An intelligent tower crane selection and position optimization method, device and equipment

Abstract

The present invention relates to the technical field of engineering construction management, and discloses an intelligent tower crane selection and position optimization method, device and equipment. The method comprises: obtaining construction environment information, and determining the number of tower cranes and the range of each tower crane to be installed according to the construction environment information; randomly generating a population according to the range of each tower crane to be installed and the model to be selected of each tower crane, the population including multiple individuals, each individual including the installation position of each tower crane and the model of each tower crane; iteratively optimizing the multiple individuals to obtain multiple optimized individuals; obtaining multiple top individuals according to the result of the iterative optimization of total cost evaluation, and selecting multiple suboptimal individuals from the multiple top individuals by clustering, thereby obtaining multiple optimal solutions for tower crane selection and position, including all solutions that can be thought of by engineering and technical personnel, which is conducive to determining the optimal solution for tower crane selection and position and reducing total cost.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering construction management, and in particular to an intelligent tower crane selection and position optimization method, device and equipment. Background Art

[0002] The selection and location of tower cranes are crucial components of construction site layout. Optimizing crane selection and location solutions significantly reduces construction costs. Large, complex projects often require the simultaneous deployment of multiple cranes within a specific area to ensure progress and timely transportation of large quantities of materials, owing to the significant investment and tight deadlines. Intelligent optimization algorithms are often used to determine the optimal solution for crane selection and location. However, commonly used intelligent optimization algorithms, such as genetic algorithms, particle swarm optimization, and ant colony algorithms, focus on finding a global optimal solution while neglecting the exploration of suboptimal solutions. Ultimately, this results in a single optimal solution for crane selection and location. Engineering technicians often question whether this optimal solution can be further optimized to lower the total crane-related costs. Summary of the Invention

[0003] In view of this, the present invention provides an intelligent tower crane selection and location optimization method, device and equipment medium to solve the problem of being unable to determine the optimal solution for tower crane selection and location and high total cost.

[0004] In a first aspect, the present invention provides a method for optimizing the selection and position of an intelligent tower crane, the method comprising:

[0005] Obtain construction environment information, and determine the number of tower cranes and the range of each tower crane to be installed based on the construction environment information;

[0006] A population is randomly generated based on the installation range of each tower crane and the model to be selected for each tower crane. The population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation location and model of each tower crane;

[0007] Iteratively optimize multiple individuals to obtain multiple optimized individuals;

[0008] Calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among all the optimized cost values.

[0009] Determine multiple top individuals based on multiple optimization cost values ​​and the relationship between the optimal cost values;

[0010] Cluster the top individuals to obtain multiple clusters;

[0011] Select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals;

[0012] According to actual needs, one individual is selected from the optimal individual and multiple suboptimal individuals as the final installation plan of the tower crane.

[0013] The intelligent tower crane selection and location optimization method provided by the present invention iteratively optimizes each installation scheme, obtains multiple top individuals based on the results of the iterative optimization according to the total cost evaluation, and selects multiple suboptimal individuals from the multiple top individuals through clustering, thereby obtaining multiple optimal solutions for tower crane selection and location, including all solutions that engineering and technical personnel can think of, which is conducive to determining the optimal solution for tower crane selection and location and reducing total cost.

[0014] In an optional implementation, determining the number of tower cranes and the range of each tower crane to be installed based on the construction environment information includes:

[0015] Obtain the maximum single-story lifting area of ​​a preset single tower crane and the floor area of ​​the building to be constructed, and determine the number of tower cranes accordingly;

[0016] The installation range of the tower crane is determined according to the plane position, the length of each side and the preset distance of the building to be built. The preset distance is the vertical distance between the installation range of the tower crane and each side of the building to be built.

[0017] In an optional embodiment, the process of determining the installation position of each tower crane includes:

[0018] Establish a plane coordinate system according to the plane position of the building to be built;

[0019] Determine the starting point of the tower crane according to the plane coordinate system and the range of the tower crane to be installed, and determine the installation point of each tower crane within the range of the tower crane to be installed;

[0020] Calculate the distance from the starting point to the installation point in the preset direction within the range to be installed, which is the installation position.

[0021] The intelligent tower crane selection and position optimization method provided by the present invention simplifies the installation position of the tower crane from a two-dimensional plane to a one-dimensional polyline, greatly improving the calculation accuracy and speed. For convex polygonal or convex defective polygonal buildings, there is no need to preset the calculation accuracy. The required accuracy can be obtained during subsequent iterative optimization without increasing the calculation time.

[0022] In an optional embodiment, iterative optimization is performed on multiple individuals to obtain multiple optimized individuals, including:

[0023] Calculate the total cost of multiple individuals and classify them into excellent individuals, ordinary individuals, and penalty individuals according to the size of the total costs;

[0024] Iterative optimization is performed on excellent individuals, ordinary individuals, and punished individuals respectively. Excellent individuals, ordinary individuals, and punished individuals correspond to different iterative optimization methods;

[0025] If the current number of iterations is less than the preset value, the iterative optimization process is repeated until the number of iterations reaches the preset value, and multiple optimized individuals are obtained.

[0026] The intelligent tower crane selection and position optimization method provided by the present invention classifies all individuals and performs iterative optimization in different ways to obtain multiple optimized individuals. The optimization process of each individual does not affect each other, and the obtained multiple optimized individuals are independent of each other, thereby improving the effectiveness of iterative optimization.

[0027] In an optional embodiment, the position and model of each tower crane in the individual is an element in the iterative optimization process, and iterative optimization is performed on the excellent individual, the ordinary individual, and the penalty individual respectively, including:

[0028] For excellent individuals, a preset number of elements are randomly selected from the excellent individuals for iterative optimization according to a first preset step size, where the preset number is less than the total number of elements;

[0029] For the common individuals, all elements in the common individuals are iteratively optimized according to a second preset step size, where the second preset step size is greater than the first preset step size;

[0030] For the punished individual, delete it and regenerate a new individual.

[0031] The intelligent tower crane selection and position optimization method provided by the present invention adopts different adaptive iterative step sizes to optimize different types of individuals, thereby accelerating the convergence speed of the individual iterative optimization process.

[0032] In an optional embodiment, determining a plurality of top individuals based on the relationship between a plurality of optimization cost values ​​and the optimal cost value includes:

[0033] Multiply the optimal cost value by the preset ratio value to obtain the excellent cost range;

[0034] Compare multiple optimized cost values ​​with the excellent cost range. If the optimized cost value is within the excellent cost range, the individual corresponding to the optimized cost value is the top individual.

[0035] The intelligent tower crane selection and position optimization method provided by the present invention selects top individuals with lower costs from all optimized individuals according to the size of the optimization cost value, which simplifies the subsequent calculation process, speeds up the calculation speed, and is conducive to obtaining the most reasonable multiple solutions.

[0036] In an optional embodiment, a top individual is selected from each cluster to obtain an optimal individual and multiple suboptimal individuals, including:

[0037] Calculate the total cost value of each top individual in each cluster;

[0038] Compare the total costs of the top individuals in each cluster, and obtain an optimal individual and multiple suboptimal individuals from each cluster based on the comparison results. The optimal individual and suboptimal individuals are the top individuals with the lowest total cost in their clusters.

[0039] The method for optimizing the selection and location of intelligent tower cranes provided by the present invention utilizes a clustering algorithm to cluster top individuals, and then selects the lowest-cost solution from all categories as the suboptimal individual. The optimal solution has the lowest total cost and good reference indicators, and can be directly used to guide construction. The suboptimal solutions each have their own advantages and disadvantages, which can provide engineering technicians with more choices and comparisons.

[0040] In a second aspect, the present invention provides an intelligent tower crane selection and position optimization device, the device comprising:

[0041] An information acquisition module is used to obtain construction environment information and determine the number of tower cranes and the range of each tower crane to be installed based on the construction environment information;

[0042] A population generation module is used to randomly generate a population based on the installation range of each tower crane and the model to be selected for each tower crane. The population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation location of each tower crane and the model of each tower crane;

[0043] Iterative optimization module, used to iteratively optimize multiple individuals to obtain multiple optimized individuals;

[0044] The optimal individual determination module is used to calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among all the optimization cost values;

[0045] A top individual determination module, used for determining a plurality of top individuals according to a plurality of optimized cost values ​​and a relationship between the optimal cost values;

[0046] Individual clustering module, used to cluster top individuals and obtain multiple clusters;

[0047] The suboptimal individual determination module is used to select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals;

[0048] The final solution determination module is used to select an individual from the optimal individual and multiple suboptimal individuals as the final installation solution of the tower crane according to actual needs.

[0049] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 1 is a flow chart of a method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention;

[0053] Figure 2 1 is a flow chart of another intelligent tower crane selection and position optimization method according to an embodiment of the present invention;

[0054] Figure 3 1 is a schematic diagram of a basic project situation in a specific embodiment of the method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention;

[0055] Figure 4 1 is a schematic diagram of a tower crane installation range in a specific embodiment of the intelligent tower crane selection and position optimization method according to an embodiment of the present invention;

[0056] Figure 5 3. It is a schematic diagram of classification of iterative optimization individuals of the intelligent tower crane selection and position optimization method according to an embodiment of the present invention;

[0057] Figure 6 1 is a flow chart of an iterative optimization process of an intelligent tower crane selection and position optimization method according to an embodiment of the present invention;

[0058] Figure 7 This is a schematic diagram of an optimal solution obtained by engineers based on experience in a specific embodiment of the method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention;

[0059] Figure 81 is a schematic diagram of an optimal solution obtained according to a specific embodiment of the method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention;

[0060] Figure 9 is a structural block diagram of an intelligent tower crane selection and position optimization device according to an embodiment of the present invention;

[0061] Figure 10 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0063] Before arranging the tower crane, the following information must be given: the coordinates of the outline of the building to be built, the coordinates of the outline of the existing building, and the coordinates of the location of obstacles (including high-voltage lines, transformers, etc.). The optional installation range of the tower crane: In engineering practice, the tower crane should be arranged as close to the building to be built as possible to improve the efficiency and value of the tower crane. Therefore, the optional installation range of the tower crane should be a set of multiple lines around the building, and on this basis, the positions where the tower crane is not suitable should be eliminated. The width of the distance between the tower crane and the building to be built should generally take into account the width of the external scaffolding and the safety distance of the tower crane. When the tower crane foundation cannot be placed under the building foundation, the size of the tower crane foundation should also be considered.

[0064] The total costs associated with a tower crane include crane rental fees, crane operator wages, crane foundation costs, blind spot handling fees, installation and dismantling fees, anti-smashing measures fees, and punitive costs. Crane rental fees refer to the total monthly rental fee for the crane; crane operator wages refer to the total wages of the crane operator; crane foundation costs include the material and construction costs of the crane foundation, and the cost of pre-embedded crane legs; blind spot handling fees refer to the additional costs incurred in transporting construction materials when the building to be constructed has a blind spot for the crane; and crane installation and dismantling fees include crane entry and exit fees, as well as the construction costs for crane installation and dismantling. (When calculating crane dismantling costs, it should be noted that if there are buildings that hinder the crane from lowering its height, such as when dismantling a crane located inside a circular building, it is necessary to consider whether additional lifting equipment is required to dismantle the crane.)

[0065] The anti-smash measures fee refers to the cost of installing anti-smash shelters when tower cranes cover high-voltage power lines, transformers, living quarters, and office areas. Anti-smash shelters are required for entrances and exits of construction buildings, regardless of whether they are within the crane's coverage area, and are therefore not considered in this article. When steel processing sheds and carpentry sheds are outside the crane's coverage area, anti-smash shelters are not required, but additional truck cranes will be required to transport materials. Construction companies generally try to place steel processing sheds and carpentry sheds within the crane's coverage area, so anti-smash measures at the processing site are not included in the calculation.

[0066] Anti-smash measures represent a significant, unearned expense for construction companies. Furthermore, even with these measures, safety remains inferior to a plan without them. Therefore, these measures should be avoided whenever possible. Because the cost of these measures is unavoidable in some projects, their cost should be analyzed and compared with other options. If the lowest-cost option has a high anti-smash cost, this should be a warning sign. This option may have room for improvement, or consideration should be given to choosing a suboptimal option with a similar cost to increase safety.

[0067] Constraints on tower crane layout: The layout plan should be considered infeasible when the tower crane has the following conditions:

[0068] 1. The two tower cranes collide or are too close to each other in the horizontal direction: the distance between the boom end of the tower crane in the lower position and the tower body of the other tower crane is less than 2 meters;

[0069] 2. The two towers collide or are too close in the vertical direction: the vertical distance between the lowest part of the high-level tower crane (the hook is raised to the highest point or the lowest part of the balance arm) and the top of the corresponding part of the low-level tower crane at the same vertical position is less than 2 meters;

[0070] 3. The tower crane collides with or is too close to buildings or other facilities: the safe distance between the moving part of the tower crane and buildings or other facilities is less than 0.6m.

[0071] When a plan violates a constraint, a significant penalty is imposed on the total cost, negating the feasibility of the plan. When the coverage areas of two towers overlap, the two towers must carefully avoid each other. This sometimes results in the cranes sitting idle, raising their hooks across another crane, or taking a detour, reducing lifting efficiency. Therefore, in engineering practice, the intersecting length of the two tower booms should generally not exceed 0.67 times the shortest boom length.

[0072] The embodiment of the present invention provides an intelligent tower crane selection and location optimization method, which obtains multiple optimization schemes through iterative optimization and clustering to achieve the effect of determining the optimal scheme for tower crane selection and location and reducing total cost.

[0073] According to an embodiment of the present invention, an embodiment of a method for optimizing the selection and position of an intelligent tower crane is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0074] In this embodiment, a method for optimizing the selection and position of an intelligent tower crane is provided, which can be used for the above-mentioned computer equipment. Figure 1 Flowchart of the method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0075] Step S101: Acquire construction environment information, and determine the number of tower cranes and the range of each tower crane to be installed according to the construction environment information.

[0076] Specifically, the construction environment information includes: the outline coordinates of the building to be built, the outline coordinates of the existing building, and the coordinates of the obstacle positions (including high-voltage lines, transformers, etc., for example only, but not limited to this). For a building, the effective coverage area of ​​each tower crane accounts for the proportion of the single-story plan area of ​​the building, where the effective coverage area refers to the area where the tower crane coverage area overlaps with the building. The tower crane coverage range refers to the circle formed by the longest distance that the tower crane hook can reach, excluding the size of the boom end outside the lifting range. The number of tower cranes is determined according to the effective coverage area of ​​the tower crane and the plan area of ​​the building to be built; the range of installation of each tower crane is determined according to the outline coordinates of the building to be built and the layout requirements of the tower crane. The layout requirements of the tower crane are the constraints for the layout of the tower crane, for example: tower cranes cannot collide with each other, existing buildings, and obstacles, for example only, but not limited to this.

[0077] In step S102, a population is randomly generated according to the installation range of each tower crane and the model to be selected for each tower crane. The population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation position and model of each tower crane.

[0078] Specifically, the randomly generated population includes multiple individuals, such as 1,000, which is only used as an example but not limited to this. The total cost of each individual is calculated, and the lowest cost of all is used as the best score of the population. Based on the best score, multiple individuals are classified according to the difference between the total cost of each individual and the best score.

[0079] Step S103: iteratively optimize the multiple individuals to obtain multiple optimized individuals.

[0080] Specifically, in order to speed up the convergence speed of each individual in the iterative optimization process, the iterative step size should be large at first and then small. In this embodiment, an adaptive step size is used, and the iterative step size is proportional to the individual performance. When the individual performance is close to the best performance, the iterative step size is small. When the individual performance is significantly different from the best performance, the iterative step size is large. For the individual with the best performance, a smaller disturbance needs to be added, and iterative optimization is performed on itself.

[0081] Step S104 , calculating and comparing the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among the optimization cost values.

[0082] Specifically, after each individual is iteratively optimized, each optimized individual has its own optimization cost value. At this time, all the optimization cost values ​​are compared, and the one with the lowest optimization cost value is taken as the optimal cost value. The optimized individual corresponding to the optimal cost value is the optimal individual.

[0083] Step S105 , determining a plurality of top individuals according to the relationship between the plurality of optimized cost values ​​and the optimal cost value.

[0084] Specifically, from all optimized individuals, multiple top individuals are selected based on the relationship between the optimization cost value and the optimal cost value. For example, individuals corresponding to optimization cost values ​​that are less than 1.1 times the optimal cost value are selected as top individuals. This is only an example and is not limited to this.

[0085] Step S106: cluster the top individuals to obtain multiple clusters.

[0086] Specifically, by comparing multiple top individuals along multiple dimensions, similar individuals are grouped together, thereby clustering the top individuals and obtaining multiple clusters. For example, if the tower cranes in two solutions are the same model and located in close proximity, the two solutions will be grouped together in one cluster. This is for example only and is not intended to be limiting.

[0087] Step S107: select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals.

[0088] Specifically, the total cost value of all top individuals in each cluster is calculated, and a top individual with the lowest total cost value is selected from each cluster. The top individual with the lowest total cost value among the selected top individuals is the optimal individual, and the rest are suboptimal individuals.

[0089] Step S108: selecting an individual from the optimal individual and multiple suboptimal individuals as the final installation solution for the tower crane according to actual needs.

[0090] Specifically, in addition to total cost, other evaluation metrics are used for tower crane selection and location determination, such as blind spot ratio, crane coverage, and intersection length ratio. These are examples, but not limited to these. The resulting optimal individual and multiple suboptimal individuals encompass all possible solutions conceivable by engineers, each with its own advantages. For example, if a suboptimal individual has a smaller blind spot ratio than the optimal individual, and the engineer determines that a smaller blind spot ratio has greater value for the overall construction, the suboptimal individual with the smaller blind spot ratio may be selected as the final tower crane installation solution.

[0091] The intelligent tower crane selection and location optimization method provided in this embodiment performs iterative optimization on each installation scheme, obtains multiple top individuals based on the results of the iterative optimization of the total cost evaluation, and selects multiple suboptimal individuals from the multiple top individuals through clustering, thereby obtaining multiple optimal solutions for tower crane selection and location, including all solutions that engineering and technical personnel can think of, which is conducive to determining the optimal solution for tower crane selection and location and reducing total cost.

[0092] In this embodiment, a method for optimizing the selection and position of an intelligent tower crane is provided, which can be used for the above-mentioned computer equipment. Figure 2 Flowchart of the method for optimizing the selection and position of an intelligent tower crane according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0093] Step S201: Acquire construction environment information, and determine the number of tower cranes and the range of each tower crane to be installed according to the construction environment information.

[0094] Specifically, the above step S201 includes:

[0095] Step S2011: Obtain the preset maximum lifting area of ​​a single tower crane on a single floor and the plan area of ​​the building to be constructed, and determine the number of tower cranes accordingly.

[0096] Specifically, a project has two buildings to be built. Building A has a plan size of 90×126m and a total of 4 floors; Building B has a plan size of 80×110m and a total of 2 floors. There is an east-west high-voltage line 53m to the south of Building A, and an existing temporary board house 55m to the east with a plan size of 55×90m. There is an existing 30-story residential building 58m to the north of the northwest corner of Building A. There is an existing underground fire water tank in the northwest corner of Building A with a plan size of 25×13m. Establish a plane coordinate system with the southwest corner of Building A as the origin, such as Figure 3 Shown is the basic situation of the project.

[0097] The tower crane is used for 10 months. Based on the type and weight of the building materials that the tower crane needs to lift in this project, combined with the requirements for the tower crane lifting efficiency of this project (for example only, but not limited to this), the maximum lifting area of ​​a single tower crane per floor is calculated. It is assumed that the maximum lifting area of ​​a single tower crane per floor should be less than 5000m 2 The floor area of ​​Building A is 11340m 2 , the floor area of ​​Building B is 8800m 2 , so it is appropriate to use 3 tower cranes for Building A and 2 tower cranes for Building B. The optional tower crane models are TC6010, TC6513, and TC7013, and their lifting radius are 60 meters, 65 meters, and 70 meters respectively. The variable cost of the tower crane includes the monthly rental of the tower crane and the monthly salary of the tower crane driver, which are RMB 36,000, RMB 50,000, and RMB 53,000 for the three models respectively. The fixed cost of the tower crane includes entry and exit fees, installation and disassembly fees, embedded support leg fees, and tower crane foundation fees, which are RMB 89,000, RMB 110,000, and RMB 146,000 for the three models respectively. Therefore, in this embodiment, each tower crane layout plan has 10 variables, that is, 10 dimensions, which are the models and positions of the five tower cranes. The tower crane model is one of TC6010, TC6513, and TC7013. The minimum value of the tower crane position is 0, and the maximum value is the total length of the line segment in the optional range of the tower crane.

[0098] In this embodiment, the cost of the high-voltage line anti-smash shed is 5,000 yuan per meter, and the cost of the living area anti-smash shed is 30 yuan per square meter (taking into account recycling). When the length or width of the single-piece lifting blind area of ​​the tower crane is less than 10 meters, workers usually do not ask for transportation fees. When the blind area is larger, it is often necessary to arrange a special crane or manual transportation. When arranging tower cranes, it is necessary to avoid excessive blind areas. Therefore, when the length or width of the blind area is greater than 10 meters, a punitive transportation fee of 1,000 yuan per square meter is imposed.

[0099] Constraints: Building A is 24 meters high, and Building B is 14 meters high, so the tower cranes have a large degree of freedom in arrangement in the height direction, and there is no need to constrain the height relationship between the tower cranes in the algorithm. For collision constraints in the horizontal direction, the heights of high-voltage lines, living areas, and fire water tanks are all less than 20 meters, and will not collide with the tower cranes. The height of the residential building is nearly 100 meters, so the coverage of the tower crane should avoid intersecting with the outer contour of the residential building. When tower crane A is within the coverage of tower crane B, it means that tower crane A may be below the boom of tower crane B, or the two towers have collided. When tower crane B is also within the coverage of tower crane A, it means that the two towers have collided. In engineering practice, the reserved distance between tower cranes should also consider the reserved safety distance of the boom end, the standard section size of the tower body, and the size of the boom end outside the lifting range. Therefore, the constraints in this embodiment include: the center distance between any two tower cranes is not less than the minimum distance between the tower cranes; the coverage of the tower crane does not intersect with the outer contour of the residential building.

[0100] Step S2012: determining the installation range of the tower crane according to the plane position, side lengths and preset distance of the building to be constructed. The preset distance is the vertical distance between the installation range of the tower crane and each side of the building to be constructed.

[0101] Specifically, the outlines of Building A and Building B are expanded by 2 meters to obtain the preliminary optional range of the tower crane position. The expansion of 2 meters is only for example, but not limited to this. The range to be installed of the tower crane can be obtained by subtracting the area affected by the fire water tank from the preliminary optional range, such as Figure 4 As shown, the dotted line is the installation range. In addition, the distance between the end of the tower crane's boom and the farthest position of the hook is 1.3 meters, and the safe distance between the moving part of the tower crane and buildings or other facilities should be no less than 0.6 meters. The outer contour of the residential building is expanded by 2 meters. Figure 4 As shown by the midpoint line, when the tower crane coverage area overlaps with the expanded outline of the residential building, it can be judged that the tower crane is too close to the residential building or has collided with it. The voltage of the high-voltage line is 10kV, and the hook should be at least 2 meters away from the high-voltage line. To ensure safety, the distance is set to 3 meters in this example, that is, the high-voltage line is offset 3 meters to the north. Figure 4 As shown by the midpoint line, the living area is only 6 meters high and will not collide with the tower crane boom. The temporary living area is enclosed by a living area fence 6 meters outside the tower crane hook, so there is no need to consider the collision between the tower crane hook and the temporary living area. Only the anti-smashing measures for the temporary living area need to be considered, and the outline of the living area will not be expanded.

[0102] In step S202, a population is randomly generated according to the installation range of each tower crane and the model to be selected for each tower crane. The population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation position and model of each tower crane.

[0103] Specifically, in step S202, the process of determining the installation position of each tower crane in each solution includes:

[0104] Step S2021: establishing a plane coordinate system according to the plane position of the building to be constructed.

[0105] Specifically, a plane coordinate system is established with the southwest corner of building A as the origin, and the positions and sizes of all buildings to be built, existing buildings, and obstacles are represented in the plane coordinate system. This is only an example, and the establishment of the plane coordinate system is not limited to this.

[0106] Step S2022: determining the starting point of the tower crane according to the plane coordinate system and the range of the tower crane to be installed, and determining the installation point of each tower crane within the range of the tower crane to be installed.

[0107] Specifically, the starting point of the tower crane is the origin of the plane coordinate system, which is only used as an example but not limited to this. The installation point of the tower crane is any point within the range to be installed.

[0108] Step S2023: Calculate the distance from the starting point to the installation point in the preset direction within the range to be installed, which is the installation position.

[0109] Specifically, the installation position of the tower crane is converted into a one-dimensional value on the range to be installed for iterative calculation, which speeds up the calculation speed and improves the accuracy.

[0110] The intelligent tower crane selection and position optimization method provided in this embodiment simplifies the installation position of the tower crane from a two-dimensional plane to a one-dimensional polyline, greatly improving the calculation accuracy and speed. For convex polygonal or convex defective polygonal buildings, there is no need to preset the calculation accuracy, and the required accuracy can be obtained during subsequent iterative optimization without increasing the calculation time.

[0111] Step S203: iteratively optimize the multiple individuals to obtain multiple optimized individuals.

[0112] Specifically, the above step S203 includes:

[0113] Step S2031 , calculating the total costs of multiple individuals, and classifying the multiple individuals into excellent individuals, ordinary individuals, and penalty individuals according to the sizes of the multiple total costs.

[0114] Specifically, a population of 1,000 individuals is randomly generated. For each individual, the total cost is calculated. The lowest cost is used as the best score for the initial population. A score twice the best score in the population is considered a good score, and a score three times the best score in the population is considered a penalty score. Individuals with a total cost less than the good score score are considered excellent, those with a total cost greater than the penalty score score are considered penalty scores, and the rest are considered average. The distinction between a good score score and a penalty score score is provided as an example and is not intended to be limiting.

[0115] Step S2032: Iteratively optimize the excellent individuals, ordinary individuals, and penalized individuals respectively. The excellent individuals, ordinary individuals, and penalized individuals correspond to different iterative optimization methods.

[0116] In this embodiment, Figure 6 As shown in the figure, the "Seeing the Good and Equaling Them" algorithm works as follows: after each exam, the class will have a best score. Before the next exam, each student will have some time to explore new learning methods and use them for the next exam, all the way to the graduation exam. The "algebra" in the figure represents the number of exams, or in other words, the number of iterations of optimization. With a sufficient number of explorations, and therefore a sufficient number of exams, each student will find the method that best suits them and achieve a relatively good score.

[0117] In some optional implementations, the position and model of each crane in the individual is an element in the iterative optimization process, and iterative optimization is performed on the excellent individual, the ordinary individual, and the penalty individual, respectively, including:

[0118] For excellent individuals, a preset number of elements are randomly selected from the excellent individuals and iteratively optimized according to a first preset step size, where the preset number is less than the total number of elements.

[0119] For ordinary individuals, all elements in the ordinary individuals are iteratively optimized according to a second preset step size, and the second preset step size is larger than the first preset step size.

[0120] For the punished individual, delete it and regenerate a new individual.

[0121] Specifically, in order to speed up the convergence of the iterative optimization process, the iterative step size should follow the principle of first large and then small. In this embodiment, an adaptive step size is used. Since the correct exploration direction and step size of the individual are not known in advance, a random decimal between -10 and 10 is introduced into the step size. The formula for the iterative step size is:

[0122] Step length = random number × individual score / best score. From this formula, we can see that the iteration step length is proportional to the individual score, so when the individual score is large, the step length is large. When the individual score is close to the best score, the step length is small.

[0123] In this example, each crane layout plan has 10 variables, or 10 elements, representing the models and locations of the five cranes. Penalized individuals, due to constraint violations, are deleted and a new one is randomly generated. For excellent individuals, a random value from the 10 elements is added to the iteration step, effectively perturbing a dimension of the excellent individual. A smaller iteration step also results in a smaller perturbation. For average individuals, all 10 elements are added to the iteration step, effectively perturbing the average individual.

[0124] The intelligent tower crane selection and position optimization method provided in this embodiment uses different adaptive iterative step sizes to optimize different types of individuals, thereby accelerating the convergence speed of the individual iterative optimization process.

[0125] Step S2033: If the current number of iterations is less than the preset value, the iterative optimization process is repeated until the number of iterations reaches the preset value, and multiple optimized individuals are obtained.

[0126] Specifically, to prevent individual element values ​​from exceeding their range after perturbation, the remainder of the element value relative to the range is calculated. This completes a single iteration of the population, and the same steps are repeated 500 times. To merge identical or similar solutions, the crane positions in each individual are arranged counterclockwise from nearest to farthest from the starting point. For example, if the crane positions in Solution A are (100, 300, 400, 200, 500) and those in Solution B are (500, 100, 300, 400, 200), then after sorting, they are both (100, 200, 300, 400, 500), so Solution A and Solution B are considered equal. This completes the entire algorithm iteration process.

[0127] The intelligent tower crane selection and position optimization method provided in this embodiment classifies all individuals and then performs iterative optimization in different ways to obtain multiple optimized individuals. The optimization process of each individual does not affect each other, and the multiple optimized individuals obtained are independent of each other, thereby improving the effectiveness of iterative optimization.

[0128] Step S204: Calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual. The optimal cost value is the lowest value among all the optimized cost values. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0129] Step S205 , determining a plurality of top individuals according to the relationship between the plurality of optimized cost values ​​and the optimal cost value.

[0130] Specifically, the above step S205 includes:

[0131] Step S2051: multiply the optimal cost value by a preset ratio value to obtain an excellent cost range.

[0132] Specifically, if the model and location of each tower crane are determined according to the experience of civil engineering, the optimal solution is as follows: Figure 7 As shown, the total cost is 2.5853 million yuan, with anti-smashing measures costing 18,259 yuan. There are no blind spot handling fees, a blind spot ratio of 2%, and coverage rates for the five cranes, 56%, 37%, 32%, 67%, and 60%, respectively. The crane intersection length ratio is 0.68. The crane intersection length is slightly higher than 0.67 due to the close proximity of the two cranes in Building B. However, since the lifting ranges of the two cranes do not overlap with those of the other cranes, this metric is not sufficient to disqualify this layout. Overall, this is a good plan.

[0133] After the optimization method provided in this embodiment is used to iteratively optimize all individuals, the optimal solution is as follows: Figure 8As shown, the optimal cost value of the optimal individual is 2.41 million yuan, the anti-smashing cost is 4,445 yuan, there is no blind spot transportation fee, the blind spot ratio is 2%, the coverage ratios of each tower crane are 30%, 36%, 47%, 68%, and 60% respectively, and the tower crane intersection length ratio is 0.68.

[0134] The civil engineer determined that the optimal solution obtained using the optimization method provided in this embodiment was superior and had no room for further optimization, resulting in a cost reduction of 175,200 yuan. The engineer failed to consider placing crane No. 1 on the east side of Building A and crane No. 2 on the north side. Generally, when manually arranging cranes, the tendency is to give the first crane a larger coverage area, allowing the later cranes greater freedom of movement. Therefore, the optimal solution obtained in this embodiment was not easily achieved.

[0135] The total cost value that is less than 1.1 times the optimal cost value is selected as the excellent cost range.

[0136] Step S2052 , comparing the multiple optimized cost values ​​with the excellent cost range. If the optimized cost value is within the excellent cost range, the individual corresponding to the optimized cost value is the top individual.

[0137] The individuals corresponding to the excellent cost range are regarded as top individuals. There are 23 solutions in total, which are sorted in ascending order of total cost value as shown in Table 1.

[0138] Table 1

[0139]

[0140] A simple comparison reveals that the total costs of the first nine options differ by only 700 yuan at most, and the crane locations are very similar. In options 1, 5, 6, and 9, crane No. 4 is a TC6010 and crane No. 5 is a TC6513. In options 2, 3, 4, 7, and 8, crane No. 4 is a TC6513 and crane No. 5 is a TC6010. These first nine options can be considered identical or mirror images, and therefore can be combined into one category.

[0141] The intelligent tower crane selection and position optimization method provided in this embodiment selects top individuals with lower costs from all optimized individuals according to the size of the optimization cost value, which simplifies the subsequent calculation process, speeds up the calculation speed, and is conducive to obtaining the most reasonable multiple solutions.

[0142] Step S206: Cluster the top individuals to obtain multiple clusters. Figure 1 Step S106 of the illustrated embodiment will not be described in detail here.

[0143] Step S207: select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals.

[0144] Specifically, the above step S207 includes:

[0145] Step S2071, calculating the total cost value of each top individual in each cluster.

[0146] Step S2072, compare the total costs of the top individuals in each cluster, and obtain an optimal individual and multiple suboptimal individuals from each cluster based on the comparison results. The optimal individual and the suboptimal individual are the top individuals with the lowest total cost in their clusters.

[0147] Specifically, a scheme with the lowest total cost is selected from each cluster as the suboptimal individual. The suboptimal individuals are scheme 10, scheme 14, scheme 22, and scheme 23 in Table 1. Table 2 shows a comparative evaluation table of the four suboptimal individuals and one optimal individual.

[0148]

[0149] For the five options in Table 2, the main change of Option 2 compared to Option 1 is that the No. 3 tower crane is enlarged by one size. When the No. 3 tower crane needs a larger lifting capacity, Option 2 can be selected; the main change of Option 3 compared to Option 1 is that the two tower cranes of Building B are arranged in a north-south layout, thereby reducing the tower crane intersection length ratio to 0.35, but two medium-sized tower cranes are used. Medium-sized tower cranes are more expensive than small-sized tower cranes and have slower boom rotation speeds. Therefore, technicians can consider whether to adopt a north-south layout based on the lifting weight requirements; Option 4 is consistent with the layout arranged by the civil engineering engineer based on experience, and is also of reference value; Option 5 is equivalent to a combination of Options 3 and 4.

[0150] The optimal solution and multiple suboptimal solutions each have their own characteristics, and engineers can choose the most appropriate layout based on actual conditions.

[0151] The method for optimizing the selection and location of intelligent tower cranes provided in this embodiment utilizes a clustering algorithm to cluster top individuals, and then selects the lowest-cost solution from all categories as the suboptimal individual. The optimal solution has the lowest total cost and good reference indicators, and can be directly used to guide construction. Each suboptimal solution has its own advantages and disadvantages, which can provide engineering technicians with more choices and comparisons.

[0152] Step S208: Select one individual from the optimal individual and multiple suboptimal individuals as the final installation solution for the tower crane according to actual needs. Figure 1 Step S108 of the illustrated embodiment will not be described in detail here.

[0153] This embodiment also provides an intelligent tower crane selection and position optimization device, which is used to implement the above-mentioned embodiments and preferred embodiments. Details already described will not be repeated here. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0154] This embodiment provides an intelligent tower crane selection and location optimization device, such as Figure 9 As shown, including:

[0155] The information acquisition module 901 is used to acquire construction environment information and determine the number of tower cranes and the range of each tower crane to be installed based on the construction environment information.

[0156] The population generation module 902 is used to randomly generate a population according to the installation range of each tower crane and the selected model of each tower crane. The population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation location of each tower crane and the model of each tower crane.

[0157] The iterative optimization module 903 is used to perform iterative optimization on multiple individuals to obtain multiple optimized individuals.

[0158] The optimal individual determination module 904 is used to calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among all the optimization cost values.

[0159] The top individual determination module 905 is used to determine multiple top individuals based on the relationship between multiple optimization cost values ​​and the optimal cost value.

[0160] The individual clustering module 906 is used to cluster top individuals to obtain multiple clusters.

[0161] The suboptimal individual determination module 907 is used to select a top individual from each cluster to obtain multiple suboptimal individuals.

[0162] The final solution determination module 908 is used to select an individual from the optimal individual and multiple suboptimal individuals as the final installation solution of the tower crane according to actual needs.

[0163] In some optional implementations, the information acquisition module 901 includes:

[0164] The tower crane quantity determination unit is used to obtain the radius of each tower crane model, the plane position of the building to be built and the length of each side, and determine the number of tower cranes based on this.

[0165] The unit for determining the range to be installed is used to determine the range to be installed of the tower crane according to the plane position, the length of each side and the preset distance of the building to be built. The preset distance is the vertical distance between the range to be installed of the tower crane and each side of the building to be built.

[0166] In some optional implementations, the population generation module 902 includes:

[0167] The coordinate system establishing unit is used to establish a plane coordinate system according to the plane position of the building to be built.

[0168] The installation point determination unit is used to determine the starting point of the tower crane according to the plane coordinate system and the range of the tower crane to be installed, and to determine the installation point of each tower crane within the range of each tower crane to be installed.

[0169] The installation position determination unit is used to calculate the distance from the starting point to the installation point in a preset direction within the range to be installed, which is the installation position.

[0170] In some optional implementations, the iterative optimization module 903 includes:

[0171] The individual classification unit is used to calculate the total cost of multiple individuals and divide the individuals into excellent individuals, ordinary individuals, and penalty individuals according to the size of the multiple total costs.

[0172] The optimization unit is used to perform iterative optimization on excellent individuals, ordinary individuals, and penalized individuals respectively. Excellent individuals, ordinary individuals, and penalized individuals correspond to different iterative optimization methods.

[0173] The optimized individual determination unit is used to repeat the iterative optimization process if the current number of iterations is less than a preset value until the number of iterations reaches the preset value, thereby obtaining multiple optimized individuals.

[0174] In some optional implementations, the top individual determination module 905 includes:

[0175] The excellent cost range determining unit is used to multiply the optimal cost value by a preset ratio value to obtain an excellent cost range.

[0176] The top individual determination unit is used to compare the size of multiple optimization cost values ​​with the excellent cost range. If the optimization cost value is within the excellent cost range, the individual corresponding to the optimization cost value is the top individual.

[0177] In some optional implementations, the suboptimal individual determination module 907 includes:

[0178] The total cost calculation unit is used to calculate the total cost value of each top individual in each cluster.

[0179] The top individual comparison unit is used to compare the total cost of the top individuals in each cluster, and obtain multiple suboptimal individuals from each cluster based on the comparison results. The suboptimal individual is the top individual with the lowest total cost in the cluster.

[0180] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0181] The intelligent tower crane selection and position optimization device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0182] The embodiment of the present invention also provides a computer device having the above Figure 9 The device shown is for optimizing the selection and location of intelligent tower cranes.

[0183] See also Figure 10 , Figure 10 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 10 As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 10 A processor 10 is taken as an example.

[0184] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0185] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0186] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0187] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0188] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0189] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0190] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. An intelligent tower crane selection and position optimization method, characterized in that: The method comprises: Acquire construction environment information, and determine the number of tower cranes and the range of each tower crane to be installed based on the construction environment information; Randomly generating a population according to the to-be-installed range of each tower crane and the to-be-selected model of each tower crane, wherein the population includes a plurality of individuals, each individual corresponds to an installation plan, and each installation plan includes the installation position of each tower crane and the model of each tower crane; Iteratively optimizing the multiple individuals to obtain multiple optimized individuals; Calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value, the individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among all the optimization cost values; Determine a plurality of top individuals according to the relationship between the plurality of optimized cost values ​​and the optimal cost value; Clustering the top individuals to obtain multiple clusters; Select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals; According to actual needs, one individual is selected from the optimal individual and the multiple suboptimal individuals as the final installation solution of the tower crane.

2. The method according to claim 1, characterized in that Determining the number of tower cranes and the installation range of each tower crane based on the construction environment information includes: Obtain the maximum single-story lifting area of ​​a preset single tower crane and the floor area of ​​the building to be constructed, and determine the number of tower cranes accordingly; The range of the tower crane to be installed is determined according to the plane position, the length of each side and the preset distance of the building to be built. The preset distance is the vertical distance between the range of the tower crane to be installed and each side of the building to be built.

3. The method according to claim 2, characterized in that The process of determining the installation position of each tower crane includes: Establishing a plane coordinate system according to the plane position of the building to be constructed; Determine the starting point of the tower crane according to the plane coordinate system and the range of the tower crane to be installed, and determine the installation point of each tower crane within the range of the tower crane to be installed; The distance from the starting point to the installation point in a preset direction is calculated on the range to be installed, which is the installation position.

4. The method according to claim 1, wherein Iteratively optimizing the multiple individuals to obtain multiple optimized individuals includes: Calculating the total costs of the multiple individuals, and classifying the multiple individuals into excellent individuals, ordinary individuals, and penalty individuals according to the magnitude of the multiple total costs; Iterative optimization is performed on excellent individuals, ordinary individuals, and punished individuals respectively. Excellent individuals, ordinary individuals, and punished individuals correspond to different iterative optimization methods; If the current number of iterations is less than the preset value, the iterative optimization process is repeated until the number of iterations reaches the preset value, and multiple optimized individuals are obtained.

5. The method according to claim 4, characterized in that The position and model of each crane in the individual are elements in the iterative optimization process. The iterative optimization is performed on the excellent individuals, ordinary individuals, and penalty individuals respectively, including: For excellent individuals, randomly selecting a preset number of elements from the excellent individuals for iterative optimization according to a first preset step size, wherein the preset number is less than the total number of the elements; For a common individual, iteratively optimizing all elements in the common individual according to a second preset step size, where the second preset step size is greater than the first preset step size; For the punished individual, delete it and regenerate a new individual.

6. The method according to claim 5, characterized in that Determining a plurality of top individuals according to the relationship between the plurality of optimized cost values ​​and the optimal cost value includes: Multiplying the optimal cost value by a preset ratio value to obtain an excellent cost range; The plurality of optimized cost values ​​are compared with the excellent cost range. If the optimized cost value is within the excellent cost range, the individual corresponding to the optimized cost value is the top individual.

7. The method according to claim 1, characterized in that Select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals, including: Calculate the total cost value of each top individual in each cluster; The total costs of the top individuals in each cluster are compared, and an optimal individual and multiple suboptimal individuals are obtained from each cluster according to the comparison results. The optimal individual and suboptimal individuals are the top individuals with the lowest total costs in their clusters.

8. An intelligent tower crane selection and position optimization device, characterized in that: The device comprises: An information acquisition module is used to obtain construction environment information and determine the number of tower cranes and the range of each tower crane to be installed based on the construction environment information; A population generation module is used to randomly generate a population based on the installation range of each tower crane and the model to be selected for each tower crane, wherein the population includes multiple individuals, each individual corresponds to an installation plan, and each installation plan includes the installation location of each tower crane and the model of each tower crane; an iterative optimization module, configured to perform iterative optimization on the plurality of individuals to obtain a plurality of optimized individuals; The optimal individual determination module is used to calculate and compare the optimization cost value of each optimized individual to obtain the optimal cost value. The individual corresponding to the optimal cost value is the optimal individual, and the optimal cost value is the lowest value among the optimization cost values. a top individual determination module, configured to determine a plurality of top individuals according to the relationship between the plurality of optimized cost values ​​and the optimal cost value; An individual clustering module, used for clustering the top individuals to obtain multiple clusters; The suboptimal individual determination module is used to select a top individual from each cluster to obtain an optimal individual and multiple suboptimal individuals; The final solution determination module is used to select an individual from the optimal individual and the multiple suboptimal individuals as the final installation solution of the tower crane according to actual needs.

9. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method according to any one of claims 1 to 7 by executing the computer instructions.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 7.

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