Data Processing System and Method for House Acceptance

Abstract

This application relates to the field of data processing. Specifically, it discloses a data processing system and method for housing acceptance. It uses data processing technology based on deep learning to compare and analyze the acceptance BIM model and the design BIM model of the housing target to be measured, respectively extracts the housing structure features of the acceptance BIM model and the design BIM model, then performs spatial attention feature enhancement processing on both, and on this basis measures the feature differences between the two, so as to intelligently determine whether the housing target to be measured meets the construction party's acceptance standards. In this way, automated data processing can be used to assist or replace traditional manual acceptance, thereby reducing labor costs and improving the efficiency and accuracy of housing acceptance.

Description

Technical Field

[0001] This application relates to the field of data processing, and more specifically, to a data processing system and method for house acceptance. Background Art

[0002] With the rapid development of the construction industry and the continuous advancement of technological innovation, the design, construction, and acceptance processes of buildings have become increasingly complex and variable. The traditional method of house acceptance mainly relies on manual verification and comparison with paper documents. This method is not only time-consuming and laborious but also vulnerable to human factors, making it difficult to ensure the accuracy and credibility of the acceptance results. Especially when faced with large-scale or complex construction projects, the traditional method often seems inadequate and difficult to efficiently complete the acceptance tasks.

[0003] In recent years, with the rapid development of information technology, it has become possible to improve the efficiency and quality of house acceptance by means of digitalization. As a digital model integrating various relevant information of a construction project, BIM technology covers geometric information, spatial relationships, geographical information, as well as the attributes and quantities of various building components. It can not only effectively improve the efficiency of building design and construction but also make the operation and maintenance of buildings more efficient and convenient.

[0004] In the house acceptance link, although BIM technology can provide a comprehensive, accurate, and real-time building information display platform, thereby greatly improving the accuracy and efficiency of the acceptance work, currently, it still relies on manual means to compare and judge the data information in the BIM model to ensure the accuracy of the acceptance results. This not only involves a huge workload but also is prone to omissions.

[0005] Therefore, there is an expectation for a data processing system and method for house acceptance that can utilize the BIM model for automated acceptance. Summary of the Invention

[0006] To solve the above technical problems, this application is proposed. The embodiments of this application provide a data processing system and method for house acceptance. It uses data processing technology based on deep learning to conduct a comparative analysis of the acceptance BIM model and the design BIM model of the house target to be measured, respectively extracts the house structure features of the acceptance BIM model and the design BIM model, then performs spatial attention feature enhancement processing on both, and on this basis, measures the feature differences between the two, so as to intelligently determine whether the house target to be measured meets the construction party's acceptance standards. In this way, automated data processing can be used to assist or replace traditional manual acceptance, thereby reducing labor costs and improving the efficiency and accuracy of house acceptance.

[0007] According to one aspect of this application, there is provided a data processing method for house acceptance, which includes:

[0008] Obtain the acceptance BIM model of the house target to be measured, and at the same time extract the design BIM model of the house target to be measured from the database;

[0009] Extract the house structure semantic features of the acceptance BIM model and the design BIM model to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map;

[0010] Perform attention enhancement on the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map;

[0011] Based on the feature differences between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map, determine whether the house target to be measured meets the construction party's acceptance standards.

[0012] In the above data processing method for house acceptance, performing attention enhancement on the acceptance BIM model structure semantic coding feature map includes: based on the pixel spatial position information of the acceptance BIM model structure semantic coding feature map, adaptively attenuate and modulate the pixel-level semantic association structure of the acceptance BIM model structure semantic coding feature map to obtain the acceptance BIM model structure feature space attenuation pixel granularity inter-semantic association weight map; based on the acceptance BIM model structure feature space attenuation pixel granularity inter-semantic association weight map, perform feature modulation on the acceptance BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map.

[0013] In the above data processing method for house acceptance, extracting the house structure semantic features of the acceptance BIM model and the design BIM model to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map includes: inputting the acceptance BIM model and the design BIM model into a BIM model twin detection network including a first house model feature extractor and a second house model feature extractor to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map.

[0014] In the above data processing method for housing acceptance, based on the pixel spatial position information of the acceptance BIM model structure semantic coding feature map, adaptively attenuating modulation is performed on the pixel-level semantic association structure of the acceptance BIM model structure semantic coding feature map to obtain a semantic association weight map between the attenuated pixel granularities of the acceptance BIM model structure feature space, including: performing feature fine-grained spatial decoupling along the channel dimension on the acceptance BIM model structure semantic coding feature map to obtain a set of acceptance BIM model pixel granularity structure semantic feature vectors; inputting any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors into a semantic association score metric network to obtain a set of semantic association score vectors between the acceptance BIM model pixel granularities; performing semantic association adaptive attenuation modulation on the set of semantic association score vectors between the acceptance BIM model pixel granularities based on the spatial span between any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors to obtain a set of semantic association score vectors between the spatially attenuated pixel granularities of the acceptance BIM model; performing dimension reconstruction and weight assignment on the set of semantic association score vectors between the spatially attenuated pixel granularities of the acceptance BIM model to obtain the semantic association weight map between the attenuated pixel granularities of the acceptance BIM model structure feature space.

[0015] In the above data processing method for housing acceptance, inputting any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors into a semantic association score metric network to obtain a set of semantic association score vectors between the acceptance BIM model pixel granularities includes: concatenating and fusing any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors, multiplying by a weight parameter matrix, and then performing a dot product with a bias vector to obtain the set of semantic association score vectors between the acceptance BIM model pixel granularities.

[0016] In the above data processing method for house acceptance, based on the spatial span between any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors, semantic association adaptive attenuation modulation is performed on the set of acceptance BIM model pixel granularity semantic association score vectors to obtain a set of acceptance BIM model spatially attenuated pixel granularity semantic association score vectors, including: performing spatial position annotation on each acceptance BIM model pixel granularity structure semantic feature vector in the set of acceptance BIM model pixel granularity structure semantic feature vectors to obtain a set of acceptance BIM model pixel spatial position data; calculating the Euclidean distance between the spatial position data of any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel spatial position data as the spatial span value of the two acceptance BIM model pixel granularity structure semantic feature vectors to obtain a set of acceptance BIM model pixel granularity spatial span values; based on each acceptance BIM model pixel granularity spatial span value in the set of acceptance BIM model pixel granularity spatial span values, performing semantic association adaptive attenuation modulation on each acceptance BIM model pixel granularity semantic association score vector in the set of acceptance BIM model pixel granularity semantic association score vectors to obtain the set of acceptance BIM model spatially attenuated pixel granularity semantic association score vectors.

[0017] In the above data processing method for house acceptance, based on each acceptance BIM model pixel granularity spatial span value in the set of acceptance BIM model pixel granularity spatial span values, semantic association adaptive attenuation modulation is performed on each acceptance BIM model pixel granularity semantic association score vector in the set of acceptance BIM model pixel granularity semantic association score vectors to obtain a set of acceptance BIM model spatially attenuated pixel granularity semantic association score vectors, including: dividing the acceptance BIM model pixel granularity spatial span value by a preset scaling factor to obtain an acceptance BIM model pixel granularity spatial span scaled value; calculating the exponential function value with e as the base and the opposite number of the acceptance BIM model pixel granularity spatial span scaled value as the exponent to obtain a semantic association spatial attenuation coefficient; calculating the acceptance BIM model pixel granularity semantic association score vector divided by the semantic association spatial attenuation coefficient to obtain the acceptance BIM model spatially attenuated pixel granularity semantic association score vector.

[0018] In the above data processing method for house acceptance, dimension reconstruction and weight assignment are performed on the set of semantic association score vectors between the spatial attenuation pixel granularities of the acceptance BIM model to obtain the semantic association weight map between the spatial attenuation pixel granularities of the acceptance BIM model structure features, including: after arranging the set of semantic association score vectors between the spatial attenuation pixel granularities of the acceptance BIM model into a semantic association score feature map of the acceptance BIM model spatial attenuation pixel granularities, inputting it into a feature dimension modulation layer based on a point convolutional layer and a weight assignment network based on the Sigmoid function to obtain the semantic association weight map between the spatial attenuation pixel granularities of the acceptance BIM model structure features.

[0019] In the above data processing method for house acceptance, feature modulation is performed on the semantic encoding feature map of the acceptance BIM model structure based on the semantic association weight map between the spatial attenuation pixel granularities of the acceptance BIM model structure features to obtain the semantic space enhanced encoding feature map of the acceptance BIM model structure, including: calculating the element-wise product of the semantic association weight map between the spatial attenuation pixel granularities of the acceptance BIM model structure features and the semantic encoding feature map of the acceptance BIM model structure to obtain the semantic space enhanced encoding feature map of the acceptance BIM model structure.

[0020] In the above data processing method for house acceptance, based on the feature difference between the semantic space enhanced encoding feature map of the acceptance BIM model structure and the semantic space enhanced encoding feature map of the design BIM model structure, it is determined whether the house target to be tested meets the acceptance standards of the construction party, including: inputting the semantic space enhanced encoding feature map of the acceptance BIM model structure and the semantic space enhanced encoding feature map of the design BIM model structure into a BIM model difference measurement network to obtain a difference semantic encoding feature map of the acceptance-design house target; inputting the difference semantic encoding feature map of the acceptance-design house target into an intelligent auxiliary acceptance module based on a classifier to obtain an auxiliary acceptance result, and the auxiliary acceptance result is used to indicate whether it meets the acceptance standards of the construction party.

[0021] According to another aspect of the present application, a data processing system for house acceptance is provided, which includes:

[0022] A BIM model acquisition module, configured to acquire the acceptance BIM model of the house target to be tested, and at the same time extract the design BIM model of the house target to be tested from the database;

[0023] A house structure semantic feature extraction module, configured to extract the house structure semantic features of the acceptance BIM model and the design BIM model to obtain a semantic encoding feature map of the acceptance BIM model structure and a semantic encoding feature map of the design BIM model structure;

[0024] An attention enhancement module for enhancing the attention of the acceptance BIM model structure semantic encoding feature map and the design BIM model structure semantic encoding feature map to obtain an acceptance BIM model structure semantic space enhanced encoding feature map and a design BIM model structure semantic space enhanced encoding feature map;

[0025] An acceptance standard evaluation module for determining whether the to-be-inspected housing object meets the construction party's acceptance standards based on the feature differences between the acceptance BIM model structure semantic space enhanced encoding feature map and the design BIM model structure semantic space enhanced encoding feature map.

[0026] Compared with the prior art, the data processing system and method for housing acceptance provided by the present application uses data processing technology based on deep learning to conduct a comparative analysis of the acceptance BIM model and the design BIM model of the to-be-inspected housing object, respectively extracts the housing structure features of the acceptance BIM model and the design BIM model, then performs spatial attention feature enhancement processing on both, and on this basis measures the feature differences between the two, so as to intelligently determine whether the to-be-inspected housing object meets the construction party's acceptance standards. In this way, automated data processing can be used to assist or replace traditional manual acceptance, thereby reducing labor costs and improving the efficiency and accuracy of housing acceptance. Description of the Drawings

[0027] By describing the embodiments of the present application in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present application will become more obvious. The drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation to the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0028] Figure 1 It is a flowchart of a data processing method for housing acceptance according to an embodiment of the present application.

[0029] Figure 2 It is a schematic diagram of data flow of a data processing method for housing acceptance according to an embodiment of the present application.

[0030] Figure 3 It is a flowchart of sub-step S3 of a data processing method for housing acceptance according to an embodiment of the present application.

[0031] Figure 4 It is a flowchart of sub-step S31 of a data processing method for housing acceptance according to an embodiment of the present application.

[0032] Figure 5 It is a flowchart of sub-step S313 of a data processing method for housing acceptance according to an embodiment of the present application.

[0033] Figure 6 It is a block diagram of a data processing system for housing acceptance according to an embodiment of the present application. Detailed implementation manners

[0034] As shown in the present application and the claims, unless the context clearly indicates otherwise, words such as "a", "an", "one", and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0035] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The modules are merely illustrative, and different aspects of the system and method can use different modules.

[0036] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations before or below are not necessarily executed precisely in sequence. On the contrary, as needed, various steps can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0037] Next, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the exemplary embodiments described herein.

[0038] In response to the technical problems described in the above background art, the present application proposes an optimized data processing method for housing acceptance, which uses data processing technology based on deep learning to compare and analyze the acceptance BIM model and the design BIM model of the housing target to be measured, respectively extracts the housing structure features of the acceptance BIM model and the design BIM model, then performs spatial attention feature enhancement processing on both, and on this basis, measures the feature differences between the two, so as to intelligently determine whether the housing target to be measured meets the construction party's acceptance standards. In this way, automated data processing can be used to assist or replace traditional manual acceptance, thereby reducing labor costs and improving the efficiency and accuracy of housing acceptance.

[0039] Figure 1 It is a flowchart of a data processing method for housing acceptance according to an embodiment of the present application. Figure 2 It is a schematic diagram of data flow of a data processing method for housing acceptance according to an embodiment of the present application. AsFigure 1 and Figure 2 As shown in Figure 2 , the data processing method for housing acceptance includes the steps of: S1, obtaining the acceptance BIM model of the housing target to be measured, and simultaneously extracting the design BIM model of the housing target to be measured from the database; S2, extracting the housing structure semantic features of the acceptance BIM model and the design BIM model to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map; S3, performing attention enhancement on the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map; S4, based on the feature differences between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map, determining whether the housing target to be measured meets the construction party's acceptance standards.

[0040] In the above data processing method for housing acceptance, in step S1, the acceptance BIM model of the housing target to be measured is obtained, and the design BIM model of the housing target to be measured is extracted from the database. It should be understood that the design BIM model represents the initial design intention and planning of the project, including detailed information such as the expected building structure, layout, material usage, etc. The acceptance BIM model is a digital model constructed based on the actually built housing, reflecting the results of the actual construction. By comparing and analyzing these two, any deviations or changes during the construction process can be intuitively found, so as to accurately evaluate whether the housing meets the design requirements and construction standards.

[0041] In the above data processing method for house acceptance, in step S2, the house structure semantic features of the acceptance BIM model and the design BIM model are extracted to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map. In a specific example of the present application, step S2 further includes: inputting the acceptance BIM model and the design BIM model into a BIM model twin detection network including a first house model feature extractor and a second house model feature extractor to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map. It should be understood that considering the large amount and complexity of the original BIM model data, direct comparison calculation is inefficient and difficult to process. Therefore, in order to reduce the computational complexity and extract the key structural features in the BIM model, the present application further uses the BIM model twin detection network to process the acceptance BIM model and the design BIM model. Among them, the BIM model twin detection network includes a first house model feature extractor and a second house model feature extractor with shared weights, both based on a three-dimensional convolutional neural network architecture, respectively used to extract the location, size, attributes and other structured information of building elements in the acceptance BIM model and the design BIM model, and generate the corresponding acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map. In addition, through this twin network architecture with shared weights, the consistency of feature extraction between the acceptance BIM model and the design BIM model can be ensured, thereby improving the reliability and accuracy of subsequent feature comparison and analysis.

[0042] In the above data processing method for house acceptance, in step S3, the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map are enhanced in attention to obtain the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map. It should be understood that considering that different building elements in the BIM model have different importance for house acceptance, for example, key nodes in the structure and important building components are usually more critical than non-structural decorative elements. Therefore, in order to improve the model's ability to capture key structural patterns in the BIM model, the present application proposes an adaptive feature enhancement method based on spatial context awareness, which dynamically adjusts its feature weights by learning the spatial distribution and context semantic association structure of different building elements in the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map, and adaptively focuses on the key structural features of the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map respectively, thereby improving the discrimination of features and the sensitivity of the model to house structure differences. Among them, Figure 3It is a flowchart of sub-step S3 of the data processing method for house acceptance according to an embodiment of the present application. As Figure 3 shown, the step S3 includes steps: S31, based on the pixel spatial position information of the acceptance BIM model structure semantic coding feature map, adaptively attenuate and modulate the pixel-level semantic association structure of the acceptance BIM model structure semantic coding feature map to obtain a semantic association weight map between the acceptance BIM model structure feature space attenuation pixel granularities; S32, based on the semantic association weight map between the acceptance BIM model structure feature space attenuation pixel granularities, perform feature modulation on the acceptance BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map.

[0043] Figure 4 It is a flowchart of sub-step S31 of the data processing method for house acceptance according to an embodiment of the present application. As Figure 4 shown, the step S31 includes steps: S311, perform feature fine-grained spatial decoupling on the acceptance BIM model structure semantic coding feature map along the channel dimension to obtain a set of acceptance BIM model pixel granularity structure semantic feature vectors; S312, input any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors into the semantic association score measurement network to obtain a set of semantic association score vectors between the acceptance BIM model pixel granularities; S313, based on the spatial span between any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors, perform semantic association adaptive attenuation modulation on the set of semantic association score vectors between the acceptance BIM model pixel granularities to obtain a set of semantic association score vectors between the acceptance BIM model space attenuation pixel granularities; S314, perform dimension reconstruction and weight assignment on the set of semantic association score vectors between the acceptance BIM model space attenuation pixel granularities to obtain the semantic association weight map between the acceptance BIM model structure feature space attenuation pixel granularities.

[0044] More specifically, the step S312 further includes: concatenating and fusing any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors, multiplying by the weight parameter matrix, and then performing a dot product with the bias vector to obtain the set of semantic association score vectors between the acceptance BIM model pixel granularities.

[0045] That is, first, fine-grained spatial decoupling is performed on the semantic encoding feature map of the acceptance BIM model structure along the channel dimension, and it is decomposed into feature vectors at the pixel granularity, thereby enhancing the model's ability to recognize local detail features. Then, considering that the local features of the same type of object usually have stronger semantic associations, therefore, in this application, the semantic association between any two semantic feature vectors of the acceptance BIM model pixel granularity structure in the set is measured through the trained network model to estimate the strength of the semantic connection between the two, generating a set of semantic association score vectors between the acceptance BIM model pixel granularities, thereby revealing the semantic connection between different building elements in the acceptance BIM model.

[0046] Figure 5 It is a flowchart of sub-step S313 of the data processing method for house acceptance according to an embodiment of the present application. As Figure 5 shown, the step S313 includes steps: S3131, performing spatial position annotation on each semantic feature vector of the acceptance BIM model pixel granularity structure in the set of semantic feature vectors of the acceptance BIM model pixel granularity structure to obtain a set of acceptance BIM model pixel spatial position data; S3132, calculating the Euclidean distance between the spatial position data of any two semantic feature vectors of the acceptance BIM model pixel granularity structure in the set of acceptance BIM model pixel spatial position data as the spatial span value between the two semantic feature vectors of the acceptance BIM model pixel granularity structure to obtain a set of acceptance BIM model pixel granularity spatial span values; S3133, based on each acceptance BIM model pixel granularity spatial span value in the set of acceptance BIM model pixel granularity spatial span values, performing semantic association adaptive attenuation modulation on each semantic association score vector between the acceptance BIM model pixel granularities in the set of semantic association score vectors between the acceptance BIM model pixel granularities to obtain the set of acceptance BIM model spatially attenuated pixel granularity semantic association score vectors.

[0047] Specifically, this application considers that even if two local features exhibit similar feature attributes, if their spatial positions are far apart, they may carry completely different information meanings. Therefore, this application further performs spatial position annotation on each semantic feature vector of the acceptance BIM model pixel granularity structure, adds its exact position information in the original BIM model, and calculates the spatial span between any two semantic feature vectors of the acceptance BIM model pixel granularity structure based on this, thereby revealing the relative position relationship between the local features of each pixel granularity, so as to filter out interfering items that are seemingly similar but actually irrelevant in the subsequent feature modulation process.

[0048] In a specific example of the present application, the step S3133 further includes: dividing the acceptance BIM model pixel granularity spatial span value by a preset scaling factor to obtain the acceptance BIM model pixel granularity spatial span scaling value; calculating the exponential function value with e as the base and the opposite number of the acceptance BIM model pixel granularity spatial span scaling value as the exponent to obtain the semantic association space attenuation coefficient; calculating the acceptance BIM model pixel granularity inter-semantic association score vector divided by the semantic association space attenuation coefficient to obtain the acceptance BIM model space attenuation pixel granularity inter-semantic association score vector. That is, based on the spatial span value between any two acceptance BIM model pixel granularity structural semantic feature vectors, adaptive attenuation modulation is performed on the semantic association score vector between the two. When two pixel granularity local features show both strong semantic correlation and are close to each other, the importance of both will be maximized; conversely, if they have a high semantic similarity but are far apart, their influence will be appropriately weakened, so as to accurately reflect the most noteworthy feature part in the BIM model.

[0049] More specifically, the step S314 further includes: after arranging the set of acceptance BIM model space attenuation pixel granularity inter-semantic association score vectors into an acceptance BIM model space attenuation pixel granularity inter-semantic association score feature map, inputting it into a feature dimension modulation layer based on a point convolutional layer and a weight assignment network based on the Sigmoid function to obtain the acceptance BIM model structural feature space attenuation pixel granularity inter-semantic association weight map. That is, after arranging the set of acceptance BIM model pixel granularity inter-semantic association score vectors after spatial attenuation modulation into a feature map form, feature dimension modulation and weight assignment are performed on it through point convolution and the Sigmoid function to generate the corresponding acceptance BIM model structural feature space attenuation pixel granularity inter-semantic association weight map. It is worth mentioning that point convolution is an operation in a convolutional neural network. By changing the number of convolutional kernels, it can reduce or increase the depth of the feature map, which helps to extract features of different scales and enhance the expression ability of the model.

[0050] Specifically, the step S32 further includes: calculating the element-wise multiplication between the acceptance BIM model structural feature space attenuation pixel granularity inter-semantic association weight map and the acceptance BIM model structural semantic encoding feature map to obtain the acceptance BIM model structural semantic space enhanced encoding feature map. That is, through element-wise multiplication operation, weighted modulation is performed on the acceptance BIM model structural semantic encoding feature map, so as to selectively amplify important information and suppress irrelevant background information or non-critical feature regions, further highlighting the expression of key structural features in the acceptance BIM model, thereby obtaining the acceptance BIM model structural semantic space enhanced encoding feature map.

[0051] Accordingly, step S3 includes: processing the acceptance BIM model structure semantic coding feature map with the following feature enhancement formula to obtain the acceptance BIM model structure semantic space enhanced coding feature map, where the feature enhancement formula is:

[0052]

[0053]

[0054]

[0055]

[0056] A ∈ R (H×W)×(H×W)×C

[0057] A' = C pw (A); A' ∈ R H×W×C

[0058] Y = σ(A′)

[0059]

[0060] F' = F ⊙ Y

[0061] where F is the acceptance BIM model structure semantic coding feature map, and F ∈ R H×W×C , where C, H, and W are the number of channels, height, and width of the acceptance BIM model structure semantic coding feature map respectively, decouple(·) represents the feature decoupling operation, X is the set of acceptance BIM model pixel granularity structure semantic feature vectors, v h,w is the acceptance BIM model pixel granularity structure semantic feature vector, and C is the length of the acceptance BIM model pixel granularity structure semantic feature vector, p h,w is the spatial position data of the acceptance BIM model pixel granularity structure semantic feature vector, where p h,w = (h, w), (h, w) are the spatial position coordinates of the acceptance BIM model pixel granularity structure semantic feature vector, P represents the set of acceptance BIM model pixel spatial position data, represents any two acceptance BIM model pixel granularity structure semantic feature vectors in the set of acceptance BIM model pixel granularity structure semantic feature vectors, W r and b r represent the weight parameter matrix and the bias vector respectively, represents the acceptance BIM model pixel granularity - to - granularity semantic association score vector, S is the set of acceptance BIM model pixel granularity - to - granularity semantic association score vectors, is the spatial span value between any two semantic feature vectors of the pixel granularity structure of the acceptance BIM model, and P' is the set of spatial span values of the pixel granularity of the acceptance BIM model. is a preset scaling factor. is the semantic association score vector between the spatially attenuated pixel granularities of the acceptance BIM model, S' is the set of semantic association score vectors between the spatially attenuated pixel granularities of the acceptance BIM model, A represents the semantic association score feature map between the spatially attenuated pixel granularities of the acceptance BIM model, and C pw (·) represents a point convolution operation, A' is the semantic association score feature map between the spatially attenuated pixel granularities of the acceptance BIM model after feature dimension modulation, σ(·) is the Sigmoid function, Y represents the semantic association weight map between the spatially attenuated pixel granularities of the structural feature space of the acceptance BIM model, ⊙ represents element-wise multiplication, and F' represents the enhanced encoded feature map of the structural semantic space of the acceptance BIM model.

[0062] In particular, by processing the structural semantic encoding feature map of the acceptance BIM model and the structural semantic encoding feature map of the design BIM model respectively through the above processing method, the present application can effectively enhance the model's ability to identify and distinguish key structural features in house acceptance, and obtain the enhanced encoded feature map of the structural semantic space of the acceptance BIM model and the enhanced encoded feature map of the structural semantic space of the design BIM model with spatial context awareness, thus contributing to more refined feature comparison.

[0063] In the above data processing method for housing acceptance, in step S4, based on the feature differences between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map, it is determined whether the housing target to be measured meets the acceptance standards of the construction party. In a specific example of the present application, step S4 further includes: inputting the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map into a BIM model difference measurement network to obtain an acceptance-design housing target difference semantic coding feature map; inputting the acceptance-design housing target difference semantic coding feature map into an intelligent auxiliary acceptance module based on a classifier to obtain an auxiliary acceptance result, and the auxiliary acceptance result is used to indicate whether it meets the acceptance standards of the construction party. It should be understood that in order to reveal the feature differences between the acceptance BIM model and the design BIM model, the present application uses a BIM model difference measurement network to conduct a comparative analysis on the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map. In a specific example of the present application, the BIM model difference measurement network generates an acceptance-design housing target difference semantic coding feature map by calculating the position-by-position difference between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map, so as to intuitively reflect the differences in structure and details between the acceptance BIM model and the design BIM model, and provide a more accurate reference basis for housing acceptance. It is worth mentioning that the classifier is based on a multi-layer perceptron (MLP) structure. Through a large amount of training, it can effectively learn the feature differences between the acceptance BIM model and the design BIM model contained in the acceptance-design housing target difference semantic coding feature map, fully understand the similarity correlation relationship between the two, and then accurately distinguish whether the housing target to be measured meets the acceptance standards of the construction party based on this, and output the corresponding acceptance result.

[0064] In a preferred example of the present application, inputting the semantic coding feature map of the acceptance-design house target difference into the intelligent auxiliary acceptance module based on a classifier to obtain an auxiliary acceptance result includes: expanding the semantic coding feature map of the acceptance-design house target difference into a semantic coding feature vector of the acceptance-design house target difference; determining a semantic coding association response matrix and a semantic coding distance response matrix of the acceptance-design house target difference based on the association value and distance value of the eigenvalues of the semantic coding feature vector of the acceptance-design house target difference; multiplying the semantic coding feature vector of the acceptance-design house target difference by the semantic coding association response matrix to obtain a semantic coding association response vector of the acceptance-design house target difference; multiplying the semantic coding distance response matrix by the transposed vector of the semantic coding feature vector of the acceptance-design house target difference to obtain a semantic coding distance response vector of the acceptance-design house target difference; multiplying the semantic coding association response matrix and the semantic coding distance response matrix, and then multiplying the result by the transposed vector of the semantic coding feature vector of the acceptance-design house target difference to obtain a semantic coding logic bias vector of the acceptance-design house target difference; performing dot product on the semantic coding association response vector, the semantic coding distance response vector, and the semantic coding logic bias vector to obtain an optimized semantic coding feature vector of the acceptance-design house target difference; inputting the optimized semantic coding feature vector of the acceptance-design house target difference into the intelligent auxiliary acceptance module based on a classifier to obtain an auxiliary acceptance result.

[0065] Correspondingly, the optimization process of the semantic coding feature vector of the acceptance-design house target difference is represented by the following optimization formula:

[0066] M 1 (i,j) = v i ×v j

[0067]

[0068] v i ,v j ∈V∈R 1×L

[0069] V 1 ∈R 1×L

[0070] V 2 ∈R L×1

[0071]

[0072] Among them, M 1 is the semantic coding association response matrix of the acceptance-design housing target difference, M 2 is the semantic coding distance response matrix of the acceptance-design housing target difference, M 1 (i,j) is the eigenvalue at the (i,j) position in the semantic coding association response matrix of the acceptance-design housing target difference, M 2 (i,j) is the eigenvalue at the (i,j) position in the semantic coding distance response matrix of the acceptance-design housing target difference, v i and v j are respectively the i-th and j-th eigenvalues in the semantic coding feature vector of the acceptance-design housing target difference, V 1 is the dot-added vector between the semantic coding association response vector of the acceptance-design housing target difference and the semantic coding distance response vector of the acceptance-design housing target difference, V 2 is the semantic coding logic bias vector of the acceptance-design housing target difference, R is the set of real numbers, L is the length of the semantic coding feature vector of the acceptance-design housing target difference, ⊙ represents pointwise multiplication by position, represents matrix multiplication, represents pointwise addition by position, V is the semantic coding feature vector of the acceptance-design housing target difference, and V' is the optimized semantic coding feature vector of the acceptance-design housing target difference.

[0073] That is to say, considering that the enhanced coding feature map of the acceptance BIM model structure semantic space and the enhanced coding feature map of the design BIM model structure semantic space respectively represent the model space enhanced semantic coding features of the acceptance BIM model and the design BIM model of the housing target to be measured, when they are input into the BIM model difference measurement network, the measurement error introduced during the modeling of the acceptance BIM model of the housing target to be measured and the spatial distribution significance introduced during the feature attention enhancement of the model semantic coding features will cause large-area simple repetitions in the BIM model difference features between the enhanced coding feature map of the acceptance BIM model structure semantic space and the enhanced coding feature map of the design BIM model structure semantic space, affecting the logical dependence of the model semantic differences and reducing the accuracy of the classification results based on the model semantic difference distribution.

[0074] Thus, by using the self - correlation matrix and self - distance matrix of the acceptance - design house target difference semantic coding feature vector after unfolding the acceptance - design house target difference semantic coding feature map as the statistical - based reference - free distribution response framework of the acceptance - design house target difference semantic coding feature map, constructing a retrieval - enhanced reverse response for the acceptance - design house target difference semantic coding feature vector to avoid simple repetition of feature distributions, and ensuring the internal mapping logic of the acceptance - design house target difference semantic coding feature vector based on the retrieval - response context to avoid the surface combinatorial mapping of the acceptance - design house target difference semantic coding feature map, a logical dependency mapping from the acceptance - design house target difference semantic coding feature map to the classification target domain is achieved while maintaining the intuitive response relevance, thereby improving the accuracy of the auxiliary acceptance result obtained by inputting the acceptance - design house target difference semantic coding feature map into the intelligent auxiliary acceptance module based on the classifier.

[0075] In summary, a data - processing method for house acceptance based on an embodiment of the present application is elucidated. It uses deep - learning - based data - processing techniques to conduct a comparative analysis on the acceptance BIM model and the design BIM model of the house target to be measured, extracts the house structure features of the acceptance BIM model and the design BIM model respectively, then performs spatial attention feature enhancement processing on both, and on this basis, measures the feature differences between the two, so as to intelligently determine whether the house target to be measured meets the construction - party acceptance standards. In this way, automated data processing can assist or replace traditional manual acceptance, thereby reducing labor costs and improving the efficiency and accuracy of house acceptance.

[0076] Furthermore, a data - processing system for house acceptance is also provided.

[0077] Figure 6 The block diagram of the data - processing system for house acceptance according to an embodiment of the present application. As Figure 6As shown in the figure, a data processing system 100 for housing acceptance according to an embodiment of the present application includes: a BIM model acquisition module 110, configured to acquire an acceptance BIM model of a housing target to be measured, and extract a design BIM model of the housing target to be measured from a database; a housing structure semantic feature extraction module 120, configured to extract housing structure semantic features of the acceptance BIM model and the design BIM model to obtain an acceptance BIM model structure semantic coding feature map and a design BIM model structure semantic coding feature map; an attention enhancement module 130, configured to perform attention enhancement on the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map to obtain an acceptance BIM model structure semantic space enhanced coding feature map and a design BIM model structure semantic space enhanced coding feature map; an acceptance standard evaluation module 140, configured to determine whether the housing target to be measured meets the construction party's acceptance standards based on the feature differences between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map.

[0078] Here, those skilled in the art can understand that the specific operations of each module in the above data processing system for housing acceptance have been described in detail above with reference to Figures 1 to 5 the description of the data processing method for housing acceptance, and therefore, the repeated description thereof will be omitted.

[0079] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, advantages, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, advantages, effects, etc. are essential for each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and easy understanding, rather than limitations. The above details do not limit the present invention to necessarily adopt the above specific details for implementation.

[0080] In the above embodiments, the descriptions of each embodiment have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0081] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0082] In addition, it is obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. The multiple elements stated in the system claims can also be implemented by one element through software or hardware.

[0083] Finally, it should be noted that the above description has been given for purposes of illustration and description. In addition, the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data processing method for house acceptance, characterized in that: include: Obtaining the acceptance BIM model of the house subject to be tested, and extracting the design BIM model of the house subject to be tested from the database; Extracting the house structure semantic features of the acceptance BIM model and the design BIM model to obtain a structural semantic coding feature graph of the acceptance BIM model and a structural semantic coding feature graph of the design BIM model; Performing attention enhancement on the acceptance BIM model structure semantic coding feature graph and the design BIM model structure semantic coding feature graph to obtain an acceptance BIM model structure semantic space enhanced coding feature graph and a design BIM model structure semantic space enhanced coding feature graph; Based on the feature difference between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map, determining whether the house subject to be tested meets the construction party's acceptance criteria; Among them, performing attention enhancement on the acceptance BIM model structure semantic coding feature map includes: based on the pixel spatial position information of the acceptance BIM model structure semantic coding feature map, adaptively attenuating and modulating the pixel-level semantic association structure of the acceptance BIM model structure semantic coding feature map to obtain the acceptance BIM model structure feature space attenuation pixel granularity semantic association weight map; based on the acceptance BIM model structure feature space attenuation pixel granularity semantic association weight map, performing feature modulation on the acceptance BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map; Among them, based on the pixel spatial position information of the acceptance BIM model structure semantic coding feature map, the pixel-level semantic association structure of the acceptance BIM model structure semantic coding feature map is adaptively attenuated and modulated to obtain the acceptance BIM model structure feature space attenuation pixel granularity semantic association weight map, including: Performing feature fine-grained spatial decoupling along the channel dimension on the acceptance BIM model structure semantic encoding feature map to obtain a set of acceptance BIM model pixel granularity structure semantic feature vectors; Input any two of the acceptance BIM model pixel granularity structure semantic feature vectors in the set of the acceptance BIM model pixel granularity structure semantic feature vectors into a semantic association score measurement network to obtain a set of semantic association score vectors between pixel granularities of the acceptance BIM model; Based on the spatial span between any two semantic feature vectors of the pixel granularity structure of the acceptance BIM model in the set of semantic feature vectors of the pixel granularity structure of the acceptance BIM model, the set of semantic association score vectors between pixel granularities of the acceptance BIM model is subjected to semantic association adaptive attenuation modulation to obtain a set of semantic association score vectors between spatial attenuation pixel granularities of the acceptance BIM model; The set of semantic association score vectors between spatial attenuation pixel granularities of the acceptance BIM model is dimensionally reconstructed and weighted to obtain a semantic association weight map between spatial attenuation pixel granularities of the acceptance BIM model structural feature.

2. The data processing method for housing acceptance according to claim 1, characterized in that: Extracting the house structure semantic features of the acceptance BIM model and the design BIM model to obtain the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map, including: The acceptance BIM model and the design BIM model are input into a BIM model twin detection network including a first house model feature extractor and a second house model feature extractor to obtain a structural semantic encoding feature graph of the acceptance BIM model and a structural semantic encoding feature graph of the design BIM model.

3. The data processing method for housing acceptance according to claim 2, characterized in that: Inputting any two of the acceptance BIM model pixel granularity structure semantic feature vectors in the set of the acceptance BIM model pixel granularity structure semantic feature vectors into the semantic association score measurement network to obtain a set of semantic association score vectors between pixel granularities of the acceptance BIM model, including: Any two of the acceptance BIM model pixel granularity structure semantic feature vectors in the set of the acceptance BIM model pixel granularity structure semantic feature vectors are cascaded and fused, multiplied by a weight parameter matrix, and then dotted with a bias vector to obtain a set of semantic association score vectors between pixel granularities of the acceptance BIM model.

4. The data processing method for housing acceptance according to claim 3 is characterized in that: Based on the spatial span between any two semantic feature vectors of the pixel granularity structure of the acceptance BIM model in the set of semantic feature vectors of the pixel granularity structure of the acceptance BIM model, a set of semantic association score vectors between pixel granularities of the acceptance BIM model is subjected to semantic association adaptive attenuation modulation to obtain a set of semantic association score vectors between spatial attenuation pixel granularities of the acceptance BIM model, including: Performing spatial position annotation on each acceptance BIM model pixel granularity structure semantic feature vector in the set of acceptance BIM model pixel granularity structure semantic feature vectors to obtain a set of acceptance BIM model pixel spatial position data; Calculating the Euclidean distance between the spatial position data of any two pixel granularity structure semantic feature vectors of the acceptance BIM model in the set of pixel spatial position data of the acceptance BIM model as the spatial span value of the two pixel granularity structure semantic feature vectors of the acceptance BIM model to obtain a set of pixel granularity spatial span values ​​of the acceptance BIM model; Based on each acceptance BIM model pixel granularity spatial span value in the set of the acceptance BIM model pixel granularity spatial span values, each acceptance BIM model pixel granularity semantic association score vector in the set of the acceptance BIM model pixel granularity semantic association adaptive attenuation modulation is performed to obtain the set of the acceptance BIM model spatial attenuation pixel granularity semantic association score vectors.

5. The data processing method for housing acceptance according to claim 4, characterized in that: Based on each acceptance BIM model pixel granularity spatial span value in the set of acceptance BIM model pixel granularity spatial span values, each acceptance BIM model pixel granularity semantic association score vector in the set of acceptance BIM model pixel granularity semantic association score vectors is subjected to semantic association adaptive attenuation modulation to obtain the set of acceptance BIM model spatial attenuation pixel granularity semantic association score vectors, including: Dividing the acceptance BIM model pixel granularity space span value by a preset scaling factor to obtain the acceptance BIM model pixel granularity space span scaling value; Calculate an exponential function value with e as base and the inverse of the scaling value of the pixel granularity space span of the acceptance BIM model as exponent to obtain a semantic association space attenuation coefficient; The semantic association score vector between pixel granularities of the acceptance BIM model is calculated and divided by the semantic association spatial attenuation coefficient to obtain the semantic association score vector between spatial attenuation pixel granularities of the acceptance BIM model.

6. The data processing method for housing inspection according to claim 5, characterized in that: The set of semantic association score vectors between spatial attenuation pixel granularities of the acceptance BIM model is dimensionally reconstructed and weighted to obtain a semantic association weight map between spatial attenuation pixel granularities of the acceptance BIM model structural features, including: After arranging the set of semantic association score vectors between spatial attenuation pixel granularities of the acceptance BIM model into a semantic association score feature map between spatial attenuation pixel granularities of the acceptance BIM model, it is input into a feature dimension modulation layer based on a point convolution layer and a weight allocation network based on a Sigmoid function to obtain a semantic association weight map between spatial attenuation pixel granularities of the acceptance BIM model structural feature.

7. The data processing method for housing inspection according to claim 6, characterized in that: The method of performing feature modulation on the acceptance BIM model structure semantic coding feature map based on the semantic association weight map between attenuated pixel granularities of the acceptance BIM model structure feature space to obtain the acceptance BIM model structure semantic space enhanced coding feature map includes: The acceptance BIM model structure feature space attenuation pixel granularity semantic association weight map and the acceptance BIM model structure semantic coding feature map are calculated by multiplying the points by position to obtain the acceptance BIM model structure semantic space enhanced coding feature map.

8. The data processing method for housing inspection according to claim 7, characterized in that: Determining whether the house to be tested meets the acceptance criteria of the construction party based on the feature difference between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map includes: Inputting the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map into a BIM model difference measurement network to obtain a difference semantic coding feature map of the acceptance-design house mark; The difference semantic coding feature map of the acceptance-design house mark is input into the classifier-based intelligent auxiliary acceptance module to obtain an auxiliary acceptance result, and the auxiliary acceptance result is used to indicate whether it meets the acceptance standard of the construction party.

9. A data processing system for house acceptance, used to execute the data processing method for house acceptance according to claim 1, characterized in that: include: A BIM model acquisition module is used to acquire the acceptance BIM model of the house subject to be tested, and at the same time extract the design BIM model of the house subject to be tested from the database; A housing structure semantic feature extraction module is used to extract the housing structure semantic features of the acceptance BIM model and the design BIM model to obtain a structural semantic coding feature graph of the acceptance BIM model and a structural semantic coding feature graph of the design BIM model; An attention enhancement module is used to perform attention enhancement on the acceptance BIM model structure semantic coding feature map and the design BIM model structure semantic coding feature map to obtain the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map; The acceptance standard evaluation module is used to determine whether the house subject to be tested meets the acceptance standard of the construction party based on the feature difference between the acceptance BIM model structure semantic space enhanced coding feature map and the design BIM model structure semantic space enhanced coding feature map.

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