Prefabricated floor slab type optimization method and system based on entropy weight method and storage medium

Abstract

The invention discloses a prefabricated floor slab shape optimization method and system based on an entropy weight method and a storage medium, and the method comprises the steps: obtaining a plane division result of each basic unit of a target prefabricated floor slab, determining the unit area of each basic unit, and calculating the fractal dimension feature parameters of each basic unit; standardizing the unit area and the fractal dimension characteristic parameter of each basic unit, and calculating the proportion of the unit area and the fractal dimension characteristic parameter in the same kind of data; calculating information entropies of the unit area and the fractal dimension characteristic parameters; calculating the unit area and the information entropy weight of the fractal dimension characteristic parameter through the information entropy Hj; calculating the importance coefficient of each basic unit; and sorting according to the importance coefficient Zi of each basic unit to perform plate type selection. Scientific type selection and optimized layout of the floor slab type are achieved, the design efficiency can be improved, the material utilization rate and the structural stability can be improved, and a new technical path is provided for the field of prefabricated buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of prefabricated building construction, and in particular to a method, system and storage medium for optimizing the type of prefabricated floor slabs based on an entropy weight method. Background Art

[0002] In recent years, with the rapid development of prefabricated building technology, prefabricated floor slabs, as an important part of prefabricated buildings, have been widely used due to their advantages such as convenient construction, energy saving and environmental protection. In building structure design, in order to meet functional and process requirements, the building plane is usually divided into several basic units of different shapes (such as rectangles, trapezoids, etc.). These basic units need to be covered by prefabricated floor slabs to form a complete load-bearing structure system. However, the current selection and layout methods of prefabricated floor slabs still have the following shortcomings: 1. Lack of scientific selection criteria: Currently, the selection of prefabricated floor slabs mainly relies on the experience and intuition of designers. When faced with complex building plans, the design schemes often lack quantitative basis, resulting in insufficient stability and reliability of the selection results.

[0003] 2. There are many types of panels and low design efficiency: In order to adapt to the diverse shapes of building planes, a common method is to divide all basic units equally. Although this method is easy to operate, it will lead to an excessive number of prefabricated panels, increase the complexity of production and construction, and is not conducive to industrial large-scale production.

[0004] 3. It is difficult to balance material utilization and structural stability: In the traditional selection method, it is difficult to balance the modular coordination of the building plane and the material utilization efficiency, which can easily lead to problems such as waste of resources or insufficient structural strength.

[0005] In response to the above problems, researchers have tried to introduce mathematical analysis and calculation methods in recent years, such as optimization algorithms and modular coordination methods, but they have not been able to effectively solve the quantitative problems in the board type optimization process.

[0006] Information entropy, as a method for quantifying system uncertainty, has been widely used in the field of optimization analysis. Fractal dimension is a mathematical tool for describing the self-similarity and complexity of geometric figures, which can characterize the structural characteristics of the basic units of the figures. However, there is no method that combines information entropy and fractal dimension to apply to the selection of prefabricated floor slabs for prefabricated buildings. Summary of the invention

[0007] In order to solve the shortcomings of the above-mentioned prior art, the present invention proposes a method, system and storage medium for optimizing the type of prefabricated floor slab based on the entropy weight method. By combining information entropy with fractal dimension, the importance of each basic unit is quantified, thereby realizing the scientific selection and optimized layout of the floor slab type. This method can not only improve design efficiency, but also improve material utilization and structural stability, providing a new technical path for the field of prefabricated buildings.

[0008] In order to achieve the above-mentioned invention object, the technical solution provided by the present invention includes: The method for optimizing the type of prefabricated floor slab based on entropy weight method comprises the following steps: Obtain the plane division results of each basic unit of the target prefabricated floor, determine the unit area of ​​each basic unit, and calculate the fractal dimension characteristic parameters of each basic unit; The unit area and fractal dimension characteristic parameters of each basic unit are standardized and their proportion in similar data is calculated; Calculate the information entropy of the unit area and fractal dimension characteristic parameters ,in, , n is the number of basic units; j is the unit area and fractal dimension characteristic parameter serial number and j=1,2; i is the basic unit serial number and i=1,2,3…; p ij is the proportion of the unit area or fractal dimension of each basic unit; α is a preset small fraction; By the information entropy H j Calculate the information entropy weight w of the characteristic parameters of unit area and fractal dimension j ; Calculate the importance coefficient of each basic unit , where y i is the data after the unit area and fractal dimension characteristic parameters of each unit are standardized; w i is the information entropy weight of the unit area and fractal dimension characteristic parameters of each unit; According to the importance coefficient Z of each basic unit i Sort by board type selection.

[0009] Preferably, the method for calculating the fractal dimension characteristic parameter includes: Calculate the fractal dimension of the entire target prefabricated floor plane as the initial fractal dimension; After removing each basic unit respectively, the unit fractal dimension of each basic unit is calculated using the calculation method of the initial fractal dimension, and the initial fractal dimension is subtracted from the unit fractal dimension to obtain the fractal dimension characteristic parameter of each basic unit.

[0010] Preferably, the method for standardizing the unit area and fractal dimension characteristic parameters of each basic unit includes: ; where x ij is the unit area or fractal dimension of each basic unit, x j is the sequence of cell areas or fractal dimensions of all cells.

[0011] Preferably, the information entropy weight w j The calculation methods include: .

[0012] Preferably, the importance coefficient Z of each basic unit i The methods for sorting and selecting board types include: According to the importance coefficient Z of each basic unit i Sort by excluding the importance coefficient Z i A basic unit below a first preset value; The importance coefficient Z i The largest basic unit is used as the basic unit, and the importance coefficient Z i The basic units whose difference does not exceed a first preset value are regarded as approximate units; The basic plate type of the prefabricated floor is determined by the size characteristics of the basic unit; and the auxiliary plate type of the prefabricated floor is determined by combining the size characteristics of each approximate unit.

[0013] The present invention also provides a system, which is used to implement the above-mentioned method for optimizing the type of prefabricated floor slab based on the entropy weight method.

[0014] The present invention also provides a storage medium, which includes a stored program, wherein when the program is run, the above-mentioned prefabricated floor slab type optimization method based on the entropy weight method is executed.

[0015] The present invention also provides an electronic device, which is used to run a program, wherein the program executes the above-mentioned prefabricated floor slab type optimization method based on the entropy weight method when running.

[0016] Beneficial Effects 1. Improve the scientificity and accuracy of prefabricated floor slab selection: By introducing the combination of entropy weight method and fractal dimension, the present invention can quantitatively evaluate the importance of each basic unit in the building plane, avoiding the subjectivity of relying on the designer's experience in the traditional method, and improving the scientificity and accuracy of the selection. The entropy weight method gives each basic unit an objective weight, making the selection process more rational and controllable.

[0017] 2. Improve design efficiency and reduce the types of plate types: The present invention can systematically analyze the basic units based on information entropy and fractal dimension and optimize the plate type modulus. By calculating the importance coefficient of the unit, the optimal plate type can be efficiently screened out, and unnecessary plate types can be reduced, significantly improving design efficiency. Compared with traditional methods, this method can effectively reduce the number of plate types and reduce the complexity of production and construction.

[0018] 3. Optimize material utilization and structural stability: By quantitatively evaluating the importance and self-similarity of each basic unit, the present invention can reasonably optimize material utilization while ensuring the stability of the building structure. This avoids the waste of resources caused by excessive segmentation or incoordination of modules in traditional methods, improves the overall material utilization efficiency, and enhances the stability and reliability of the structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for optimizing the type of prefabricated floor slabs based on an entropy weight method provided in a preferred embodiment of the present invention; Figure 2 A schematic diagram of obtaining the plane division result of each basic unit of the target prefabricated floor provided in another preferred embodiment of the present invention; Figure 3 It is a schematic diagram of the plane partitioning result of calculating the overall fractal dimension and calculating the fractal dimension by removing one basic unit provided in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0021] Embodiment 1 like Figure 1 As shown, the present invention provides a method for optimizing the type of prefabricated floor slab based on the entropy weight method, comprising the steps of: S1. Obtain the plane division results of each basic unit of the target prefabricated floor, determine the unit area of ​​each basic unit, and calculate the fractal dimension characteristic parameters of each basic unit.

[0022] The plane division result of each basic unit of the target prefabricated floor slab can be obtained by manually or automatically drawing and dividing the architectural plan using architectural design software (such as AutoCAD, Revit, SketchUp, etc.). Specifically, the basic units of the prefabricated floor slab are often divided according to architectural design rules and modules. The specific implementation method of plane division based on certain rules according to the modular requirements of the building and the rules relied on by those skilled in the art are not the focus of the present invention, so they are not described here.

[0023] The fractal dimension is a mathematical concept used to describe the dimension of geometric figures with self-similarity and complex structure. Unlike traditional integer dimensions (such as one, two, three, etc.), the fractal dimension can be non-integer, usually between integers, and is used to quantify the complexity or irregularity of objects at different scales. It is characterized by self-similarity, irregularity and non-integer dimension. In the present invention, the fractal dimension can accurately describe the self-similarity and complexity of each basic unit in the building plane. The traditional building plane design method relies on the experience and intuition of the designer, while through fractal dimension analysis, the contribution of each unit to the entire building plane structure can be quantified, thereby providing a scientific basis for the selection of floor slab type. There are many methods for calculating fractal dimension, the most common of which are Hausdorff dimension and box-counting method. The following is a specific example to illustrate how the present invention uses Hausdorff dimension to calculate fractal dimension.

[0024] First, the fractal dimension of the entire target prefabricated floor plane is calculated as the initial fractal dimension: ; Wherein, D is the Hausdorff dimension D, N(r) is the number of squares required after cutting the entire target prefabricated floor plane with a square with a side length of r, and r is the side length of the square used for cutting.

[0025] Use squares with different side lengths r to cover the target figure, and count the number of squares N(r) required to cover the entire figure; draw a scatter plot in a double logarithmic coordinate system with the horizontal axis as ln(r) and the vertical axis as ln(N(r)). Perform linear regression on the scatter plot and calculate the slope of the fitting line. The absolute value of the slope is the fractal dimension D.

[0026] Then, in order to evaluate the influence of a certain basic unit on the self-similarity of the overall graphic, the fractal dimension characteristic parameter D′ of each basic unit is calculated using the calculation method of the initial fractal dimension after removing each basic unit. For a single basic unit, the difference ΔD=D−D′ between the two is used as the fractal dimension characteristic index of each basic unit, which characterizes the influence of the unit on the overall self-similarity.

[0027] S2. Standardize the unit area and fractal dimension characteristic parameters of each basic unit and calculate its proportion in similar data.

[0028] The purpose of the standardization process is mainly to ensure the comparability and consistency of data of different types of basic units (such as basic units of prefabricated floor slabs), so as to make subsequent calculations and analysis more scientific, accurate and efficient. Those skilled in the art should know that common standardization methods include standard deviation standardization, maximum value standardization and quantile standardization. In some preferred embodiments, a preferred standardization method is provided as follows: ; Among them, y ij is the standardized unit area or fractal dimension characteristic parameter of each basic unit, x ij is the unit area or fractal dimension of each basic unit, x j is the sequence of cell areas or fractal dimensions of all cells.

[0029] This step calculates the proportion of the standardized results of the unit area and fractal dimension characteristic parameters in similar data. In fact, it quantifies the relative importance of each unit in the entire building design and can reflect their contribution to the overall plane layout. This is the basis for subsequent entropy weight analysis and plate type optimization. For example, units with larger areas may have a greater impact on the bearing capacity of the overall structure, while units with higher fractal dimensions may represent more complex structures that require special design and components. Therefore, calculating the proportion can help designers accurately determine which units need more attention in plate type selection and design. The calculation method of the proportion is as follows: , where p ij is the proportion of the unit area or fractal dimension of each basic unit, y ij It is the standardized characteristic parameter of unit area or fractal dimension of each basic unit.

[0030] S3, calculating the information entropy of the unit area and fractal dimension characteristic parameters ,in, , n is the number of basic units; j is the unit area and fractal dimension characteristic parameter serial number and j=1,2; i is the basic unit serial number and i=1,2,3…; p ijis the proportion of the unit area or fractal dimension of each basic unit; α is a preset small fraction. The preset small fraction α is set to avoid the appearance of 0 in the logarithmic term, and its value can be 0.0001, etc.

[0031] The information entropy is a basic concept in information theory, which is used to quantify the uncertainty, randomness or complexity of information. Currently, information entropy is widely used in many fields, especially in data analysis, machine learning and communication. In the present invention, the fractal dimension and information entropy are used in combination, mainly to more comprehensively quantify the complexity and importance of different units in architectural design, wherein the fractal dimension is used to describe the geometric shape and self-similarity of the basic unit, reflecting the spatial complexity of the unit, and the information entropy is used to quantify the "information amount" or "uncertainty" of these units, and the relative importance of each unit is evaluated by the entropy value. The combined use can help technical personnel in the field to more accurately optimize and select the plate type of prefabricated floor slabs, ensuring that the design and selection of each unit have a clear basis.

[0032] S4, through the information entropy H j Calculate the information entropy weight w of the characteristic parameters of unit area and fractal dimension j This step is to quantify the relative importance of each dimension (unit area and fractal dimension characteristic parameters) in the selection of the entire precast floor slab type. By calculating the information entropy weight of each dimension, it is possible to assign a suitable weight to the contribution of each basic unit in the subsequent optimization.

[0033] In some preferred embodiments, the information entropy weight w j The calculation methods include: Among them, the information entropy weight w j It reflects the relative importance of the jth dimension (area or fractal dimension) in all basic units. A dimension with a larger information entropy indicates that the dimension has a higher uncertainty or complexity in all basic units, so it should be given a larger weight. If the standardized value of the area dimension is distributed more evenly (that is, the information entropy is higher), the weight of the area dimension will be larger, indicating that the area has a greater impact on the selection of floor slab type. On the contrary, if the data of a certain dimension (such as fractal dimension) is highly concentrated (that is, the information entropy is lower), its weight will be smaller, indicating that the dimension has less impact on the overall selection.

[0034] S5. Calculate the importance coefficient of each basic unit , where y i is the data after the unit area and fractal dimension characteristic parameters of each unit are standardized; w iis the information entropy weight of the unit area and fractal dimension characteristic parameters of each unit. This step quantifies the relative importance of each basic unit in the optimization of the prefabricated floor slab type by combining the information entropy weight and the standardized data. By calculating the importance coefficient of each basic unit, those skilled in the art can select the optimal slab type according to the importance ranking. Units with larger importance coefficients are usually used as basic units for slab type design, while units with smaller importance coefficients may choose standardized prefabricated floor slab types.

[0035] S6. According to the importance coefficient Z of each basic unit i In the present invention, the importance coefficient Z of each basic unit is used to select the board type. i The specific method of selecting the plate type by sorting is not the core content of the present invention and can be set by technicians in this field according to actual engineering requirements. i The basic units are sorted by value, and the appropriate precast floor slab type is selected according to the sorting results. The specific implementation details and selection strategy of this process can be flexibly adjusted according to the different requirements, resource conditions and technical specifications of the project.

[0036] In some preferred embodiments, a method is provided based on the importance coefficient Z of each basic unit. i This method determines which units require special design (basic units) and which can use simplified or standardized design (approximate units). This method can improve production efficiency and reduce costs by optimizing the design while ensuring the safety and functional requirements of the building structure. This method clarifies the design principles of basic units and auxiliary units, and provides a scientific basis for the subsequent selection of floor slab types. Specifically include: S601, according to the importance coefficient Z of each basic unit i Sort by excluding the importance coefficient Z i Basic units below the first preset value. By excluding low-importance units, the board selection process is simplified and resources and energy are concentrated on those units that have a significant impact on the overall design.

[0037] S602, the importance coefficient Z i The largest basic unit is used as the basic unit, and the importance coefficient Z i The basic units whose difference does not exceed the first preset value are regarded as approximate units.

[0038] The base unit usually bears a large load or has a high complexity, so it is necessary to select a dedicated plate type according to its specific size, shape and functional requirements. The size and functional characteristics of the base unit will play a key role in the plate type selection to ensure that it can be effectively integrated into the overall building structure.

[0039] The basic units belonging to the same approximate unit have similar structures or sizes, which allows the use of the same or similar prefabricated panel types for design. By utilizing their similarities, the panel types can be unified or simplified in design to improve design efficiency.

[0040] S603, determining the basic plate type of the prefabricated floor slab based on the size characteristics of the basic unit; and combining the size characteristics of each approximate unit to determine the auxiliary plate type of the prefabricated floor slab.

[0041] The dimensional characteristics include, but are not limited to, parameters such as geometric shape, area, side length, etc. These parameters are important bases for plate type design. By determining the dimensional characteristics of the basic unit, the basic plate type of the entire prefabricated floor can be derived. The auxiliary plate type can be flexibly adjusted and designed according to the number and specific shape of the approximate units to ensure its coordination with the basic plate type.

[0042] Embodiment 2 This embodiment is developed on the basis of the above-mentioned embodiment 1. This embodiment further illustrates the method for optimizing the type of prefabricated floor slab based on the entropy weight method of the present invention through a specific example.

[0043] Obtain the plane division results of each basic unit of the target precast floor, such as Figure 2 As shown, each basic unit is named, the same units can be named uniformly, and the unit area of ​​each basic unit is determined. The results are shown in Table 1 below.

[0044] Table 1 Basic unit name and area statistics like Figure 3 As shown in the figure, first calculate the initial fractal dimension of the whole, then remove one of the basic units in turn, recalculate the unit fractal dimension of the remaining graphics, and subtract the initial fractal dimension from the unit fractal dimension to obtain the fractal dimension characteristic parameter of each basic unit, so as to obtain the influence of the unit on the self-similarity. The results are shown in Table 2 below, where the serial number 0 represents the entire target prefabricated floor.

[0045] Table 2 Calculation results of fractal dimension and fractal dimension characteristic index After combining Table 1 and Table 2 above, the basic data of the fractal dimension characteristic parameters and area double dimensions of each basic unit are obtained as shown in Table 3 below.

[0046] Table 3 Basic data table of fractal dimension characteristic parameters and area dual dimensions of each basic unit The fractal dimension characteristic parameters and area data of each basic unit in Table 3 are standardized, and the results are shown in Table 4.

[0047] Table 4. Standardization results of fractal dimension characteristic parameters and area data of each basic unit The fractal dimension characteristic parameters and area of ​​the basic unit are taken as two independent dimensions, and the proportion of each data in each independent dimension is calculated respectively. The results are shown in Table 5 below.

[0048] Table 5 Calculation results of the proportion of fractal dimension characteristic parameters and area independent dimension The information entropy of independent dimension 1-fractal dimension characteristic parameter and independent dimension 2-area are calculated respectively, where =0.6624, =0.6029, and then calculate the information entropy weight of the unit area and fractal dimension characteristic parameters, where, =0.4595, =0.5405.

[0049] The importance coefficient of each basic unit is calculated and ranked, and the results are shown in Table 6 below.

[0050] Table 6 Calculation results of importance coefficients of each basic unit It can be seen from Table 6 that A1 is significantly important, and the importance coefficients of A2 and A3 are not much different. Therefore, when selecting precast panels, the specific panel type of the precast panels should be determined based on the basic size of A1, and A2 and A3 can be combined for specific selection based on size characteristics.

[0051] 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 optimizing the type of prefabricated floor slab based on entropy weight method, characterized in that: Includes steps: Obtain the plane division results of each basic unit of the target prefabricated floor, determine the unit area of ​​each basic unit, and calculate the fractal dimension characteristic parameters of each basic unit; The unit area and fractal dimension characteristic parameters of each basic unit are standardized and their proportion in similar data is calculated; Calculate the information entropy of the unit area and fractal dimension characteristic parameters ,in, , n is the number of basic units; j is the unit area and fractal dimension characteristic parameter serial number and j=1,2; i is the basic unit serial number and i=1,2,3…; p ij is the proportion of the unit area or fractal dimension of each basic unit; α is a preset small fraction; By the information entropy H j Calculate the information entropy weight w of the characteristic parameters of unit area and fractal dimension j ; Calculate the importance coefficient of each basic unit , where y i is the data after the unit area and fractal dimension characteristic parameters of each unit are standardized; w i is the information entropy weight of the unit area and fractal dimension characteristic parameters of each unit; According to the importance coefficient Z of each basic unit i Sort by board type selection.

2. The method for optimizing the type of prefabricated floor slab based on the entropy weight method according to claim 1, characterized in that: The calculation method of the fractal dimension characteristic parameter includes: Calculate the fractal dimension of the entire target prefabricated floor plane as the initial fractal dimension; After removing each basic unit respectively, the unit fractal dimension of each basic unit is calculated using the calculation method of the initial fractal dimension, and the initial fractal dimension is subtracted from the unit fractal dimension to obtain the fractal dimension characteristic parameter of each basic unit.

3. The method for optimizing the type of prefabricated floor slab based on the entropy weight method according to claim 1, characterized in that: The method for standardizing the unit area and fractal dimension characteristic parameters of each basic unit includes: ; where x ij is the unit area or fractal dimension of each basic unit, x j is the sequence of cell areas or fractal dimensions of all cells.

4. The method for optimizing the type of prefabricated floor slab based on the entropy weight method according to claim 1 or 3, characterized in that: The information entropy weight w j The calculation methods include: .

5. The method for optimizing the type of prefabricated floor slab based on entropy weight method according to claim 1, characterized in that: According to the importance coefficient Z of each basic unit i The methods for sorting and selecting board types include: According to the importance coefficient Z of each basic unit i Sort by excluding the importance coefficient Z i A basic unit below a first preset value; The importance coefficient Z i The largest basic unit is used as the basic unit, and the importance coefficient Z i The basic units whose difference does not exceed a first preset value are regarded as approximate units; The basic plate type of the prefabricated floor is determined by the size characteristics of the basic unit; and the auxiliary plate type of the prefabricated floor is determined by combining the size characteristics of each approximate unit.

6. The prefabricated floor slab type optimization system based on entropy weight method is characterized by: The prefabricated floor slab type optimization system is used to implement the method described in any one of claims 1 to 5.

7. A storage medium, characterized in that: The storage medium includes a stored program, wherein the program executes the method according to any one of claims 1 to 5 when executed.

8. An electronic device, characterized in that: The electronic device is used to run a program, wherein the program executes the method according to any one of claims 1 to 5 when running.

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