A comprehensive performance evaluation method and device for steel formwork materials

Abstract

The present invention relates to the field of steel formwork detection technology, and in particular to a method and device for evaluating the comprehensive performance of steel formwork materials. The method comprises: obtaining geometric parameters and material parameters of the steel formwork, determining a three-dimensional geometric model of the steel formwork based on the geometric parameters and material parameters, meshing the three-dimensional geometric model of the steel formwork to obtain the meshed three-dimensional geometric model of the steel formwork, inputting corresponding comprehensive performance evaluation parameters into each node in the meshed geometric model of the steel formwork to obtain a simulation model of the steel formwork material, performing feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix, inputting the multi-dimensional feature matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material. Through the above configuration, the present invention can accurately evaluate the comprehensive performance of the steel formwork material.

Description

Technical Field

[0001] The present invention relates to the technical field of steel template detection, and in particular to a comprehensive performance evaluation method and device for steel template materials. Background Art

[0002] Steel formwork is widely used in the construction industry. With the advancement of urbanization and the acceleration of infrastructure construction, market demand for steel formwork continues to rise, and its types and specifications are becoming increasingly diverse. In addition to traditional flat formwork, special-shaped and large formwork are constantly emerging, and manufacturing processes and materials are constantly innovating.

[0003] However, traditional steel formwork evaluation methods have significant limitations. They often rely on a single metric, such as focusing solely on strength or stiffness, which fails to fully reflect overall performance. They lack systematicity, with each metric being independent and inter-correlations not considered. They are highly subjective, with inconsistent evaluation criteria and significant influence from evaluators. Furthermore, they are unable to adapt to developments in construction technology and new project quality requirements.

[0004] Based on this, the present invention proposes a comprehensive performance evaluation method and device for steel formwork materials to solve the above technical problems. Summary of the Invention

[0005] The present invention describes a comprehensive performance evaluation method and device for steel template materials, which can accurately evaluate the comprehensive performance of steel template materials.

[0006] According to a first aspect, the present invention provides a method for evaluating the comprehensive performance of a steel formwork material, the method comprising:

[0007] Obtain the geometric parameters and material parameters of the steel formwork;

[0008] Determining a three-dimensional geometric model of the steel formwork based on the geometric parameters and the material parameters;

[0009] Meshing the three-dimensional geometric model of the steel template to obtain a meshed three-dimensional geometric model of the steel template; wherein the meshed three-dimensional geometric model of the steel template includes a plurality of mesh nodes;

[0010] Inputting corresponding comprehensive performance evaluation parameters into each node in the geometric model of the meshed steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus and toughness;

[0011] Performing feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space;

[0012] The multi-dimensional characteristic matrix is ​​input into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material.

[0013] According to a second aspect, the present invention provides a comprehensive performance evaluation device for steel formwork materials, comprising:

[0014] an acquisition unit configured to acquire geometric parameters and material parameters of the steel formwork;

[0015] a first data processing unit configured to determine a three-dimensional geometric model of the steel formwork based on the geometric parameters and the material parameters;

[0016] a second data processing unit configured to mesh the three-dimensional geometric model of the steel template to obtain a meshed three-dimensional geometric model of the steel template; wherein the meshed three-dimensional geometric model of the steel template includes a plurality of mesh nodes;

[0017] a third data processing unit configured to input corresponding comprehensive performance evaluation parameters into each node in the meshed geometric model of the steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness;

[0018] a fourth data processing unit configured to perform feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space;

[0019] The fifth data processing unit is configured to input the multi-dimensional feature matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material.

[0020] In a third aspect, an embodiment of this specification further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of this specification is implemented.

[0021] In a fourth aspect, an embodiment of this specification further provides a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute the method described in any embodiment of this specification.

[0022] According to the method and device for comprehensive performance evaluation of steel formwork materials provided by the present invention, first, the geometric and material parameters of the steel formwork are acquired. Geometric parameters include the dimensions and shape characteristics of the steel formwork; material parameters include the material composition and microstructure. Based on these parameters, a 3D geometric model of the steel formwork is constructed using advanced 3D modeling technology. This model accurately reflects the geometric formwork's geometry in real space. Subsequently, the constructed 3D geometric model of the steel formwork is meshed. Using an advanced meshing algorithm, the model is discretized into multiple regular or irregular units, resulting in a meshed 3D geometric model of the steel formwork. This model consists of a large number of mesh nodes, which serve as the basic units for subsequent analysis. Next, a series of evaluation parameters closely related to the comprehensive performance of the steel formwork are assigned to each node in the meshed model, thereby constructing a simulation model of the steel formwork material. These comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness, which comprehensively reflect the mechanical properties and deformation characteristics of the steel formwork material under different stress conditions. Afterwards, by calculating and processing the physical quantities of each node in the model, a multi-dimensional characteristic matrix that can characterize the comprehensive performance of the steel formwork material in three-dimensional space is extracted. This characteristic matrix contains the stress distribution, strain state, and energy dissipation at different positions, reflecting the performance characteristics of the steel formwork material from multiple angles. Finally, the extracted multi-dimensional characteristic matrix is ​​input into a pre-built and verified comprehensive performance evaluation model for steel formwork materials. This evaluation model is based on advanced machine learning algorithms or numerical analysis methods. After training and optimization with a large amount of experimental data, it can accurately analyze and judge the input characteristic matrix, thereby outputting accurate comprehensive performance evaluation results of steel formwork materials. Through the above configuration, the present invention can accurately evaluate the comprehensive performance of steel formwork materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the 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.

[0024] Figure 1 A schematic flow chart of a method for evaluating the comprehensive performance of a steel formwork material according to one embodiment is shown;

[0025] Figure 2 A schematic block diagram of a device for evaluating comprehensive performance of steel formwork materials according to one embodiment is shown. DETAILED DESCRIPTION

[0026] The solution provided by the present invention is described below in conjunction with the accompanying drawings.

[0027] Figure 1 The flow chart of the comprehensive performance evaluation method of steel formwork materials according to one embodiment is shown. It is understood that the method can be executed by any device, equipment, platform, or equipment cluster with computing and processing capabilities. Figure 1 As shown, the method includes:

[0028] Step 100: obtaining geometric parameters and material parameters of the steel template;

[0029] Step 102: Determine a three-dimensional geometric model of the steel formwork based on the geometric parameters and material parameters;

[0030] Step 104: Meshing the three-dimensional geometric model of the steel formwork to obtain a meshed three-dimensional geometric model of the steel formwork; wherein the meshed three-dimensional geometric model of the steel formwork includes a plurality of mesh nodes;

[0031] Step 106: Inputting corresponding comprehensive performance evaluation parameters into each node of the meshed geometric model of the steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness;

[0032] Step 108: extracting features from the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space;

[0033] Step 110: Input the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material.

[0034] In this embodiment, the geometric and material parameters of the steel formwork are first acquired. Geometric parameters include the dimensions and shape characteristics of the steel formwork; material parameters include the material composition and structure. Based on these parameters, a 3D geometric model of the steel formwork is constructed using advanced 3D modeling techniques. This model accurately reflects the geometric formwork in real space. Subsequently, the constructed 3D geometric model of the steel formwork is meshed. Using advanced meshing algorithms, the model is discretized into multiple regular or irregular cells, resulting in a meshed 3D geometric model of the steel formwork. This model consists of a large number of mesh nodes, which serve as the basic units for subsequent analysis. Next, a series of evaluation parameters closely related to the comprehensive performance of the steel formwork are assigned to each node in the meshed model, thereby constructing a simulation model of the steel formwork material. These comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness. They comprehensively reflect the mechanical properties and deformation characteristics of the steel formwork material under different stress conditions. Subsequently, by calculating and processing the physical quantities of each node in the model, a multidimensional feature matrix is ​​extracted that characterizes the comprehensive performance of the steel formwork material in 3D space. This characteristic matrix contains the stress distribution, strain state, and energy dissipation at different locations, reflecting the performance characteristics of the steel formwork material from multiple perspectives. Finally, the extracted multi-dimensional characteristic matrix is ​​input into a pre-built and verified comprehensive performance evaluation model for steel formwork materials. This evaluation model is based on advanced machine learning algorithms or numerical analysis methods. After training and optimization with a large amount of experimental data, it can accurately analyze and judge the input characteristic matrix, thereby outputting an accurate comprehensive performance evaluation result for the steel formwork material. Through the above configuration, the present invention can accurately evaluate the comprehensive performance of steel formwork materials.

[0035] In one embodiment of the present invention, after inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain the comprehensive performance evaluation result of the steel formwork material, the method further includes:

[0036] Acquire image data of steel formwork;

[0037] Input the image data of the steel template into a preset steel template defect detection model to obtain the number and type of defects on the steel template surface;

[0038] Inputting the number and type of defects on the steel template surface into the steel template surface evaluation result equation group to obtain the steel template surface evaluation result;

[0039] Based on the evaluation results of the steel formwork surface and the comprehensive performance evaluation results of the steel formwork material, the final comprehensive performance evaluation results of the steel formwork material are determined.

[0040] In this embodiment, first, image data of the steel template is obtained with the help of image acquisition equipment such as high-resolution industrial cameras. This data contains multi-dimensional visual information such as the texture, color and potential defects of the steel template surface. Subsequently, the image data is input into a steel template defect detection model that has been pre-trained and verified based on deep learning algorithms such as convolutional neural networks (CNN). By learning a large number of labeled defect images, the model has the ability to identify and distinguish different types of defects such as cracks, holes, and sand holes. After analysis and processing, it outputs the number and specific types of defects on the steel template surface. Then, the number and type of defects are used as input parameters and substituted into the steel template surface evaluation result equation system constructed based on the relevant theory and practical experience of steel template surface quality. This equation system comprehensively considers the degree of influence of different types of defects on surface performance and the quantitative relationship between the number of defects and surface quality. After calculation and solution, the steel template surface evaluation result presented in the form of quantitative indicators is obtained. Finally, the comprehensive performance evaluation result of the final steel template material is determined by the evaluation result of the steel template surface and the comprehensive performance evaluation result of the steel template material.

[0041] In one embodiment of the present invention, the evaluation result equation group of the steel template surface is constructed by the following formula:

[0042]

[0043] 0<D i ≤m, D i ≥βE i , 0≤S i ≤S max

[0044] 1≤T≤T max , T=1+γln(t+1)

[0045] Where S is the evaluation result of the steel template surface, T is the time coefficient, n is the number of defects on the steel template surface, and w i is the weight of the i-th defect, wj is the weight of the j-th defect, C i j is the interaction coefficient matrix between defects, D i is the spatial distribution coefficient of the i-th type defect, D j is the spatial distribution coefficient of the jth type of defect, S i is the quantitative score of the i-th defect, Sj is the quantitative score of the j-th defect, a i is the lower limit of the weight of the i-th defect, b i The upper limit of the weight of the i-th defect, r ij is the weight correlation coefficient, k is the upper limit of the interaction coefficient matrix, is the initial interaction coefficient of the i-th and j-th defects, a is the adjustment coefficient of the dynamic adjustment of the interaction coefficient, p is the threshold of the dynamic adjustment of the interaction coefficient, m is the upper limit of the spatial distribution coefficient, E i is the cumulative spatial distribution coefficient of the i-th type defect, β is the dynamic adjustment coefficient, S max is the upper limit of the quantitative score of the i-th type defect, T max is the upper limit of the time coefficient, γ is the growth coefficient in the time coefficient growth model, and t is the service life of the steel formwork.

[0046] In this embodiment, w i is the weight of the i-th type of defect, which is determined based on actual experience or expert judgment to ensure that this type of defect has a certain minimum importance in the comprehensive evaluation. j is the weight of the jth type of defect, which limits the weight of this type of defect to not be too high and ensures the rationality of weight distribution. ij is the weight correlation coefficient, which is used to measure the correlation between the weight of the i-th defect and the j-th defect of the i-th defect, and constrain the impact of the change in the weight of the related defect category on the weight of the target defect category. k is the upper limit of the interaction coefficient matrix, which prevents the interaction between different defects from being overly exaggerated and ensures a reasonable assessment of the interaction impact. a is the adjustment coefficient for the dynamic adjustment of the interaction coefficient, which is used to control the increase in the interaction coefficient when the severity of the two types of defects reaches a certain level. m is the upper limit of the spatial distribution coefficient, which avoids abnormal fluctuations in the comprehensive score due to defect distribution factors. E i is the cumulative spatial distribution coefficient of the i-th type of defect. Considering the accumulation of defects in different areas of the steel template, when the number or severity of the i-th type of defects in the local area exceeds a certain threshold, the coefficient will increase. β is a dynamic adjustment coefficient, which is used to constrain the relationship between the spatial distribution coefficient of the defect and the cumulative spatial distribution coefficient, reflecting the greater impact of the concentrated distribution of defects on the surface strength. max is the upper limit of the quantification score of the i-th type of defect, to prevent the distortion of the comprehensive score due to excessive quantification of individual defects. max The upper limit of the time coefficient is used to prevent the time coefficient from growing without limit and ensure the reasonable modeling of the influence of time on defects. γ is the growth coefficient in the time coefficient growth model, which controls the speed at which the time coefficient increases with the use time of the steel formwork. The evaluation result equation group of the steel formwork surface can accurately solve the evaluation result of the steel formwork surface, thereby improving the accuracy of the comprehensive performance evaluation result of the final steel formwork material.

[0047] In one embodiment of the present invention, determining the final comprehensive performance evaluation result of the steel formwork material based on the evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material further includes:

[0048] The evaluation results of the steel formwork surface and the comprehensive performance evaluation results of the steel formwork material are integrated according to the preset weights to obtain the final comprehensive performance evaluation results of the steel formwork material.

[0049] In this embodiment, since the comprehensive performance evaluation results of the steel formwork material do not take into account factors such as the surface defects of the steel formwork, the evaluation results of the steel formwork surface and the comprehensive performance evaluation results of the steel formwork material must be fused according to preset weights to obtain the final comprehensive performance evaluation results of the steel formwork material as the final result to accurately evaluate the comprehensive performance of the steel formwork material.

[0050] In one embodiment of the present invention, before inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain the comprehensive performance evaluation result of the steel formwork material, the method further includes:

[0051] Determine multiple eigenvalues ​​and multiple eigenvectors according to a multi-dimensional characteristic matrix;

[0052] Sort multiple eigenvalues ​​and multiple eigenvectors according to preset rules to obtain a sorted feature matrix;

[0053] Based on the sorted feature matrix, determine a simplified feature matrix;

[0054] The simplified characteristic matrix is ​​subjected to dimensionality reduction to obtain a reduced-dimensional characteristic matrix, and the reduced-dimensional characteristic matrix is ​​used as an input quantity of a preset comprehensive performance evaluation model of steel formwork materials.

[0055] In this embodiment, eigenvalue decomposition is first performed on the original multi-dimensional feature matrix. Matrix algebraic methods are used to extract the eigenvalues ​​of each dimension and their corresponding eigenvectors. This operation effectively analyzes the primary direction of variation and variance contribution in the data space, providing a mathematical foundation for subsequent analysis. A descending sorting rule is established based on the eigenvalue values. The eigenvectors are rearranged from highest to lowest eigenvalues, constructing an eigenvector matrix with decreasing eigenvalues. This sorting strategy adheres to the fundamental statistical principle that larger eigenvalues ​​contribute more to the data variance. Feature dimensions are screened based on the cumulative contribution of the eigenvalues, typically with a preset information retention threshold of 95%. By successively accumulating the eigenvalue contributions, the minimum feature subset that retains the core information of the original data is determined, forming a dimensionally reduced feature matrix. Finally, the reduced feature matrix is ​​mapped to a lower-dimensional space. This process preserves the key information in the data while eliminating multicollinearity between dimensions, generating an optimized feature dataset suitable for input into the evaluation model. This improves the computational speed of the evaluation model.

[0056] In one embodiment of the present invention, determining multiple eigenvalues ​​and multiple eigenvectors based on a multi-dimensional feature matrix includes:

[0057] Perform data standardization on the multi-dimensional feature matrix to obtain a standardized data matrix;

[0058] Based on the standardized data matrix, the covariance matrix is ​​determined;

[0059] Perform eigendecomposition on the covariance matrix to obtain multiple eigenvalues ​​and multiple eigenvectors.

[0060] In this embodiment, the original multi-dimensional feature data is first dimensionlessly processed. By subtracting the mean value of each feature from each eigenvalue and dividing it by its standard deviation, the processed data is made to conform to a standard normal distribution with a mean of 0 and a variance of 1. A covariance matrix is ​​calculated based on the standardized data. The diagonal elements of this matrix reflect the degree of dispersion of each feature dimension, while the off-diagonal elements reflect the correlation between different features. The specific calculation method is to multiply the transpose of the standardized data matrix by itself and then divide it by the number of samples minus one. The covariance matrix is ​​then subjected to eigendecomposition, resulting in a set of eigenvalues ​​and their corresponding eigenvectors, arranged in descending order of magnitude. Each eigenvalue represents the variance contribution of the corresponding principal component, and the eigenvectors form a set of mutually orthogonal unit vectors, forming a new coordinate system for the data space. The number of key principal components is determined based on the cumulative contribution rate of the eigenvalues, typically selecting the top k principal components with a cumulative contribution rate of 95%. These principal components are composed of the eigenvectors corresponding to the largest k eigenvalues. Through projection transformation, effective dimensionality reduction of the original data is achieved while preserving key information.

[0061] In one embodiment of the present invention, the preset comprehensive performance evaluation model of the steel formwork material is a convolutional neural network model.

[0062] In this embodiment, in the technical system for comprehensive performance evaluation of steel template materials, a convolutional neural network (CNN) model is pre-set as the core evaluation tool. As a highly representative and powerful algorithm architecture in the field of deep learning, the convolutional neural network model has become an ideal choice for evaluating the comprehensive performance of steel template materials by virtue of its outstanding ability in processing complex pattern recognition and feature extraction. The convolutional neural network model has a unique network structure, including components such as convolution layers, pooling layers, and fully connected layers. The convolution layer can automatically extract local features from the input data, and capture key information of the steel template material at different scales and dimensions through the sliding operation of the convolution kernel on the data, such as the microstructural features inside the material, local changes in stress distribution, etc. The pooling layer downsamples the feature map, reduces the data dimension, and improves the computational efficiency and generalization ability of the model, just like screening out the most critical part from a lot of information, so that the model can focus on the core features of the performance of the steel template material. The fully connected layer integrates and maps the extracted features and outputs the final evaluation results. During the training process, the model continuously adjusts its parameters and weights based on a large amount of experimental data and actual cases in order to learn the complex mapping relationship between the various performance indicators of steel formwork materials and the input features. This data-driven learning method enables the convolutional neural network model to adapt to the performance evaluation needs of steel formwork materials of different types and specifications, and has strong flexibility and adaptability. By inputting the multi-dimensional feature matrix of steel formwork materials into this carefully trained and verified convolutional neural network model, the model can comprehensively consider the various performance factors of the material and conduct comprehensive, accurate and efficient analysis and judgment. Compared with traditional evaluation methods, the convolutional neural network model can process large-scale, high-dimensional data and unearth the complex relationships hidden behind the data, thereby outputting more accurate and reliable comprehensive performance evaluation results of steel formwork materials, providing a scientific and powerful decision-making basis for the selection and application of steel formwork materials in fields such as construction.

[0063] According to another embodiment, the present invention provides a comprehensive performance evaluation device for steel formwork materials. Figure 2 A schematic block diagram of a comprehensive performance evaluation device for steel formwork materials according to an embodiment is shown. It is understood that the device can be implemented by any device, equipment, platform, and equipment cluster with computing and processing capabilities. Figure 2 As shown, the device includes: an acquisition unit 200, a first data processing unit 202, a second data processing unit 204, a third data processing unit 206, a fourth data processing unit 208 and a fifth data processing unit 210. The main functions of each component unit are as follows:

[0064] an acquisition unit configured to acquire geometric parameters and material parameters of the steel formwork;

[0065] a first data processing unit configured to determine a three-dimensional geometric model of the steel formwork based on the geometric parameters and the material parameters;

[0066] a second data processing unit configured to mesh the three-dimensional geometric model of the steel template to obtain a meshed three-dimensional geometric model of the steel template; wherein the meshed three-dimensional geometric model of the steel template includes a plurality of mesh nodes;

[0067] a third data processing unit configured to input corresponding comprehensive performance evaluation parameters into each node in the meshed geometric model of the steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness;

[0068] a fourth data processing unit configured to perform feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space;

[0069] The fifth data processing unit is configured to input the multi-dimensional feature matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material.

[0070] In one embodiment of the present invention, after inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material, the method further includes:

[0071] Acquire image data of steel formwork;

[0072] Inputting the image data of the steel template into a preset steel template defect detection model to obtain the number and type of defects on the surface of the steel template;

[0073] Inputting the number and type of defects on the steel template surface into the steel template surface evaluation result equation group to determine the steel template surface evaluation result;

[0074] Based on the evaluation results of the steel formwork surface and the comprehensive performance evaluation results of the steel formwork material, the final comprehensive performance evaluation results of the steel formwork material are determined.

[0075] In one embodiment of the present invention, the evaluation result equation group of the steel template surface is constructed by the following formula:

[0076]

[0077] 0<D i ≤m,Di ≥βE i , 0≤S i ≤S max

[0078] 1≤T≤T max , T=1+γln(t+1)

[0079] Where, S is the evaluation result of the steel template surface, T is the time coefficient, n is the number of defects on the steel template surface, w i is the weight of the i-th defect, w j is the weight of the j-th defect, C ij is the interaction coefficient matrix between defects, D i is the spatial distribution coefficient of the i-th type defect, D j is the spatial distribution coefficient of the jth type of defect, S i is the quantitative score of the i-th type of defect, S j is the quantitative score of the j-th type of defect, a i is the lower limit of the weight of the i-th type defect, b i The upper limit of the weight of the i-th type defect, r ij is the weight correlation coefficient, k is the upper limit of the interaction coefficient matrix, is the initial interaction coefficient of the i-th and j-th defects, a is the adjustment coefficient of the dynamic adjustment of the interaction coefficient, p is the threshold of the dynamic adjustment of the interaction coefficient, m is the upper limit of the spatial distribution coefficient, E i is the cumulative spatial distribution coefficient of the i-th type defect, β is the dynamic adjustment coefficient, S max is the upper limit of the quantitative score of the i-th type defect, T max is the upper limit of the time coefficient, γ is the growth coefficient in the time coefficient growth model, and t is the service life of the steel formwork.

[0080] In one embodiment of the present invention, determining the final comprehensive performance evaluation result of the steel formwork material based on the evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material further includes:

[0081] The evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material are fused according to preset weights to obtain a final comprehensive performance evaluation result of the steel formwork material.

[0082] In one embodiment of the present invention, before inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material, the method further includes:

[0083] Determining a plurality of eigenvalues ​​and a plurality of eigenvectors according to the multi-dimensional characteristic matrix;

[0084] Sorting the plurality of eigenvalues ​​and the plurality of eigenvectors according to a preset rule to obtain a sorted eigenmatrix;

[0085] Determining a simplified feature matrix based on the sorted feature matrix;

[0086] The simplified characteristic matrix is ​​subjected to dimensionality reduction to obtain a reduced-dimensional characteristic matrix, and the reduced-dimensional characteristic matrix is ​​used as an input quantity of the preset comprehensive performance evaluation model of the steel formwork material.

[0087] In one embodiment of the present invention, determining a plurality of eigenvalues ​​and a plurality of eigenvectors according to the multi-dimensional feature matrix includes:

[0088] Performing data standardization on the multi-dimensional feature matrix to obtain a standardized data matrix;

[0089] determining a covariance matrix based on the standardized data matrix;

[0090] Performing eigendecomposition on the covariance matrix to obtain the multiple eigenvalues ​​and the multiple eigenvectors.

[0091] In one embodiment of the present invention, the preset comprehensive performance evaluation model of the steel formwork material is a convolutional neural network model.

[0092] According to another embodiment, there is also provided a computer readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute a combination of Figure 1 The method described.

[0093] According to another embodiment, an electronic device is provided, comprising a memory and a processor, wherein the memory stores an executable code, and when the processor executes the executable code, the system realizes the combination of Figure 1 The method described.

[0094] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are described briefly because they are generally similar to the method embodiments. For relevant portions, refer to the description of the method embodiments.

[0095] Those skilled in the art will appreciate that, in one or more of the above examples, the functions described herein may be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions may be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0096] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A comprehensive performance evaluation method for steel formwork materials, characterized in that: The method comprises: Obtain the geometric parameters and material parameters of the steel formwork; Determining a three-dimensional geometric model of the steel formwork based on the geometric parameters and the material parameters; Meshing the three-dimensional geometric model of the steel template to obtain a meshed three-dimensional geometric model of the steel template; wherein the meshed three-dimensional geometric model of the steel template includes a plurality of mesh nodes; Inputting corresponding comprehensive performance evaluation parameters into each node in the three-dimensional geometric model of the meshed steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus and toughness; Performing feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space; Inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material; After inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material, the method further includes: Acquire image data of steel formwork; Inputting the image data of the steel template into a preset steel template defect detection model to obtain the number and type of defects on the surface of the steel template; Inputting the number and type of defects on the steel template surface into the steel template surface evaluation result equation group to obtain the steel template surface evaluation result; Determining a final comprehensive performance evaluation result of the steel formwork material based on the evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material; The evaluation result equation group of the steel template surface is constructed by the following formula: a i ≤w i ≤b i ,w i ≥0 ∑ j≠i r ij w j ≤w i 0<C ij ≤k, 0<D i ≤m,D i ≥βE i ,0≤S i ≤S max 1≤T≤T max ,T=1+γln(t+1) Where, S is the evaluation result of the steel template surface, T is the time coefficient, n is the number of defects on the steel template surface, w i is the weight of the i-th defect, w j is the weight of the j-th defect, C ij is the interaction coefficient matrix between defects, D i is the spatial distribution coefficient of the i-th type defect, D j is the spatial distribution coefficient of the jth type of defect, S i is the quantitative score of the i-th type of defect, S j is the quantitative score of the j-th type of defect, a i is the lower limit of the weight of the i-th type defect, b i The upper limit of the weight of the i-th defect, r ij is the weight correlation coefficient, k is the upper limit of the interaction coefficient matrix, is the initial interaction coefficient of the i-th and j-th defects, a is the adjustment coefficient of the dynamic adjustment of the interaction coefficient, p is the threshold of the dynamic adjustment of the interaction coefficient, m is the upper limit of the spatial distribution coefficient, E i is the cumulative spatial distribution coefficient of the i-th type defect, β is the dynamic adjustment coefficient, S max is the upper limit of the quantitative score of the i-th type defect, T max is the upper limit of the time coefficient, γ is the growth coefficient in the time coefficient growth model, and t is the service life of the steel formwork.

2. The method according to claim 1, characterized in that The step of determining a final comprehensive performance evaluation result of the steel formwork material based on the evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material further includes: The evaluation result of the steel formwork surface and the comprehensive performance evaluation result of the steel formwork material are fused according to preset weights to obtain a final comprehensive performance evaluation result of the steel formwork material.

3. The method according to claim 2, characterized in that Before inputting the multi-dimensional characteristic matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material, the method further includes: Determining a plurality of eigenvalues ​​and a plurality of eigenvectors according to the multi-dimensional characteristic matrix; Sorting the plurality of eigenvalues ​​and the plurality of eigenvectors according to a preset rule to obtain a sorted eigenmatrix; Determining a simplified feature matrix based on the sorted feature matrix; The simplified characteristic matrix is ​​subjected to dimensionality reduction to obtain a reduced-dimensional characteristic matrix, and the reduced-dimensional characteristic matrix is ​​used as an input quantity of the preset comprehensive performance evaluation model of the steel formwork material.

4. The method according to claim 3, characterized in that The determining of a plurality of eigenvalues ​​and a plurality of eigenvectors according to the multi-dimensional feature matrix includes: Performing data standardization on the multi-dimensional feature matrix to obtain a standardized data matrix; determining a covariance matrix based on the standardized data matrix; Performing eigendecomposition on the covariance matrix to obtain the multiple eigenvalues ​​and the multiple eigenvectors.

5. The method according to claim 4, characterized in that The preset comprehensive performance evaluation model of the steel formwork material is a convolutional neural network model.

6. A comprehensive performance evaluation device for steel formwork materials, characterized in that: Based on the method according to any one of claims 1 to 5, comprising: an acquisition unit configured to acquire geometric parameters and material parameters of the steel formwork; a first data processing unit configured to determine a three-dimensional geometric model of the steel formwork based on the geometric parameters and the material parameters; a second data processing unit configured to mesh the three-dimensional geometric model of the steel template to obtain a meshed three-dimensional geometric model of the steel template; wherein the meshed three-dimensional geometric model of the steel template includes a plurality of mesh nodes; a third data processing unit configured to input corresponding comprehensive performance evaluation parameters into each node in the meshed three-dimensional geometric model of the steel formwork to obtain a simulation model of the steel formwork material; wherein the comprehensive performance evaluation parameters include tensile strength, compressive strength, flexural strength, elastic modulus, and toughness; a fourth data processing unit configured to perform feature extraction on the simulation model of the steel formwork material to obtain a multi-dimensional feature matrix; wherein the multi-dimensional feature matrix is ​​used to characterize the comprehensive performance of the steel formwork material in three-dimensional space; The fifth data processing unit is configured to input the multi-dimensional feature matrix into a preset comprehensive performance evaluation model of the steel formwork material to obtain a comprehensive performance evaluation result of the steel formwork material.

7. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the method according to any one of claims 1 to 5.

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