Supporting point selection method and system for irregular grid structure and related equipment

Abstract

The invention discloses a fulcrum selection method and system for an irregular grid structure and related equipment, and the method comprises the steps: firstly building a finite element model of the irregular grid structure, counting the number of nodes, and numbering the nodes; then, in combination with site construction conditions, (1) a fulcrum selectable range is given; (2) fulcrum number range; (3) intelligent algorithm parameters such as the maximum number of iterations; then selecting a structure optimization index as a population fitness function; then selecting a constraint processing function; finally, corresponding parameters are substituted into the irregular grid structure finite element model for iteration, a finite element software platform is automatically called to calculate structural stress and output a calculation result, structural optimization indexes are calculated according to the stress result, constraint processing is carried out on individuals which do not meet constraints, and an optimal fulcrum arrangement scheme is output after repeated iteration. The scheme of the invention can effectively overcome the tedious calculation process of the existing fulcrum determination method, can quickly determine the optimal fulcrum scheme of the structure, and is convenient for engineers to use.

Description

Technical Field

[0001] The invention relates to the construction and installation process of a large-span grid structure, and in particular to the selection of structural fulcrums in overall installation methods such as jacking and hoisting of the large-span grid structure. Background Art

[0002] With the rapid development of social economy, the construction of large public buildings has become more frequent. At the same time, along with the innovation and upgrading of design concepts and aesthetic ideas, asymmetric, long cantilevered and high-clearance architectural shapes are increasing. Irregular large-span grid structures have become an important structural form for realizing design concepts. During the construction of such grid structures, jacking, hoisting and other installation methods are widely used.

[0003] Among them, this type of installation method involves the determination of the fulcrums of irregular structures. The number and position of the fulcrums are directly related to the structural safety, fulcrum reaction force and the selection of construction machinery. However, the determination of the fulcrum scheme for irregular grid structures currently often relies on the experience of engineers, who select from multiple fulcrum schemes based on the structural response and construction site conditions, which involves repeated calculations of the number and position of the fulcrums.

[0004] Therefore, the existing method for determining the fulcrum of an irregular structure is extremely inefficient because it needs to rely on the experience of engineers to make the determination. Therefore, it can be seen that how to improve the efficiency of determining the fulcrum of an irregular structure is a problem that needs to be solved in this field. Summary of the invention

[0005] In view of the problem that the existing schemes are based on manual experience to select and determine the fulcrums of irregular grid structures, the purpose of the present invention is to provide a fulcrum selection scheme for irregular grid structures. The scheme is based on an innovative intelligent algorithm and can automatically give the corresponding most suitable fulcrums for irregular grid structures for reference by engineering personnel, thereby greatly reducing reliance on engineers' personal experience and improving efficiency.

[0006] In order to achieve the above object, the present invention provides a fulcrum selection method for an irregular grid structure, the fulcrum selection method comprising the following steps:

[0007] Step 1: For the target irregular grid structure, establish the corresponding structural finite element model and set the corresponding loads and other conditions;

[0008] Step 2: Determine the optional range parameters of the fulcrum, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions;

[0009] Step 3: Constructing the population fitness function in the established structural finite element model based on the structural optimization index;

[0010] Step 4: Construct the constraint processing function in the established structural finite element model based on the performance requirements of the target irregular grid structure;

[0011] Step 5: Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and constrain the individuals that do not meet the constraints. After repeated iterations, output the optimal support point arrangement plan.

[0012] In some embodiments of the present invention, the structural finite element model is constructed based on a genetic algorithm in step (1).

[0013] In some embodiments of the present invention, the iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population number n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm.

[0014] In some embodiments of the present invention, in step (3), a population fitness function is constructed based on structural performance indicators such as structural strain energy and maximum reaction force of support points.

[0015] In some embodiments of the present invention, in step (4), a constraint processing function is constructed based on performance indicators such as strength of structural members, maximum deformation of the entire structure, and stability.

[0016] In some embodiments of the present invention, the structural finite element model in step (5) is iteratively run, specifically comprising the following steps:

[0017] (5.1) The structural finite element model is first initialized based on the input support optional range parameters, support number range parameters, and various iterative operation parameters to form the support distribution position corresponding to the initial population;

[0018] (5.2) Set corresponding boundary conditions for the formed support point positions, and then perform structural force calculations based on the load and other conditions pre-set when constructing the structural finite element model;

[0019] (5.3) Based on the structural force calculation results in step (5.2), combined with the population fitness function constructed in step (3), the structural optimization index is calculated;

[0020] (5.4) updating a new population of fulcrum distribution according to the structural optimization index calculated and determined in step (5.3);

[0021] (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population;

[0022] (5.6) Based on the above steps, the maximum number of iterations m is input and repeated iterations are performed to generate the optimal fulcrum arrangement scheme.

[0023] In order to achieve the above object, the present invention also provides a selection system for irregular grid structure fulcrums, the selection system is composed of a finite element model construction module, a parameter setting module, a population fitness function construction module, a constraint processing function construction module, and an iteration and constraint processing module.

[0024] The finite element model building module is configured to establish a corresponding structural finite element model for the target irregular grid structure, and set corresponding loads and other conditions;

[0025] The parameter setting module is configured to determine the optional range parameters of the fulcrum, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions;

[0026] The population fitness function construction module is configured to be able to construct a population fitness function in the established structural finite element model based on the structural optimization index;

[0027] The constraint processing function construction module is configured to be able to construct a constraint processing function adapted to the established structural finite element model based on the performance requirements of the target irregular grid structure;

[0028] The iteration and constraint processing module cooperate with each other to form data interaction with the finite element model construction module, the parameter setting module, the population fitness function construction module, and the constraint processing function construction module, and can call the structural finite element model established by the finite element model construction module, and introduce various parameters determined by the parameter setting module, the population fitness function constructed by the population fitness function construction module and the constraint processing function constructed by the constraint processing function construction module as parameter inputs, and iterate accordingly to calculate the structural force and output the calculation results, calculate the structural optimization index according to the force results, and perform constraint processing on the individuals that do not meet the constraints, and output the optimal fulcrum arrangement plan after repeated iterations.

[0029] In some embodiments of the present invention, the iteration and constraint processing module is composed of a support point distribution position calculation submodule, a structural force calculation submodule, a structural optimization index calculation submodule, a population update submodule, a constraint processing submodule and an iteration processing submodule.

[0030] The fulcrum distribution position calculation submodule is configured to call the structural finite element model, and first perform an initialization operation based on the input fulcrum optional range parameters, fulcrum quantity range parameters, and various iterative operation parameters to form a fulcrum distribution position corresponding to the initial population;

[0031] The structural force calculation submodule is configured to interact with the support point distribution position calculation submodule data, and can set corresponding boundary conditions for the formed support point positions, and then perform structural force calculation in combination with the load and other conditions pre-set when the structural finite element model is constructed;

[0032] The structural optimization index calculation submodule is configured to interact with the structural force calculation submodule data, and can calculate the structural optimization index based on the structural force result calculated by the structural force calculation submodule and the population fitness function constructed by the population fitness function construction module;

[0033] The population updating submodule is configured to interact with the structural optimization index calculating submodule data, and can update a new population of fulcrum distribution according to the structural optimization index calculated and determined by the structural optimization index calculating submodule;

[0034] The constraint processing submodule is configured to interact with the population updating submodule data, and can perform constraint processing on the pivot distribution population formed by the population updating submodule based on the constraint processing function constructed by the constraint processing function construction module to form a new population;

[0035] The iterative processing submodule is configured to interact with the fulcrum distribution position calculation submodule, the structural force calculation submodule, the structural optimization index calculation submodule, the population update submodule, and the constraint processing submodule data, and coordinate the fulcrum distribution position calculation submodule, the structural force calculation submodule, the structural optimization index calculation submodule, the population update submodule, and the constraint processing submodule to perform repeated iterations according to the iterative operation parameters set by the parameter setting module, and finally output the optimal fulcrum arrangement plan.

[0036] In order to achieve the above object, the present invention also provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps of the above fulcrum selection method are implemented.

[0037] In order to achieve the above object, the present invention also provides a processor, which is used to run a program, and the steps of the above fulcrum selection method are executed when the program is running.

[0038] In order to achieve the above-mentioned purpose, the present invention also provides a terminal device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program code is loaded and executed by the processor to implement the steps of the above-mentioned fulcrum selection method.

[0039] In order to achieve the above object, the present invention also provides a computer program product, which is suitable for executing the steps of the above fulcrum selection method when executed on a data processing device.

[0040] The fulcrum selection scheme for irregular grid structures provided by the present invention is based on intelligent fulcrum selection means. It can automatically provide the most suitable fulcrum for the structure for reference by engineers through an innovative intelligent selection algorithm on the basis of providing preliminary fulcrum parameters, thereby reducing dependence on the personal experience of engineers, overcoming the cumbersome calculation process of existing fulcrum determination methods, being easy for engineers to use, and greatly saving their time and energy.

[0041] The fulcrum selection scheme for irregular grid structures provided by the present invention is suitable for the selection and determination of structural fulcrums in overall installation projects such as jacking and hoisting of large-span grid structures. It can quickly provide fulcrum arrangement schemes and corresponding structural calculation results, reduce reliance on engineers' subjective judgments, and save time; at the same time, it can also reduce construction machinery and tools and avoid construction risks.

[0042] Furthermore, when the solution provided by the present invention is implemented specifically, it innovatively uses the finite element calculation of the complex bar structure as the basis for fitness function calculation on the basis of the self-iteration and automatic optimization function of the intelligent optimization algorithm, and further calls the finite element calculation and intelligent optimization algorithm, which can effectively overcome the cumbersome calculation process of the existing support point determination method. At the same time, the support point arrangement formed is directly related to the structural verification result, has high credibility, and is convenient for engineering application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] Figure 1 is a basic flow chart of the fulcrum selection method for irregular grid structures in the present invention;

[0045] Figure 2 A system principle diagram of a fulcrum selection system for an irregular grid structure in the present invention;

[0046] Figure 3 It is a schematic diagram of the composition of the iteration and constraint processing module in the present invention;

[0047] Figure 4 A flowchart of the optimization of the fulcrum of the irregular grid structure in the example of the present invention;

[0048] Figure 5 A schematic diagram of an irregular grid structure for which a fulcrum needs to be determined in an example of the present invention;

[0049] Figure 6 It is a schematic diagram of the initial space distribution of points in the grid structure in the example of the present invention;

[0050] Figure 7 It is a schematic diagram of the optimized distribution structure of the support point arrangement in the example of the present invention;

[0051] Figure 8 It is the calculation result corresponding to the optimized fulcrum arrangement in the example of the present invention. DETAILED DESCRIPTION

[0052] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below with reference to specific diagrams.

[0053] In view of the structural characteristics of irregular large-span grid structures, in order to be able to quickly and accurately obtain the fulcrums of irregular large-span grid structures, the solution of the present invention abandons conventional means and innovatively constructs a finite element model of the irregular grid structure based on an improved genetic algorithm. On this basis, the preliminary fulcrum parameters are combined, and then intelligent calculations are performed through intelligent algorithms to automatically find the best solution, which can automatically determine the optimal fulcrum arrangement solution.

[0054] Accordingly, the present invention provides a method for selecting a fulcrum for an irregular grid structure, which is mainly composed of the following steps in cooperation with each other:

[0055] Step 1: For the target irregular grid structure, establish the corresponding structural finite element model and set the corresponding loads and other conditions;

[0056] Step 2: Determine the optional range parameters of the fulcrum, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions;

[0057] Step 3: Constructing the population fitness function in the established structural finite element model based on the structural optimization index;

[0058] Step 4: Construct the constraint processing function in the established structural finite element model based on the performance requirements of the target irregular grid structure;

[0059] Step 5: Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and constrain the individuals that do not meet the constraints. After repeated iterations, output the optimal support point arrangement plan.

[0060] On this basis, the implementation scheme of the present invention and the corresponding technical features are described in detail below.

[0061] In some embodiments of the present invention, when constructing the structural finite element model in step (1), each node in the optional support range in the constructed structural finite element model is set with a clear and fixed node number for algorithm retrieval; furthermore, the constructed structural finite element model is configured to be able to run independently outside each support selection program, and the supports in the support range need to be deleted before entering the support selection program.

[0062] In some embodiments of the present invention, when determining the corresponding optional range parameters of the fulcrum in step (2), the fulcrum targeted is a group of fulcrum nodes in the irregular grid structure that can be used as structural support points during construction. At the same time, the fulcrum range is determined according to the actual situation of the construction site, and excludes areas where support systems such as tire frames and jacks cannot be supported due to ground facilities, foundation bearing capacity or structural collision, thereby ensuring the accuracy of the determined optional range parameters of the fulcrum.

[0063] As an example, the fulcrum determined here may have a range of N points, and each point includes a spatial coordinate.

[0064] Furthermore, in the range of the number of fulcrums determined in step (2), the number of fulcrums is specifically a positive integer interval including a minimum number of fulcrums and a maximum number of fulcrums. The minimum number of fulcrums here is 1, and the maximum number is determined based on historical data and the actual situation of the construction site. Generally speaking, the more fulcrums there are, the better the structural stress, but the higher the construction cost required. Therefore, in this scheme, the preferred number of fulcrums can be gradually selected from 3 upwards until the structural stress meets the requirements.

[0065] Furthermore, the present solution further reduces the interval length, thereby improving the rapid convergence of the support point optimization in the subsequent calculation process.

[0066] As an example, the range of the number of fulcrums determined here is specifically [m1, m2], where m1=2, m2=5.

[0067] Furthermore, the iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population number n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm. These four parameters can effectively adapt to the iterative operation of the structural finite element model.

[0068] The population number n is configured as the possible number of solutions to the fulcrum determination problem. To ensure the accuracy of subsequent running results, the population number n is preferably greater than 20.

[0069] The maximum number of iterations m is configured to control the length of the intelligent algorithm search process during the iterative operation of the structural finite element model. When the number of iterations reaches the maximum number of iterations, the iteration of the structural finite element model will stop searching and output the best solution currently found. To ensure the accuracy of the subsequent operation results, the maximum number of iterations m here is preferably above 50.

[0070] The crossover probability Pc is configured to determine the frequency of crossover operations between two individuals in the iterative operation of the structural finite element model. A larger crossover probability is conducive to maintaining the diversity of the population and promoting the generation of new individuals, but it may also destroy excellent individuals. A smaller crossover probability may result in a too slow search speed. In order to ensure the accuracy of the subsequent operation results, the crossover probability Pc here is preferably determined between 0.4 and 0.99.

[0071] The mutation probability Pm is configured to determine the frequency of individual gene mutations in the iterative operation of the structural finite element model. For the mutation operation in the iterative operation, an excessively large mutation probability may cause the search to tend to be random and destroy the excellent model of the population. To ensure the accuracy of the subsequent operation results, the mutation probability Pm here is preferably determined to be 0.05.

[0072] In some embodiments of the present invention, when constructing the population fitness function based on the structural optimization index in step (3), it can be specifically constructed based on structural performance indicators such as structural strain energy and maximum reaction force of the support.

[0073] As an example, here the fitness function is set according to the inverse of the maximum reaction force of the construction support group as follows:

[0074]

[0075] In the formula, Fitness is the fitness value of the population; P i,max is the node reaction at the maximum reaction point i.

[0076] In some embodiments of the present invention, when the constraint processing function is constructed based on the performance requirements of the target irregular grid structure in step (4), it is constructed specifically based on performance indicators such as structural member strength, maximum deformation of the entire structure, and stability.

[0077] As an example, a multi-condition constraint function of structural safety is established here based on structural strength and deformation. If any aspect exceeds the limit, the constraint processing is executed:

[0078] σ max >σ limits (Formula 2)

[0079] u max >u limits (Formula 3)

[0080] In the formula, σ max is the maximum stress of the structure; limits is the maximum allowable stress of the structure; u max is the maximum deformation force of the structure; u limits The maximum allowable deformation of the structure.

[0081] In some embodiments of the present invention, in step (5), the structural finite element model is iteratively run, specifically comprising the following steps:

[0082] (5.1) The structural finite element model is first initialized based on the input support optional range parameters, support number range parameters, and various iterative operation parameters to form the support distribution position corresponding to the initial population;

[0083] (5.2) Set corresponding boundary conditions for the formed support point positions, and then perform structural force calculations based on the load and other conditions pre-set when constructing the structural finite element model;

[0084] (5.3) Based on the structural force calculation results in step (5.2), combined with the population fitness function constructed in step (3), the structural optimization index is calculated;

[0085] (5.4) updating a new population of fulcrum distribution according to the structural optimization index calculated and determined in step (5.3);

[0086] (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population;

[0087] (5.6) Based on the above steps, the maximum number of iterations m is input and repeated iterations are performed to generate the optimal fulcrum arrangement scheme.

[0088] Furthermore, when forming the fulcrum distribution positions corresponding to the initial population in step (5.1), the present invention includes:

[0089] First, an N-bit ordered list with all original values ​​0 is formed with the input support point optional range parameter N, where each bit corresponds to a real node in the finite element model;

[0090] Next, randomly change m of the positions to 1, and configure the current position to have a support point. The boundary conditions corresponding to the support point, such as hinge or consolidation, are determined by the actual conditions of the construction site.

[0091] Next, the above random process is repeated n times to form n 0-1 lists, which constitute the initial population and then form the initial position of the fulcrum distribution.

[0092] Furthermore, the present invention, when performing structural force calculation in step (5.2), includes:

[0093] First, read the 0-1 list formed in step (5.1), and set the corresponding boundary conditions at the corresponding pivot positions based on the API method;

[0094] Next, based on the load and other conditions that have been pre-set in step 1, the structural force is calculated based on the finite element method and the calculation results are output. As an example, the finite element module here can perform structural calculations based on rod units, beam units and other units and output results such as stress and strain.

[0095] Furthermore, when calculating the structural optimization index in step (5.3) of the present invention, the corresponding structural optimization index may be selected from structural strain energy or maximum reaction force of the support point, etc.

[0096] On this basis, the structural optimization index is calculated based on the structural force value calculated in step (5.2) and the population fitness function determined in step (3) (such as formula 1). When the population fitness function is used for calculation, its population fitness is the inverse of the maximum force of the fulcrum, so the smaller the force on the fulcrum, the higher the population fitness.

[0097] Furthermore, when constructing a new population of fulcrum distribution in step (5.4), the present invention updates the 0-1 list of fulcrum distribution formed in step (5.1) through selection operation, crossover operation and mutation operation in sequence to form a new population of fulcrum arrangement based on the structural optimization index calculated in step (5.3).

[0098] The specific implementation scheme of the selection, crossover and mutation operations is not limited here and can be determined according to actual needs.

[0099] Furthermore, when the present invention performs constraint processing on the new population of fulcrum distribution established in step (5.4) in step (5.5), firstly, each individual in the new population is analyzed and judged to determine whether it satisfies the structural fulcrum constraint condition formed by the corresponding constraint processing function in step (4);

[0100] Next, for the individuals that do not meet the constraints, the corresponding constraint processing function in step (4) is used to perform constraint processing until all individuals meet the constraints, thus forming a new population.

[0101] Specifically, the process of implementing constraint processing in the solution of the present invention is as follows:

[0102] (5.4.1) First, sort the existing pivots from low to high according to the reaction force, remove the pivot with the smallest reaction force (ranked as 1), randomly add new pivots, update the chromosome code, perform finite element calculations, and examine the structural calculation results of the new pivot arrangement. If the constraints are met, the chromosome is repaired;

[0103] (5.4.2) If the constraint condition is still not met, remove the fulcrum with the second smallest reaction force (ranked as 2), randomly add a new fulcrum, update the chromosome code, perform finite element calculation, and repeat the cycle until the constraint condition is met and the chromosome is repaired;

[0104] (5.4.3) If the requirements are still not met after the pivot traversal is completed, analyze and determine whether the number of pivots has reached the maximum number m2. If not, randomly add a pivot, perform finite element calculation, and perform chromosome repair until the constraints are met.

[0105] The constraint processing scheme here designs a two-stage constraint processing process: first, without increasing the number of fulcrums, the fulcrum positions are dynamically adjusted to try to meet the structural safety limit; secondly, if the requirements cannot be met by adjusting the fulcrum positions alone, the number of fulcrums of the structure is gradually increased to accelerate the speed of chromosome repair, thereby achieving the technical effect of streamlining the number of fulcrums and quickly repairing chromosomes.

[0106] The fulcrum selection method for an irregular grid structure provided by the present invention can be used in a specific application to form a corresponding software program to form a corresponding fulcrum selection system for an irregular grid structure. When the software program is running, it will execute the fulcrum selection method for an irregular grid structure and store it in a corresponding storage medium for the processor to retrieve and execute.

[0107] like Figure 2 As shown, the fulcrum selection system 100 for irregular grid structures formed thereby is specifically composed of a finite element model construction module 110, a parameter setting module 120, a population fitness function construction module 130, a constraint processing function construction module 140, and an iteration and constraint processing module 150 that cooperate with each other.

[0108] The finite element model building module 110 in the system is configured to establish a corresponding structural finite element model for the target irregular grid structure and set corresponding loads and other conditions.

[0109] The parameter setting module 120 in the present system is configured to determine the optional range parameters of the fulcrums, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions.

[0110] The population fitness function construction module 130 in the present system is configured to be able to construct a population fitness function in the established structural finite element model based on the structural optimization index.

[0111] The constraint processing function construction module 140 in the present system is configured to construct a constraint processing function adapted to the established structural finite element model based on the performance requirements of the target irregular grid structure.

[0112] The iteration and constraint processing module 150 in the system cooperates with each other to form data interaction with the finite element model construction module 110, the parameter setting module 120, the population fitness function construction module 130, and the constraint processing function construction module 140, and can call the structural finite element model established by the finite element model construction module 110, and introduce various parameters determined by the parameter setting module 120, the population fitness function constructed by the population fitness function construction module 130 and the constraint processing function constructed by the constraint processing function construction module 140 as parameter inputs, and iterate accordingly to calculate the structural force and output the calculation results, calculate the structural optimization index according to the force results, and perform constraint processing on the individuals that do not meet the constraints, and output the optimal fulcrum arrangement plan after repeated iterations.

[0113] As a further explanation, when the finite element model construction module 110 in the present system is specifically implemented, when constructing the structural finite element model, each node in the optional fulcrum range in the structural finite element model is set with a clear and fixed node number for algorithm retrieval; furthermore, the constructed structural finite element model is configured to be able to run independently outside each fulcrum selection program, and the supports in the fulcrum range need to be deleted before entering the fulcrum selection program.

[0114] The population fitness function construction module 130 in the present system can be constructed based on structural performance indicators such as structural strain energy and maximum reaction force of support points during specific implementation.

[0115] As an example, the population fitness function is constructed using the inverse of the maximum reaction force of the structural support.

[0116]

[0117] In the formula, Fitness is the fitness value of the population; P i,max is the node reaction at the maximum reaction point i of the structure.

[0118] The constraint processing function building module 140 in the system is built according to performance indicators such as the strength of structural members, the maximum deformation of the entire structure, and stability during specific implementation.

[0119] As an example, the strength of the structural member and the maximum deformation of the entire structure can be used to establish a constraint processing function, which is a multi-condition constraint: if any of the conditions in (Equation 2) and (Equation 3) is met, the constraint processing is executed:

[0120] σ max >σ limits (Formula 2)

[0121] u max >u limits (Formula 3)

[0122] In the formula, σ max is the maximum stress of the structure; limits is the maximum allowable stress of the structure; u max is the maximum deformation force of the structure; u limits The maximum allowable deformation of the structure.

[0123] See also Figure 3 When the iteration and constraint processing module 150 in this system is implemented specifically, it is composed of a support point distribution position calculation submodule 151, a structural force calculation submodule 152, a structural optimization index calculation submodule 153, a population update submodule 154, a constraint processing submodule 155 and an iteration processing submodule 156 that cooperate with each other.

[0124] Among them, the fulcrum distribution position calculation submodule 151 is configured to call the structural finite element model, and first perform initialization operation based on the input fulcrum optional range parameters, fulcrum number range parameters, and various iterative operation parameters to form the fulcrum distribution position corresponding to the initial population.

[0125] The structural force calculation submodule 152 is configured to interact with the support point distribution position calculation submodule 151 in data, and can set corresponding boundary conditions for the support point positions formed by the support point distribution position calculation submodule 151, and then perform structural force calculation in combination with the load and other conditions pre-set when the structural finite element model is constructed.

[0126] The structural optimization index calculation submodule 153 is configured to interact with the structural force calculation submodule 152 in data, and can calculate the structural optimization index based on the structural force results calculated by the structural force calculation submodule 152 and the population fitness function constructed by the population fitness function construction module 130.

[0127] The population updating submodule 154 is configured to interact with the structural optimization index calculating submodule 153 in data, and can update a new population of fulcrum distribution according to the structural optimization index calculated and determined by the structural optimization index calculating submodule 153 .

[0128] The constraint processing submodule 155 is configured to interact with the population updating submodule 154 and can perform constraint processing on the pivot distribution population formed by the population updating submodule 154 based on the constraint processing function constructed by the constraint processing function construction module 140 to form a new population.

[0129] The iterative processing submodule 156 is configured to interact with the fulcrum distribution position calculation submodule 151, the structural force calculation submodule 152, the structural optimization index calculation submodule 153, the population update submodule 154, and the constraint processing submodule 155, and coordinate the repeated iterations among the fulcrum distribution position calculation submodule 151, the structural force calculation submodule 152, the structural optimization index calculation submodule 153, the population update submodule 154, and the constraint processing submodule 155 according to the iterative operation parameters set by the parameter setting module 120 (such as the four parameters of the population number n, the maximum number of iterations m, the crossover probability Pc, and the mutation probability Pm), and finally output the optimal fulcrum arrangement plan.

[0130] The implementation process of the fulcrum selection scheme for irregular grid structures provided by the present invention is described in detail below through specific application examples.

[0131] This example builds a corresponding fulcrum selection system for irregular grid structures based on the above-mentioned solution, and performs corresponding operating environment construction and system operation deployment.

[0132] See also Figure 4 In this example, the process of optimizing the pivot points of an irregular grid structure based on the pivot point selection system is as follows:

[0133] (1) First, an irregular grid structure finite element model is established for the target irregular grid structure, such as Figure 5 shown.

[0134] (2) Based on the constructed finite element model, chromosome encoding is performed: chromosomes are generated according to the possible positions of the support points in the input parameters, such as Figure 6 As shown in the figure, the number of chromosome digits is equal to the number of possible positions N of the pivot point distribution. Each digit in the chromosome can have two values: 1 or 0. 1 corresponds to the existence of a pivot point at that point, and 0 corresponds to the absence of a pivot point.

[0135] (3) Establish multi-condition constraints; generate chromosome constraint conditions based on structural strength, deformation and stability verification, and execute constraint processing when the generated chromosome code and the corresponding support point calculation results do not meet the constraints.

[0136] Specifically, the constraint processing process in this example is as follows:

[0137] ① Sort the existing pivots from low to high according to the reaction force, remove the pivot with the smallest reaction force (ranked as 1), randomly add new pivots, update the chromosome code, perform finite element calculations, and examine the structural calculation results of the new pivot arrangement. If the constraints are met, the chromosome repair is complete;

[0138] ② If the constraint conditions are still not met, remove the fulcrum with the second smallest reaction force (ranked as 2), randomly add new fulcrums, update the chromosome code, perform finite element calculations, and repeat the cycle until the constraint conditions are met and the chromosome is repaired;

[0139] ③ If the requirements are still not met after the pivot traversal is completed, check whether the number of pivots reaches the maximum number m2 at this time, then randomly add one pivot, perform finite element calculation, and perform chromosome repair until the constraints are met.

[0140] (5) To facilitate construction and reduce risks, the fitness function is set according to the inverse of the maximum reaction force of the construction support group as follows:

[0141]

[0142] In the formula, Fitness is the fitness value of the population; P i,max is the node reaction at the maximum reaction point i of the structure.

[0143] (6) Based on the finite element model of the engineering structure and the input parameters, the fitness index of the pivot arrangement is calculated; then a selection operation based on the roulette strategy is performed, followed by crossover, mutation and recombination operations to form a new pivot arrangement population; then constraint processing is performed to form a new population, and repeated iterations are performed to converge to obtain the optimal chromosome arrangement of the population.

[0144] (7) Decode the obtained chromosome into a pivot point arrangement, and output the corresponding pivot point number, position, and corresponding finite element calculation results, such as Figure 7 and Figure 8 As shown, it is for reference by engineers.

[0145] Based on the above-mentioned fulcrum selection scheme for irregular grid structures, an embodiment of the present invention further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the steps of the above-mentioned fulcrum selection method for irregular grid structures are implemented.

[0146] An embodiment of the present invention further provides a processor, which is used to run a program, wherein the program executes the steps of the above-mentioned fulcrum selection method for an irregular grid structure when running.

[0147] An embodiment of the present invention also provides a terminal device, which includes a processor, a memory, and a program stored in the memory and executable on the processor, wherein the program code is loaded and executed by the processor to implement the steps of the above-mentioned fulcrum selection method for irregular grid structures.

[0148] The present invention also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the above-mentioned support point selection method for an irregular grid structure.

[0149] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0151] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0152] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0153] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0155] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0156] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0157] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0158] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0159] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0160] The above-mentioned method of the present invention, or a specific system unit, or a part of the same, is a pure software architecture, which can be arranged on a physical medium, such as a hard disk, an optical disk, or any electronic device (such as a smart phone, a computer-readable storage medium) through program code. When the machine loads the program code and executes it (such as a smart phone loading and executing it), the machine becomes a device for implementing the present invention. The above-mentioned method and device of the present invention can also be transmitted in the form of program code through some transmission media, such as cables, optical fibers, or any transmission mode. When the program code is received, loaded and executed by a machine (such as a smart phone), the machine becomes a device for implementing the present invention.

[0161] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for selecting a pivot point for an irregular grid structure, characterized in that: The fulcrum selection method comprises the following steps: Step 1: For the target irregular grid structure, establish the corresponding structural finite element model and set the corresponding loads and other conditions; Step 2: Determine the optional range parameters of the fulcrum, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions; Step 3: Constructing the population fitness function in the established structural finite element model based on the structural optimization index; Step 4: Construct the constraint processing function in the established structural finite element model based on the performance requirements of the target irregular grid structure; Step 5: Introduce the parameters determined in step (2), the population fitness function constructed in step (3), and the constraint processing function constructed in step (4) into the structural finite element model established in step (1), and iterate accordingly to calculate the structural force and output the calculation results. Calculate the structural optimization index based on the force results, and constrain the individuals that do not meet the constraints. After repeated iterations, output the optimal support point arrangement plan.

2. The method for selecting a fulcrum for an irregular grid structure according to claim 1, characterized in that: In the step (1), a structural finite element model is constructed based on a genetic algorithm.

3. The method for selecting fulcrums for an irregular grid structure according to claim 1, characterized in that: The iterative operation parameters of the structural finite element model determined in step (2) specifically include four parameters: population number n, maximum number of iterations m, crossover probability Pc, and mutation probability Pm.

4. The method for selecting fulcrums for an irregular grid structure according to claim 1, characterized in that: In the step (3), a population fitness function is constructed based on structural performance indicators such as structural strain energy and maximum reaction force of support points.

5. The method for selecting fulcrums for an irregular grid structure according to claim 1, characterized in that: In the step (4), the constraint processing function is constructed based on performance indicators such as the strength of the structural members, the maximum deformation of the entire structure, and the stability.

6. The method for selecting fulcrums for an irregular grid structure according to claim 1, characterized in that: In step (5), the structural finite element model is iteratively run, specifically including the following steps: (5.1) The structural finite element model is first initialized based on the input support optional range parameters, support number range parameters, and various iterative operation parameters to form the support distribution position corresponding to the initial population; (5.2) Set corresponding boundary conditions for the formed support point positions, and then perform structural force calculations based on the load and other conditions pre-set when constructing the structural finite element model; (5.3) Based on the structural force calculation results in step (5.2), combined with the population fitness function constructed in step (3), the structural optimization index is calculated; (5.4) updating a new population of fulcrum distribution according to the structural optimization index calculated and determined in step (5.3); (5.5) Based on the constraint processing function constructed in step (4), the fulcrum distribution population formed in step (5.4) is constrained to form a new population; (5.6) Based on the above steps, the maximum number of iterations m is input and repeated iterations are performed to generate the optimal fulcrum arrangement scheme.

7. A system for selecting support points of irregular grid structures, characterized in that: The selection system is composed of a finite element model construction module, a parameter setting module, a population fitness function construction module, a constraint processing function construction module, and an iteration and constraint processing module. The finite element model building module is configured to establish a corresponding structural finite element model for the target irregular grid structure and set corresponding loads and other conditions; The parameter setting module is configured to determine the optional range parameters of the fulcrum, the range parameters of the number of fulcrums, and the iterative operation parameters of the structural finite element model in combination with the on-site construction conditions; The population fitness function construction module is configured to be able to construct a population fitness function in the established structural finite element model based on the structural optimization index; The constraint processing function construction module is configured to be able to construct a constraint processing function adapted to the established structural finite element model based on the performance requirements of the target irregular grid structure; The iteration and constraint processing module cooperates with each other to form data interaction with the finite element model construction module, the parameter setting module, the population fitness function construction module, and the constraint processing function construction module, and can call the structural finite element model established by the finite element model construction module, and introduce various parameters determined by the parameter setting module, the population fitness function constructed by the population fitness function construction module and the constraint processing function constructed by the constraint processing function construction module as parameter inputs, and iterates accordingly to calculate the structural force and output the calculation results, calculates the structural optimization index according to the force results, and performs constraint processing on the individuals that do not meet the constraints, and outputs the optimal fulcrum arrangement plan after repeated iterations.

8. The system for selecting support points of an irregular grid structure according to claim 7, characterized in that: The iteration and constraint processing module is composed of a support point distribution position calculation submodule, a structural force calculation submodule, a structural optimization index calculation submodule, a population update submodule, a constraint processing submodule and an iteration processing submodule. The fulcrum distribution position calculation submodule is configured to call the structural finite element model, and first perform an initialization operation based on the input fulcrum optional range parameters, fulcrum quantity range parameters, and various iterative operation parameters to form a fulcrum distribution position corresponding to the initial population; The structural force calculation submodule is configured to interact with the support point distribution position calculation submodule data, and can set corresponding boundary conditions for the formed support point positions, and then perform structural force calculation in combination with the load and other conditions pre-set when the structural finite element model is constructed; The structural optimization index calculation submodule is configured to interact with the structural force calculation submodule data, and can calculate the structural optimization index based on the structural force result calculated by the structural force calculation submodule and the population fitness function constructed by the population fitness function construction module; The population updating submodule is configured to interact with the structural optimization index calculating submodule data, and can update a new population of fulcrum distribution according to the structural optimization index calculated and determined by the structural optimization index calculating submodule; The constraint processing submodule is configured to interact with the population updating submodule data, and can perform constraint processing on the pivot distribution population formed by the population updating submodule based on the constraint processing function constructed by the constraint processing function construction module to form a new population; The iterative processing submodule is configured to interact with the fulcrum distribution position calculation submodule, the structural force calculation submodule, the structural optimization index calculation submodule, the population update submodule, and the constraint processing submodule data, and coordinate the fulcrum distribution position calculation submodule, the structural force calculation submodule, the structural optimization index calculation submodule, the population update submodule, and the constraint processing submodule to perform repeated iterations according to the iterative operation parameters set by the parameter setting module, and finally output the optimal fulcrum arrangement plan.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps of the fulcrum selection method for an irregular grid structure described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that When executed on a data processing device, it is suitable for executing the steps of the fulcrum selection method for an irregular grid structure as claimed in any one of claims 1 to 6.

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