to the design of a 500kV double-circuit transmission line straight-line tower, 54 meters high, with a design wind speed of 30 m / s. The company had a large stockpile of 9-meter standard-length Q420 high-strength angle steel. Through optimization by this system, the lower part of the tower automatically matched a K-type topology suitable for the 9-meter main material; however, at the slope change points, since the 9-meter stockpile was exhausted, the system automatically abruptly changed the topology to a cross-X-type topology suitable for the remaining 6-meter short-length stock, and automatically adjusted the partition height. Compared to traditional manual design, the final output scheme, although theoretically increasing the structural weight by slightly by 1.5%, reduced the overall manufacturing cost by approximately 12% due to the highly efficient use of stagnant inventory and reduced cutting waste. Furthermore, all nodes met the installation space requirements for electric wrenches, verifying the significant effectiveness of this method in solving supply chain constraints in complex engineering projects.

[0050] This embodiment also provides a computer system and a computer-readable storage medium for executing the above-described angle steel tower structure optimization design method. Given that this invention relates to large-scale combinatorial optimization search, nonlinear finite element analysis, and dynamic matrix operations, its hardware architecture needs to possess corresponding high-performance data processing capabilities to ensure that the algorithm converges within an engineering-acceptable timeframe.

[0051] In this embodiment, the computer system includes at least one processor, a memory, and a bus connecting different system components. The processor, as the computing core, is configured to execute computer program instructions stored in the memory. For the specific computing requirements of this invention, the processor is specifically configured to efficiently handle floating-point operations and matrix operations to support the aforementioned inventory state matrix. Real-time updates and node space topology entropy Complex geometric calculations are required. In practical deployments, considering the computational power consumption of global nonlinear buckling analysis, the processor can be a multi-core central processing unit (CPU), a graphics processing unit (GPU), or an application-specific integrated circuit (ASIC) for accelerating specific mathematical operations.

[0052] In this invention, the memory serves as a non-volatile computer-readable storage medium, used to store computer programs, an initial raw material inventory database, a standard profile library, and a topology configuration library. More importantly, during operation, the memory allocates a dynamic data buffer to store in real-time the evolving state matrix of the remaining raw material inventory as the optimization process progresses. This memory-resident mechanism ensures that when evaluating the nesting of tens of thousands of individuals in a population, the algorithm can read the current resource boundary conditions with extremely low latency, thereby avoiding performance bottlenecks caused by frequent disk I / O operations.

[0053] In this embodiment, a computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements any one of the steps in the foregoing embodiments of the present invention. Specifically, after reading the instructions, the processor first executes the initialization module to construct a parameterized discrete model of the angle steel tower and an initial inventory matrix in memory; then it enters the evolution calculation module to perform calculations for each logical function segment. The structural response is calculated by calling the finite element solver, and the geometric analysis subroutine is called in parallel to calculate the congestion term and spatial angular potential energy. and the overlapping potential energy of the node plates Next, the processor executes the one-dimensional nesting logic to calculate the inventory matching index. The population is updated according to the survival of the fittest rule; finally, the processor executes the global verification module and the post-processing module to map the final mathematical solution into an engineering data file containing construction drawing geometric information and a pre-layout procurement list.

[0054] In this invention, the computer system is also equipped with standard data input / output interfaces. The input interface receives real-time inventory data (such as in Excel or SQL database format) from an external ERP system, as well as load parameters set by the designer. The output interface connects to a display device or network printing device to visually display the optimized 3D structural model, detailed node construction diagrams, and material procurement BOM (Bill of Materials). High-speed data exchange is achieved between the processor, memory, and input / output interfaces via an internal bus, ensuring that instructions stored in the medium can be accurately loaded and drive the automated operation of the entire physical design process.

[0055] Furthermore, those skilled in the art will understand that all or part of the processes in the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, including but not limited to: read-only memory (ROM), random access memory (RAM), disk, or optical disk. When the program runs, through the scheduling of the operating system, the various mathematical models and logical steps defined above are transformed into physical-level transistor switching actions and electrical signal transmissions, thereby completing the transformation from abstract algorithms to concrete physical design results.

[0056] In this embodiment, it should be specifically noted that the mathematical models and algorithmic logic described in detail in the foregoing embodiments demonstrate the preferred implementation of the present invention in a specific engineering scenario. However, the technical concept of the present invention is not limited to the specific mathematical expression form described above. For example, in calculating the topological entropy of the node space... At that time, regarding the potential energy of spatial angle The core technical feature lies in constructing a nonlinear penalty function that monotonically increases as the included angle of the components decreases. Although this embodiment uses a reciprocal power function form... However, in other embodiments, a tangent function or an exponential function can also be used, as long as it can mathematically simulate the "potential energy barrier" effect, that is, generate an evaluation value that tends to infinity when the included angle approaches the critical value, and should be regarded as an equivalent substitution of the technical solution of the present invention.

[0057] In this invention, regarding the raw material inventory status matrix The dimension definition is illustrated in this embodiment using a two-dimensional matrix of "specifications" and "length" as an example. In more complex supply chain environments, this matrix can be extended to a three-dimensional or even multi-dimensional tensor that includes "material grade" or "furnace batch number". In this case, the inventory matching index... The calculation logic will correspondingly increase the penalty for material mixing costs or batch management costs. This expansion of dimensions does not deviate from the core inventive point of this invention, "design driven by inventory status," and still falls within the scope of protection of the claims of this invention.

[0058] In this embodiment, regarding the solution engine used for the step-by-step evolutionary optimization, in addition to the aforementioned improved co-evolutionary algorithm, those skilled in the art can also use particle swarm optimization (PSO), simulated annealing (SA), or ant colony optimization (ACO) to optimize the design variables. Regardless of the metaheuristic algorithm used, as long as it follows the temporal coupling logic of "segmented sequential optimization" and "the constraints of the next segment depend on the remaining inventory state after the previous segment's optimization," it implements the core steps regarding dynamic inventory decay in the claims of this invention.

[0059] Furthermore, the "angle steel tower" described in this invention should be interpreted broadly, as its technical solution is also applicable to similar spatial truss structures made of profiles, such as substation frames or communication towers. These structures also suffer from the complexity of node construction due to the intersection of members, as well as the waste problem caused by the fixed-length cutting of raw materials. The node spatial topological entropy model and inventory coordination mechanism provided by this invention have a universal technical effect in solving the optimization design of such discrete truss structures.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A computer-aided optimization design method for angle steel tower structures, characterized in that, Includes the following steps: Step S1, Model Discretization and Parameter Initialization: Establish a parameterized model of the angle steel tower, discretize the angle steel tower along the height direction into several logical functional segments, and establish an initial raw material inventory state matrix that includes raw material specifications, fixed length and initial quantity. Step S2, Segment-by-segment evolution optimization based on inventory status: Iterative optimization design of each of the logical functional segments is performed sequentially according to a preset order; When optimizing the current logical function segment, constraints are applied based on the remaining raw material inventory status matrix after the optimization of the previous logical function segment. In step S2, the optimization of the current logical function segment includes: generating candidate design individuals that include topological configuration and component cross-section; Calculate the node spatial topological entropy of all nodes in the candidate design individual, and use the node spatial topological entropy to quantify the construction complexity of the node construction; Perform real-time one-dimensional nesting analysis, calculate the consumption of the candidate design individual on the current raw material inventory status matrix, obtain the inventory matching degree index, and update and generate the predicted remaining raw material inventory status matrix. Based on the node space topology entropy and the inventory matching index, an objective function is constructed to select the optimal design scheme for the current logical function segment. Step S3, Global Verification and Output: After all logical function segments are optimized, assemble the full tower model for global force verification and output manufacturing-level data including structural construction drawings and pre-layout procurement lists.

2. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, In step S1, the initial raw material inventory status matrix is ​​a multi-dimensional matrix, and the elements in the matrix represent the available quantity of raw materials with specific cross-sectional specifications and specific fixed lengths. In step S2, the update of the remaining raw material inventory state matrix follows an inventory decay model: The remaining raw material inventory status matrix after optimization of the current logical function segment is equal to the remaining raw material inventory status matrix of the previous logical function segment minus the raw material consumption matrix determined by the optimal design scheme of the current logical function segment after material nesting analysis. If the calculation result contains a negative value, the current design scheme is deemed infeasible.

3. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, In step S2, the calculation logic for the inventory matching index is as follows: Based on the component length list of the candidate design individuals, retrieve the corresponding standard-length raw materials from the current remaining raw material inventory status matrix; If the proportion of demand for standard-length raw materials in the current remaining raw material inventory status matrix that are below the safety stock threshold exceeds a preset ratio, or if the generated cutting waste rate is higher than a preset threshold, then the value of the inventory matching index is increased, and a penalty is imposed on the objective function.

4. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, In step S2, the calculation of the node spatial topological entropy includes a weighted summation of the following three dimensions: Convergence congestion term: Logarithmic growth value calculated based on the total number of members converging at the target node; Spatial angular potential energy: a potential energy function calculated based on the difference between the spatial angle between the converging members and the preset minimum critical angle threshold for installation; Node plate overlap potential energy: The degree of interference calculated based on the overlap area of ​​the projected profiles of the converging members on the reference projection plane determined by the resultant vector of the identified main member and the converging diagonal member.

5. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, The method for calculating the spatial angular potential energy satisfies the following characteristics: Traverse all pairs of adjacent members at the target node and calculate their spatial angles; When the absolute value of the difference between the spatial angle and the minimum critical angle threshold for installation approaches zero, the function value of the spatial angular potential energy approaches infinity. When the spatial angle is less than the minimum critical angle threshold for installation, the spatial angular potential energy is infinite.

6. The computer-aided optimization design method for angle steel tower structures according to claim 4, characterized in that, The method for calculating the overlapping potential energy of the node plates satisfies the following characteristics: The ends of each component converging at the target node are projected onto the reference projection plane to obtain the projected profile of each component; Calculate the sum of the intersection areas between each projected contour, and use the ratio of the sum of the intersection areas to the estimated total area of ​​the node plate as the basis for measuring the processing complexity of the node plate.

7. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, In step S2, the objective function is a minimization function, which is composed of the following weighted components: The theoretical structural weight of the current logical functional segment; The weight of cutting waste calculated based on real-time one-dimensional nesting analysis; The sum of the node spatial topology entropy of all nodes within the current logical function segment, and the inventory matching index.

8. The computer-aided angle steel tower structure optimization design method according to claim 1, characterized in that, Step S2 also includes a dynamic topological mutation mechanism: When optimizing the current logical function segment, if a candidate design individual that meets the mechanical constraints and whose inventory matching index is lower than the preset matching tolerance threshold cannot be found based on the current remaining raw material inventory state matrix, a new topology with different rod length characteristics from the preset topology library is selected to replace the current logical function segment, and cross-section sampling and evolution are performed again.

9. The computer-aided optimization design method for angle steel tower structures according to claim 1, characterized in that, In step S3, the global force verification includes performing nonlinear buckling analysis on the assembled full tower model; If the global force check fails, the stiffness weight coefficient in the objective function is adjusted, the raw material inventory state matrix is ​​reset to the initial raw material inventory state matrix described in step S1, and the process returns to step S2 to perform step-by-step evolution optimization again.

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 method as described in any one of claims 1 to 9.

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