Method, System and Storage Medium for Optimizing Prefabricated Floor Slab Types Based on Principal Component Analysis

Abstract

The present invention discloses a method, system and storage medium for optimizing the slab type of precast floor slabs based on the principal component method. The method includes: obtaining the plane division results of each basic unit of the target precast floor slab, determining the unit area of each basic unit and calculating the fractal dimension index of each basic unit; performing standardization processing on the unit area and fractal dimension index of each basic unit; constructing a covariance matrix and calculating the variance of the data group after standardization of the unit area and fractal dimension index of each basic unit; calculating the principal component weights of the two dimensions of the unit area and fractal dimension index through the variance of the data group; calculating the influence coefficient of each basic unit, and sorting according to the level of the influence coefficient of each basic unit, and selecting the slab type through the order. The present invention calculates the influence level of each basic unit in multiple dimensions by constructing a covariance matrix, so as to realize the modular coordination selection of precast slab types, improve the use efficiency of materials and the stability of the structure.

Description

Technical Field

[0001] The present invention belongs to the technical field of prefabricated building construction. Specifically, it relates to a method, system and storage medium for optimizing the slab type of precast floor slabs based on the principal component method. Background Art

[0002] In recent years, with the rapid development of prefabricated building technology, precast floor slabs, as an important part of prefabricated buildings, have been widely used due to their advantages such as convenient construction, energy conservation and environmental protection. In building structure design, to meet the functional and technological 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 precast floor slabs to form a complete load-bearing structure system. However, the current methods for selecting and arranging precast floor slabs still have the following deficiencies:

[0003] 1. Lack of scientific selection criteria: Currently, the selection of precast floor slabs mainly relies on the experience and intuition of designers. When facing a complex building plane, the design scheme often lacks quantitative basis, resulting in insufficient stability and reliability of the selection results.

[0004] 2. There are many slab types and low design efficiency: To adapt to the diverse shapes of building planes, a common method is to evenly divide all basic units. Although this method is easy to operate, it will result in too many slab types of precast floor slabs, increasing the complexity of production and construction and being unfavorable for large-scale industrial production.

[0005] 3. It is difficult to balance material utilization rate and structural stability: In traditional selection methods, it is difficult to balance the modular coordination of the building plane and the material utilization efficiency, easily causing problems such as resource waste or insufficient structural strength.

[0006] In response to the above problems, in recent years, researchers have tried to introduce mathematical analysis and calculation methods. The principal component analysis method is a mathematical statistical method that can transform high-dimensional data into low-dimensional data through linear transformation and is widely used in the field of optimization analysis. However, there has not yet been a selection of precast floor slabs for prefabricated buildings using the principal component analysis method. Summary of the Invention

[0007] The purpose of the present invention is to provide a method, system and storage medium for optimizing the slab type of precast floor slabs based on the principal component method. It uses the principal component analysis method to calculate the influence degree of each basic unit in multiple dimensions by constructing a covariance matrix, so as to realize the modular coordination selection of precast slab types, improve the material use efficiency and the structural stability.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] The method for optimizing the slab type of precast floor slabs based on the principal component method includes the following steps:

[0010] S1. Obtain the plane division results of each basic unit of the target precast floor slab, determine the unit area of each basic unit, and calculate the fractal dimension index of each basic unit;

[0011] S2. Standardize the unit area and fractal dimension index of each basic unit;

[0012] S3. Construct the covariance matrix of the unit area and fractal dimension index of each basic unit, and calculate the variance of the data group after standardizing the unit area and fractal dimension index of each basic unit;

[0013] S4. Calculate the principal component weights of the two dimensions of the unit area and fractal dimension index through the variance of the data group;

[0014] S5. Calculate the influence coefficient of each basic unit, sort according to the level of the influence coefficient of each basic unit, and select the slab type according to the order.

[0015] Preferably, in step S1, the calculation method of the fractal dimension index includes:

[0016] S101. Calculate the fractal dimension of the overall plane of the target precast floor slab and use it as the initial fractal dimension;

[0017] S102. Remove one basic unit alone, and use the calculation method of the initial fractal dimension to calculate the fractal dimension of the remaining units again. Subtract the fractal dimension of the remaining units from the initial fractal dimension to obtain the fractal dimension index of the removed basic unit;

[0018] S103. Repeat step S102 to calculate the fractal dimension index of each basic unit.

[0019] Preferably, in step S2, the standardization method of the unit area and fractal dimension index of each basic unit includes:

[0020] ;

[0021] Among them, is the standardization parameter of the unit area or fractal dimension index of each basic unit; n is the number of basic units, i is the serial number of the basic unit and i = 1, 2, 3...; j is the serial number of the unit area and fractal dimension index and j = 1, 2; is the unit area or fractal dimension index of each basic unit.

[0022] Preferably, in step S3, the covariance matrix based on the two dimensions of the unit area and fractal dimension index of each basic unit is a 2x2 matrix:

[0023] ;

[0024] Among them, is the unit area variable; is the fractal dimension index variable; and are the variances of the unit area and the fractal dimension index of each basic unit respectively; and are the covariances of the unit area and the fractal dimension index of each basic unit respectively;

[0025] Since the unit area and the fractal dimension index of each basic unit are two independent and uncorrelated dimensions, therefore, the and are both zero, so the above formula can be expressed as:

[0026] ;

[0027] Among them, and are the variances of the data groups after standardization of the unit area and the fractal dimension index of each basic unit respectively.

[0028] Preferably, the calculation method of the variances of the data groups after standardization of the unit area and the fractal dimension index of each basic unit includes:

[0029] ;

[0030] Among them, is the average value of the data groups after standardization of the unit area and the fractal dimension index of each basic unit.

[0031] Preferably, in step S4, the calculation method of the principal component weights includes:

[0032] ;

[0033] Among them, is the principal component weight of the unit area or the fractal dimension index of each basic unit; is the variance of the data group after standardization of the unit area or the fractal dimension index of each basic unit.

[0034] Preferably, in step S5, the calculation method of the influence degree coefficient includes:

[0035] ;

[0036] Among them, is the influence degree coefficient of each basic unit.

[0037] Preferably, in step S5, the method of selecting the slab type according to the sorting of the influence coefficient of each basic unit includes:

[0038] S501, sort according to the influence coefficient of each basic unit, and exclude the basic units whose influence coefficient is lower than the first preset value;

[0039] S502, take the basic unit with the largest influence coefficient as the basic unit, and take the basic units whose influence coefficients differ by no more than the first preset value as approximate units;

[0040] S503, determine the basic slab type of the precast floor slab according to the dimensional characteristics of the basic unit, and respectively combine the dimensional characteristics of each approximate unit to determine the auxiliary slab type of the precast floor slab.

[0041] The present invention also provides a system for implementing the above-mentioned method for optimizing the slab type of precast floor slabs based on the principal component method.

[0042] The present invention also provides a storage medium, which includes a stored program. When the program runs, it executes the above-mentioned method for optimizing the slab type of precast floor slabs based on the principal component method.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] The present invention uses the principal component analysis method to quantify the variation degree of each basic unit in different dimensions, and then determines its influence on the overall engineering characteristics, which helps to deeply understand the internal laws and complexities of all basic units of the precast slab; through the vector normalization processing of different independent dimensions, the overall covariance matrix is constructed, so as to calculate the weight factors of the unit area and fractal dimension indexes, and then judge the importance contribution of all basic units to the overall plane, realize the modular coordination of the precast slab type, and improve the material use efficiency and structural stability; based on the two independent dimensions of the unit area and fractal dimension indexes of the precast floor slab, the full-range test of the precast floor slab plane can be realized, the test efficiency is high, and at the same time, by obtaining the influence degree indexes of each basic unit, the importance order of various basic units can be accurately evaluated, ensuring the test accuracy, and providing an effective basis for the selection of precast components. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0046] Figure 1 is a schematic flow chart of the method for optimizing the slab type of precast floor slabs based on the principal component method provided in a preferred embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the plane division result of each basic unit of the target precast floor slab provided in another preferred embodiment of the present invention;

[0048] Figure 3 Schematic diagram of the plane division result of removing one basic unit to calculate the fractal dimension provided in a preferred embodiment of the present invention;

[0049] Figure 4 Schematic diagram of the calculation method flow of the fractal dimension index provided in a preferred embodiment of the present invention;

[0050] Figure 5 Schematic diagram of the method flow for selecting a slab type according to the sorting of the influence degree coefficients of each basic unit provided in a preferred embodiment of the present invention. Detailed implementation manners

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1

[0053] As Figure 1 shown, this embodiment provides a method for optimizing the slab type of a precast floor slab based on the principal component method, including the steps:

[0054] S1. Obtain the plane division result of each basic unit of the target precast floor slab, determine the unit area of each basic unit, and calculate the fractal dimension index of each basic unit.

[0055] It should be noted that the precast floor slab has two independent and irrelevant dimensions, namely natural attributes and engineering attributes. In this embodiment, the method of statistically calculating the unit area is used to solve the measurement of the characteristic indexes of natural attributes, and the method of calculating the fractal dimension of each basic unit is used to solve the measurement of the characteristic indexes of the engineering structure plane complexity, that is, engineering attributes.

[0056] Among them, the unit area of each basic unit is determined by obtaining the plane division result of each basic unit of the target precast floor slab. The plane division result can be obtained by using architectural design software (such as AutoCAD, Revit, SketchUp, etc.) to manually or automatically draw and divide the architectural floor plan. Specifically, the basic units of the precast floor slab are often divided according to architectural design rules and modules; those skilled in the art follow the modular requirements of the building and perform the specific implementation method and the rules relied on for plane division based on certain rules, which are not the key objects of investigation in this invention, so they will not be elaborated here.

[0057] On the other hand, there are various calculation methods for the fractal dimension index of each basic unit. The most common ones include: Hausdorff Dimension method, Box-counting Method, Information Dimension method, and Correlation Dimension method. Since the above calculation methods for the fractal dimension are all prior arts, the operation process will not be elaborated in detail in this embodiment, but will be illustrated by examples in subsequent embodiments.

[0058] S2, standardize the unit area and fractal dimension index of each basic unit.

[0059] The purpose of the above standardization process is mainly to ensure the data comparability and consistency of different types of basic units (such as each basic unit of the precast floor slab), so that subsequent calculations and analyses are more scientific, accurate, and efficient.

[0060] Those skilled in the art should know that common standardization methods include Min-Max standardization, Z-Score standardization, decimal scaling standardization, logarithmic standardization, standard deviation standardization, maximum standardization, quantile standardization, etc. Since the above data standardization methods are all prior arts, the operation process will not be elaborated in detail in this embodiment, but will be illustrated by examples in subsequent embodiments.

[0061] S3, construct a covariance matrix of the unit area and fractal dimension index of each basic unit, and calculate the variance of the data group after standardization of the unit area and fractal dimension index of each basic unit.

[0062] Among them, the purpose of constructing the covariance matrix is mainly to capture the linear relationship between variables in the data, and based on this, find the main change directions (principal components) of the data. By solving the eigenvalues and eigenvectors of the two independent dimensions of the unit area and the fractal dimension index, the eigenvalue represents the proportion of variance explained by the principal components in the data, and the eigenvector represents the direction of the principal components. Generally, the maximum eigenvalue is taken as the basis for subsequent evaluation, that is, the largest factor of the principal component; and the numerical proportional relationship of its corresponding eigenvector can be considered as the weight proportional relationship under this principal component, so as to calculate the proportion of the principal component weights of the two independent dimensions respectively, providing an effective basis for the subsequent selection of precast floor slabs.

[0063] Those skilled in the art should be aware that calculating the variance values of the data groups of the two independent dimensions respectively according to the covariance matrix is a prior art. Therefore, examples of this part of the technical content will be given in the subsequent embodiments and will not be elaborated here.

[0064] S4. Calculate the principal component weights of the two dimensions of the unit area and the fractal dimension index through the variance of the data group.

[0065] The above calculation of the principal component weights is to quantify the relative importance of each independent dimension (unit area and fractal dimension index) in the selection of the precast floor slab type. By calculating the principal component weights of each independent dimension, the influence degree of each basic unit in the entire precast slab plane can be determined subsequently, providing a basis for the final selection of the precast floor slab.

[0066] In this embodiment, the calculation result of the principal component weight is the ratio of the variance value of the unit area or the fractal dimension index to the sum of the variance values of the unit area and the fractal dimension index. The specific calculation formula is disclosed in the subsequent embodiments and will not be elaborated here.

[0067] S5. Calculate the influence degree coefficients of each basic unit, sort them according to the level of the influence degree coefficients of each basic unit, and select the slab type through the order.

[0068] In this step, the influence degree coefficient is calculated based on the two dimensions of the corresponding unit area and the fractal dimension index of each basic unit. This value indicates the importance degree of each basic unit in the precast floor slab. The module number of the precast floor slab type can be determined by the size of this value, so as to realize the module coordination of the precast slab type, improve the material use efficiency and structural stability of the precast floor slab; this step quantifies the influence degree of each basic unit in the selection of the precast floor slab type by combining the principal component weights and the standardized data. Those skilled in the art can select the optimal slab type according to the sorting of the influence degree coefficients. The units with larger influence degree coefficients are usually used as the basic units for slab type design, while the units with smaller influence degree coefficients may choose the standardized precast floor slab types.

[0069] On the other hand, the specific method of selecting the slab type according to the sorting of the influence coefficient of each basic unit is not the core content of the present invention and can be set by those skilled in the art according to the actual engineering requirements. Specifically, the designer can sort each basic unit according to the calculated influence coefficient value and select a suitable precast floor slab type according to the sorting result. The specific implementation details and selection strategies of this process can be flexibly adjusted according to different requirements, resource conditions and technical specifications of the project.

[0070] Generally speaking, in this embodiment, the principal component analysis method is used to quantify the variation degree of each basic unit in different dimensions, and then determine its influence on the overall engineering characteristics, which helps to deeply understand the internal laws and complexities of all basic units of the precast slab; through the vector normalization processing of different independent dimensions, the overall covariance matrix is constructed, so as to calculate the weight factors of the unit area and fractal dimension indexes, and then judge the importance contribution of all basic units to the overall plane, realize the modular coordination of the precast slab type, and improve the material utilization efficiency and the structural stability; based on the two independent dimensions of the unit area and fractal dimension indexes of the precast floor slab, the full-range test of the precast floor slab plane can be realized, with high test efficiency. At the same time, by obtaining the influence degree indexes of each basic unit, the importance order of various basic units can be accurately evaluated, the test accuracy can be guaranteed, and an effective basis can be provided for the selection of precast components.

[0071] Embodiment 2

[0072] This embodiment is a further preferred scheme of Embodiment 1. In this embodiment, the method for calculating the fractal dimension index in step S1 adopts the Hausdorff dimension method. However, since the direct calculation of the Hausdorff dimension is relatively complex, in actual applications, it is approximately calculated by the box-counting method. The specific steps are as follows:

[0073] S101, calculate the fractal dimension of the overall plane of the target precast floor slab and use it as the initial fractal dimension;

[0074] Divide the target graph into several square boxes with side length r, and count the number of boxes N(r) required to cover the entire graph; gradually reduce the size r of the box (for example, r = 1, 0.5, 0.25,...), for each r, respectively count the number of boxes N(r) required to cover the entire graph; take the logarithm of the box side length r and the number of boxes N(r), and draw and relationship diagram, and fit the slope through linear regression. This slope is the fractal dimension D:

[0075] ;

[0076] Among them, D is the Hausdorff dimension, r is the side length of the square box for covering, and N(r) is the number of square boxes with side length r required to cover the entire plane of the target precast floor slab.

[0077] S102. To evaluate the influence of a certain basic unit on the self-similarity of the overall graph, remove one basic unit alone, and use the calculation method of the initial fractal dimension to calculate the fractal dimension D' of the remaining units again. For a single basic unit, the difference ΔD = D - D' between the two is used as the fractal dimension index of this basic unit, which characterizes the influence degree of this unit on the overall self-similarity.

[0078] S103. Repeat the step S102 to calculate the fractal dimension index of each basic unit.

[0079] In this embodiment, the Hausdorff dimension is an important concept in fractal geometry, which is mainly used to describe the complexity and roughness of geometric objects, and is very suitable for the complex, irregular and self-similar structures such as precast floor slabs in the present invention.

[0080] Embodiment 3

[0081] This embodiment is a further preferred solution of Embodiment 2. In this embodiment, the softmax normalization method is adopted for the normalization processing method of the unit area and fractal dimension index of each basic unit in the step S2, and its calculation formula is as follows:

[0082] ;

[0083] Among them, is the normalization parameter of the unit area or fractal dimension index of each basic unit; n is the number of basic units, i is the serial number of the basic unit and i = 1, 2, 3...; j is the serial number of the unit area and fractal dimension index and j = 1, 2; is the unit area or fractal dimension index of each basic unit.

[0084] The main advantage of the Softmax function is that it can convert the initial data output into a probability distribution, so that the output value of each category can be interpreted as the probability of this category. This not only makes the final output result intuitive and easy to understand, but also helps to handle various classification problems, while maintaining the smoothness and numerical stability of the gradient, which is convenient for subsequent analysis and optimization of data.

[0085] Embodiment 4

[0086] This embodiment is a further preferred solution of Embodiment 3. In this embodiment, the covariance matrix based on the two dimensions of the unit area and fractal dimension index of each basic unit in the step S3 is a 2×2 matrix:

[0087] ;

[0088] Wherein, is the unit area variable; is the fractal dimension index variable; and are the variances of the unit area and the fractal dimension index of each basic unit, respectively; and are the covariances of the unit area and the fractal dimension index of each basic unit, respectively.

[0089] It should be clear that the establishment of the above covariance matrix is based on the unit area and the fractal dimension index data of each basic unit after being standardized in step S2. This data set includes two variables (unit area and fractal dimension index ) and n samples.

[0090] Furthermore, since the unit area and the fractal dimension index of each basic unit are two independent and uncorrelated dimensions, therefore, the and are both zero, so the above formula can be expressed as:

[0091] ;

[0092] Wherein, and are the variances of the data groups after the unit area and the fractal dimension index of each basic unit are standardized, respectively.

[0093] The calculation method of the variances of the data groups after the unit area and the fractal dimension index of each basic unit are standardized includes:

[0094] ;

[0095] Wherein, is the average value of the data groups after the unit area and the fractal dimension index of each basic unit are standardized.

[0096] Specifically, splitting the above calculation process for the variance of the data group, specifically:

[0097] First, organize to obtain the data set matrix after the unit area and the fractal dimension index of each basic unit are standardized:

[0098] ;

[0099] Wherein, each row is a sample and each column is a variable.

[0100] Then, calculate the mean value of each variable:

[0101] ;

[0102] And subtract the mean value from each variable to obtain a centered data matrix:

[0103] ;

[0104] Then, construct a covariance matrix according to the covariance matrix calculation formula:

[0105] C = ;

[0106] where is the transpose of the centered data matrix.

[0107] The specific form of the covariance matrix C is:

[0108] ;

[0109] That is ;

[0110] where the diagonal elements and are the variances of the two variables respectively; the off - diagonal elements and are the covariances between the two variables.

[0111] Then,

[0112] = ;

[0113] = ;

[0114] Since the cell area and the fractal dimension index of each basic unit are two independent and uncorrelated dimensions, therefore, the and are both zero, and the finally constructed covariance matrix is:

[0115] ;

[0116] Through the above covariance matrix C, its eigenvalues λ (in the order of principal components) and eigenvectors V (in the direction of principal components) can be solved. Generally, the maximum value of the eigenvalues is taken as the basis for subsequent evaluation, that is, the maximum factor of the principal component, and the numerical proportional relationship of the corresponding eigenvector V can be regarded as the weight proportional relationship under this principal component.

[0117] Furthermore, the calculation method of the principal component weights in step S4 includes:

[0118]

[0119] Among them, is the principal component weight of the unit area or fractal dimension index of each basic unit; is the data group variance of the unit area or fractal dimension index of each basic unit after standardization.

[0120] The above principal component weight reflects the relative importance of the j-th dimension (unit area or fractal dimension index) among all basic units. A dimension with a larger principal component weight indicates a higher degree of uncertainty or complexity in that dimension among all basic units. Therefore, a larger weight should be assigned.

[0121] Furthermore, the calculation method of the influence degree coefficient in step S5 includes:

[0122] ;

[0123] Among them, is the influence degree coefficient of each basic unit.

[0124] The above influence degree coefficient reflects the influence degree of each basic unit on the overall precast floor slab in the dimensions of unit area and fractal dimension index. The larger the value of the influence degree coefficient, the more important the basic unit is in the precast floor slab structure. Therefore, in the subsequent slab type selection, the basic size of this basic unit should be used to determine the slab type modulus of the precast floor slab.

[0125] Embodiment 5

[0126] This embodiment is a further supplementary scheme of Embodiment 1. In step S5, a method for slab type selection based on the sorting of the influence degree coefficients of each basic unit is given. This method determines which units need special design (basic units) and which can use simplified or standardized designs (approximate units). This method can improve production efficiency and reduce costs through optimized design while ensuring the safety and functional requirements of the building structure. This method clarifies the design principles of basic units and auxiliary units, providing a scientific basis for subsequent floor slab type selection. Specifically, it includes:

[0127] S501, Sort according to the influence degree coefficients of each basic unit, and exclude the basic units whose influence degree coefficients are lower than the first preset value. By excluding low-influence units, the slab type selection process is simplified, and resources and energy are concentrated on those units that have a significant impact on the overall design.

[0128] S502, Take the basic unit with the largest influence degree coefficient as the basic unit, and take the basic units whose influence degree coefficients differ by no more than the first preset value as approximate units.

[0129] The basic units usually bear relatively large loads or have high complexity. Therefore, it is necessary to select special plate types according to their specific size, shape, and functional requirements. The size and functional characteristics of the basic units will play a key role in the selection of plate types to ensure their effective integration into the overall building structure.

[0130] The basic units belonging to the same approximate unit have similar structures or sizes, allowing the use of the same or similar precast plate types for design. By utilizing their similarity, the design of the plate types can be unified or simplified to improve the design efficiency.

[0131] S503, determine the basic plate type of the precast floor slab based on the size characteristics of the basic unit, and combine the size characteristics of each approximate unit respectively to determine the auxiliary plate type of the precast floor slab.

[0132] The size 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 size characteristics of the basic unit, the basic plate type of the entire precast floor slab can be deduced. The auxiliary plate type can be flexibly adjusted according to the number and specific shape of the approximate units to ensure its coordination with the basic plate type.

[0133] Embodiment 6

[0134] This embodiment combines the technical solutions of the above embodiments and further illustrates the method for optimizing the plate type of the precast floor slab based on the principal component method of the present invention through a specific example.

[0135] Obtain the plane division results of each basic unit of the target precast floor slab, and determine the unit area of each basic unit. As Figure 2 shown, name each basic unit. The same units can be named uniformly, and determine the unit area of each basic unit. The results are shown in Table 1 below.

[0136] Table 1 Statistical table of basic unit names and areas

[0137]

[0138] As Figure 3 shown, first calculate the fractal dimension of the entire plane of the target precast floor slab, then sequentially remove one basic unit alone, and recalculate the fractal dimension of the remaining units. Subtract the initial fractal dimension from the unit fractal dimension to obtain the fractal dimension index of each basic unit, and then the influence degree of the unit on self-similarity can be obtained. The results are shown in Table 2 below, where A0 represents the whole of the target precast floor slab.

[0139] Table 2 Calculation results table of fractal dimension and fractal dimension index

[0140]

[0141] After merging and sorting the above Table 1 and Table 2, the basic data of the fractal dimension index and the unit area two-dimension of each basic unit are shown in Table 3 below.

[0142] Table 3 Basic data table of the fractal dimension index and the unit area two-dimension of each basic unit

[0143]

[0144] The fractal dimension index and the unit area data of each basic unit in Table 3 are standardized, and the results are shown in Table 4.

[0145] Table 4 Standardization result table of the fractal dimension index and the unit area data of each basic unit

[0146]

[0147] By constructing the covariance matrix, the variances of the two independent dimensions of the fractal dimension index and the unit area of the basic unit are calculated, and the results are shown in Table 5 below.

[0148] Table 5 Variance calculation results of the fractal dimension index and the unit area independent dimension

[0149]

[0150] The principal component weights of the fractal dimension index and the unit area two-dimension are calculated through the above variance values, and the results are shown in Table 6 below.

[0151] Table 6 Calculation result table of the principal component weights of the independent dimension

[0152]

[0153] The influence degree coefficients of each basic unit are calculated through the product of the standardized data group and the weights of different dimensions, and are sorted according to the size, so as to clarify the influence order of each basic unit in the target precast floor slab. Then, according to the order of the influence degree, the modulus of the precast slab type is determined, and the results are shown in Table 7 below.

[0154] Table 7 Calculation result table of the influence degree coefficients of each basic unit

[0155]

[0156] It can be seen from this that A1 is significantly important. Therefore, when selecting the type of precast floor slab, the specific slab type should be determined according to the basic size of A1, and A2 to A4 can be matched by combining according to the size characteristics. If the modulus does not match, the specific slab type of the precast floor slab is determined according to the basic size of A3, and A2 and A4 are matched, and so on.

[0157] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the slab type of precast floor slabs based on the principal component method, characterized in that Including the steps: S1. Obtain the plane division results of each basic unit of the target precast floor slab, determine the unit area of each basic unit, and calculate the fractal dimension index of each basic unit. The fractal dimension index is a characteristic index for measuring the plane complexity of the precast floor slab. The calculation method of the fractal dimension index includes: S101. Calculate the fractal dimension of the overall plane of the target precast floor slab and use it as the initial fractal dimension; S102. Remove one of the basic units separately, and use the calculation method of the initial fractal dimension to calculate the fractal dimension of the remaining units again. Subtract the fractal dimension of the remaining units from the initial fractal dimension to obtain the fractal dimension index of the removed basic unit; S103. Repeat the step S102 to calculate the fractal dimension index of each basic unit; S2. Standardize the unit area and fractal dimension index of each basic unit; S3. Construct a covariance matrix of the unit area and fractal dimension index of each basic unit, and calculate the variance of the data group after standardizing the unit area and fractal dimension index of each basic unit; S4. Calculate the principal component weights of the two dimensions of the unit area and fractal dimension index through the variance of the data group; S5. Calculate the influence degree coefficient of each basic unit, sort according to the level of the influence degree coefficient of each basic unit, and select the slab type through the order.

2. The method for optimizing the precast floor slab type based on the principal component method according to claim 1, characterized in that In step S2, the method for standardizing the unit area and fractal dimension index of each basic unit includes: ； Among them, is the standardized parameter of the unit area or fractal dimension index of each basic unit; n is the number of basic units; i is the serial number of the basic unit and i = 1, 2, 3...; j is the serial number of the unit area and fractal dimension index and j = 1, 2; is the unit area or fractal dimension index of each basic unit.

3. The method for optimizing the precast floor slab type based on the principal component method according to claim 2, wherein In step S3, the covariance matrix based on the two dimensions of the unit area and fractal dimension index of each basic unit is a 2x2 matrix: ； Among them, is the unit area variable; is the fractal dimension index variable; and are the variances of the unit area and the fractal dimension index of each basic unit, respectively; and are the covariances of the unit area and the fractal dimension index of each basic unit, respectively; Since the cell area and the fractal dimension index of each basic cell are two independent dimensions that are not related to each other, therefore, the and are both zero, so the above formula can be expressed as: ； Among them, and are the variances of the data groups after standardizing the unit area and fractal dimension index of each basic unit, respectively.

4. The method for optimizing the precast floor slab type based on the principal component method according to claim 3, characterized in that The calculation method of the variance of the data group after standardizing the unit area and fractal dimension index of each basic unit includes: ； Among them, is the average value of the data group after standardizing the unit area and fractal dimension index of each basic unit.

5. The method for optimizing the precast floor slab type based on the principal component method according to claim 4, characterized in that In step S4, the calculation method of the principal component weight includes: ； Among them, is the principal component weight of the unit area or fractal dimension index of each basic unit; is the data group variance of the unit area or fractal dimension index of each basic unit after standardization.

6. The method for optimizing the precast floor slab type based on the principal component method according to claim 5, wherein In step S5, the calculation method of the influence degree coefficient includes: ； Among them, is the influence coefficient of each basic unit.

7. The method for optimizing the precast floor slab type based on the principal component method according to claim 1, wherein In step S5, the method for selecting the slab type according to the sorting of the influence degree coefficient of each basic unit includes: S501. Sort according to the influence degree coefficient of each basic unit, and exclude the basic units with an influence degree coefficient lower than the first preset value; S502. Take the basic unit with the largest influence degree coefficient as the basic unit, and take the basic units with an influence degree coefficient difference not exceeding the first preset value as approximate units; S503. Determine the basic slab type of the precast floor slab based on the size characteristics of the basic unit, and combine the size characteristics of each approximate unit to determine the auxiliary slab type of the precast floor slab.

8. A precast floor slab type optimization system based on the principal component method, characterized in that The precast floor slab type selection system is used to implement the method described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when running.

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