Intelligent control method for installation configuration of large-span steel structure

Abstract

The invention discloses an intelligent control method for the installation configuration of a large-span steel structure, and relates to the related field of constructional engineering, and the method comprises the steps: extracting the design configuration parameters of each rod piece; establishing a computational analysis model; performing construction simulation to obtain a predicted configuration; a design configuration is extracted, and a configuration difference value between the design configuration and the predicted configuration is calculated; mounting configuration parameters are adjusted, construction simulation iteration is conducted till the configuration difference value is smaller than or equal to the configuration deviation threshold, and a construction scheme is obtained; site construction is conducted, actual configuration parameters are obtained in real time, and a corrected construction scheme is obtained according to the actual configuration parameters; and carrying out installation configuration control. The technical problem that dynamic factors in the construction process are ignored in existing large-span steel structure installation configuration control, and consequently large deviation exists between the prediction result and the actual situation is solved, various dynamic factors in the construction process are integrated, the actual configuration of the structure is accurately predicted, real-time adjustment is conducted according to the prediction result, and the construction efficiency is improved. And the installation precision of the structure is ensured.

Description

Technical Field

[0001] The present application relates to fields related to construction engineering, and in particular to a method for intelligent control of the installation position of large-span steel structures. Background Art

[0002] Due to their unique spatial form, excellent mechanical properties, and elegant appearance, large-span steel structures are widely used in large public buildings such as stadiums, exhibition halls, and airport terminals. During the existing large-span steel structure installation process, construction workers perform static calculations and analysis based on the structural parameters in the design drawings to determine the theoretical configuration of the structure. This method ignores the influence of various dynamic factors during construction, such as temperature fluctuations and construction loads, resulting in significant deviations between the predicted results and the actual situation.

[0003] In the current related technologies, the installation position control of large-span steel structures has the technical problem of ignoring the dynamic factors in the construction process, resulting in a large deviation between the predicted results and the actual situation. Summary of the Invention

[0004] The present application provides an intelligent control method for the installation position of a large-span steel structure. The method extracts the design position parameters from the design drawings, establishes a calculation and analysis model of the overall structural state, performs construction simulation to obtain the predicted position after completion, calculates the position difference between the design position and the predicted position, and adjusts the installation position parameters of the rod according to the difference. The construction simulation is iterated until the position difference meets the requirements. During the construction process, the actual position parameters of each rod are monitored in real time, and real-time construction simulation iterative adjustments are made to obtain a revised construction plan. Based on the revised construction plan, the installation position control of the large-span steel structure and other technical means are used to achieve the technical effect of comprehensively considering various dynamic factors in the construction process, accurately predicting the actual position of the structure, making real-time adjustments according to the prediction results, and ensuring the installation accuracy of the structure.

[0005] The present application provides an intelligent control method for the installation position of a large-span steel structure, comprising: extracting design position parameters of each steel structure member in a design drawing; establishing a computational analysis model of the overall structural state of the large-span steel structure based on the design position parameters; performing a construction simulation based on the computational analysis model to obtain a predicted position after completion; extracting a design position from the design drawing, and calculating a position difference between the design position and the predicted position; adjusting the installation position parameters of the member based on the position difference, performing construction simulation iterations until the position difference is less than or equal to a position deviation threshold, and obtaining a construction plan; performing on-site construction according to the construction plan, obtaining the actual position parameters of each member during the construction process through real-time monitoring, performing real-time construction simulation based on the actual position parameters, predicting the real-time position after completion, performing real-time construction simulation iterative adjustments of the installation position parameters based on the real-time position difference between the real-time position and the design position, and obtaining a revised construction plan; and performing installation position control of the large-span steel structure based on the revised construction plan.

[0006] In a possible implementation, the following processing is performed: the design configuration parameters include the coordinates, rotation angles, and structural dimensions of the rods.

[0007] In a possible implementation, a computational analysis model of the overall structural state of the large-span steel structure is established based on the design configuration parameters, and the following processing is performed: based on the structural dimensions, the deadweight of the rod is calculated and obtained; based on a preset installation process, the additional loads applied during the installation process are obtained, and the additional loads include construction equipment loads, temporary support loads and environmental loads; based on the design configuration parameters, the deadweight and the additional loads, a computational analysis model of the overall structural state of the large-span steel structure is established.

[0008] In a possible implementation, a construction simulation is performed based on the computational analysis model to obtain a predicted configuration after completion, and the following processing is performed: a construction process analysis is performed based on the computational analysis model to predict the configuration after construction completion; and a usage status analysis is performed based on the configuration after construction completion to obtain a predicted configuration of the structure after completion under a preset usage load and a preset irresistible load.

[0009] In a possible implementation, based on the computational analysis model, a construction process analysis is performed, the construction completion position is predicted, and the following processing is performed: based on the computational analysis model, a construction sequence position impact analysis, a construction operation position impact analysis, and a construction additional load position impact analysis are performed to predict construction position changes; based on the construction position changes, a stress distribution position impact analysis is performed to predict the construction completion position.

[0010] In a possible implementation, the installation position parameter of the rod is adjusted according to the position difference value, construction simulation iteration is performed until the position difference value is less than or equal to the position deviation threshold, a construction scheme is obtained, and the following processing is performed: the adjustment step and the adjustment direction are determined according to the position difference value; the design position parameter is adjusted according to the adjustment step and the adjustment direction, to obtain an updated position parameter; construction simulation is performed according to the updated position parameter, and an updated position difference value is obtained; if the updated position difference value is greater than the position deviation threshold, iterative adjustment of the installation position parameter is performed until the updated position difference value is less than or equal to the position deviation threshold, to obtain the construction scheme.

[0011] In a possible implementation, the adjustment step and the adjustment direction are determined according to the position difference value, and the following processing is performed: historical position difference values and corresponding optimal adjustment steps and optimal adjustment directions are collected, a training data set is constructed according to the corresponding relationship of the historical data; the training data set is used to train a neural network, to obtain an adjustment strategy model; and the position difference value is input into the adjustment strategy model, to output the adjustment step and the adjustment direction.

[0012] In a possible implementation, the training data set is used to train a neural network, to obtain an adjustment strategy model, and the following processing is performed: historical position difference values are extracted from the training data set, and a historical position difference value vector is generated; the historical position difference value vector is converted into a historical position difference value matrix; and the training data set updated according to the historical position difference value matrix is input into a pre-trained convolutional neural network for transfer learning training, to obtain the adjustment strategy model.

[0013] The large-span steel structure installation position intelligent control method provided in the present application first extracts design position parameters of each rod of the steel structure in design drawings, then establishes a calculation and analysis model of the overall structure state of the large-span steel structure according to the design position parameters, then performs construction simulation based on the calculation and analysis model, obtains a predicted position after completion, then extracts a design position from the design drawings, calculates a position difference value between the design position and the predicted position, adjusts the installation position parameter of the rod according to the position difference value, performs construction simulation iteration until the position difference value is less than or equal to a position deviation threshold, obtains a construction scheme, then performs on-site construction according to the construction scheme, real-time monitoring is performed to obtain actual position parameters of each rod in the construction process, real-time construction simulation is performed according to the actual position parameters, a real-time position after completion is predicted, real-time construction simulation iteration adjustment of the installation position parameter is performed according to a real-time position difference value between the real-time position and the design position, to obtain a corrected construction scheme, and finally the installation position control of the large-span steel structure is performed based on the corrected construction scheme, so that various dynamic factors in the construction process are comprehensively considered, the actual position of the structure is accurately predicted, real-time adjustment is performed according to the prediction result, and the technical effect of ensuring the installation precision of the structure is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the methods according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A flow chart of the intelligent control method for the installation configuration of a large-span steel structure provided in an embodiment of the present application.

[0016] Figure 2 A schematic diagram of the process of obtaining a construction plan in the large-span steel structure installation position intelligent control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0018] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] In the following description, reference is made to “some embodiments” which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict. 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 clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0020] The embodiment of the present application provides an intelligent control method for the installation configuration of a large-span steel structure, such as Figure 1 As shown, the method includes:

[0021] Step S100, extract the design configuration parameters of each member of the steel structure in the design drawing.

[0022] Specifically, through computer-aided design (CAD) software or building information modeling (BIM) technology, the geometric dimensions, position coordinates, cross-section characteristics and other design configuration parameters of each member of the steel structure are extracted one by one from the design drawing by using the measurement tool or attribute query function of the software, and are arranged into an electronic table or database form for subsequent analysis and calculation.

[0023] In one possible implementation, step S100 further includes step S110, and the design configuration parameters include the coordinates, rotation angles and structural dimensions of the members. Specifically, the position of a member in space is determined by its coordinates, which are defined in three-dimensional space and include values in X, Y and Z directions. In CAD or BIM software, these coordinate values can be obtained by selecting the member and viewing its attributes. The rotation angle describes the rotation angle of the member relative to its initial position or reference direction, and is expressed in degrees (°) or radians (rad). The structural dimensions include the length, width, height (or diameter for circular cross-section) and other geometric parameters of the member. These dimensions determine the physical properties and load-carrying capacity of the member. In CAD or BIM software, these dimension values can be obtained by using the measurement tool or attribute query function. This implementation ensures the accuracy and reliability of the subsequent calculation and analysis model by extracting the design configuration parameters such as the coordinates, rotation angles and structural dimensions of the members. By accurately extracting these parameters, the as-built configuration of the long-span steel structure can be more effectively predicted, and real-time adjustments can be made during the construction process to ensure that the final structure meets the design requirements.

[0024] Step S200, according to the design configuration parameters, establish a calculation and analysis model of the overall structural state of the long-span steel structure.

[0025] Specifically, the extracted member parameters are input into finite element analysis (FEA) software such as ANSYS, SAP2000, etc., the physical and mechanical properties of the material are defined, the boundary conditions and load conditions are set, and the overall structural state calculation and analysis model of the long-span steel structure is established.

[0026] In one possible implementation, according to the design configuration parameters, a calculation and analysis model of the overall structural state of the long-span steel structure is established, and step S200 further includes step S210 of calculating the self-weight of the member according to the structural dimensions. Specifically, according to the extracted structural dimensions (such as length, cross-sectional area, wall thickness, etc.) of the member, combined with the density of the steel used, the mass of each member is calculated.

[0027] At step S220, according to the preset installation process, the additional load applied in the installation process is obtained, and the additional load includes a construction equipment load, a temporary support load, and an environmental load. Specifically, according to the preset installation process, the additional load (in the installation process, in addition to the self weight of the rod, other external forces acting on the structure) involved in each construction stage is listed in detail, and the load is derived from the weight of the construction equipment (such as a crane, a sling, etc.), the weight of the temporary support structure (such as a scaffold, a support frame, etc.), and the influence of environmental factors (such as wind load, snow load, temperature stress, etc.). The size of the load is estimated by referring to relevant design specifications or standards. For example, for wind load, local meteorological data can be consulted, and wind load calculation formula is used to estimate according to wind speed and wind direction.

[0028] At step S230, a calculation and analysis model of the overall structure state of the long-span steel structure is established according to the design shape parameter, the self weight, and the additional load. Specifically, the obtained design shape parameter, the self weight of the rod, and the additional load are taken as input data, and a calculation and analysis model of the overall structure state of the long-span steel structure is established by using a structure analysis software (such as ANSYS, SAP2000, etc.). The software can automatically calculate key parameters such as internal force distribution, deformation, and stability of the structure according to the information. This implementation provides accurate structure state information and analysis tools by establishing a calculation and analysis model, and can perform virtual testing and optimization design on the structure before construction to reduce risks and uncertainties in the construction process.

[0029] At step S300, construction simulation is performed based on the calculation and analysis model to obtain a predicted shape after completion.

[0030] Specifically, the construction process of the long-span steel structure is simulated by using the construction simulation function of the finite element analysis software, including hoisting, sliding, lifting, jacking, and other construction technologies, the rod installation, load application, and deformation in each construction stage are simulated, and finally the predicted shape after completion is obtained, that is, the predicted shape is the expected geometric shape and position of the long-span steel structure after completion calculated by the construction simulation software.

[0031] In one possible implementation, based on the calculation and analysis model, construction simulation is performed to obtain a predicted shape after completion, and step S300 further includes step S310 of performing construction process analysis based on the calculation and analysis model to predict a construction completion shape. Specifically, the construction process is divided into multiple construction stages, and each stage corresponds to a certain construction step or change of construction conditions. The finite element analysis or structural dynamics analysis is performed step by step for each stage to simulate the deformation and stress of the steel structure in each stage. The predicted shape after completion (construction completion shape) is obtained by accumulating the deformations of the stages.

[0032] At step S320, based on the construction completion configuration, a use state analysis is performed to obtain a predicted configuration of the structure under a preset use load and a preset non-resistive load after completion. Specifically, various load conditions that the structure may encounter during the use stage are determined, including regular use loads (regular loads borne by the structure during use, such as floor live loads, equipment loads, etc.) and non-resistive loads (extreme or unpredictable loads that the structure may encounter, such as wind loads, snow loads, and earthquake loads under extreme weather conditions, etc.). These loads are applied to the construction completion configuration to perform finite element analysis or structural dynamics analysis, to obtain the deformation and stress conditions of the structure under these loads, and to obtain the predicted configuration. This implementation predicts the configuration after completion of the construction process through construction process analysis, and performs use state analysis based on the configuration, to evaluate the configuration change of the structure during long-term use, which helps to optimize the construction scheme and ensure the quality and safety of the structure.

[0033] In a possible implementation, based on the calculation analysis model, construction process analysis is performed to predict the construction completion configuration, and step S310 further includes step S311, based on the calculation analysis model, construction sequence configuration influence analysis, construction operation configuration influence analysis, and construction additional load configuration influence analysis are performed to predict the construction configuration change. Specifically, the construction process is divided into multiple stages, each stage corresponding to a certain construction step. Through finite element analysis, the influence of each construction step on the structure configuration is simulated, and these influences are accumulated to predict the configuration change of the entire construction process. For example, which members are installed first, which members are installed later, and how the installation sequence of each member affects the overall structure configuration. The influence of specific operations in the construction process, such as welding, bolt connection, etc., on the structure configuration is analyzed. These operations may cause local deformation or stress concentration of the structure, and the influences are evaluated through finite element analysis. The influence of additional loads applied during the construction process, such as the weight of construction equipment, the arrangement of temporary supports, and environmental factors (such as wind load, temperature change) on the structure configuration is analyzed, and these additional loads may cause additional deformation of the structure, which is predicted through finite element analysis.

[0034] At step S312, stress distribution configuration influence analysis is performed according to the construction configuration change to predict the construction completion configuration. Specifically, based on the construction configuration change predicted in step S311, stress distribution analysis is performed, including evaluating the stress state of each member and node, and how these stress states change as the construction process progresses. Through finite element analysis, the stress distribution and configuration state after construction completion are predicted. This implementation gradually and deeply analyzes the configuration change during the construction process through finite element analysis, improving the accuracy of configuration prediction.

[0035] Step S400: extracting a design configuration from a design drawing, and calculating a configuration difference between the design configuration and the predicted configuration.

[0036] Specifically, the design configuration is extracted from the design drawings through CAD or BIM software, and the comparison tool in the software or script writing is used to compare the coordinate values ​​or shape differences of the design configuration and the predicted configuration at each key point or overall shape to calculate the configuration difference, that is, the configuration difference refers to the difference between the design configuration and the predicted configuration.

[0037] Step S500 , adjusting the installation configuration parameters of the rod according to the configuration difference, performing construction simulation iterations until the configuration difference is less than or equal to a configuration deviation threshold, and obtaining a construction plan.

[0038] Specifically, according to the configuration difference, the installation configuration parameters of the rods in the model, such as the initial position and inclination angle, are adjusted. Then, the finite element analysis software or real-time construction simulation software is reused to simulate the construction process of the large-span steel structure to obtain a new predicted configuration. The new predicted configuration is then compared with the designed configuration to obtain a new configuration difference. This process is repeated until the configuration difference is less than or equal to the configuration deviation threshold (the allowable configuration difference range, which requires adjustment if it exceeds this range, such as ±5mm), and a construction plan that meets the requirements is obtained.

[0039] like Figure 2 As shown, in a possible implementation, the installation configuration parameters of the rod are adjusted according to the configuration difference, and the construction simulation iteration is performed until the configuration difference is less than or equal to the configuration deviation threshold, and a construction plan is obtained. Step S500 further includes step S510, which determines the adjustment step size and adjustment direction according to the configuration difference. Specifically, the gradient (i.e., derivative or partial derivative) of the configuration difference relative to the design configuration parameters is calculated, which indicates the direction and rate of error change with the design parameters. The adjustment step size of each step is determined based on the size and direction of the gradient and the preset learning rate (or step size factor). The adjustment direction is determined by the sign of the gradient, and the direction in which the gradient is negative indicates the direction in which the error is reduced.

[0040] Step S520: Adjust the design configuration parameters according to the adjustment step size and the adjustment direction to obtain updated configuration parameters. Specifically, multiply the current design configuration parameters by the adjustment step size and the adjustment direction to obtain updated design configuration parameters.

[0041] Step S530: Perform a construction simulation based on the updated configuration parameters and obtain an updated configuration difference. Specifically, a finite element analysis or other structural analysis method is used to perform a simulation based on the updated design configuration parameters. After the simulation is complete, the configuration difference between the new predicted configuration and the design configuration is calculated.

[0042] Step S540, if the updated shape difference value is greater than the shape deviation threshold, iterative adjustment of the installation shape parameters is performed until the updated shape difference value is less than or equal to the shape deviation threshold, and a construction scheme is obtained. Specifically, the updated shape difference value is compared with the preset shape deviation threshold. If the updated shape difference value is less than or equal to the shape deviation threshold, it is considered that a satisfactory result has been converged, the iteration is stopped, the updated shape parameter corresponding to the updated shape difference value is obtained, and the updated shape parameter is taken as the construction scheme for subsequent processing and installation of each member. If the updated shape difference value is greater than the shape deviation threshold, the process returns to step S510, and the design shape parameter is iteratively adjusted until the updated shape difference value is less than or equal to the shape deviation threshold, and the corresponding construction scheme is obtained. This implementation ensures that the installation shape of the long-span steel structure is as close as possible to the design shape, thereby meeting the engineering requirements, through iterative adjustment.

[0043] In a possible implementation, the adjustment step and the adjustment direction are determined according to the shape difference value. Step S510 further includes step S511 of collecting historical shape difference values and corresponding optimal adjustment steps and optimal adjustment directions, and constructing a training data set according to the corresponding relationship of the historical data. Specifically, data is collected from past projects, including the shape difference value at each iteration, the adjustment step and the adjustment direction taken, and whether these adjustments result in a decrease in the shape difference value. The adjustment step and the adjustment direction that result in a significant decrease in the shape difference value are selected as the optimal solution to form the training data set.

[0044] Step S512, the training data set is used to train the neural network to obtain an adjustment strategy model. Specifically, a neural network architecture is selected, such as a multi-layer perceptron (MLP), a convolutional neural network (CNN), or a recurrent neural network (RNN), the training data set is input into the neural network, and the weights and biases of the neural network are adjusted through a backpropagation algorithm until the performance of the model on the validation set reaches a satisfactory level. During the training process, the performance of the model on the validation set is periodically evaluated to prevent overfitting.

[0045] Step S513, the shape difference value is input into the adjustment strategy model, and the adjustment step and the adjustment direction are output. Specifically, the shape difference value in the current iteration is input as input data into the trained adjustment strategy model, and the model outputs the predicted adjustment step and the adjustment direction according to the input shape difference value. According to the output of the model, the design shape parameter is adjusted, and the iteration process is continued. This implementation uses a neural network to predict the adjustment step and the adjustment direction. The neural network can automatically learn the rules in the historical data and guide future adjustments according to these rules, thereby improving the accuracy and efficiency of determining the adjustment step and the adjustment direction.

[0046] In a possible implementation, the neural network is trained by using the training data set to obtain an adjustment strategy model, and step S512 further includes the following step S5121: extracting historical position difference values from the training data set and generating a historical position difference value vector. Specifically, all data records containing position difference values are filtered from the historical data set. The extracted position difference values are converted into vector form. If the position difference value is a multi-dimensional data (for example, containing difference values in X, Y and Z directions), each position difference value can be converted into a three-dimensional vector. If the position difference value is one-dimensional data, it is directly converted into a one-dimensional vector. In order to improve the training efficiency and performance of the model, the data is standardized to scale the data to a suitable range (such as between 0 and 1) and eliminate the dimensional differences between different features.

[0047] Step S5122: converting the historical position difference value vector into a historical position difference value matrix. Specifically, a plurality of historical position difference value vectors are spliced into a large matrix in a certain order, each row representing a data record (that is, a historical position difference value vector), and each column representing a feature (that is, a certain dimension of the position difference value). If the number of position difference values in the data set is insufficient to form a large enough matrix, the size of the matrix is expanded by using data enhancement techniques such as data replication, data interpolation and the like.

[0048] Step S5123: inputting the training data set updated according to the historical position difference value matrix into a pre-trained convolutional neural network for transfer learning training to obtain an adjustment strategy model. Specifically, a pre-trained convolutional neural network that performs well on similar tasks is selected as a feature extractor. For example, a VGG, ResNet or the like model pre-trained on an image classification task can be selected. A new fully connected layer is added to the pre-trained model as an output layer for predicting the adjustment step and the adjustment direction. Then, the updated training data set is used for fine-tuning the model. During the fine-tuning process, some parameters of the pre-trained model can be frozen to reduce the amount of calculation and speed up the training process. This implementation extracts the position difference values from the historical data set and converts them into vector and matrix forms, which can be conveniently used for neural network training. By using the transfer learning technology and using the pre-trained convolutional neural network as a feature extractor, the learning process of the adjustment strategy model is accelerated and the prediction accuracy is improved.

[0049] Step S600: performing site construction according to the construction scheme, monitoring and obtaining actual position parameters of each member in the construction process in real time, performing real-time construction simulation according to the actual position parameters, predicting a real-time position after completion, performing real-time construction simulation iterative adjustment of the installation position parameters according to real-time position difference values between the real-time position and the design position, and obtaining a revised construction scheme.

[0050] Specifically, actual construction is carried out according to the construction plan. During the construction process, sensors, laser rangefinders, total stations, and other monitoring equipment are used to monitor the actual configuration parameters of each member in real time and input them into the real-time construction simulation software. The software then adjusts the calculation and analysis model based on the monitoring data in real time, predicting the real-time configuration after completion and comparing it with the designed configuration. If the difference between the real-time configuration and the designed configuration exceeds a configuration deviation threshold, the member's installation configuration parameters are adjusted, and real-time construction simulation iterations are performed until the real-time configuration difference is less than or equal to the configuration deviation threshold. This results in a revised construction plan, which contains the installation configuration parameters corresponding to the real-time configuration difference that is less than or equal to the configuration deviation threshold.

[0051] Step S700: performing installation configuration control of the large-span steel structure based on the revised construction plan.

[0052] Specifically, on-site construction is organized according to the revised construction plan. During the construction process, monitoring equipment is used to track and feedback the actual construction situation in real time to ensure that the installation position of each rod meets the design requirements. If deviations are found, adjustments are made in real time to ensure that the position of the overall structure meets the design requirements. The embodiment of the present application extracts the design position parameters in the design drawings, establishes a calculation and analysis model of the overall structural state, performs construction simulation to obtain the predicted position after completion, calculates the position difference between the design position and the predicted position, and adjusts the installation position parameters of the rod according to the difference, performs construction simulation iterations until the position difference meets the requirements, monitors the actual position parameters of each rod in real time during the construction process, and performs real-time construction simulation iterative adjustments to obtain a revised construction plan, and performs installation position control of large-span steel structures based on the revised construction plan. This achieves the technical effect of comprehensively considering various dynamic factors in the construction process, accurately predicting the actual position of the structure, and performing real-time adjustments based on the prediction results to ensure the installation accuracy of the structure.

[0053] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. 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.

Claims

1. An intelligent control method for the installation configuration of a large-span steel structure, characterized in that: The method comprises: Extract the design configuration parameters of each steel structure member in the design drawings; Establishing a calculation and analysis model for the overall structural state of the long-span steel structure based on the design configuration parameters; Conducting construction simulation based on the computational analysis model to obtain predicted configuration after completion; Extracting a design configuration from a design drawing, and calculating a configuration difference between the design configuration and the predicted configuration; Adjusting the installation configuration parameters of the rod according to the configuration difference, performing construction simulation iterations until the configuration difference is less than or equal to a configuration deviation threshold, and obtaining a construction plan; Perform on-site construction according to the construction plan, monitor and obtain actual configuration parameters of each rod during the construction process in real time, perform real-time construction simulation based on the actual configuration parameters, predict the real-time configuration after completion, and perform real-time construction simulation iterative adjustment of the installation configuration parameters based on the real-time configuration difference between the real-time configuration and the designed configuration to obtain a revised construction plan; The installation configuration control of the large-span steel structure is performed based on the revised construction plan.

2. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 1, characterized in that: The design configuration parameters include the coordinates, rotation angles and structural dimensions of the rods.

3. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 2, characterized in that: Based on the design configuration parameters, a calculation and analysis model for the overall structural state of the long-span steel structure is established, including: Calculate and obtain the deadweight of the rod according to the structural dimensions; According to the preset installation process, the additional loads applied during the installation process are obtained, wherein the additional loads include construction equipment loads, temporary support loads, and environmental loads; A calculation and analysis model for the overall structural state of the long-span steel structure is established based on the design configuration parameters, the deadweight and the additional load.

4. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 1, characterized in that: Based on the calculation and analysis model, a construction simulation is performed to obtain the predicted configuration after completion, including: Based on the calculation and analysis model, the construction process is analyzed to predict the completed construction position; Based on the completed construction configuration, a usage status analysis is performed to obtain a predicted configuration of the completed structure under a preset usage load and a preset irresistible load.

5. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 4, characterized in that: Based on the calculation and analysis model, the construction process is analyzed and the construction completion position is predicted, including: Based on the calculation and analysis model, the construction sequence configuration impact analysis, construction operation configuration impact analysis, construction additional load configuration impact analysis are carried out to predict the construction configuration change; Based on the construction configuration changes, stress distribution configuration impact analysis is performed to predict the completed construction configuration.

6. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 1, characterized in that: Adjusting the installation configuration parameters of the rod according to the configuration difference, performing construction simulation iterations until the configuration difference is less than or equal to a configuration deviation threshold, and obtaining a construction plan, including: Determining an adjustment step size and an adjustment direction according to the configuration difference; Adjust the designed configuration parameters according to the adjustment step size and the adjustment direction to obtain updated configuration parameters; Performing construction simulation according to the updated configuration parameters and obtaining updated configuration differences; If the updated configuration difference is greater than the configuration deviation threshold, iterative adjustment of the installation configuration parameters is performed until the updated configuration difference is less than or equal to the configuration deviation threshold, and a construction plan is obtained.

7. The method for intelligent control of the installation configuration of a large-span steel structure according to claim 6, characterized in that: Determining an adjustment step size and an adjustment direction according to the configuration difference includes: Collect historical configuration differences and the corresponding optimal adjustment step and optimal adjustment direction, and construct a training data set based on the corresponding relationship of historical data; Using the training data set to train a neural network to obtain an adjustment strategy model; The configuration difference is input into the adjustment strategy model, and the adjustment step size and adjustment direction are output.

8. The method for intelligent control of installation configuration of a large-span steel structure according to claim 7, characterized in that: The neural network is trained using the training data set to obtain an adjustment strategy model, including: Extracting historical configuration differences from the training data set and generating a historical configuration difference vector; Converting the historical configuration difference vector into a historical configuration difference matrix; The training data set updated according to the historical configuration difference matrix is ​​input into the pre-trained convolutional neural network for transfer learning training to obtain an adjustment strategy model.

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