Fabricated steel structure assembly optimization method and system fused with BIM (Building Information Modeling) technology

Abstract

The invention discloses a BIM technology fused fabricated steel structure assembly optimization method and system, and relates to the technical field of data processing.The method comprises the steps that fabricated steel structure design information of a target assembly project is obtained, and a fabricated steel structure digital twinborn model is constructed; collecting a multi-angle image of a to-be-assembled node, extracting surface defects and connection quality features, and determining a node quality factor; and carrying out assemblability authentication based on the quality factor, if the authentication is passed, carrying out analogue simulation through an assembly type steel structure digital twinborn model, and optimizing an assembly scheme in combination with real-time environmental data. The technical problems that in traditional steel structure assembling in the prior art, node quality evaluation is not accurate, an assembling scheme lacks intelligent optimization and dynamic adjustment, and the assembling precision and the construction efficiency are affected are solved, the node quality is accurately evaluated by fusing the BIM technology, the assembling scheme is optimized in real time, and the construction efficiency is improved. The steel structure assembling precision and the construction efficiency are improved, and the construction risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an assembly optimization method and system for a prefabricated steel structure integrating BIM technology. Background Art

[0002] With the rapid development of the construction industry, prefabricated steel structures have become an important application in the field of modern construction due to their high efficiency, sustainability and flexibility. However, the traditional steel structure assembly process often faces problems such as low assembly accuracy, low operating efficiency and worker operation errors, which affect the overall construction quality and progress.

[0003] The traditional assembly process lacks intelligent quality monitoring and real-time adjustment mechanisms, and is often unable to flexibly respond to dynamic changes that occur during on-site construction, such as environmental factors, node quality fluctuations, etc., affecting the overall efficiency and quality of prefabricated steel structure projects. Summary of the invention

[0004] The present application provides a method and system for optimizing the assembly of prefabricated steel structures incorporating BIM technology, which is used to solve the technical problems in the prior art of inaccurate node quality assessment in traditional steel structure assembly, lack of intelligent optimization and dynamic adjustment of assembly plans, and affecting assembly accuracy and construction efficiency.

[0005] The first aspect of the present application provides an assembly optimization method for prefabricated steel structures that integrates BIM technology, the method comprising: obtaining prefabricated steel structure design information of a target assembly project, inputting the prefabricated steel structure design information into a BIM model for analysis and simulation, and constructing a digital twin model of the prefabricated steel structure; calling a high-definition industrial camera to perform multi-angle image acquisition on Q steel structure nodes to be assembled, and obtaining a set of Q node images, wherein Q is a positive integer greater than or equal to 1; from the two dimensions of surface defects and connection quality, in combination with the prefabricated steel structure digital twin model, feature extraction and analysis are performed on the Q node image sets to determine the Q node quality factors; based on the size of the Q node quality factors, the Q steel structure nodes to be assembled are certified for assemblability, and if the assemblability certification is passed, assembly simulation is performed based on the prefabricated steel structure digital twin model, and the assembly scheme is optimized in combination with the actual assembly environment information to obtain a target assembly scheme; and the prefabricated steel structure assembly of the target assembly project is executed based on the target assembly scheme.

[0006] In a second aspect of the present application, a prefabricated steel structure assembly optimization system integrating BIM technology is provided, the system comprising: a digital twin model construction module, the digital twin model construction module is used to obtain the prefabricated steel structure design information of the target assembly project, input the prefabricated steel structure design information into the BIM model for analysis and simulation, and construct a digital twin model of the prefabricated steel structure; a multi-angle image acquisition module, the multi-angle image acquisition module is used to call a high-definition industrial camera to perform multi-angle image acquisition on Q steel structure nodes to be assembled, and obtain a set of Q node images, where Q is a positive integer greater than or equal to 1; a feature extraction and analysis module, the feature extraction and analysis module is used to extract and analyze the image from the table Based on the two dimensions of surface defects and connection quality, the digital twin model of the prefabricated steel structure is combined to perform feature extraction and analysis on the Q node image sets to determine the quality factors of the Q nodes; an assembly scheme optimization module, the assembly scheme optimization module is used to perform assemblable certification on the Q steel structure nodes to be assembled based on the sizes of the Q node quality factors. If the assemblable certification is passed, an assembly simulation is performed based on the digital twin model of the prefabricated steel structure, and the assembly scheme is optimized in combination with the actual assembly environment information to obtain a target assembly scheme; an assembly scheme execution module, the assembly scheme execution module is used to execute the prefabricated steel structure assembly of the target assembly project based on the target assembly scheme.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The assembly optimization method and system of prefabricated steel structure integrated with BIM technology provided in this application relates to the field of data processing technology. The digital twin model of prefabricated steel structure is constructed by BIM technology, and node images are collected by high-definition industrial cameras, quality features are extracted and quality assessment is performed, and assembly certification is performed based on the assessment results. If passed, assembly simulation is performed, and the assembly plan is optimized in combination with real-time environmental data. It solves the technical problems of inaccurate node quality assessment in traditional steel structure assembly in the prior art, lack of intelligent optimization and dynamic adjustment of assembly plans, and affecting assembly accuracy and construction efficiency. It achieves the technical effect of integrating BIM technology, accurately assessing node quality and optimizing assembly plans in real time, improving the accuracy and construction efficiency of steel structure assembly, and reducing construction risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1A schematic diagram of a flow chart of a prefabricated steel structure assembly optimization method integrating BIM technology provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the structure of a prefabricated steel structure assembly optimization system integrating BIM technology provided in an embodiment of the present application.

[0012] Explanation of the accompanying drawings: digital twin model construction module 11, multi-angle image acquisition module 12, feature extraction and analysis module 13, assembly plan optimization module 14, assembly plan execution module 15. DETAILED DESCRIPTION

[0013] The present application provides a method and system for optimizing the assembly of prefabricated steel structures incorporating BIM technology, which is used to solve the technical problems in the prior art of inaccurate node quality assessment in traditional steel structure assembly, lack of intelligent optimization and dynamic adjustment of assembly plans, and affecting assembly accuracy and construction efficiency.

[0014] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0016] Embodiment 1, as Figure 1 As shown, the present application provides an assembly optimization method for prefabricated steel structures integrating BIM technology, the method comprising:

[0017] P10: Obtain the prefabricated steel structure design information of the target assembly project, input the prefabricated steel structure design information into the BIM model for analysis and simulation, and construct a digital twin model of the prefabricated steel structure.

[0018] Optionally, it is necessary to first obtain the prefabricated steel structure design information of the target assembly project. The prefabricated steel structure design information includes detailed parameters such as various components, dimensions, connection methods, material specifications, etc. of the steel structure. Among them, the design information is usually provided by a structural engineer and generated based on architectural design drawings or design software. By integrating the building information model (BIM) technology, these design information can be digitized and input into the BIM system for processing. BIM technology is a technology based on 3D modeling that can integrate all relevant data throughout the life cycle of a building to achieve information visualization and collaborative work.

[0019] In the BIM model, design information is not only static geometric data, but also includes multi-dimensional information such as physical properties, construction technology, time schedule, cost budget, etc. A dynamic, three-dimensional digital twin model of prefabricated steel structure is constructed through these data. Digital twin refers to the technology of accurately mapping physical objects or systems in the real world through virtual models. In this step, the digital twin model of prefabricated steel structure can reflect the various design elements of prefabricated steel structure and possible dynamic changes during the construction process in real time, such as the mechanical response of steel structure, structural force changes, material consumption, etc.

[0020] The prefabricated steel structure design information is input into the BIM model for analysis and simulation. The system will verify and optimize the structure through computer-aided design (CAD) and finite element analysis (FEA) and other technologies to build a digital twin model of the prefabricated steel structure. For example, finite element analysis can be used to analyze the stress distribution, deformation and possible structural failure risks of each node, so that potential structural problems can be identified before actual assembly. Through these simulations, the stability, strength and durability of the steel structure can be predicted to ensure the feasibility of the design solution.

[0021] In addition, the digital twin model of the prefabricated steel structure will also serve as the data platform for the entire assembly project, running through all stages of the assembly process. Through the digital twin model of the prefabricated steel structure, project managers can obtain information such as assembly progress, resource consumption, and personnel scheduling in real time, thereby performing efficient management and scheduling.

[0022] P20: Call a high-definition industrial camera to collect multi-angle images of Q steel structure nodes to be assembled, and obtain a set of Q node images, where Q is a positive integer greater than or equal to 1.

[0023] Specifically, a high-definition industrial camera is used to collect images from multiple angles of the steel structure nodes to be assembled, and high-resolution image data of each node is obtained to provide the original image basis for subsequent analysis and quality assessment. In order to ensure that the collected data is accurate and comprehensive, each steel structure node to be assembled should be photographed from multiple different angles. This not only avoids image distortion caused by angle problems, but also fully presents the details of the node surface and connection parts, ensuring the accuracy of subsequent analysis.

[0024] In this process, the high-definition industrial cameras used usually have the characteristics of high resolution and high frame rate, which can clearly capture the tiny defects on the node surface, such as cracks, deformation, corrosion and other surface quality problems. The selection of high-definition cameras is crucial, especially in industrial environments, which require them to maintain stable image acquisition quality under complex field conditions. In addition,

[0025] Call a high-definition industrial camera to collect multi-angle images of Q steel structure nodes to be assembled, and form a database containing Q node image sets, where Q represents the number of nodes to be assembled and Q is a positive integer greater than or equal to 1. Each image in the image set corresponds to the capture of the node at different angles and different fields of view. In order to avoid any possible occlusion or blind spots, the key parts of each node should be fully recorded during the acquisition process. These images will become the basic data source for subsequent feature extraction and quality analysis.

[0026] After image acquisition is completed, these image sets will be transmitted to the back-end system for further processing and analysis. These images not only provide intuitive feedback on the current status of the node, but also provide accurate data support for quality assessment and assembly certification in subsequent steps.

[0027] P30: From the two dimensions of surface defects and connection quality, the digital twin model of the prefabricated steel structure is combined to perform feature extraction and analysis on the Q node image sets to determine the quality factors of the Q nodes.

[0028] Furthermore, step P30 of the embodiment of the present application further includes:

[0029] P31: Use the surface defect analysis network layer to identify the Q node image sets respectively to obtain Q surface quality coefficients; P32: Based on the digital twin model of the prefabricated steel structure, identify the key connection points of the Q steel structure nodes to be assembled, obtain Q key connection point sets, and use the feature extraction algorithm to traverse the Q key connection point sets to extract connection point processing features, and obtain Q connection point processing feature clusters; P33: Traverse the Q connection point processing feature clusters to identify the connection quality and obtain Q node connection coefficients; P34: Based on the Q surface quality coefficients and the Q node connection coefficients, perform comprehensive quality identification on the Q steel structure nodes to be assembled, and determine Q node quality factors; P35: Determine whether the Q node quality factors are less than the preset node quality factors. If so, the assembly certification fails, and a warning message is generated, which is sent to the staff to repair the steel structure nodes; if not, the assembly certification passes.

[0030] It should be understood that starting from the two core dimensions of surface defects and connection quality, combined with the digital twin model and image recognition technology, modern technical means are used to extract features and calculate the quality factor of the node to comprehensively evaluate the quality of each steel structure node to be assembled.

[0031] First, the image of each node to be assembled is processed using a surface defect analysis network (such as a deep convolutional neural network) to identify possible defects on the node surface. Surface defects include cracks, rust, scratches, deformation, etc., which may affect the overall performance of the steel structure. Through automated learning and image analysis at the network layer, the system can efficiently extract defect information from a large amount of image data and then calculate the surface quality coefficient of each node. The surface quality coefficient is a quantitative indicator that reflects the severity of node surface defects. The higher the value, the better the node surface quality and the fewer defects.

[0032] Next, the connection quality of the steel structure nodes is directly related to the stability and safety of the entire structure. Therefore, the digital twin model of the assembled steel structure is used to identify the key connection points of each node to be assembled. The key connection points of each node are located based on the design data, the connection parts that need to be focused on are determined, and a set of Q key connection points of the nodes is obtained. Furthermore, the feature extraction algorithm is used to traverse the set of Q key connection points of the nodes to extract the processing features of the connection points, including important parameters such as the shape, size, and processing accuracy of the connection. These features constitute Q connection point processing feature clusters, which are used to describe the processing quality and process status of the connection points.

[0033] Furthermore, the Q extracted connection point processing feature clusters are used to further identify the connection quality. The core of this process is to evaluate the processing quality of each connection point and identify whether it meets the design requirements. The identification process may include testing the size, angle, welding quality, etc. of the connection parts to generate a connection coefficient for each node, which is a quantitative indicator reflecting the quality of the node connection. The higher the connection coefficient, the better the connection quality and the more reliable the node connection performance.

[0034] Through the above two analyses, the surface quality coefficient and node connection coefficient of each node are obtained, that is, Q surface quality coefficients and Q node connection coefficients. These two types of coefficients will serve as the basis for evaluating the overall quality of the node, and each node will be comprehensively identified for quality. For example, the surface quality coefficient and the connection coefficient are fused through a weighted algorithm to obtain Q comprehensive node quality factors. The node quality factor is a key indicator for evaluating whether a node is suitable for assembly. The larger the value, the higher the quality of the node and the more suitable it is for assembly.

[0035] Finally, the system determines whether the node quality factor of each node meets the requirements based on the preset standards. If the quality factor of a node is less than the preset threshold, it means that the node has serious quality problems and cannot meet the assembly requirements. At this time, the system will fail the assembly certification and generate an early warning message, which will be sent to the staff in a timely manner to remind them to repair or replace the node. On the contrary, if the quality factor of the node is greater than or equal to the preset standard, the system will determine that the node has passed the assembly certification and is ready to enter the next stage of actual assembly.

[0036] Through the above analysis and judgment, a comprehensive evaluation of each node from surface quality to connection quality is achieved, ensuring that the steel structure nodes during the assembly process can meet the design requirements, reducing the risk of quality problems during the assembly process, and improving the overall assembly accuracy and efficiency.

[0037] Furthermore, step P32 of the embodiment of the present application also includes:

[0038] P32-1: Traverse the Q steel structure nodes to be assembled and perform neighborhood matching in the digital twin model of the prefabricated steel structure to obtain the neighborhood of the Q steel structure nodes to be assembled and the assembly parameter set of the Q steel structure nodes to be assembled; P32-2: Extract Q design information of the Q steel structure nodes to be assembled, and perform frequent fault item mining in combination with the assembly parameter set of the Q steel structure nodes to be assembled, and determine the risk coefficients of the Q steel structure nodes to be assembled according to the mining analysis results; P32-3: Analyze the number of key connection points based on the size of the risk coefficients of the Q steel structure nodes to be assembled, and determine the number of Q key connection points; P32-4: Take the number of Q key connection points as the first constraint, identify the key connection points of nodes in combination with the neighborhood of the Q steel structure nodes to be assembled and the Q steel structure nodes to be assembled, and obtain the key connection point set of the Q nodes.

[0039] Specifically, the connection quality analysis of the steel structure nodes to be assembled can be further deepened to improve the accuracy of node quality assessment and ensure the smooth progress of the assembly process.

[0040] First, the Q steel structure nodes to be assembled are neighborhood matched in the digital twin model of the assembled steel structure. The goal of neighborhood matching is to identify the relationship between adjacent nodes, especially their assembly parameters such as connection method and assembly direction. Through spatial positioning and data association in the digital twin model, the system can accurately obtain the assembly information of adjacent nodes of each node during the assembly process. These assembly parameters include connection methods (such as bolt connection, welding, riveting, etc.), connection directions (such as horizontal, vertical, inclined, etc.), and possible connection parts (such as connecting plates, number of bolts, etc.). This information is crucial for subsequent assembly analysis, because the assembly method of adjacent nodes may affect the stability of the overall structure and the interaction between nodes.

[0041] Next, the design information of each of the Q steel structure nodes to be assembled is extracted, and the frequent fault items are mined in combination with the assembly parameter set obtained above. The failure frequency of different nodes during the design, processing and assembly process, such as connection failure, assembly error, etc., is analyzed by data mining technology. By analyzing historical data and failure cases of similar projects, high-frequency items that may fail at each node are mined. According to the mining and analysis results, a risk coefficient is calculated for each node to be assembled, and the risk coefficients of the Q steel structure nodes to be assembled are determined. The coefficient reflects the probability of problems occurring in the node during the assembly process. Nodes with high risk coefficients usually require more stringent quality control and monitoring.

[0042] Furthermore, based on the risk factor of each node, the number of critical connection points is analyzed. The number of connection points directly affects the complexity of the assembly process and the stability of the node. Especially in nodes with complex connection methods, too many connection points may lead to the accumulation of assembly errors. The system determines the number of critical connection points for each node by analyzing the design and connection characteristics of the node, and identifies the connection points that need special attention based on the size of the node's risk factor. Nodes with high risk factors may require more attention to avoid potential problems in assembly.

[0043] Finally, the key connection points of the nodes are identified by combining the aforementioned number of key connection points and node neighborhood information. This process uses the aforementioned number of key connection points as a constraint and combines the neighborhood matching results to accurately determine the set of key connection points for each node. During this identification process, the system examines the assembly relationship between the node and the adjacent nodes to ensure that the identified key connection points are the most critical part of the entire assembly process. This method can effectively avoid quality problems caused by omissions or errors in connection points during node assembly.

[0044] Through the above steps, not only can the key connection points of each node be accurately identified, but also potential assembly problems can be predicted and dealt with in advance during the design phase, significantly improving the efficiency and quality control level of the assembly process.

[0045] Furthermore, step P32-4 of the embodiment of the present application also includes:

[0046] P32-41: Using the neighborhood of the Q steel structure nodes to be assembled and the index of the Q steel structure nodes to be assembled, combined with the digital twin model of the assembled steel structure, neighborhood connection points are identified to obtain Q neighborhood connection point sets; P32-42: Traversing the Q steel structure nodes to be assembled, connection point extraction is performed to obtain Q node connection point sets; P32-43: Dividing the number of connection points in the Q neighborhood connection point sets by the number of connection points in the Q node connection point sets, respectively, to obtain the ratio of Q neighborhood connection points, and using the ratio of Q neighborhood connection points as the second constraint for identifying key connection points; P32-44: Based on the first constraint and the second constraint, key connection points of the Q node connection point sets are identified to obtain the key connection point sets of the Q nodes.

[0047] Optionally, further deepen the analysis of node connection points to ensure the accuracy of node connections and the quality of assembly. First, based on the digital twin model, the neighborhood connection points are identified by combining the neighborhood information of Q steel structure nodes to be assembled and the indexes of Q steel structure nodes to be assembled. The core of this step is to identify the connection points between each node to be assembled and its adjacent nodes. These connection points are the key connection parts between nodes and may include important connection locations such as bolt holes and welding points. Through the spatial data of the digital twin model, the system can accurately locate and identify these connection points and generate a set of Q neighborhood connection points. The identification of neighborhood connection points not only takes into account the spatial proximity relationship, but also needs to be combined with assembly drawings and design data to ensure that all possible connection points can be accurately identified during the actual assembly process.

[0048] Next, traverse the Q steel structure nodes to be assembled and extract the connection points for each node. This process is to automatically extract all relevant connection points through in-depth analysis of the node design information to form a set of Q node connection points. The connection points of each node may involve different types of connection methods (such as bolt connection, welding, riveting, etc.), and the quality and position of these connection points are crucial to the stability of the overall assembly of the node. Therefore, the extraction of connection points not only focuses on the number of points, but also ensures the precise positioning of each connection point.

[0049] After the node connection points are extracted, the number of connection points in the Q neighborhood connection point sets and the number of connection points in the Q node connection point sets are compared to calculate the neighborhood connection point ratio. Specifically, the number of connection points in each node neighborhood is divided by the number of connection points in the node connection point set to obtain the neighborhood connection point ratio. This ratio reflects the connection density of each node in the neighborhood environment. A higher ratio may indicate that the connection between the node and its neighboring nodes is relatively close, which may affect the relationship between nodes in the assembly process.

[0050] Finally, based on the two aforementioned constraints, namely the first constraint (number of critical connection points) and the second constraint (proportion of neighborhood connection points), the critical connection points of the Q node connection points are identified. Among them, the first constraint comes from the analysis of the number of critical connection points in the previous step. This constraint can ensure that the node connection points currently concerned have sufficient criticality, while the second constraint ensures that the relationship between the node and the neighborhood is reasonably considered through the proportion of neighborhood connection points. Through the comprehensive application of these two constraints, the system can accurately identify the set of critical connection points of each node, which determines the stability of the node connection and the success rate of assembly.

[0051] Furthermore, steps P32-44 of the embodiment of the present application also include:

[0052] P32-441: Based on the first constraint and the second constraint, the Q node connection point sets are randomly sampled to obtain Q initial node key connection point sets; P32-442: Three-dimensional simulation is performed according to the positions of the connection points in the Q initial node key connection point sets to obtain Q initial node key connection point models; P32-443: Based on the prefabricated steel structure digital twin model, the three-dimensional models of the Q steel structures to be assembled are retrieved to obtain Q steel structure models to be assembled; P32-444: Similarity matching is performed on the Q initial node key connection point models and the Q steel structure models to be assembled to obtain Q matching fitnesses; P32-445: When the Q matching fitnesses are greater than or equal to a preset matching fitness threshold, the Q initial node key connection point sets are used as the Q initial node key connection point sets.

[0053] In a possible embodiment of the present application, by combining model simulation, three-dimensional matching and fitness evaluation, it is ensured that the key connection points of each node are correctly identified to support efficient assembly and quality control.

[0054] First, based on the previously determined constraints, namely the first constraint (the number of critical connection points) and the second constraint (the proportion of neighborhood connection points), Q node connection point sets are randomly selected to obtain Q initial node critical connection point sets. The purpose of random selection is to provide preliminary critical connection points for further analysis. These connection point sets represent the most critical positions in the assembly process. In this way, the initially selected connection points will serve as the basis for subsequent simulation and verification.

[0055] Next, a three-dimensional simulation is performed based on the positions of the connection points in the set of Q initial node key connection points. The positions of these connection points can be mapped to the digital twin model using three-dimensional modeling technology to generate Q initial node key connection point models. Through three-dimensional simulation, the position and distribution of each connection point in three-dimensional space can be more intuitively displayed, which is conducive to subsequent connection point matching and fitness evaluation, and helps to check whether the connection between nodes meets the design requirements and whether there may be spatial conflicts or assembly problems.

[0056] After obtaining the initial node key connection point model, the system retrieves Q 3D models of the steel structures to be assembled based on the digital twin model of the assembled steel structure. These 3D models are generated based on the design and actual processing data, and contain all the geometric information and physical properties of the nodes. Through these 3D models, the overall assembly process of the steel structure is simulated to identify the assembly requirements and environmental factors of each node.

[0057] After having Q initial node key connection point models and Q steel structure models to be assembled, the two sets of models are matched by a similar matching algorithm. The goal of the matching is to evaluate the fitness between the initial key connection point model and the steel structure model to be assembled, and the fitness evaluation can take into account the accuracy of the geometric shape, size and connection parts of the model. Each model matching pair is assigned a matching fitness, which reflects the degree of matching between the initial connection point model and the actual model to be assembled. The higher the matching fitness, the more consistent the position and state of the connection point are with the actual assembly requirements.

[0058] Finally, according to the preset matching fitness threshold, it is determined whether the Q matching fitnesses meet the requirements. When all matching fitnesses are greater than or equal to the set threshold, it means that these initial node key connection points meet the assembly requirements and can be used as the final key connection points. If the matching fitness does not reach the threshold, it may be necessary to readjust the connection points or further optimize the model until the ideal matching effect is achieved.

[0059] When it is confirmed that the matching fitness meets the standard, the set of Q initial node key connection points will be used as the final set of key connection points, that is, the parts that need to be focused on during the assembly process, so as to achieve comprehensive identification and optimization of the steel structure node connection points and ensure the assembly quality.

[0060] Furthermore, step P34 of the embodiment of the present application also includes:

[0061] A quality identification network layer is pre-constructed, and the quality identification network layer is used to map and identify the Q surface quality coefficients and the Q node connection coefficients respectively to obtain the Q node quality factors.

[0062] Optionally, a pre-built quality recognition network layer can be introduced to map and identify the surface quality and connection quality of the nodes to be assembled using a deep learning model, thereby calculating the quality factor of each node.

[0063] Before implementing this step, you first need to pre-build a quality recognition network layer. The quality recognition network layer is a deep neural network structure that is specifically used to process and analyze quality data from different sensors or image acquisition systems. This network layer will perform supervised training based on the surface quality coefficient and node connection coefficient of the node, and learn how to extract key features that affect node quality from the original data. Among them, the surface quality coefficient comes from historical image analysis data (such as the detection results of defects such as cracks and corrosion), while the node connection coefficient reflects the processing quality and assembly conditions of the node connection parts.

[0064] After the training of the quality identification network layer is completed, the next step is to map and identify the Q surface quality coefficients and Q node connection coefficients. Specifically, the surface quality coefficient and the node connection coefficient are used as input, and feature extraction and pattern recognition are performed through different processing layers of the quality identification network layer. This process is similar to the convolution operation in image recognition. The quality identification network layer will automatically identify the key factors affecting the node quality based on the input quality data. For example, for surface quality, it is recognized that there are many cracks or deformation patterns on the surface of a node, and for node connection, it can identify the problems that may be caused by the accuracy of the connection part or the connection method.

[0065] After mapping and identification, the node quality factor of each node to be assembled is generated according to the output results. For example, the corresponding node quality factor can be generated by quantitative calculation based on the severity and importance of each quality defect. The node quality factor is a comprehensive evaluation index that reflects the overall performance of the node in terms of surface defects and connection quality. The higher the value of the node quality factor, the better the quality of the node and the lower the risk of problems during the assembly process; conversely, nodes with lower quality factors may have larger defects or assembly problems and require further inspection or repair. This process can use a regression model or classification model in deep learning, in which each input surface quality coefficient and node connection coefficient are regarded as feature vectors, which are trained and optimized through the network to automatically process large amounts of data and accurately evaluate node quality, thereby improving the efficiency and accuracy of node quality analysis.

[0066] P40: Based on the sizes of the Q node quality factors, the Q steel structure nodes to be assembled are certified for assembly. If the assembly certification is passed, assembly simulation is performed based on the digital twin model of the assembled steel structure, and the assembly plan is optimized based on the actual assembly environment information to obtain the target assembly plan.

[0067] Furthermore, step P40 of the embodiment of the present application also includes:

[0068] P41: Collect environmental data based on Internet of Things devices and sensors deployed at the assembly site to obtain real-time assembly environment information; P42: Perform assembly simulation based on the preset assembly plan, real-time assembly environment information and the digital twin model of the prefabricated steel structure, and use finite element analysis to analyze the assembly status of Q steel structure nodes to be assembled to obtain analysis results; P43: Optimize the preset assembly plan based on the analysis results to obtain the target assembly plan.

[0069] It should be understood that the decision on whether to pass the assembly certification is based on the preliminary evaluation results of the quality factors of Q nodes, and the assembly simulation is carried out using the digital twin model of the prefabricated steel structure to optimize the assembly plan and ensure the accuracy and efficiency of the node assembly.

[0070] First, by analyzing the size of the quality factors of Q nodes, each node to be assembled is certified for assembly. The quality factor usually combines multiple dimensions such as surface quality and connection quality. Therefore, the higher the quality factor of the node, the better its assembly condition, and vice versa, there may be quality problems. If the quality factor of the node meets the preset standard and the certification is passed, the system will enter the subsequent assembly simulation and optimization process. If the node fails to pass the certification, the system will issue an early warning, prompting the staff to trim the node to ensure quality.

[0071] When the node passes the assembly certification, the system will then collect environmental data in real time through IoT devices and sensors deployed at the assembly site. These devices include sensors such as temperature, humidity, vibration, and air pressure, which can provide accurate on-site environmental information and evaluate external conditions that may affect node assembly during the assembly process. For example, temperature changes may affect the thermal expansion and contraction of steel structures, and humidity may affect the bonding quality or corrosion of connection parts. These environmental data provide a practical environmental basis for subsequent simulations.

[0072] Next, the assembly simulation is performed using the digital twin model of the assembled steel structure and the environmental data collected in real time. In this step, finite element analysis technology plays a key role. Finite element analysis is to decompose the assembly structure into multiple small units and calculate the mechanical response of each unit to predict the performance of the entire structure. In this step, finite element analysis is mainly used to analyze the assembly status of Q nodes of the steel structure to be assembled, and to evaluate factors such as stress, deformation, and force transmission of the nodes during the actual assembly process. These analysis results can reveal possible assembly problems, such as whether the nodes will be deformed due to excessive assembly force, or whether the connection parts can withstand the expected load.

[0073] According to the results of finite element analysis, the preset assembly scheme is optimized. The optimization process includes adjusting the assembly sequence, improving the connection method, optimizing the selection of assembly tools and equipment, adjusting process parameters, etc. The optimized scheme is the target assembly scheme, which can maximize the assembly efficiency, ensure the accuracy of node assembly, and minimize the risks in the assembly process. It will provide detailed guidance for the construction site, making the assembly process more efficient, safe and economical.

[0074] Through the above steps, the entire assembly process can not only ensure that the quality of each node meets the standards, but also can be dynamically adjusted and optimized according to the actual environment, thereby ensuring the accuracy and efficiency of the assembly process.

[0075] P50: Execute the assembly of the prefabricated steel structure of the target assembly project based on the target assembly plan.

[0076] Specifically, based on the target assembly plan obtained through previous optimization, the assembly task is started to actually install and assemble the prefabricated steel structure, including the determination of the assembly sequence, the scheduling of assembly equipment, the application of process parameters, etc., to ensure the accurate assembly and efficient completion of the steel structure.

[0077] Through the target assembly scheme, the assembly order of nodes and components is first determined to reduce working hours and improve assembly efficiency in the best way. The assembly order is the result of comprehensive optimization based on multi-dimensional factors such as the quality factor, environmental data and assembly difficulty of each node. At the same time, select suitable assembly equipment, such as cranes, hoists, welding equipment, bolt wrenches, etc. The selection of these equipment will be optimized according to the actual conditions such as the type of assembled nodes, weight, assembly position, etc. And extract process parameters such as connection method, connection strength, bolt torque, welding temperature and other process parameters, which must meet the design requirements of the steel structure and be strictly controlled during on-site construction.

[0078] In the actual assembly process, with the help of the digital twin model of the prefabricated steel structure and the real-time monitoring system, all assembly tasks will be accurately tracked in a virtual and real environment. By using IoT devices and on-site sensors, data from the assembly site is collected in real time, and this data will be continuously fed back to the system to ensure that all work in the assembly process proceeds smoothly as planned.

[0079] In the actual assembly process, precise measuring tools and control systems can be used to ensure that each node and component meets the design accuracy requirements during the installation process. Through the automated control system, equipment parameters and assembly processes can be adjusted in real time to prevent structural problems caused by assembly deviations or errors. For example, laser measuring equipment or sensors are used to detect the accuracy of nodes to ensure that their position and direction are accurate. In the process of connecting steel structure nodes, real-time data collection and feedback are used to ensure that the quality of each connection point meets the predetermined standards.

[0080] The entire assembly process requires strict safety management and risk control measures. Track the safety status of the site in real time and issue early warnings for abnormal situations. Use dynamic monitoring equipment to monitor environmental changes (such as temperature, humidity, vibration, etc.) at the assembly site, adjust operating strategies in a timely manner, and avoid safety hazards caused by environmental changes.

[0081] In summary, the embodiments of the present application have at least the following technical effects:

[0082] This application obtains the design information of the prefabricated steel structure of the target assembly project, constructs a digital twin model of the prefabricated steel structure, collects multi-angle images of the nodes to be assembled, extracts surface defects and connection quality characteristics, determines the node quality factor, and performs assembly certification based on the quality factor. If the certification is passed, simulation is performed through the digital twin model of the prefabricated steel structure, and the assembly plan is optimized in combination with real-time environmental data.

[0083] The technical effect of integrating BIM technology, accurately evaluating node quality and optimizing assembly plans in real time, improving the accuracy and construction efficiency of steel structure assembly, and reducing construction risks has been achieved.

[0084] Embodiment 2 is based on the same inventive concept as the assembly optimization method of prefabricated steel structure integrating BIM technology in the above embodiment. Figure 2 As shown, the present application provides an assembly optimization system for prefabricated steel structures integrating BIM technology. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0085] The digital twin model construction module 11 is used to obtain the prefabricated steel structure design information of the target assembly project, input the prefabricated steel structure design information into the BIM model for analysis and simulation, and construct a digital twin model of the prefabricated steel structure.

[0086] The multi-angle image acquisition module 12 is used to call a high-definition industrial camera to perform multi-angle image acquisition on Q steel structure nodes to be assembled, and obtain Q node image sets, where Q is a positive integer greater than or equal to 1.

[0087] The feature extraction and analysis module 13 is used to perform feature extraction and analysis on the Q node image sets from two dimensions of surface defects and connection quality in combination with the assembled steel structure digital twin model to determine the Q node quality factors.

[0088] The assembly scheme optimization module 14 is used to perform assembly certification on the Q steel structure nodes to be assembled based on the sizes of the Q node quality factors. If the assembly certification is passed, assembly simulation is performed based on the digital twin model of the assembled steel structure, and the assembly scheme is optimized in combination with the actual assembly environment information to obtain the target assembly scheme.

[0089] The assembly scheme execution module 15 is used to execute the assembly of the prefabricated steel structure of the target assembly project based on the target assembly scheme.

[0090] Furthermore, the feature extraction and analysis module 13 is also used to perform the following steps:

[0091] The surface defect analysis network layer is used to identify the Q node image sets respectively to obtain Q surface quality coefficients; based on the digital twin model of the prefabricated steel structure, the key connection points of the Q steel structure nodes to be assembled are identified to obtain Q key connection point sets, and the feature extraction algorithm is used to traverse the Q key connection point sets to extract connection point processing features to obtain Q connection point processing feature clusters; the Q connection point processing feature clusters are traversed to identify the connection quality to obtain Q node connection coefficients; based on the Q surface quality coefficients and the Q node connection coefficients, the Q steel structure nodes to be assembled are comprehensively identified to determine Q node quality factors; it is determined whether the Q node quality factors are less than the preset node quality factors. If so, the assembly certification fails, and a warning message is generated, which is sent to the staff for steel structure node repair; if not, the assembly certification passes.

[0092] Furthermore, the feature extraction and analysis module 13 is also used to perform the following steps:

[0093] The Q steel structure nodes to be assembled are traversed to perform neighborhood matching in the digital twin model of the prefabricated steel structure, and the neighborhoods of the Q steel structure nodes to be assembled and the assembly parameter sets of the Q steel structure nodes to be assembled are obtained; Q design information of the Q steel structure nodes to be assembled are extracted, and frequent fault items are mined in combination with the assembly parameter sets of the Q steel structure nodes to be assembled, and the risk coefficients of the Q steel structure nodes to be assembled are determined according to the mining analysis results; the number of key connection points is analyzed based on the size of the risk coefficients of the Q steel structure nodes to be assembled, and the number of Q key connection points is determined; with the number of the Q key connection points as the first constraint, the key connection points of the nodes are identified in combination with the neighborhoods of the Q steel structure nodes to be assembled and the Q steel structure nodes to be assembled, and the key connection point set of the Q nodes is obtained.

[0094] Furthermore, the feature extraction and analysis module 13 is also used to perform the following steps:

[0095] Using the neighborhoods of the Q steel structure nodes to be assembled and the indexes of the Q steel structure nodes to be assembled, combined with the digital twin model of the assembled steel structure, neighborhood connection points are identified to obtain Q neighborhood connection point sets; the Q steel structure nodes to be assembled are traversed to extract connection points to obtain Q node connection point sets; the number of connection points in the Q neighborhood connection point sets is divided by the number of connection points in the Q node connection point sets to obtain Q neighborhood connection point ratios, and the Q neighborhood connection point ratios are used as the second constraint for identifying key connection points; key connection points of the Q node connection point sets are identified based on the first constraint and the second constraint to obtain the Q node key connection point sets.

[0096] Furthermore, the feature extraction and analysis module 13 is also used to perform the following steps:

[0097] Based on the first constraint and the second constraint, the Q node connection point sets are randomly sampled to obtain Q initial node key connection point sets; three-dimensional simulation is performed according to the positions of the connection points in the Q initial node key connection point sets to obtain Q initial node key connection point models; based on the prefabricated steel structure digital twin model, the three-dimensional models of the Q steel structures to be assembled are retrieved to obtain Q steel structure models to be assembled; similarity matching is performed on the Q initial node key connection point models and the Q steel structure models to be assembled to obtain Q matching fitnesses; when the Q matching fitnesses are greater than or equal to a preset matching fitness threshold, the Q initial node key connection point sets are used as the Q initial node key connection point sets.

[0098] Furthermore, the feature extraction and analysis module 13 is also used to perform the following steps:

[0099] A quality identification network layer is pre-constructed, and the quality identification network layer is used to map and identify the Q surface quality coefficients and the Q node connection coefficients respectively to obtain the Q node quality factors.

[0100] Furthermore, the assembly scheme optimization module 14 is also used to perform the following steps:

[0101] Environmental data is collected based on Internet of Things devices and sensors deployed at the assembly site to obtain real-time assembly environment information; assembly simulation is performed according to a preset assembly plan, real-time assembly environment information and the digital twin model of the assembled steel structure, and the assembly status of Q steel structure nodes to be assembled is analyzed using finite element analysis to obtain analysis results; the preset assembly plan is optimized according to the analysis results to obtain the target assembly plan.

[0102] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0104] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The assembly optimization method of prefabricated steel structure integrating BIM technology is characterized by: The method comprises: Obtaining the prefabricated steel structure design information of the target assembly project, inputting the prefabricated steel structure design information into the BIM model for analysis and simulation, and constructing a digital twin model of the prefabricated steel structure; Call a high-definition industrial camera to collect multi-angle images of Q steel structure nodes to be assembled, and obtain a set of Q node images, where Q is a positive integer greater than or equal to 1; From the two dimensions of surface defects and connection quality, the digital twin model of the assembled steel structure is combined to perform feature extraction and analysis on the Q node image sets to determine the quality factors of the Q nodes; Based on the sizes of the Q node quality factors, the Q steel structure nodes to be assembled are certified for assembly. If the assembly certification is passed, assembly simulation is performed based on the assembled steel structure digital twin model, and the assembly scheme is optimized in combination with the actual assembly environment information to obtain a target assembly scheme; The prefabricated steel structure assembly of the target assembly project is performed based on the target assembly plan.

2. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 1, characterized in that: From the two dimensions of surface defects and connection quality, the digital twin model of the assembled steel structure is combined to perform feature extraction and analysis on the Q node image sets to determine the Q node quality factors, including: Using the surface defect analysis network layer to respectively identify the Q node image sets, and obtain Q surface quality coefficients; Based on the digital twin model of the assembled steel structure, the Q nodes of the steel structure to be assembled are identified with key node connection points to obtain a set of Q key node connection points, and a feature extraction algorithm is used to traverse the set of Q key node connection points to extract connection point processing features to obtain Q connection point processing feature clusters; Traversing the Q connection point processing feature clusters to identify connection quality and obtain Q node connection coefficients; Performing comprehensive quality identification on the Q nodes of the steel structure to be assembled based on the Q surface quality coefficients and the Q node connection coefficients, and determining Q node quality factors; Determine whether the Q node quality factors are less than the preset node quality factors. If so, the assembly certification fails, and a warning message is generated, which is sent to the staff to perform steel structure node repairs; If not, the assembly certification can be passed.

3. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 2, characterized in that: Based on the digital twin model of the assembled steel structure, the key connection points of the Q steel structure nodes to be assembled are identified to obtain a set of Q key connection points of the nodes, including: Traversing the Q steel structure nodes to be assembled and performing neighborhood matching in the assembled steel structure digital twin model to obtain neighborhoods of the Q steel structure nodes to be assembled and assembly parameter sets of the Q steel structure nodes to be assembled; Extracting Q design information of the Q steel structure nodes to be assembled, performing frequent fault item mining in combination with the assembly parameter sets of the Q steel structure nodes to be assembled, and determining risk coefficients of the Q steel structure nodes to be assembled according to the mining analysis results; Performing a key connection point quantity analysis based on the risk coefficients of the Q steel structure nodes to be assembled, and determining the number of Q key connection points; Taking the number of the Q key connection points as the first constraint, the Q node neighborhoods of the steel structures to be assembled and the Q nodes of the steel structures to be assembled are combined to perform node key connection point identification to obtain the set of the Q node key connection points.

4. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 3 is characterized in that: include: Using the Q steel structure node neighborhoods to be assembled and the Q steel structure node indexes to be assembled, in combination with the assembled steel structure digital twin model, neighborhood connection point identification is performed to obtain Q neighborhood connection point sets; Traversing the Q nodes of the steel structure to be assembled to extract connection points, and obtaining a set of Q node connection points; respectively dividing the number of connection points in the Q neighborhood connection point sets by the number of connection points in the Q node connection point sets to obtain Q neighborhood connection point ratios, and using the Q neighborhood connection point ratios as the second constraint for key connection point identification; Based on the first constraint and the second constraint, key connection points of the Q node connection point sets are identified to obtain the Q node key connection point sets.

5. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 4 is characterized in that: include: Randomly extract the Q node connection point sets based on the first constraint and the second constraint to obtain Q initial node key connection point sets; Perform three-dimensional simulation according to the positions of the connection points in the set of Q initial node key connection points to obtain models of the Q initial node key connection points; Retrieving the three-dimensional models of the Q steel structures to be assembled based on the digital twin model of the assembled steel structure to obtain Q models of the steel structures to be assembled; Performing similarity matching on the Q initial node key connection point models and the Q to-be-assembled steel structure models to obtain Q matching fitnesses; When the Q matching fitnesses are greater than or equal to a preset matching fitness threshold, the Q initial node key connection point sets are used as the Q initial node key connection point sets.

6. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 1, characterized in that: include: Collect environmental data based on IoT devices and sensors deployed at the assembly site to obtain real-time assembly environment information; Perform assembly simulation according to the preset assembly scheme, real-time assembly environment information and the digital twin model of the assembled steel structure, and use finite element analysis to analyze the assembly status of Q steel structure nodes to be assembled to obtain analysis results; The preset assembly scheme is optimized according to the analysis result to obtain the target assembly scheme.

7. The assembly optimization method for prefabricated steel structures integrating BIM technology as claimed in claim 2, characterized in that: A quality identification network layer is pre-constructed, and the quality identification network layer is used to map and identify the Q surface quality coefficients and the Q node connection coefficients respectively to obtain the Q node quality factors.

8. The prefabricated steel structure assembly optimization system integrating BIM technology is characterized by: The system comprises: A digital twin model construction module, wherein the digital twin model construction module is used to obtain the prefabricated steel structure design information of the target assembly project, input the prefabricated steel structure design information into the BIM model for analysis and simulation, and construct a digital twin model of the prefabricated steel structure; A multi-angle image acquisition module, which is used to call a high-definition industrial camera to perform multi-angle image acquisition on Q nodes of the steel structure to be assembled, and obtain a set of Q node images, where Q is a positive integer greater than or equal to 1; A feature extraction and analysis module, wherein the feature extraction and analysis module is used to perform feature extraction and analysis on the Q node image sets from two dimensions of surface defects and connection quality in combination with the assembled steel structure digital twin model to determine the quality factors of the Q nodes; An assembly scheme optimization module, wherein the assembly scheme optimization module is used to perform assembly certification on the Q steel structure nodes to be assembled based on the sizes of the Q node quality factors. If the assembly certification is passed, an assembly simulation is performed based on the assembled steel structure digital twin model, and the assembly scheme is optimized in combination with the actual assembly environment information to obtain a target assembly scheme; An assembly scheme execution module is used to execute the assembly of the prefabricated steel structure of the target assembly project based on the target assembly scheme.

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