Building structure model optimization method, device, equipment and medium

Abstract

The present application relates to the technical field of model optimization, and provides a building structure model optimization method, device, equipment and medium, which can establish a parameterized graphic element building model according to design variables, and generate a plurality of chromosomes according to the parameterized graphic element building model to realize digital and modelized representation of the design variables; a genetic algorithm is used to iteratively optimize the plurality of chromosomes based on an optimization objective function and constraint conditions to obtain a target chromosome, and the target chromosome is decoded to obtain optimal design variable values, an optimization strategy for a target building model is generated according to the optimal design variable values, so that the optimization direction and boundary are determined through the optimization objective function and constraint conditions, global exploration of the design space is realized through the genetic algorithm, the local optimal trap is effectively jumped out, and fast, accurate and automatic building structure model optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of model optimization technology, and in particular to a method, apparatus, equipment and medium for optimizing building structure models. Background Technology

[0002] Concrete core tube-steel module is a highly promising composite structure, but its design process is also very complex, and it is a typical multivariable and multi-constraint optimization problem.

[0003] In traditional design processes, engineers primarily rely on personal experience and specifications for trial calculations. This "trial and error" method has the following significant drawbacks:

[0004] (1) The design space is huge and it is difficult to find the global optimal solution: the number of combinations of variables grows exponentially, and manual trial calculation can only explore a very small part of the design space. It is almost impossible to find the global optimal solution. Therefore, what is often obtained is a feasible solution that meets the specifications but may not be economical.

[0005] (2) The optimization goal is singular and cannot be comprehensively balanced: manual design usually takes safety as the primary goal and it is difficult to systematically take into account multiple conflicting performance indicators such as project cost, total steel consumption, carbon emissions, and structural stiffness at the same time.

[0006] (3) Long iteration cycle and slow response: Every adjustment to the design scheme requires tedious modeling and calculation analysis, which is inefficient and cannot quickly respond to the needs of multi-scheme comparison in the early stage of design.

[0007] Therefore, there is an urgent need for a practical optimization method that can be directly applied to engineering projects, executed automatically, and obtain the optimal building structure design scheme. Summary of the Invention

[0008] In view of the above, it is necessary to provide a method, apparatus, equipment and medium for optimizing building structure models, which aims to solve the problem of not being able to directly and quickly and accurately optimize building structure models for engineering automation.

[0009] A method for optimizing a building structure model, the method comprising:

[0010] In response to the optimization command for the target building model, the structural design parameters to be optimized are obtained as design variables;

[0011] A parametric primitive building model is established based on the design variables, and multiple chromosomes are generated based on the parametric primitive building model.

[0012] The objective function and constraints are determined based on the penalty mechanism.

[0013] A genetic algorithm is used to iteratively optimize the multiple chromosomes based on the objective function and the constraints to obtain the target chromosome.

[0014] The target chromosome is decoded to obtain the optimal design variable values, and an optimization strategy for the target building model is generated based on the optimal design variable values.

[0015] A building structure model optimization device, the building structure model optimization device comprising:

[0016] The acquisition unit is used to acquire the structural design parameters to be optimized as design variables in response to the optimization instructions on the target building model;

[0017] The generation unit is used to establish a parametric primitive building model based on the design variables, and to generate multiple chromosomes based on the parametric primitive building model.

[0018] The determination unit is used to determine the optimization objective function and constraints based on the penalty mechanism;

[0019] An iterative unit is used to perform iterative optimization on the multiple chromosomes based on the optimization objective function and the constraints using a genetic algorithm to obtain the target chromosome.

[0020] The generation unit is further configured to decode the target chromosome to obtain the optimal design variable values, and generate an optimization strategy for the target building model based on the optimal design variable values.

[0021] A computer device, the computer device comprising:

[0022] A memory that stores at least one instruction; and a processor that executes the instructions stored in the memory to implement the building structure model optimization method.

[0023] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the building structure model optimization method.

[0024] As can be seen from the above technical solutions, this invention can establish a parametric primitive building model based on design variables, and generate multiple chromosomes based on the parametric primitive building model to realize the digital and model-based representation of design variables. By using a genetic algorithm, the multiple chromosomes are iteratively optimized based on the objective function and constraints to obtain the target chromosome, and the target chromosome is decoded to obtain the optimal design variable value. Based on the optimal design variable value, an optimization strategy for the target building model is generated. Thus, the direction and boundary of optimization are clarified by optimizing the objective function and constraints, and the global exploration of the design space is realized through the genetic algorithm, effectively escaping the local optimum trap, and realizing fast, accurate and automated optimization of the building structure model. Attached Figure Description

[0025] Figure 1 This is a flowchart of a preferred embodiment of the building structure model optimization method of the present invention;

[0026] Figure 2 This is a schematic diagram of the target building model of the present invention;

[0027] Figure 3 This is a functional block diagram of a preferred embodiment of the building structure model optimization device of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of a computer device that implements the building structure model optimization method of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0030] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the building structure model optimization method of the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0031] The building structure model optimization method is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0032] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc.

[0033] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0034] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0035] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0036] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0037] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0038] S10, in response to the optimization instruction for the target building model, obtains the structural design parameters to be optimized as design variables.

[0039] In this embodiment, the target building model can be a "concrete core tube-steel module" building model.

[0040] In this embodiment, the optimization instructions can be triggered by relevant designers according to actual needs.

[0041] In this embodiment, the design variables may include, but are not limited to, one or more of the following combinations of variables:

[0042] Core tube wall thickness, concrete grade, and cross-sectional index number of beam and column components in the steel module (corresponding to a preset steel section).

[0043] For example: Please refer to Figure 2 This is a schematic diagram of the target building model of the present invention. Figure 2 This study focuses on optimizing a 15-story office building with a hybrid "concrete core tube-steel module" structure. The building's structural features are as follows: within a rectangular building area, one side consists of a cast-in-place reinforced concrete core tube, bearing the primary lateral force resistance; the other side comprises standardized steel structural module units, connected to the core tube to form a unified structure. The optimization objective is to minimize the total structural cost of the steel module area while meeting all safety and performance requirements. Accordingly, the core tube wall thickness from floors 1 to 15 can be considered a design variable, ranging from {400, 450, 500, 550, 600} mm, encoded using integers 0, 1, 2, 3, and 4. Furthermore, the steel module components are grouped to reduce the dimensionality of the variables. The building is divided into four zones along its height (floors 1-4, 5-8, 9-12, and 13-15). Within each zone, the component types (such as corner columns, edge columns, interior columns, main beams, and secondary beams) form a group, resulting in a total of 16 component groups. Each group selects a section from a preset section library containing 100 commonly used H-beams. The design variable for each group is the index number of the selected section in the library, which can be encoded using integers from 0 to 99.

[0044] S11, establish a parametric primitive building model based on the design variables, and generate multiple chromosomes based on the parametric primitive building model.

[0045] In this embodiment, establishing a parametric primitive building model based on the design variables includes:

[0046] Establish the mapping relationship between the design variables and component attributes;

[0047] The automated drawing parsing tool is invoked to identify the architectural plan sketch corresponding to the target building model, thereby obtaining the building structure information;

[0048] The data processing program is invoked to process the building structure information to obtain an initial topology graph; wherein, the initial topology graph uses components as nodes and the connection relationships between components as edges;

[0049] Establish the association between the design variables and the nodes in the initial topology graph;

[0050] Attribute data is obtained from the configuration database according to the mapping relationship, and the attribute data is added to the corresponding node of the initial topology graph according to the association relationship to obtain the parameterized primitive building model.

[0051] For example, the mapping relationship can be: the core tube wall thickness code 3 corresponds to a wall thickness of 550mm, and the steel component section code 45 corresponds to the size of the 45 H-beam in the steel section library, etc.

[0052] The building structure information may include the core tube boundary, steel module column grid nodes (such as the node coordinates corresponding to a 6m×8m column spacing), etc.

[0053] The initial topology diagram may include nodes such as beam members and column members, as well as edges such as connecting lines between nodes (e.g., the connection relationship between beams and columns).

[0054] The aforementioned relationships may include, but are not limited to, the binding of wall thickness variables to the core tube wall and the binding of corresponding section index number variables to the corner columns of floors 1-4.

[0055] The configuration database may include a material library (such as concrete strength parameters), a section library (such as H-beam dimensions or mechanical parameters), etc.

[0056] Accordingly, the attribute data may include, but is not limited to, component geometric attributes such as cross-sectional dimensions, material properties, etc.

[0057] Furthermore, the structural analysis kernel can be invoked to perform preliminary calculations on the model and verify whether the variable-driven model meets the structural code constraints (such as inter-story drift angle ≤ 1 / 800, component strength meeting the standard). If there are violations (such as the section not meeting the bearing capacity requirements), the design variables are adjusted, and the attributes are rebound and the model is updated until the model is compliant.

[0058] Furthermore, compliant parametric building models can be transformed into visual graphics or generated into deliverable BIM (Building Information Modeling) models, thereby clearly presenting the correspondence between each component and design variables (such as labeling the core tube wall thickness and steel component cross-section model).

[0059] Through the above embodiments, the design variables can be digitally and modeled, providing a standardized input carrier for subsequent genetic algorithm iterative optimization. At the same time, with the help of automated modeling tools, the tedious operation of manual modeling can be reduced, and the efficiency and accuracy of model generation can be improved.

[0060] In this embodiment, generating multiple chromosomes based on the parameterized primitive building model includes:

[0061] Obtain the variable value of each design variable from the parametric primitive architectural model;

[0062] The variable values ​​of each design variable are encoded according to a preset format to obtain the multiple chromosomes;

[0063] Each chromosome represents a complete architectural design scheme, and the value of each design variable corresponds to a gene segment within the corresponding chromosome.

[0064] The preset format may include binary format, integer format, etc.

[0065] For example, in chromosome {3,45,40,35,88,76,...}, the first digit 3 corresponds to a core cylinder wall thickness of 550mm, and the second digit 45 corresponds to section 45 of the steel structure of the corner columns of layers 1-4.

[0066] In this way, each chromosome represents an independent and complete set of design variable values, that is, each chromosome corresponds to a complete architectural design scheme.

[0067] S12, Determine the optimization objective function and constraints based on the penalty mechanism.

[0068] In this embodiment, determining the optimization objective function and constraints based on the penalty mechanism includes:

[0069] Obtain the total project cost function, and add a penalty term to the total project cost function to obtain the optimization objective function;

[0070] The performance indicators required by the building structure design code are converted into the aforementioned constraints.

[0071] The penalty items include macroscopic displacement penalty items, microscopic component verification penalty items, and period ratio index penalty items.

[0072] For example, the total project cost function may include the unit price and quantity of concrete, steel bars, and different types of steel components. By weighting the product of each unit price and the quantity used, the corresponding total project cost can be obtained.

[0073] Wherein, the macroscopic displacement penalty term can be the product of the macroscopic displacement weight and the macroscopic displacement penalty term index value; the microscopic component verification penalty term can be the product of the microscopic component weight and the microscopic component verification penalty term index value; and the period ratio index penalty term can be the product of the period ratio weight and the period ratio index penalty term index value.

[0074] The macroscopic displacement weight, the microscopic component weight, and the period ratio weight can be optimal values ​​selected based on a large number of experiments.

[0075] Accordingly, the penalty term can be the sum of the macroscopic displacement penalty term, the microscopic component verification penalty term, and the period ratio index penalty term.

[0076] The constraints may include, but are not limited to, component strength, overall stability, local stability, node bearing capacity, and inter-story drift angle limits.

[0077] Through the above embodiments, the direction and boundaries of optimization can be clearly defined, the economic indicators and performance indicators can be quantitatively correlated, a unified evaluation standard can be provided for subsequent fitness assessment, and the optimization scheme can be both economical and safe.

[0078] S13, using a genetic algorithm, the multiple chromosomes are iteratively optimized based on the objective function and the constraints to obtain the target chromosome.

[0079] In this embodiment, the genetic algorithm can simulate the "survival of the fittest" principle in biological evolution and find the optimal solution through iteration.

[0080] Specifically, the genetic algorithm is used to iteratively optimize the multiple chromosomes based on the objective function and the constraints, resulting in the following target chromosomes:

[0081] Based on the multiple chromosomes, and under the premise of satisfying the constraints, an initial population with a preset number of individuals is randomly generated; wherein each individual corresponds to one chromosome.

[0082] The fitness of each individual in the initial population is evaluated to obtain a fitness score for each individual;

[0083] Genetic operations are performed based on the fitness score of each individual until the termination condition is met, at which point the genetic operations are stopped.

[0084] The chromosome corresponding to the individual with the highest fitness score obtained so far is determined as the target chromosome;

[0085] The termination conditions include reaching a preset number of iteration rounds and / or the highest fitness score for a consecutive preset number of rounds reaching convergence.

[0086] The preset number can be configured comprehensively based on the actual chromosome size and accuracy requirements. For example, within the range of design variable values, a set (e.g., 100 chromosomes) satisfying basic geometric constraints can be randomly generated to form an initial population.

[0087] The preset number of iteration rounds and the preset number of rounds can be configured comprehensively according to the actual chromosome size and accuracy requirements. For example, the preset number of iteration rounds can be configured to 200, and the preset number of rounds can be configured to 20. That is, after repeatedly performing fitness evaluation and genetic operations, until the maximum number of iteration rounds of 200 is reached, and / or the improvement of the optimal solution in 20 consecutive rounds is less than a minimum threshold, the algorithm is confirmed to have converged and the iteration is terminated.

[0088] In this embodiment, the fitness assessment of each individual in the initial population to obtain a fitness score for each individual includes:

[0089] Obtain the chromosome corresponding to each individual;

[0090] Decode the chromosome corresponding to each individual to obtain the sequence of design variable values ​​for each individual;

[0091] The automated modeling program is invoked to generate a structural analysis model for each individual based on the sequence of design variable values ​​for each individual.

[0092] The structural analysis software kernel is invoked to perform structural calculations on the structural analysis model corresponding to each individual, and the structural calculation results corresponding to each individual are obtained.

[0093] The optimization objective function value for each individual is determined based on the structural calculation results for each individual, and is used as the fitness score for each individual.

[0094] In addition, after obtaining the structural calculation results for each individual, it is also possible to check whether each structural calculation result meets the constraints, so as to further ensure safety and compliance.

[0095] The more severe the violation of constraints, the lower the fitness of the individual.

[0096] In this embodiment, performing genetic operations based on the fitness score of each individual includes:

[0097] In each round of genetic operations, a tournament selection strategy is used to select multiple parents from the initial population; wherein, in each selection of the parents, a specified number of individuals are randomly selected from the initial population as candidate individuals, and the candidate individual with the highest fitness score is selected as a parent.

[0098] The multiple parent generations are combined in pairs to obtain multiple parent generation groups;

[0099] A two-point crossover strategy is adopted to exchange gene fragments between two parents in each parent group with a preset crossover probability to generate multiple offspring.

[0100] A random reset mutation strategy is adopted to randomly change the gene segment of each offspring with a preset mutation probability to obtain a new chromosome, and a new population is constructed based on the new chromosome;

[0101] The new population is merged into the initial population to perform the next round of genetic operations.

[0102] For example, a tournament selection strategy can be used to select individuals with high fitness from the current population (e.g., 100 individuals) as parents, thus ensuring a higher probability of passing on superior genes to the next generation. Specifically, three individuals are randomly selected from the population, i.e., a three-person tournament is held. The fitness scores of these three individuals are compared, and the individual with the highest fitness score is selected as the parent. This process is repeated 100 times (to match the population size), ultimately resulting in 100 parent individuals (it is permissible to repeatedly select the same individual with a high fitness score, i.e., retaining elite individuals multiple times).

[0103] Then, a two-point crossover strategy was adopted to generate offspring with the superior traits of both parents by exchanging gene fragments between parent individuals, thus exploring new combinations of design variables. Specifically, the 100 selected parent individuals were randomly divided into pairs (50 groups in total). Crossover was performed on the parent groups with a 90% crossover probability (i.e., 90% of the parent groups would undergo crossover, and two crossover points were randomly selected for each parent group, exchanging gene fragments between the two points). 10% of the parent individuals were directly retained as offspring to avoid the destruction of superior genes. For example: Parent 1 chromosome is {3,45,40,35,...} (3 represents a wall thickness of 550mm, 45 represents a prismatic cross section of layers 1-4, number 45), and Parent 2 chromosome is {0,50,38,42,...} (0 represents a wall thickness of 400mm, 50 represents a prismatic cross section of layers 1-4, number 50). If the crossover point is the 2nd and 4th genes, then after the exchange, Offspring 1 will be {3,50,38,35,...}, and Offspring 2 will be {0,45,40,42,...}.

[0104] Finally, a random reset mutation strategy is adopted. By randomly changing the offspring genes, the homogeneity of the population genes is broken, preventing the algorithm from getting trapped in local optima (such as the steel component cross-sections in a certain area always being limited to a few types), thereby maintaining population diversity. Specifically, for each gene locus of the offspring chromosome, if the mutation probability is 0.05 (i.e., each gene locus has a 5% probability of mutation, a low probability that avoids destroying superior genes), the gene value is reset to a random value within the range of the corresponding design variable. For example, in the offspring chromosome {3,50,38,35,...}, if the gene "50 for the corner column cross-section of layers 1-4" triggers mutation, and the value range of this gene is 0-99, it is randomly reset to 62. After mutation, the offspring are {3,62,38,35,...} (corresponding to the corner column cross-section of layers 1-4 being changed to number 62).

[0105] Thus, the new generation of chromosomes not only retains the superior genes of the parent generation, but also introduces new genes through mutations, providing a diverse range of candidate schemes for the next round of fitness assessment.

[0106] Through the above embodiments, the retention, recombination, and innovation of superior genes in the population can be achieved through the sequential execution of selection, crossover, and mutation, ultimately approaching the global optimum. By leveraging the population search and random mutation characteristics of genetic algorithms, global exploration of the design space is realized, effectively escaping the trap of local optima. Simultaneously, automated iteration significantly shortens the optimization cycle, improving optimization efficiency and accuracy.

[0107] S14, decode the target chromosome to obtain the optimal design variable value, and generate an optimization strategy for the target building model based on the optimal design variable value.

[0108] In this embodiment, after the genetic operation is terminated, the individual with the highest fitness in the final population can be output, and its chromosome can be decoded to obtain a set of optimal design variable values, thereby forming a clear "component selection table".

[0109] The final optimized strategy can be the lowest cost design scheme that satisfies all safety requirements.

[0110] In this embodiment, the optimization strategies can be compared and presented in the form of tables and three-dimensional model diagrams to intuitively demonstrate the optimization effect.

[0111] In this embodiment, the optimization strategy can also be sent to a designated terminal for comprehensive engineering review to ensure that it meets all specification requirements and construction details not quantified in the optimization model, so as to guarantee the absolute safety and reliability of the final design.

[0112] This embodiment is directly geared towards engineering design, and the output results are specific component models and material parameters. It can be seamlessly integrated with the preceding drawing recognition, element modeling technology and the backend BIM visualization technology to form a powerful integrated intelligent design platform that combines "design-analysis-optimization-delivery", which significantly improves the technical level and engineering efficiency of complex structural design.

[0113] This embodiment can be easily extended to multi-objective optimization. For example, by calculating the Pareto front solution set, decision-makers can be provided with a set of excellent, non-substitutable solutions that weigh different objectives (such as minimum cost and maximum safety margin).

[0114] As can be seen from the above technical solutions, this invention can establish a parametric primitive building model based on design variables, and generate multiple chromosomes based on the parametric primitive building model to realize the digital and model-based representation of design variables. By using a genetic algorithm, the multiple chromosomes are iteratively optimized based on the objective function and constraints to obtain the target chromosome, and the target chromosome is decoded to obtain the optimal design variable value. Based on the optimal design variable value, an optimization strategy for the target building model is generated. Thus, the direction and boundary of optimization are clarified by optimizing the objective function and constraints, and the global exploration of the design space is realized through the genetic algorithm, effectively escaping the local optimum trap, and realizing fast, accurate and automated optimization of the building structure model.

[0115] like Figure 3 The diagram shown is a functional block diagram of a preferred embodiment of the building structure model optimization device of the present invention. The building structure model optimization device 11 includes an acquisition unit 110, a generation unit 111, a determination unit 112, and an iteration unit 113. The module / unit referred to in this invention is a series of computer program segments that can be executed by a processor and perform a fixed function, and are stored in memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0116] The acquisition unit 110 is used to acquire the structural design parameters to be optimized as design variables in response to the optimization instruction of the target building model.

[0117] The generation unit 111 is used to establish a parametric primitive building model based on the design variables, and to generate multiple chromosomes based on the parametric primitive building model.

[0118] The determining unit 112 is used to determine the optimization objective function and constraints based on the penalty mechanism;

[0119] The iterative unit 113 is used to perform iterative optimization on the multiple chromosomes based on the optimization objective function and the constraints using a genetic algorithm to obtain the target chromosome.

[0120] The generation unit 111 is further configured to decode the target chromosome to obtain the optimal design variable value, and generate an optimization strategy for the target building model based on the optimal design variable value.

[0121] As can be seen from the above technical solutions, this invention can establish a parametric primitive building model based on design variables, and generate multiple chromosomes based on the parametric primitive building model to realize the digital and model-based representation of design variables. By using a genetic algorithm, the multiple chromosomes are iteratively optimized based on the objective function and constraints to obtain the target chromosome, and the target chromosome is decoded to obtain the optimal design variable value. Based on the optimal design variable value, an optimization strategy for the target building model is generated. Thus, the direction and boundary of optimization are clarified by optimizing the objective function and constraints, and the global exploration of the design space is realized through the genetic algorithm, effectively escaping the local optimum trap, and realizing fast, accurate and automated optimization of the building structure model.

[0122] like Figure 4 The diagram shown is a schematic diagram of the computer device used to implement the building structure model optimization method of the present invention.

[0123] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a building structure model optimization program.

[0124] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0125] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0126] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as the code of a building structure model optimization program, but also to temporarily store data that has been output or will be output.

[0127] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing building structure model optimization programs) and calls data stored in the memory 12 to perform various functions of the computer device 1 and process data.

[0128] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes these applications to implement the steps in the various building structure model optimization method embodiments described above, for example... Figure 1 The steps are shown.

[0129] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into an acquisition unit 110, a generation unit 111, a determination unit 112, and an iteration unit 113.

[0130] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the building structure model optimization method described in the various embodiments of this invention.

[0131] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0132] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0133] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0134] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0135] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 4 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0136] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0137] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish a communication connection between the computer device 1 and other computer devices.

[0138] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0139] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0140] It will be understood by those skilled in the art that Figure 4The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0141] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a building structure model optimization method, and the processor 13 can execute the multiple instructions to achieve:

[0142] In response to the optimization command for the target building model, the structural design parameters to be optimized are obtained as design variables;

[0143] A parametric primitive building model is established based on the design variables, and multiple chromosomes are generated based on the parametric primitive building model.

[0144] The objective function and constraints are determined based on the penalty mechanism.

[0145] A genetic algorithm is used to iteratively optimize the multiple chromosomes based on the objective function and the constraints to obtain the target chromosome.

[0146] The target chromosome is decoded to obtain the optimal design variable values, and an optimization strategy for the target building model is generated based on the optimal design variable values.

[0147] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0148] It should be noted that all data involved in this case was legally obtained. Software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0149] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0150] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0151] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0152] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0153] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0154] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0155] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing a building structure model, characterized in that, The building structure model optimization method includes: In response to the optimization command for the target building model, the structural design parameters to be optimized are obtained as design variables; A parametric primitive building model is established based on the design variables, and multiple chromosomes are generated based on the parametric primitive building model. The objective function and constraints are determined based on the penalty mechanism. A genetic algorithm is used to iteratively optimize the multiple chromosomes based on the objective function and the constraints to obtain the target chromosome. The target chromosome is decoded to obtain the optimal design variable values, and an optimization strategy for the target building model is generated based on the optimal design variable values. The target building model is a concrete core tube-steel module building model, and the design variables include: core tube wall thickness, concrete grade, and cross-sectional index number of beam and column members in the steel module. The step of establishing a parametric primitive architectural model based on the design variables includes: Establish the mapping relationship between the design variables and component attributes; The automated drawing parsing tool is invoked to identify the architectural plan sketch corresponding to the target building model, thereby obtaining the building structure information; The data processing program is invoked to process the building structure information to obtain an initial topology graph; wherein, the initial topology graph uses components as nodes and the connection relationships between components as edges; Establish the association between the design variables and the nodes in the initial topology graph; Attribute data is obtained from the configuration database according to the mapping relationship, and the attribute data is added to the corresponding node of the initial topology graph according to the association relationship to obtain the parameterized primitive building model; The determination of the optimization objective function and constraints based on the penalty mechanism includes: Obtain the total project cost function, and add a penalty term to the total project cost function to obtain the optimization objective function; The performance indicators required by the building structure design code are converted into the aforementioned constraints. The penalty items include macroscopic displacement penalty items, microscopic component verification penalty items, and period ratio index penalty items.

2. The building structure model optimization method as described in claim 1, characterized in that, The generation of multiple chromosomes based on the parameterized primitive architectural model includes: Obtain the variable value of each design variable from the parametric primitive architectural model; The variable values ​​of each design variable are encoded according to a preset format to obtain the multiple chromosomes; Each chromosome represents a complete architectural design scheme, and the value of each design variable corresponds to a gene segment within the corresponding chromosome.

3. The building structure model optimization method as described in claim 1, characterized in that, The genetic algorithm is used to iteratively optimize the multiple chromosomes based on the objective function and the constraints, resulting in the following target chromosomes: Based on the multiple chromosomes, and under the premise of satisfying the constraints, an initial population with a preset number of individuals is randomly generated; wherein each individual corresponds to one chromosome. The fitness of each individual in the initial population is evaluated to obtain a fitness score for each individual; Genetic operations are performed based on the fitness score of each individual until the termination condition is met, at which point the genetic operations are stopped. The chromosome corresponding to the individual with the highest fitness score obtained so far is determined as the target chromosome; The termination conditions include reaching a preset number of iteration rounds and / or the highest fitness score for a consecutive preset number of rounds reaching convergence.

4. The building structure model optimization method as described in claim 3, characterized in that, The fitness assessment of each individual in the initial population, resulting in a fitness score for each individual, includes: Obtain the chromosome corresponding to each individual; Decode the chromosome corresponding to each individual to obtain the sequence of design variable values ​​for each individual; The automated modeling program is invoked to generate a structural analysis model for each individual based on the sequence of design variable values ​​for each individual. The structural analysis software kernel is invoked to perform structural calculations on the structural analysis model corresponding to each individual, and the structural calculation results corresponding to each individual are obtained. The optimization objective function value for each individual is determined based on the structural calculation results for each individual, and is used as the fitness score for each individual.

5. The building structure model optimization method as described in claim 3, characterized in that, The genetic operations performed based on each individual's fitness score include: In each round of genetic operations, a tournament selection strategy is used to select multiple parents from the initial population; wherein, in each selection of the parents, a specified number of individuals are randomly selected from the initial population as candidate individuals, and the candidate individual with the highest fitness score is selected as a parent from the candidate individuals; The multiple parent generations are combined in pairs to obtain multiple parent generation groups; A two-point crossover strategy is adopted to exchange gene fragments between two parents in each parent group with a preset crossover probability to generate multiple offspring. A random reset mutation strategy is adopted to randomly change the gene fragments of each offspring with a preset mutation probability to obtain new chromosomes, and a new population is constructed based on the new chromosomes; The new population is merged into the initial population to perform the next round of genetic operations.

6. A building structure model optimization device, characterized in that, The building structure model optimization device includes: The acquisition unit is used to acquire the structural design parameters to be optimized as design variables in response to the optimization instructions on the target building model; The generation unit is used to establish a parametric primitive building model based on the design variables, and to generate multiple chromosomes based on the parametric primitive building model. The determination unit is used to determine the optimization objective function and constraints based on the penalty mechanism; An iterative unit is used to perform iterative optimization on the multiple chromosomes based on the optimization objective function and the constraints using a genetic algorithm to obtain the target chromosome. The generation unit is also used to decode the target chromosome to obtain the optimal design variable value, and generate an optimization strategy for the target building model based on the optimal design variable value; The target building model is a concrete core tube-steel module building model, and the design variables include: core tube wall thickness, concrete grade, and cross-sectional index number of beam and column members in the steel module. The step of establishing a parametric primitive architectural model based on the design variables includes: Establish the mapping relationship between the design variables and component attributes; The automated drawing parsing tool is invoked to identify the architectural plan sketch corresponding to the target building model, thereby obtaining the building structure information; The data processing program is invoked to process the building structure information to obtain an initial topology graph; wherein, the initial topology graph uses components as nodes and the connection relationships between components as edges; Establish the association between the design variables and the nodes in the initial topology graph; Attribute data is obtained from the configuration database according to the mapping relationship, and the attribute data is added to the corresponding node of the initial topology graph according to the association relationship to obtain the parameterized primitive building model; The determination of the optimization objective function and constraints based on the penalty mechanism includes: Obtain the total project cost function, and add a penalty term to the total project cost function to obtain the optimization objective function; The performance indicators required by the building structure design code are converted into the aforementioned constraints. The penalty items include macroscopic displacement penalty items, microscopic component verification penalty items, and period ratio index penalty items.

7. A computer device, characterized in that, The computer device includes: A memory for storing at least one instruction; and a processor for executing the instructions stored in the memory to implement the building structure model optimization method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the building structure model optimization method as described in any one of claims 1 to 5.

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