SPCS prefabricated part bearing capacity analysis system

Abstract

The invention relates to a bearing capacity analysis system of an SPCS prefabricated part. The method comprises the following steps: acquiring stress distribution diagrams of an SPCS prefabricated part under various different load conditions, and introducing an image processing algorithm based on artificial intelligence and deep learning at a rear end to analyze the stress distribution diagrams, so as to automatically identify and quantify stress distribution characteristics of the prefabricated part under different load conditions; therefore, the structural strength of the SPCS prefabricated part, such as a cavity prefabricated wall, is evaluated, so that a result whether the prefabricated part meets a preset design standard or not can be quickly obtained, and in this way, the intelligent degree of bearing force analysis of the SPCS prefabricated part is improved, comprehensive automatic evaluation of the prefabricated part is achieved, and the working efficiency of the SPCS prefabricated part is improved. And reliable data support is provided for building design and construction processes.

Description

Technical Field

[0001] The present application relates to the technical field of SPCS prefabricated components, and in particular, to a bearing capacity analysis system for SPCS prefabricated components. Background Art

[0002] SPCS (Structural Prefabricated Component System) is a prefabricated component commonly used in modern construction technology. It improves construction efficiency and reduces on-site construction time and costs by pre-manufacturing and assembly. This system can ensure the consistency and reliability of building quality and contribute to environmental protection because it reduces waste generation on the construction site.

[0003] Structural strength analysis is a crucial link in the process of building design and construction. It is not only related to the safety of the building, but also directly affects its durability and service life. For prefabricated components, they will be subjected to various loads during transportation, installation and final use, such as wind load, snow load, live load, etc. Therefore, it is very necessary to analyze the bearing capacity of these prefabricated components under different load conditions.

[0004] In traditional engineering analysis, finite element analysis (FEA) or other numerical simulation techniques are usually relied on. Although these methods can provide relatively detailed mechanical information, they may encounter problems such as high computing resource requirements and long time consumption when dealing with complex geometric shapes and nonlinear material behaviors, and often require a lot of professional knowledge. In addition, due to the complexity of actual working conditions, traditional methods may find it difficult to fully consider all possible load combinations to evaluate the bearing capacity of components.

[0005] Therefore, an optimized load-bearing analysis system for SPCS precast components is desired. Summary of the invention

[0006] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, the present application provides a bearing capacity analysis system for SPCS prefabricated components, the system comprising:

[0008] A component stress distribution diagram acquisition module is used to obtain a stress distribution diagram of the SPCS prefabricated component to be tested under the first load condition;

[0009] A stress distribution map segmentation module, used for performing grid segmentation on the stress distribution map to obtain a set of stress distribution grid particle size distribution maps;

[0010] A stress distribution feature global analysis module, used for performing feature capture and global analysis based on the stress distribution space domain on each stress distribution grid size distribution map in the set of stress distribution grid size distribution maps to obtain a set of contextual stress distribution grid size distribution associated feature vectors;

[0011] A spatial domain stress distribution significant aggregation representation module is used to input the set of contextual stress distribution grid particle size distribution associated feature vectors into a feature significant aggregation network based on cluster contribution field analysis to obtain a spatial domain stress distribution significant aggregation representation vector;

[0012] The component structural strength detection module is used to determine whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements based on the significant aggregation representation vector of the spatial domain stress distribution.

[0013] Optionally, the stress distribution feature global analysis module includes: a stress distribution local feature extraction unit, which is used to input each stress distribution grid grain size distribution map in the set of stress distribution grid grain size distribution maps into a stress distribution local feature extractor based on a convolution layer to obtain a set of stress distribution grid grain size distribution associated feature vectors; a stress distribution space domain global encoding unit, which is used to input the set of stress distribution grid grain size distribution associated feature vectors into a converter-based stress distribution space domain global encoder to obtain a set of context stress distribution grid grain size distribution associated feature vectors.

[0014] Optionally, the spatial domain stress distribution significant aggregation representation module includes: a cluster analysis unit, which is used to perform cluster analysis on the set of contextual stress distribution grid grain size distribution associated feature vectors to obtain a stress distribution grid grain size distribution associated feature self-supervised cluster representation vector; an implicit cluster contribution factor calculation unit, which is used to calculate the implicit cluster contribution factor of each contextual stress distribution grid grain size distribution associated feature vector in the set of contextual stress distribution grid grain size distribution associated feature vectors relative to the stress distribution grid grain size distribution associated feature self-supervised cluster representation vector to obtain a sequence of implicit cluster contribution factors of stress distribution grid grain size distribution associated feature; a vector arrangement unit, which is used to arrange the stress distribution grid grain size distribution associated feature vectors into a vectorized arrangement unit; The sequence of characteristic implicit clustering contribution factors is vectorized to obtain the stress distribution grid granularity distribution associated feature clustering contribution field distribution vector; the clustering contribution field explicit modeling unit is used to input the stress distribution grid granularity distribution associated feature clustering contribution field distribution vector into the clustering contribution field explicit modeling module based on the self-attention mechanism to obtain the stress distribution grid granularity distribution associated feature clustering contribution field modulation weight vector; the position weighted sum calculation unit is used to use each eigenvalue in the stress distribution grid granularity distribution associated feature clustering contribution field modulation weight vector as the weight, and calculate the position weighted sum of the set of context stress distribution grid granularity distribution associated feature vectors to obtain the spatial domain stress distribution significant aggregation representation vector.

[0015] Optionally, the cluster analysis unit is used to calculate the mean vector of the set of context stress distribution grid grain size distribution associated feature vectors to obtain the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector.

[0016] Optionally, the implicit clustering contribution factor calculation unit is used to: calculate the absolute value of the positional division between the contextual stress distribution grid grain size distribution associated feature vector and the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector to obtain the stress distribution grid grain size self-supervised clustering interactive association feature vector; calculate the logarithmic function value of each position eigenvalue in the stress distribution grid grain size self-supervised clustering interactive association feature vector with a base of 2 to obtain the stress distribution grid grain size self-supervised clustering interactive logarithmic representation vector; use the each position eigenvalue in the stress distribution grid grain size self-supervised clustering interactive logarithmic representation vector as a weighting coefficient, calculate the weighted sum of the eigenvalues ​​at each position in the contextual stress distribution grid grain size distribution associated feature vector to obtain the stress distribution grid grain size distribution associated feature clustering contribution representation factor; calculate the exponential function value of the stress distribution grid grain size distribution associated feature clustering contribution representation factor with a base of the natural constant e to obtain the stress distribution grid grain size distribution associated feature implicit clustering contribution factor.

[0017] Optionally, the cluster contribution field explicit modeling unit is used to: calculate the matrix multiplication between the stress distribution grid granularity distribution associated feature cluster contribution field distribution vector and the query weight matrix, key weight matrix and value weight matrix respectively to obtain the query stress distribution cluster contribution field distribution vector, the key stress distribution cluster contribution field distribution vector and the value stress distribution cluster contribution field distribution vector; after calculating the multiplication between the query stress distribution cluster contribution field distribution vector and the transposed vector of the key stress distribution cluster contribution field distribution vector, the obtained matrix is ​​divided by the square root of the length of the key stress distribution cluster contribution field distribution vector by position to obtain the query-key stress distribution cluster contribution field semantic interaction representation matrix; use the Softmax function to normalize the query-key stress distribution cluster contribution field semantic interaction representation matrix to obtain the normalized query-key stress distribution cluster contribution field semantic interaction representation matrix; calculate the multiplication between the value stress distribution cluster contribution field distribution vector and the query-key stress distribution cluster contribution field semantic interaction representation matrix to obtain the stress distribution grid granularity distribution associated feature cluster contribution field domain modulation weight vector.

[0018] Optionally, the component structural strength detection module is used to: input the spatial domain stress distribution significant aggregation representation vector into a classifier-based bearing capacity analysis module to obtain a classification result, and the classification result is used to indicate whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements.

[0019] Optionally, the component structure strength detection module includes: a fully connected encoding unit, used to use multiple fully connected layers of the classifier-based stress analysis module to fully connect the spatial domain stress distribution significant aggregation representation vector to obtain an encoded classification feature vector; a classification unit, used to pass the encoded classification feature vector through the Softmax classification function of the classifier-based stress analysis module to obtain the classification result.

[0020] By adopting the above technical scheme, by obtaining the stress distribution diagram of SPCS prefabricated components under a variety of different load conditions, and introducing an image processing algorithm based on artificial intelligence and deep learning at the back end to analyze the stress distribution diagram, the stress distribution characteristics of the prefabricated components under different load conditions can be automatically identified and quantified, thereby evaluating the structural strength of SPCS prefabricated components, such as cavity prefabricated walls, so as to more quickly determine whether the prefabricated components meet the predetermined design standards. In this way, the intelligence level of the bearing capacity analysis of SPCS prefabricated components is improved, so as to realize a comprehensive and automatic evaluation of prefabricated components and provide reliable data support for the building design and construction process.

[0021] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0023] Figure 1 It is a block diagram of a bearing capacity analysis system of a SPCS prefabricated component according to an exemplary embodiment.

[0024] Figure 2 It is a flow chart of a bearing capacity analysis method of a SPCS prefabricated component according to an exemplary embodiment.

[0025] Figure 3 It is a flow chart of the construction process of SPCS prefabricated cavity column according to an exemplary embodiment.

[0026] Figure 4 The figure is a flow chart of the construction process of a SPCS prefabricated cavity wall according to an exemplary embodiment.

[0027] Figure 5 It is a structural schematic diagram of a cavity prefabricated wall panel structure according to an exemplary embodiment.

[0028] Figure 6 It is a schematic structural diagram of a cavity prefabricated column structure according to an exemplary embodiment.

[0029] Figure 7 It is a block diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0031] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0032] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0033] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0037] SPCS is a prefabricated component technology used in construction, which aims to improve construction efficiency and reduce on-site construction time and cost. Cavity prefabricated wall (SPCS shear wall) is an assembled shear wall technology that is improved and upgraded after a lot of theoretical research and experimental verification based on the European double-skin wall and national standard composite shear wall technology. When this wall panel is produced in the factory, the flipping process is adopted to anchor the integral steel cage into the concrete blades on both sides, forming an integral prefabricated wall panel component composed of "load-bearing blades, load-bearing steel cages, and cavities between blades". The prefabricated components are connected by setting overlapping steel bars in the cavity and pouring concrete on site to form an integral composite load-bearing shear wall. This system uses the principle of concrete superposition to combine vertical superimposed components, horizontal superimposed components, wall edge restraint components, etc. into a whole through cast-in-place concrete, giving full play to the respective advantages of precast concrete components and cast-in-place concrete. The overall performance is safe and reliable, ensuring the same seismic resistance as the cast-in-place structural system. At the same time, it has good building insulation, waterproofing, sound insulation and other functions, realizing the combination of cast-in-place concrete and high-precision precast components.

[0038] In the process of building design and construction, the stress and structural strength analysis of SPCS prefabricated components is still a crucial link, which is not only related to the safety of the building, but also directly affects its durability and service life. However, traditional bearing capacity analysis methods usually rely on finite element analysis (FEA) or other numerical simulation techniques. Although these methods can provide more detailed mechanical information, they may encounter problems such as high computing resource requirements and long time consumption when dealing with complex geometric shapes and nonlinear material behaviors, and it is difficult to fully consider all possible load combinations.

[0039] In response to the above technical problems, the technical concept of the present application is to obtain the stress distribution diagram of SPCS prefabricated components under a variety of different load conditions, and introduce an image processing algorithm based on artificial intelligence and deep learning at the back end to analyze the stress distribution diagram, so as to automatically identify and quantify the stress distribution characteristics of prefabricated components under different load conditions, thereby evaluating SPCS prefabricated components, such as the structural strength of cavity prefabricated walls, so as to more quickly determine whether the prefabricated components meet the predetermined design standards. In this way, the intelligence level of the bearing capacity analysis of SPCS prefabricated components is improved, so as to achieve a comprehensive and automatic evaluation of prefabricated components and provide reliable data support for the building design and construction process.

[0040] Figure 1 FIG. 1 is a block diagram of a bearing capacity analysis system for a SPCS prefabricated component according to an exemplary embodiment. Figure 1 As shown, the system 100 includes:

[0041] The component stress distribution diagram acquisition module 101 is used to obtain the stress distribution diagram of the SPCS prefabricated component to be tested under the first load condition;

[0042] A stress distribution map segmentation module 102 is used to perform grid segmentation on the stress distribution map to obtain a set of stress distribution grid particle size distribution maps;

[0043] A stress distribution feature global analysis module 103 is used to perform feature capture and global analysis based on the stress distribution space domain on each stress distribution grid size distribution map in the set of stress distribution grid size distribution maps to obtain a set of contextual stress distribution grid size distribution associated feature vectors;

[0044] A spatial domain stress distribution significant aggregation representation module 104 is used to input the set of contextual stress distribution grid particle size distribution associated feature vectors into a feature significant aggregation network based on cluster contribution field analysis to obtain a spatial domain stress distribution significant aggregation representation vector;

[0045] The component structural strength detection module 105 is used to determine whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements based on the significant aggregation representation vector of the spatial domain stress distribution.

[0046] Specifically, in the technical solution of the present application, first, a stress distribution diagram of the SPCS prefabricated component to be tested under a first load condition is obtained, and then the stress distribution diagram is gridded to obtain a set of stress distribution grid particle size distribution diagrams.

[0047] Furthermore, since the stress distribution map contains rich information, such as the magnitude, direction and distribution pattern of stress, the stress distribution grid grain size distribution map obtained by grid segmentation also has such characteristics. Based on this, in order to extract the implicit feature information of the grid grain size distribution about the stress distribution from each stress distribution grid grain size distribution map, in the technical solution of the present application, each stress distribution grid grain size distribution map in the set of the stress distribution grid grain size distribution map is input into the stress distribution local feature extractor based on the convolution layer to obtain a set of stress distribution grid grain size distribution associated feature vectors. In particular, in stress analysis, the stress distribution in the local area often has a strong correlation. The convolution layer can capture the features in these local areas by means of a sliding window, which helps to better understand the local details of the stress distribution. Therefore, through the processing of the stress distribution local feature extractor based on the convolution layer, the local stress distribution pattern and associated feature information of the stress distribution grid grain size in each stress distribution grid grain size distribution map can be captured respectively, which provides a basis for the subsequent structural strength detection of prefabricated components.

[0048] In one embodiment of the present application, the stress distribution feature global analysis module includes: a stress distribution local feature extraction unit, which is used to input each stress distribution grid grain size distribution map in the set of stress distribution grid grain size distribution maps into a stress distribution local feature extractor based on a convolutional layer to obtain a set of stress distribution grid grain size distribution associated feature vectors; a stress distribution space domain global encoding unit, which is used to input the set of stress distribution grid grain size distribution associated feature vectors into a converter-based stress distribution space domain global encoder to obtain the set of context stress distribution grid grain size distribution associated feature vectors.

[0049] Then, considering that when actually analyzing the stress distribution map to extract stress distribution correlation features, since the convolution layer extracts the local distribution and correlation features of stress through the local receptive field, this makes them perform well in processing short-distance dependencies, but is relatively weak in capturing the relationship between long-distance stress local management features. In stress analysis, it is sometimes necessary to consider the stress distribution across multiple grid cells, that is, long-distance dependencies, which are beyond the scope of the local receptive field. Therefore, in order to overcome some inherent limitations of the convolution layer in order to better capture and utilize the global contextual relationship between the local correlation features of the stress distribution grid granularity, in the technical solution of the present application, the set of stress distribution grid granularity distribution correlation feature vectors is further input into the stress distribution spatial domain global encoder based on the converter to obtain a set of contextual stress distribution grid granularity distribution correlation feature vectors.

[0050] It should be understood that since each contextual stress distribution grid grain size distribution associated feature vector in the set of the contextual stress distribution grid grain size distribution associated feature vectors respectively contains grid grain size local contextual associated feature distribution information related to stress distribution, but these features are not all important for the subsequent prefabricated component structural strength detection task, therefore, in the process of local contextual associated feature aggregation of different stress distribution grid grains, it is necessary to identify and enhance important features to improve the accuracy of feature aggregation analysis. Based on this, in the technical solution of the present application, the set of the contextual stress distribution grid grain size distribution associated feature vectors is further input into a feature significant aggregation network based on cluster contribution field analysis to obtain a spatial domain stress distribution significant aggregation representation vector. It is worth mentioning that the feature significant aggregation network based on cluster contribution field analysis is a feature aggregation technology based on simulated field characteristic analysis, which aims to use the simulated field analysis theory to perform field-based explicit modeling on the cluster contribution of each contextual stress distribution grid grain size distribution associated feature vector to improve the accuracy of feature aggregation analysis, thereby improving the performance and generalization ability of the model.

[0051] In one embodiment of the present application, the spatial domain stress distribution significant aggregation representation module includes: a cluster analysis unit, which is used to perform cluster analysis on the set of contextual stress distribution grid grain size distribution associated feature vectors to obtain a stress distribution grid grain size distribution associated feature self-supervised cluster representation vector; an implicit cluster contribution factor calculation unit, which is used to calculate the implicit cluster contribution factor of each contextual stress distribution grid grain size distribution associated feature vector in the set of contextual stress distribution grid grain size distribution associated feature vectors relative to the stress distribution grid grain size distribution associated feature self-supervised cluster representation vector to obtain a sequence of implicit cluster contribution factors of stress distribution grid grain size distribution associated features; a vector arrangement unit, which is used to arrange the stress distribution grid grain size distribution associated feature vectors into a vectorized arrangement unit. The sequence of implicit clustering contribution factors of distribution-associated features is vectorized to obtain a stress distribution grid-size distribution-associated feature clustering contribution field distribution vector; a clustering contribution field explicit modeling unit is used to input the stress distribution grid-size distribution-associated feature clustering contribution field distribution vector into a clustering contribution field explicit modeling module based on a self-attention mechanism to obtain a stress distribution grid-size distribution-associated feature clustering contribution field modulation weight vector; a position-weighted sum calculation unit is used to calculate the position-weighted sum of the set of contextual stress distribution grid-size distribution-associated feature vectors using each eigenvalue in the stress distribution grid-size distribution-associated feature clustering contribution field modulation weight vector as a weight to obtain the spatial domain stress distribution significant aggregation representation vector.

[0052] Specifically, the feature significant aggregation network based on cluster contribution field analysis first uses an unsupervised learning method to perform cluster analysis on the set of contextual stress distribution grid grain size distribution associated feature vectors, automatically discovers the stress distribution grid grain size contextual associated feature patterns and structures in the set, and generates a stress distribution grid grain size distribution associated feature self-supervised cluster representation vector. It should be understood that self-supervised clustering can reveal the intrinsic structure of each stress distribution grid grain size contextual associated feature, which helps the model learn meaningful feature representations without explicit labels, and enhances the model's understanding of the stress distribution grid grain size contextual associated feature distribution. Furthermore, by calculating the implicit clustering contribution factor between the contextual stress distribution grid grain size distribution associated feature vector and the stress distribution grid grain size distribution associated feature self-supervised cluster representation vector, it is used to reflect the contribution of each contextual stress distribution grid grain size distribution associated feature vector to the cluster to which it belongs. Accordingly, the calculation of the implicit clustering contribution factor helps to identify and quantify the role of each contextual stress distribution grid grain size distribution associated feature vector in the clustering process, and provides a basis for subsequent feature selection and weight allocation, so that the model can pay more attention to the features that contribute significantly to the clustering. Next, the calculated sequence of implicit clustering contribution factors of the stress distribution grid particle size distribution associated features is converted into a stress distribution grid particle size distribution associated feature clustering contribution field distribution vector, that is, the sequence of implicit clustering contribution factors of the stress distribution grid particle size distribution associated features is integrated into the vector field by data integration to construct a clustering contribution field. Subsequently, the stress distribution grid particle size distribution associated feature clustering contribution field distribution vector is explicitly modeled by the self-attention mechanism to generate a stress distribution grid particle size distribution associated feature clustering contribution field modulation weight vector. That is, the self-attention mechanism is used to perform explicit clustering contribution modeling based on the field autocorrelation global analysis of the implicit clustering contribution factors of each stress distribution grid particle size distribution associated feature in the clustering contribution field, which can strengthen the focus on feature vectors with large clustering contributions, while suppressing unimportant features, thereby improving the quality of feature representation and the accuracy of subsequent structural strength detection tasks. Finally, the set of contextual stress distribution grid grain size distribution associated feature vectors is weightedly fused using the stress distribution grid grain size distribution associated feature clustering contribution field modulation weight vector to generate a spatial domain stress distribution significant aggregation representation vector. The spatial domain stress distribution significant aggregation representation vector provides a comprehensive feature representation, which not only contains the original stress distribution grid grain size contextual associated feature information, but also integrates the knowledge learned in the clustering process, so that the model can more accurately capture the key grid areas and features of stress distribution when processing complex tasks, thereby improving the performance and generalization ability of the model.

[0053] In one embodiment of the present application, the cluster analysis unit is used to: calculate the mean vector of the set of context stress distribution grid grain size distribution associated feature vectors to obtain the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector.

[0054] Furthermore, in one embodiment of the present application, the implicit clustering contribution factor calculation unit is used to: calculate the absolute value of the positional division between the contextual stress distribution grid grain size distribution associated feature vector and the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector to obtain the stress distribution grid grain size self-supervised clustering interactive association feature vector; calculate the logarithmic function value of each position eigenvalue in the stress distribution grid grain size self-supervised clustering interactive association feature vector with a base of 2 to obtain the stress distribution grid grain size self-supervised clustering interactive logarithmic representation vector; use the each position eigenvalue in the stress distribution grid grain size self-supervised clustering interactive logarithmic representation vector as a weighting coefficient, calculate the weighted sum of the each position eigenvalue in the contextual stress distribution grid grain size distribution associated feature vector to obtain the stress distribution grid grain size distribution associated feature clustering contribution representation factor; calculate the exponential function value of the stress distribution grid grain size distribution associated feature clustering contribution representation factor with a base of the natural constant e to obtain the stress distribution grid grain size distribution associated feature implicit clustering contribution factor.

[0055] Furthermore, in one embodiment of the present application, the cluster contribution field explicit modeling unit is used to: calculate the matrix multiplication between the stress distribution grid particle size distribution associated feature cluster contribution field distribution vector and the query weight matrix, the key weight matrix and the value weight matrix respectively to obtain the query stress distribution cluster contribution field distribution vector, the key stress distribution cluster contribution field distribution vector and the value stress distribution cluster contribution field distribution vector; calculate the multiplication between the query stress distribution cluster contribution field distribution vector and the transposed vector of the key stress distribution cluster contribution field distribution vector, and divide the obtained matrix by position The square root of the length of the bond stress distribution cluster contribution field distribution vector is used to obtain the query-bond stress distribution cluster contribution field semantic interaction representation matrix; the query-bond stress distribution cluster contribution field semantic interaction representation matrix is ​​normalized using the Softmax function to obtain the normalized query-bond stress distribution cluster contribution field semantic interaction representation matrix; the multiplication between the value stress distribution cluster contribution field distribution vector and the query-bond stress distribution cluster contribution field semantic interaction representation matrix is ​​calculated to obtain the stress distribution grid granularity distribution associated feature cluster contribution field domain modulation weight vector.

[0056] Specifically, the set of the context stress distribution grid particle size distribution associated feature vectors is input into the feature significant aggregation network based on cluster contribution field analysis and processed with the following feature significant aggregation formula to obtain the spatial domain stress distribution significant aggregation representation vector;

[0057] Among them, the feature significant aggregation formula is:

[0058] X={x1,x2,...,x k , ..., x n}

[0059]

[0060] v d ={d1; d2; ...; d j ;...;d n}

[0061]

[0062] v q =W q v d

[0063] v k =W k v d

[0064] v v =W b v d

[0065]

[0066] Where X is the set of associated feature vectors of the context stress distribution grid size distribution, x1, x2, x k , x n are the first, second, kth and nth context stress distribution grid size distribution associated feature vectors in the set of context stress distribution grid size distribution associated feature vectors, respectively, x i is the i-th context stress distribution grid size distribution associated feature vector in the set of context stress distribution grid size distribution associated feature vectors, N is the number of feature vectors in the set of context stress distribution grid size distribution associated feature vectors, x c is the stress distribution grid size distribution associated feature self-supervised clustering representation vector, x j is the jth context stress distribution grid grain size distribution associated feature vector in the set of context stress distribution grid grain size distribution associated feature vectors, is the eigenvalue of the kth position in the associated eigenvector of the jth context stress distribution grid particle size distribution, is the eigenvalue of the kth position in the stress distribution grid particle size distribution associated feature self-supervised clustering representation vector, log represents the logarithmic function value with base 2, exp represents the natural exponential function value, d j is the implicit clustering contribution factor of the stress distribution grid size distribution associated feature corresponding to the j-th context stress distribution grid size distribution associated feature vector, v d is the stress distribution grid size distribution associated feature clustering contribution field distribution vector obtained by quantizing multiple stress distribution implicit clustering contribution factors, W q , W k and W b are the query weight matrix, key weight matrix and value weight matrix respectively, v q 、v k and v v are the query stress distribution cluster contribution field distribution vector, the key stress distribution cluster contribution field distribution vector and the value stress distribution cluster contribution field distribution vector respectively, L is the length of the key stress distribution cluster contribution field distribution vector, Softmax(·) is the Softmax function, is vector multiplication, v w The stress distribution grid size distribution associated feature clustering contribution field modulation weight vector, x f The stress distribution in the spatial domain is significantly aggregated representing the vector.

[0067] Then, the spatial domain stress distribution significant aggregation representation vector is input into the classifier-based bearing capacity analysis module to obtain the classification result, and the classification result is used to indicate whether the structural strength of the SPCS prefabricated component to be tested meets the preset requirements. It is worth mentioning that the classification result, that is, whether the structural strength of the SPCS prefabricated component to be tested meets the preset requirements, is the bearing capacity analysis and detection result of the SPCS prefabricated component to be tested under the first load condition. In particular, by deeply analyzing the stress distribution diagram of the SPCS prefabricated component to be tested under other different load conditions, the structural strength detection result of the prefabricated component under different load conditions can be obtained, so as to realize the final prefabricated component structural strength quality detection. In this way, the stress distribution characteristics of the prefabricated component under different load conditions can be automatically identified and quantified, so as to evaluate the structural strength of the SPCS prefabricated component, such as the cavity prefabricated wall, so as to more quickly obtain the result of whether the prefabricated component meets the predetermined design standard. In this way, the intelligence level of the bearing capacity analysis of the SPCS prefabricated component is improved, so as to realize the comprehensive automatic evaluation of the prefabricated component and provide reliable data support for the building design and construction process.

[0068] In one embodiment of the present application, the component structural strength detection module is used to: input the spatial domain stress distribution significant aggregation representation vector into a classifier-based bearing capacity analysis module to obtain a classification result, and the classification result is used to indicate whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements.

[0069] Furthermore, in one embodiment of the present application, the component structure strength detection module includes: a fully connected encoding unit, used to use multiple fully connected layers of the classifier-based stress analysis module to fully connect the spatial domain stress distribution significant aggregation representation vector to obtain an encoded classification feature vector; a classification unit, used to pass the encoded classification feature vector through the Softmax classification function of the classifier-based stress analysis module to obtain the classification result.

[0070] In a preferred example, the significant aggregation representation vector of the spatial stress distribution is passed through a classifier-based bearing capacity analysis module to obtain a classification result, which includes:

[0071] Calculating the sum of the absolute values ​​of each eigenvalue of the spatial domain stress distribution significant aggregation representation vector to obtain a first spatial domain stress distribution significant aggregation and modulation value, and calculating the square root of the sum of the squares of each eigenvalue of the spatial domain stress distribution significant aggregation representation vector to obtain a second spatial domain stress distribution significant aggregation and modulation value;

[0072] After performing point subtraction on the spatial domain stress distribution significant aggregation representation vector and the second spatial domain stress distribution significant aggregation and modulation value, respectively perform point multiplication with the number of eigenvalues ​​of the spatial domain stress distribution significant aggregation representation vector and the reciprocal of the first spatial domain stress distribution significant aggregation and modulation value, and take the reciprocal of each eigenvalue to obtain the first spatial domain stress distribution significant aggregation phase conversion vector;

[0073] After point-wise subtraction of the spatial domain stress distribution significant aggregation representation vector and the first spatial domain stress distribution significant aggregation and modulation value, point-wise multiplication is performed with the square root of the number of eigenvalues ​​of the spatial domain stress distribution significant aggregation representation vector and the reciprocal of the second spatial domain stress distribution significant aggregation and modulation value, and the reciprocal of each eigenvalue is taken to obtain the second spatial domain stress distribution significant aggregation phase transformation vector; and

[0074] Subtract the dot product vector of the second spatial domain stress distribution significant aggregation phase transformation vector from the first spatial domain stress distribution significant aggregation phase transformation vector and the weighted hyperparameter to obtain an optimized spatial domain stress distribution significant aggregation representation vector; and

[0075] The optimized spatial domain stress distribution significant aggregation representation vector is passed through the classifier-based bearing capacity analysis module to obtain the classification result.

[0076] Here, the spatial domain stress distribution is significantly aggregated and represented by a vector, denoted as V, which is optimally represented as:

[0077]

[0078] v i ∈V∈R n

[0079] Where V is the significant aggregation representation vector of the stress distribution in the spatial domain, v i are the eigenvalues ​​of the significant aggregation representation vector of the stress distribution in the spatial domain, α is the significant aggregation and modulation value of the stress distribution in the first spatial domain, β is the significant aggregation and modulation value of the stress distribution in the second spatial domain, α -1 is the inverse of the significant aggregation and modulation value of the stress distribution in the first spatial domain, n is the number of eigenvalues ​​of the significant aggregation representation vector of the stress distribution in the spatial domain, V1 is the significant aggregation phase transformation vector of the stress distribution in the first spatial domain, [·] ⊙-1 is the inverse of each eigenvalue, β -1 is the inverse of the significant aggregation and modulation value of the stress distribution in the second spatial domain, V2 is the significant aggregation phase transformation vector of the stress distribution in the second spatial domain, ω is a weighted hyperparameter, V′ is the optimized significant aggregation representation vector of the stress distribution in the spatial domain, Subtract by position, ⊙ multiply by position.

[0080] That is, considering that each contextual stress distribution grid grain size distribution associated feature vector in the set of contextual stress distribution grid grain size distribution associated feature vectors respectively represents the local stress distribution contextual semantic associated features determined based on gridding segmentation of the stress distribution map, when it is passed through the feature significant aggregation network based on clustering contribution field analysis, the difference in clustering contribution of each contextual stress distribution grid grain size distribution associated feature vector will cause the spatial domain stress distribution significant aggregation representation vector to lack detailed semantic aggregation, affecting the accuracy of its feature expression.

[0081] Therefore, in the preferred example, the difference of the characteristic value of the spatial domain stress distribution significant aggregation representation vector relative to the feature set of the spatial domain stress distribution significant aggregation representation vector as a whole is used as the semantic change intensity information, and the phase-like conversion corresponding to the position-based intensity modulation is performed through different and modulation representation forms, so that the aggregation enhancement of the semantic change phase perception can improve the axial aggregation receptive field along the feature aggregation direction by performing a spatial translation operation based on alternating stacking under the scale balance of the vector set of the spatial domain stress distribution significant aggregation representation vector, thereby improving the perception effect of the aggregation semantics of the spatial domain stress distribution significant aggregation representation vector on the detail semantic change, so as to improve the expression effect of the spatial domain stress distribution significant aggregation representation vector, and improve the accuracy of the classification result obtained by the classifier-based bearing capacity analysis module. In this way, based on the stress distribution characteristics of the prefabricated components under different load conditions, the structural strength detection of the SPCS prefabricated components can be more accurately performed, the intelligence level of the bearing capacity analysis of the SPCS prefabricated components is improved, and reliable data support is provided for the building design and construction process.

[0082] In summary, the above scheme is adopted to obtain the stress distribution diagram of SPCS prefabricated components under a variety of different load conditions, and introduce an image processing algorithm based on artificial intelligence and deep learning at the back end to analyze the stress distribution diagram, so as to automatically identify and quantify the stress distribution characteristics of prefabricated components under different load conditions, thereby evaluating SPCS prefabricated components, such as the structural strength of cavity prefabricated walls, so as to more quickly determine whether the prefabricated components meet the predetermined design standards. In this way, the intelligence level of the bearing capacity analysis of SPCS prefabricated components is improved, so as to realize a comprehensive and automatic evaluation of prefabricated components and provide reliable data support for the building design and construction process.

[0083] Figure 2 is a flow chart showing a method for analyzing the bearing capacity of a SPCS prefabricated component according to an exemplary embodiment. Figure 2 As shown, the method includes:

[0084] Step S201, obtaining a stress distribution diagram of the SPCS prefabricated component to be tested under a first load condition;

[0085] Step S202, gridding and segmenting the stress distribution map to obtain a set of stress distribution grid particle size distribution maps;

[0086] Step S203, performing feature capture and global analysis based on the stress distribution space domain on each stress distribution grid grain size distribution map in the set of stress distribution grid grain size distribution maps to obtain a set of contextual stress distribution grid grain size distribution associated feature vectors;

[0087] Step S204, inputting the set of contextual stress distribution grid particle size distribution associated feature vectors into a feature significant aggregation network based on cluster contribution field analysis to obtain a spatial domain stress distribution significant aggregation representation vector;

[0088] Step S205: Determine whether the structural strength of the SPCS prefabricated component to be tested meets the preset requirements based on the significant aggregation representation vector of the spatial domain stress distribution.

[0089] Among them, the SPCS assembled integral steel welded mesh composite structure technology mainly includes cavity prefabricated wall and cavity prefabricated column technology.

[0090] The cavity prefabricated wall is an improved and upgraded prefabricated shear wall technology based on the European double-skin wall and national standard composite shear wall technology, through a large amount of theoretical research and experimental verification.

[0091] The cavity prefabricated wall is turned over in the factory, and the integral steel cage is anchored into the concrete blades on both sides to form an integral prefabricated wall panel component including "load-bearing blades, load-bearing steel cages, and cavities between blades". The prefabricated components are connected by setting overlapping steel bars in the cavity and pouring concrete on site in the cavity to form an integral superimposed load-bearing shear wall.

[0092] The system uses the principle of concrete superposition to combine vertical superimposed components, horizontal superimposed components, wall edge restraint components, etc. into a whole through cast-in-place concrete, giving full play to the respective advantages of precast concrete components and cast-in-place concrete. The overall performance is safe and reliable, ensuring the same seismic resistance as the cast-in-place structural system as well as building insulation, waterproofing, sound insulation and other properties, and realizing the perfect combination of cast-in-place concrete and high-precision precast components.

[0093] The cavity prefabricated column uses professional centrifugal equipment to strictly control the centrifugal process to achieve a "square outside and square inside" cross-section, which greatly reduces the weight of the component and reduces the requirements for tower cranes on the construction site. At the same time, the rough surface inside the cavity is set to ensure that the post-cast concrete on site is tightly overlapped with the prefabricated part.

[0094] The cavity prefabricated column adopts the "prefabrication + cast-in-place" superimposed stress-bearing method to achieve the same stress-bearing performance as cast-in-place. The column shell not only participates in the stress, but also replaces the on-site formwork. The steel bar structure is consistent with the cast-in-place column. It has the advantages of convenient construction, fast speed, reliable connection, easy inspection, and safe stress performance.

[0095] The steel bars and components of the SPCS assembled integral steel welded mesh composite structure complete set technology are suitable for standardized design, intelligent production, and rapid installation. They truly realize the requirements of prefabricated buildings that are manufactured in factories and quickly installed on site. They can be widely used in shear walls, frames, frame-shear walls and other structural systems, and are suitable for residential, public buildings, commercial, factory buildings, basements, municipal water conservancy and other building categories.

[0096] SPCS prefabricated cavity column construction process Figure 3 As shown in the figure, the construction process of SPCS prefabricated cavity wall is as follows Figure 4 shown.

[0097] In this application, the cavity prefabricated wall (SPCS shear wall) technology is based on the double-skin wall and national standard composite shear wall technology, and is improved and upgraded through a large amount of theoretical research and experimental verification to form a prefabricated shear wall technology.

[0098] like Figure 5 As shown in the figure, the blades of the cavity wall panel components are connected by an integral steel cage, which is consistent with the steel bar shape in the integral cast-in-place shear wall. The wall panels are turned over in the factory, and the integral steel cages are anchored into the concrete blades on both sides, forming an integral prefabricated wall panel component including "load-bearing blades, load-bearing steel cages, and cavities between blades". The characteristics of the cavity prefabricated shear wall are "cavity prefabricated components + lap steel bars + post-cast composite concrete". The integral steel cage is integrally anchored in the blade, forming an integral strong tension connection for the blades on both sides, which makes up for the defect that the truss steel bars in the national standard composite shear wall are shallowly anchored in the blades on both sides and cannot achieve effective tension connection.

[0099] The horizontal and vertical connecting steel bars between the cavity prefabricated wall panels are all in a closed ring shape, and vertical dowel bars are added to the ends of the horizontal connecting steel bars to enhance the anchoring effect and facilitate on-site component installation.

[0100] The hollow column components are produced in the factory by centrifugal method. The column reinforcement cage and the concrete column shell are formed in one factory to form an integral prefabricated column component including "load-bearing column shell, load-bearing reinforcement cage, and column shell cavity". Figure 6 , where 1 is the prefabricated part, 2 is the cavity part, and 3 is the formed steel cage.

[0101] The cavity prefabricated column adopts the "prefabrication + cast-in-place" superimposed stress-bearing method to achieve the same stress-bearing performance as cast-in-place. The column shell not only participates in the stress, but also replaces the on-site formwork. The prefabricated components are mechanically connected by column longitudinal reinforcement, the first-level connection joint, and the connection section is directly exposed, which is convenient, reliable and easy to inspect.

[0102] In the present application, the blades of the cavity wall panel components are connected by an integral steel cage, which is consistent with the shape of the integral cast-in-place shear wall steel bars. The steel cage is composed of a ladder-shaped horizontal steel mesh and longitudinal steel bars. The steel cage is anchored as a whole in the blade to form an integral strong pull connection for the blades on both sides. The horizontal and vertical connecting steel bars between the prefabricated wall panels are all closed rings, and vertical dowel bars are added at the ends of the horizontal connecting steel bars to enhance the anchoring effect and facilitate on-site component installation.

[0103] It effectively solves many pain points of solid precast concrete components, such as inconvenient vertical steel bar connection, difficult on-site construction, uncontrollable safety and quality, and poor market recognition. It also effectively solves the problem that the national standard double-skin wall is shallowly anchored in the leaf plate through truss steel bars, and the steel mesh on both sides cannot be effectively pulled together.

[0104] The cavity prefabricated column is different from the traditional prefabricated column which is prefabricated as a whole + sleeve grouting connection. It adopts the implementation scheme of prefabrication on all sides, cast-in-place in the middle, and retaining the cast-in-place section at the bottom. The steel bars in the cast-in-place section are mechanically connected.

[0105] The central cavity greatly reduces the weight of the component, effectively solving the problem of heavy weight and difficult construction and hoisting of traditional prefabricated columns; the concrete of the core area of ​​the beam-column joint, the bottom of the column, and the cavity in the middle of the column are poured on-site to ensure better integration of the prefabricated part and the cast-in-place part, thereby improving the overall safety.

[0106] In this application, the main technical indicators include: engineering quality control, engineering progress control, engineering cost control, supervision of production safety management, contract management, and organization and coordination. The main performance indicators include main performance indicators, technical application conditions, comparison and positioning with similar technical systems, and related cost indicators.

[0107] The cavity prefabricated wall adopts a steel cage that is consistent with the cast-in-place structure, and its mechanical performance is infinitely close to that of the cast-in-place shear wall. The structural system is equipped with a complete set of intelligent equipment such as production, transportation, hoisting, and construction, forming a complete set of solutions covering the entire industry chain, which can be widely used in prefabricated buildings of various building categories.

[0108] Compared with traditional solid prefabricated walls, cavity prefabricated wall components are produced using integral forming equipment, reverse technology, and double-sided mold tables, with higher precision and a smoother and flatter surface. The vertical component cavities and connection nodes are cast in place as a whole, with good structural integrity, which solves the common quality problems of prefabricated buildings such as easy cracking and poor waterproof performance. Compared with the national standard double-skin wall, the steel bar structure is optimized, the integrity is improved, and the risk of cracking caused by on-site mold expansion is avoided.

[0109] The cavity prefabricated wall adopts steel cage forming equipment, which improves the production efficiency by more than 30% compared with manual binding. The prefabricated part not only bears the force but also serves as the formwork, which greatly reduces the on-site formwork and reinforcement work. The component is light in weight, easy to transport, hoist and install, and the plate size is large, which improves the utilization rate of the tower crane and increases the construction speed.

[0110] SPCS assembled integral welded composite structure complete set of technology can be used for shear wall, frame, frame-shear wall and other structural systems, and can be widely used in residential, public, commercial, factory, basement, municipal water conservancy and other building categories.

[0111] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 7 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0113] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0114] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0115] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0116] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0117] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0118] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0119] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the test parameter acquisition module can also be described as a "module for acquiring device test parameters corresponding to the target device".

[0121] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0122] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] In the application scenario of the SPCS prefabricated component bearing capacity analysis system, first, the stress distribution diagram of the SPCS prefabricated component to be tested under the first load condition is obtained; then, the obtained stress distribution diagram is input into a server deployed with the SPCS prefabricated component bearing capacity analysis algorithm, wherein the server can process the stress distribution diagram based on the SPCS prefabricated component bearing capacity analysis algorithm to determine whether the structural strength of the SPCS prefabricated component to be tested meets the preset requirements.

[0124] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0125] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0126] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely exemplary forms of implementation. With respect to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

1. A bearing capacity analysis system for SPCS prefabricated components, characterized in that: include: A component stress distribution diagram acquisition module is used to obtain a stress distribution diagram of the SPCS prefabricated component to be tested under the first load condition; A stress distribution map segmentation module, used for performing grid segmentation on the stress distribution map to obtain a set of stress distribution grid particle size distribution maps; A stress distribution feature global analysis module, used for performing feature capture and global analysis based on the stress distribution space domain on each stress distribution grid size distribution map in the set of stress distribution grid size distribution maps to obtain a set of contextual stress distribution grid size distribution associated feature vectors; A spatial domain stress distribution significant aggregation representation module is used to input the set of contextual stress distribution grid particle size distribution associated feature vectors into a feature significant aggregation network based on cluster contribution field analysis to obtain a spatial domain stress distribution significant aggregation representation vector; The component structural strength detection module is used to determine whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements based on the significant aggregation representation vector of the spatial domain stress distribution.

2. The bearing capacity analysis system of SPCS prefabricated components according to claim 1 is characterized in that: The stress distribution characteristics global analysis module includes: A stress distribution local feature extraction unit, used for inputting each stress distribution grid particle size distribution map in the set of stress distribution grid particle size distribution maps into a stress distribution local feature extractor based on a convolution layer to obtain a set of stress distribution grid particle size distribution associated feature vectors; The stress distribution space domain global encoding unit is used to input the set of stress distribution grid particle size distribution associated feature vectors into the stress distribution space domain global encoder based on the converter to obtain the set of context stress distribution grid particle size distribution associated feature vectors.

3. The bearing capacity analysis system of SPCS prefabricated components according to claim 2 is characterized in that: The spatial domain stress distribution significant aggregation representation module includes: A cluster analysis unit, used for performing cluster analysis on the set of context stress distribution grid grain size distribution associated feature vectors to obtain a stress distribution grid grain size distribution associated feature self-supervised cluster representation vector; An implicit clustering contribution factor calculation unit is used to calculate the implicit clustering contribution factor of each contextual stress distribution grid grain size distribution associated feature vector in the set of the contextual stress distribution grid grain size distribution associated feature vector relative to the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector to obtain a sequence of implicit clustering contribution factors of stress distribution grid grain size distribution associated features; A vectorized arrangement unit, used for vectorizing the sequence of implicit clustering contribution factors of the stress distribution grid particle size distribution association characteristics to obtain a stress distribution grid particle size distribution association characteristic clustering contribution field distribution vector; A cluster contribution field explicit modeling unit, used for inputting the stress distribution grid granularity distribution associated feature cluster contribution field distribution vector into a cluster contribution field explicit modeling module based on a self-attention mechanism to obtain a stress distribution grid granularity distribution associated feature cluster contribution field domain modulation weight vector; The position-weighted sum calculation unit is used to calculate the position-weighted sum of the set of context stress distribution grid particle size distribution associated feature vectors using each eigenvalue in the stress distribution grid particle size distribution associated feature cluster contribution field modulation weight vector as a weight to obtain the spatial domain stress distribution significant aggregation representation vector.

4. The bearing capacity analysis system of SPCS prefabricated components according to claim 3 is characterized in that: The cluster analysis unit is used to calculate the mean vector of the set of context stress distribution grid grain size distribution associated feature vectors to obtain the stress distribution grid grain size distribution associated feature self-supervised cluster representation vector.

5. The bearing capacity analysis system of SPCS prefabricated components according to claim 4 is characterized in that: The implicit clustering contribution factor calculation unit is used to: Calculating the absolute value of the positional division between the contextual stress distribution grid grain size distribution associated feature vector and the stress distribution grid grain size distribution associated feature self-supervised clustering representation vector to obtain a stress distribution grid grain size self-supervised clustering interactively associated feature vector; Calculating the logarithmic function value of each position eigenvalue in the stress distribution grid granularity self-supervised clustering interactive correlation eigenvector with base 2 to obtain the stress distribution grid granularity self-supervised clustering interactive logarithmic representation vector; Taking the eigenvalues ​​of each position in the stress distribution grid granularity self-supervised clustering interactive logarithm representation vector as weighting coefficients, calculating the weighted sum of the eigenvalues ​​of each position in the contextual stress distribution grid granularity distribution associated eigenvector to obtain a stress distribution grid granularity distribution associated feature clustering contribution representation factor; The exponential function value of the stress distribution grid particle size distribution associated feature cluster contribution representation factor is calculated with the natural constant e as the base to obtain the stress distribution grid particle size distribution associated feature implicit cluster contribution factor.

6. The bearing capacity analysis system of SPCS prefabricated components according to claim 5 is characterized in that: The cluster contribution field explicit modeling unit is used for: Calculate the matrix multiplication between the stress distribution grid particle size distribution associated feature cluster contribution field distribution vector and the query weight matrix, the key weight matrix and the value weight matrix respectively to obtain the query stress distribution cluster contribution field distribution vector, the key stress distribution cluster contribution field distribution vector and the value stress distribution cluster contribution field distribution vector; After calculating the multiplication between the query stress distribution cluster contribution field distribution vector and the transposed vector of the bond stress distribution cluster contribution field distribution vector, the obtained matrix is ​​divided by the square root of the length of the bond stress distribution cluster contribution field distribution vector by position to obtain a query-bond stress distribution cluster contribution field semantic interaction representation matrix; The query-bond stress distribution cluster contribution field semantic interaction representation matrix is ​​normalized by using a Softmax function to obtain a normalized query-bond stress distribution cluster contribution field semantic interaction representation matrix; The multiplication between the value stress distribution cluster contribution field distribution vector and the query-key stress distribution cluster contribution field semantic interaction representation matrix is ​​calculated to obtain the stress distribution grid particle size distribution associated feature cluster contribution field domain modulation weight vector.

7. The bearing capacity analysis system of SPCS prefabricated components according to claim 6 is characterized in that: The component structural strength detection module is used to: input the spatial domain stress distribution significant aggregation representation vector into the classifier-based bearing capacity analysis module to obtain a classification result, and the classification result is used to indicate whether the structural strength of the SPCS prefabricated component to be detected meets the preset requirements.

8. The bearing capacity analysis system of SPCS prefabricated components according to claim 7 is characterized in that: The component structure strength detection module comprises: A fully connected encoding unit, configured to perform fully connected encoding on the spatial domain stress distribution significant aggregation representation vector using multiple fully connected layers of the classifier-based bearing capacity analysis module to obtain an encoded classification feature vector; The classification unit is used to pass the encoded classification feature vector through the Softmax classification function of the classifier-based bearing capacity analysis module to obtain the classification result.

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