Nuclear power engineering intelligent construction site management system and method based on BIM technology

Abstract

The invention relates to the field of nuclear power engineering construction, in particular to a nuclear power engineering intelligent construction site management system and method based on a BIM technology. The system comprises a data acquisition and integration module, an analysis and decision module, a safety monitoring module and a progress management module. The method comprises the steps of collecting data of a construction site in real time, establishing a BIM model, and integrating the data into the BIM model; analyzing the collected data through a deep learning model; and in combination with the analyzed data and the BIM model, real-time monitoring of the safety of the construction site is realized. The 4D BIM technology is utilized, time information is integrated into a BIM model, and visual management of the project progress is achieved. The overall precision of the management system is improved, construction site resources are utilized more effectively, waste is reduced, and the construction efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of nuclear power engineering construction, and in particular to a nuclear power engineering intelligent construction site management system and method based on BIM technology. Background Art

[0002] A nuclear power project site refers to the site used to build a nuclear power plant, including areas such as nuclear reactor construction, auxiliary facilities construction, and power generation system construction. On a nuclear power project site, there are usually various engineering facilities and equipment, as well as a large number of engineers, technicians, and managers who work together to complete the construction tasks of the nuclear power project. Therefore, nuclear power project site management is very important.

[0003] With the increasing popularity and advancement of BIM technology, the process of construction project management moving towards refined and digital management is gradually accelerating. For construction projects, the completeness of the planning stage plays a decisive role in the construction stage.

[0004] However, due to the large number of professional jobs, complex working environment and long construction period at nuclear power construction sites, traditional management systems are unable to focus on more critical information, have low recognition and classification accuracy, and are not able to provide decision support at an ideal speed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a nuclear power project intelligent construction site management system and method based on BIM technology, improve the overall accuracy of the management system, more effectively utilize construction site resources, reduce waste, and improve construction efficiency.

[0006] The present invention provides a nuclear power project intelligent construction site management system based on BIM technology, comprising:

[0007] Data collection and integration module, used to collect data from the construction site in real time, build a BIM model, and integrate this data into the BIM model;

[0008] Analysis and decision-making module, which is used to analyze the collected data through deep learning models to support decision-making during the construction process;

[0009] Safety monitoring module, which combines the analyzed data with the BIM model to achieve real-time monitoring of construction site safety;

[0010] The progress management module is used to integrate time information into the BIM model using 4D BIM technology to achieve visual management of project progress.

[0011] In a specific embodiment of the present invention, the data collection and integration module collects data from the construction site in real time through sensors and equipment installed on the construction site.

[0012] The present invention provides a nuclear power project intelligent construction site management method based on BIM technology, comprising the following steps:

[0013] Step S1: collect data from the construction site in real time, establish a BIM model, and integrate these data into the BIM model;

[0014] Step S2: Analyze the collected data through the deep learning model;

[0015] Step S3: combining the analyzed data with the BIM model to achieve real-time monitoring of construction site safety;

[0016] Step S4: Using 4D BIM technology, time information is integrated into the BIM model to achieve visual management of the project progress.

[0017] In a specific embodiment of the present invention, step S2 specifically includes:

[0018] S21. Data preprocessing: extract relevant data from the BIM model and standardize the data;

[0019] S22. Deep learning model construction: Use multi-layer perceptron to build a classification model;

[0020] S23, model training: use a large amount of labeled BIM data to train the model;

[0021] S24. Model validation: Test the performance of the model on an independent validation set to ensure its accuracy and generalization ability;

[0022] S25. Data analysis: Identify and analyze the data in the BIM model through the trained model.

[0023] In a specific embodiment of the present invention, step S22 specifically includes:

[0024] S221: The input data is convolved through the convolution kernel to generate spatial features;

[0025] S222: Apply activation function to the feature to increase nonlinearity;

[0026] S223: Applying a pooling operation to reduce the spatial size of the feature;

[0027] S224: Calculate the query, key and value, calculate the attention weight and apply it to the weighted feature;

[0028] S225: Flatten the weighted features into a one-dimensional vector to input into a multi-layer perceptron;

[0029] S226: Process the one-dimensional vector through a series of fully connected layers and activation functions to obtain the output.

[0030] In a specific implementation of the present invention, in step S221, the specific calculation formula is as follows:

[0031] F=I*K+b

[0032] Among them, * represents the convolution operation, b is the bias term, I represents the input data, K represents the convolution kernel, and F represents the spatial feature.

[0033] In a specific implementation of the present invention, in step S222, the specific calculation formula is as follows:

[0034] F′=σ(F)

[0035] Among them, σ represents the activation function, F represents the spatial feature, and F′ represents the spatial feature after being processed by the activation function.

[0036] In a specific implementation of the present invention, in step S223, the specific calculation formula is as follows:

[0037] P = MaxPooling(F′)

[0038] Among them, P represents the result of the pooling operation.

[0039] In a specific embodiment of the present invention, in step S224, the specific calculation formula is as follows:

[0040] Q=W Q F′, K = W K F′, V = W V ·F′

[0041]

[0042] Among them, Q represents the query vector, K represents the key vector, V represents the value vector, A represents the computational power weight matrix, F″ represents the weighted feature, and d k represents the dimension of the key vector, W Q , W K , W V represents the weight matrix; T represents the transpose operation;

[0043] The specific calculation formula of the step S225 is as follows:

[0044] x=Flatten(F″)

[0045] Here, x represents a one-dimensional vector.

[0046] In a specific embodiment of the present invention, the specific calculation formula of step S226 is as follows:

[0047] y=σ(FC(σ(FC(σ(...σ(FC(x))...)))))

[0048] Among them, FC represents the fully connected layer and y represents the output.

[0049] Compared with the prior art, the BIM-based nuclear power engineering intelligent construction site management system and method of the present invention has the following beneficial effects:

[0050] (1) The present invention can provide support for the entire life cycle of construction projects and facilities in conjunction with the BIM model through the complex feature extraction capabilities of the established deep learning model, more accurately identify and classify various elements on the construction site, and improve the overall accuracy of the management system. The attention mechanism can help the model focus on more critical information, thereby providing decision support more quickly. Through precise data processing and analysis, construction site resources can be used more effectively, waste can be reduced, and construction efficiency can be improved.

[0051] (2) The present invention extracts local features of input data through convolutional layers, highlights important information through the attention mechanism, and finally makes the final prediction through MLP. It can effectively process complex data such as images and voices, and achieve high-precision recognition and classification in various tasks, improve the model's monitoring ability and accuracy of construction site environment and activities, and improve the model's ability to allocate resources, manage progress, and plan tasks on the construction site. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a specific module block diagram of the present invention;

[0053] Figure 2 A flowchart of the analysis steps of the analysis and decision module of the present invention;

[0054] Figure 3 A block diagram of the specific steps for constructing the deep learning model of the present invention. DETAILED DESCRIPTION

[0055] In order to further understand the present invention, the embodiments of the present invention are described below in conjunction with examples, but it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the present invention.

[0056] As attached Figure 1 To Attachment Figure 3 As shown:

[0057] The present invention provides a nuclear power engineering intelligent construction site management system based on BIM technology, including a data acquisition and integration module, an analysis and decision module, a safety monitoring module and a progress management module;

[0058] Data collection and integration module: collect data from the construction site in real time, establish a BIM model, and integrate these data into the BIM model, such as image data, voice data, environmental data, etc.;

[0059] Analysis and decision-making module: Analyze the collected data through deep learning models to support decision-making during the construction process;

[0060] Safety monitoring module: Combines the analyzed data with the BIM model to achieve real-time monitoring of construction site safety;

[0061] Progress management module: Using 4D BI M technology, time information is integrated into the BI M model to achieve visual management of project progress.

[0062] The data collection and integration module collects data from the construction site in real time through sensors and equipment installed on the construction site.

[0063] An embodiment of the present invention further provides a method for managing a nuclear power project intelligent construction site based on BIM technology, comprising the following steps:

[0064] Step S1: collect data from the construction site in real time, establish a BIM model, and integrate these data into the BIM model;

[0065] Step S2: Analyze the collected data through the deep learning model;

[0066] Step S3: combining the analyzed data with the BIM model to achieve real-time monitoring of construction site safety;

[0067] Step S4: Using 4D BIM technology, time information is integrated into the BIM model to achieve visual management of the project progress.

[0068] The step S2 is specifically as follows:

[0069] S21. Data preprocessing: extract relevant data from the BIM model and standardize the data;

[0070] S22. Deep learning model construction: Use multi-layer perceptron to build a classification model;

[0071] The specific steps of building a deep learning model are as follows:

[0072] S221, convolution layer: The input data is convolved through the convolution kernel to generate spatial features, which is responsible for extracting local features from the input data;

[0073] The specific calculation formula is as follows:

[0074] F=I*K+b

[0075] Among them, * represents the convolution operation, b is the bias term, I represents the input data, K represents the convolution kernel, and F represents the spatial feature.

[0076] S222, Activation function: Apply activation function to features, increase nonlinearity, introduce nonlinearity, and enable the model to learn more complex feature representations;

[0077] The specific calculation formula is as follows:

[0078] F′=σ(F)

[0079] Among them, σ represents the activation function, F represents the spatial feature, and F′ represents the spatial feature after being processed by the activation function.

[0080] S223, Pooling layer: Pooling operation is applied to reduce the spatial size of features, which reduces the amount of computation and helps the model obtain more abstract feature representations.

[0081] The specific calculation formula is as follows:

[0082] P = MaxPooling(F′)

[0083] Among them, P represents the result of the pooling operation.

[0084] S224, Attention Mechanism: Calculates queries, keys, and values, calculates attention weights and applies them to weighted features, allowing the model to focus on the most important features, improving the model's sensitivity to key information and recognition accuracy;

[0085] The specific calculation formula is as follows:

[0086] Q=W Q F′, K = W K F′, V = W V ·F′

[0087]

[0088] Among them, Q represents the query vector, K represents the key vector, V represents the value vector, A represents the computational power weight matrix, F″ represents the weighted feature, and d k represents the dimension of the key vector, W Q , W K , W V represents the weight matrix; T represents the transpose operation;

[0089] S225, flattening: flattening the weighted features into a one-dimensional vector for input into a multi-layer perceptron (MLP);

[0090] The specific calculation formula is as follows:

[0091] x=Flatten(F″)

[0092] Here, x represents a one-dimensional vector, which is used as input to the MLP.

[0093] S226, Multilayer Perceptron: Processes a one-dimensional vector through a series of fully connected layers and activation functions to obtain output, further abstracts features through a series of fully connected layers, and performs classification or other prediction tasks.

[0094] The specific calculation formula is as follows:

[0095] y=σ(FC(σ(FC(σ(...σ(FC(x))...)))))

[0096] Among them, FC represents the fully connected layer and y represents the output.

[0097] S23, model training: use a large amount of labeled BIM data to train the model;

[0098] S24. Model validation: Test the performance of the model on an independent validation set to ensure its accuracy and generalization ability;

[0099] S25. Data analysis: Identify and analyze the data in the BIM model through the trained model.

[0100] From the above, we can see that through the complex feature extraction capabilities of the established deep learning model, it can be coordinated with the BIM model to provide support throughout the life cycle of construction projects and facilities, more accurately identify and classify various elements on the construction site, and improve the overall accuracy of the management system. The attention mechanism can help the model focus on more critical information, thereby providing decision support more quickly. Through precise data processing and analysis, site resources can be used more effectively, waste can be reduced, and construction efficiency can be improved.

[0101] The convolutional layer is responsible for extracting local features of the input data, and the attention mechanism highlights important information by weighting these features. Finally, MLP acts as a classifier and makes final predictions based on the weighted features. Such a model structure can effectively process complex data such as images and voices, improve the model's monitoring capabilities and accuracy for construction site environments and activities, and timely discover potential safety risks. It can also achieve high-precision recognition and classification in a variety of tasks, and improve the model's capabilities for resource allocation, progress management, and task planning on construction sites.

[0102] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0103] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A nuclear power project intelligent construction site management system based on BIM technology, characterized in that: include: Data collection and integration module, used to collect data from the construction site in real time, build a BIM model, and integrate this data into the BIM model; Analysis and decision-making module, which is used to analyze the collected data through deep learning models to support decision-making during the construction process; Safety monitoring module, which combines the analyzed data with the BIM model to achieve real-time monitoring of construction site safety; The progress management module is used to integrate time information into the BIM model using 4D BIM technology to achieve visual management of project progress.

2. The BIM-based intelligent construction site management system for nuclear power projects according to claim 1 is characterized in that: The data collection and integration module collects data from the construction site in real time through sensors and equipment installed on the construction site.

3. A method for managing a nuclear power project intelligent site based on BIM technology, characterized in that: The following steps are involved: Step S1: collect data from the construction site in real time, establish a BIM model, and integrate these data into the BIM model; Step S2: Analyze the collected data through the deep learning model; Step S3: combining the analyzed data with the BIM model to achieve real-time monitoring of construction site safety; Step S4: Using 4D BIM technology, time information is integrated into the BIM model to achieve visual management of the project progress.

4. The method for managing a nuclear power project intelligent site based on BIM technology according to claim 1 is characterized in that: The step S2 specifically includes: S21. Data preprocessing: extract relevant data from the BIM model and standardize the data; S22. Deep learning model construction: Use multi-layer perceptron to build a classification model; S23, model training: use a large amount of labeled BIM data to train the model; S24. Model validation: Test the performance of the model on an independent validation set to ensure its accuracy and generalization ability; S25. Data analysis: Identify and analyze the data in the BIM model through the trained model.

5. The BIM-based intelligent construction site management system for nuclear power projects according to claim 4 is characterized in that: The step S22 specifically includes: S221: The input data is convolved through the convolution kernel to generate spatial features; S222: Apply activation function to the feature to increase nonlinearity; S223: Applying a pooling operation to reduce the spatial size of the feature; S224: Calculate query, key and value, calculate attention weight and apply to weighted features; S225: Flatten the weighted features into a one-dimensional vector to input into a multi-layer perceptron; S226: Process the one-dimensional vector through a series of fully connected layers and activation functions to obtain the output.

6. The BIM-based intelligent construction site management system for nuclear power projects according to claim 5 is characterized in that: In step S221, the specific calculation formula is as follows: F=I*K+b Among them, * represents the convolution operation, b is the bias term, I represents the input data, K represents the convolution kernel, and F represents the spatial feature.

7. The method for managing a nuclear power project intelligent site based on BIM technology according to claim 5 is characterized in that: In step S222, the specific calculation formula is as follows: F′=σ(F) Among them, σ represents the activation function, F represents the spatial feature, and F′ represents the spatial feature after being processed by the activation function.

8. The method for managing a nuclear power project intelligent site based on BIM technology according to claim 5 is characterized in that: In step S223, the specific calculation formula is as follows: P = MaxPooling(F′) Among them, P represents the result of the pooling operation.

9. The method for managing a nuclear power project intelligent site based on BIM technology according to claim 5 is characterized in that: In step S224, the specific calculation formula is as follows: Q=W Q ·F′,K=W K ·F′,V=W V ·F′ Among them, Q represents the query vector, K represents the key vector, V represents the value vector, A represents the computational power weight matrix, F″ represents the weighted feature, and d k represents the dimension of the key vector, W Q , W K , W V represents the weight matrix; T represents the transpose operation; The specific calculation formula of the step S225 is as follows: x=Flatten(F″) Here, x represents a one-dimensional vector.

10. The method for managing a nuclear power project intelligent site based on BIM technology according to claim 5, characterized in that: The specific calculation formula of step S226 is as follows: y=σ(FC(σ(FC(σ(...σ(FC(x))...))))) Among them, FC represents the fully connected layer and y represents the output.

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