Intelligent installation control method and system for ultra-long large-span steel structure grid roof

Abstract

The invention provides an intelligent installation control method and system for an ultra-long and large-span steel structure grid roof, and relates to the technical field of industrial control. Through analysis of an installation process of the ultra-long and large-span steel structure grid roof, a first type of installation nodes and a second type of installation nodes are determined, and basic installation information of steel members is collected; and respectively performing simulation modeling on the first type of installation nodes and the second type of installation nodes, constructing a first dynamic model and a second dynamic model, and performing installation control on the ongoing target installation nodes by using the first dynamic model or the second dynamic model. The technical problem that in the prior art, due to the fact that an ultra-long large-span steel structure grid roof is not high in industrial control intelligence, the overall construction safety and precision are insufficient is solved. The technical effects of intelligent construction decision making and industrial control and improvement of the safety and precision of overall construction are achieved.

Description

Technical Field

[0001] The present invention relates to the field of industrial control technology, and in particular to an intelligent installation control method and system for an ultra-long span steel structure grid roof. Background Art

[0002] With the increasing number of ultra-long-span public buildings, steel grid roofs, as a key load-bearing and containment system, have been widely used in important buildings such as large stadiums, convention and exhibition centers, and transportation hubs. These structures are typically assembled at high altitudes from a large number of steel components, placing extremely high demands on the accuracy of component installation, the stability of node connections, and the safety of the construction process. Currently, the installation of ultra-long-span steel grid roofs relies primarily on manual control based on experience and static construction plans, without the ability to flexibly adjust to the actual conditions at the construction site. This not only leads to low construction efficiency but also increases the risk of construction control. Furthermore, traditional industrial control methods struggle to effectively address uncertainties such as weather changes and structural deformation. Even slight deformations of the structure during load-bearing can lead to a significant decrease in installation accuracy.

[0003] The existing technology has the technical problem that the industrial control of ultra-long span steel structure grid roof is not highly intelligent, resulting in insufficient overall construction safety and precision. Summary of the Invention

[0004] The present application provides an intelligent installation control method and system for an ultra-long span steel structure grid roof, which is used to solve the technical problem in the prior art that the industrial control of the ultra-long span steel structure grid roof is not highly intelligent, resulting in insufficient overall construction safety and precision.

[0005] In view of the above problems, the present application provides an intelligent installation control method and system for an ultra-long span steel structure grid roof.

[0006] The first aspect of the present application provides an intelligent installation control method for an ultra-long long span steel structure grid roof, the method comprising: reading and analyzing the installation process of the ultra-long long span steel structure grid roof, determining a first type of installation node and a second type of installation node, wherein the first type of installation node is a node corresponding to a support construction method, and the second type of installation node is an installation node corresponding to a cantilever construction method; collecting basic installation information of steel components for the first type of installation node and the second type of installation node respectively; performing simulation modeling of hoisting parameters in response to dynamic weather changes for the first type of installation node based on the basic installation information, and constructing a first dynamic model; performing simulation modeling of cantilever parameters in a dynamic cantilever stage and simulation modeling of stable cantilever parameters in a static cantilever stage for the second type of installation node based on the basic installation information, and constructing a second dynamic model; and controlling the installation of the ongoing target installation node using the first dynamic model or the second dynamic model.

[0007] The second aspect of the present application provides an intelligent installation control system for an ultra-long long span steel structure grid roof, the system comprising: an installation node determination module for reading and analyzing the installation process of the ultra-long long span steel structure grid roof, and determining a first type of installation node and a second type of installation node, wherein the first type of installation node is a node corresponding to the support construction method, and the second type of installation node is an installation node corresponding to the cantilever construction method; an information collection module for collecting basic installation information of steel components for the first type of installation node and the second type of installation node respectively; a first dynamic model construction module for performing simulation modeling of hoisting parameters in response to dynamic weather changes for the first type of installation node based on the basic installation information, and constructing a first dynamic model; a second dynamic model construction module for performing simulation modeling of cantilever parameters in the dynamic cantilever stage and simulation modeling of stable cantilever parameters in the static cantilever stage for the second type of installation node based on the basic installation information, and constructing a second dynamic model; an installation control module for performing installation control of the ongoing target installation node with the first dynamic model or the second dynamic model.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in the embodiment of the present application reads and analyzes the installation process of the ultra-long span steel structure grid roof to determine the first type of installation node and the second type of installation node. The first type of installation node is the node corresponding to the support construction method, and the second type of installation node is the installation node corresponding to the cantilever construction method. The basic installation information of the steel components is collected for the first type of installation node and the second type of installation node respectively; based on the basic installation information, the hoisting parameters of the first type of installation node are simulated and modeled to respond to dynamic weather changes to construct a first dynamic model; based on the basic installation information, the cantilever parameters of the dynamic cantilever stage and the stable cantilever parameters of the static cantilever stage are simulated and modeled for the second type of installation node to construct a second dynamic model; and the first dynamic model or the second dynamic model is used to control the installation of the target installation node in progress. The method achieves intelligent construction decision-making and industrial control, improves the safety and accuracy of the overall construction, and improves the technical effect of installation efficiency. 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 1 A flow chart of the intelligent installation control method for the ultra-long span steel structure grid roof provided in this application; Figure 2 This is a schematic diagram of the structure of the intelligent installation control system for the ultra-long span steel structure grid roof provided in this application.

[0011] Description of the accompanying drawings: installation node determination module 11, information collection module 12, first dynamic model construction module 13, second dynamic model construction module 14, installation control module 15. DETAILED DESCRIPTION

[0012] This application provides an intelligent installation control method and system for ultra-long span steel grid roofs. This method addresses the existing technical issues of insufficient intelligent industrial control for ultra-long span steel grid roofs, resulting in insufficient overall construction safety and precision. This method achieves intelligent construction decision-making and industrial control, improving overall construction safety and precision, and enhancing installation efficiency.

[0013] Below, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the accompanying drawings.

[0014] Example 1, as Figure 1 As shown, the present application provides an intelligent installation control method for an ultra-long span steel structure grid roof, the method comprising: The installation process of the ultra-long span steel structure grid roof is read and analyzed to determine the first type of installation nodes and the second type of installation nodes, wherein the first type of installation nodes are nodes corresponding to the support construction method, and the second type of installation nodes are installation nodes corresponding to the cantilever construction method.

[0015] Specifically, the ultra-long span steel structure grid roof is a roof system assembled by connecting multiple steel components in a grid-like structure. It is often used in large-span buildings such as gymnasiums and exhibition halls. When assembling steel components, they need to be assembled one by one. Each node represents a component, including roof panels, purlins, roof trusses, brackets, skylight frames, supports and other components. Collect technical documents, construction drawings, construction plans and engineering cases related to the target ultra-long span steel structure grid roof. These materials usually contain detailed installation steps, technical requirements, node connection methods, material specifications and other information. Then read the collected data to obtain the installation process information of the steel structure grid roof, and use installation process analysis tools, such as AI-assisted drawing analysis module and AI-StructureCAD tool to analyze multiple components and their connection relationships. If the node is located on an existing support platform or ground support structure, it is marked as a first-type installation node. If the node installation relies on the gradual construction of a cantilevered structure without direct support, it is marked as a second-type installation node. An installation node refers to the connection point corresponding to a component during the installation process. Each node generally corresponds to the connection location of one component to another. For example, in the grid structure of a certain exhibition hall, the nodes in the middle area of ​​the roof truss rely on temporary scaffolding support and can be used as first-type installation nodes. However, the roof panels in the edge skylight area cantilever beyond the completed roof truss and have no independent support conditions, so they are second-type installation nodes. By analyzing and classifying the structural type and support method of the installation nodes of ultra-long span steel grid roofs, it is not only possible to improve the relevance and accuracy of subsequent simulation modeling, but also to optimize installation path planning, realize intelligent construction decision-making and industrial control, and improve the safety and accuracy of overall construction.

[0016] Basic installation information of the steel components is collected for the first type of installation nodes and the second type of installation nodes respectively.

[0017] Furthermore, the basic installation information of any steel component includes the structural parameters of the steel component, the installation position of the steel component, the connection points with other structures, and the connection methods.

[0018] Specifically, after completing the installation node classification, for each steel component corresponding to the first and second type installation nodes to be constructed, the basic installation information of multiple steel components is collected from the target steel structure grid roof data. Basic installation information refers to data that must be mastered before the component is officially installed and affects the installation process, including steel component structural parameters, steel component installation location, connection points with other structures, and connection methods. Steel component structural parameters, such as component geometry and weight, are used to determine the lifting method and construction machinery configuration. The installation position refers to the spatial coordinates and orientation of the component within the grid roof, which determines installation accuracy and structural compatibility. The connection point is the location where the component connects to other components or the underlying structure, such as the node number and connection surface coordinates. The connection method refers to the specific installation method of the component, such as welding, bolting, and plug-in connection. It also includes the installation and connection process requirements, such as whether to connect immediately after lifting into place or to wait until other structures are in place for welding. The installation and connection process is determined by the construction plan prepared in advance by project technicians, which determines the installation time, dependent components, and construction logic of each component, and can be uploaded through the user terminal. By collecting and obtaining basic installation information of steel components, we can more accurately understand the characteristics and installation requirements of each component, and improve the accuracy and consistency of component positioning and installation.

[0019] Based on the basic installation information, simulation modeling of hoisting parameters responding to dynamic weather changes is performed for the first type of installation node to construct a first dynamic model.

[0020] Furthermore, based on the basic installation information, simulation modeling of the lifting parameters responding to dynamic weather changes is performed for the first type of installation node to construct a first dynamic model, including: determining the target lifting equipment for the installation of the super-long span steel structure grid roof, and collecting lifting construction modeling data; performing digital modeling with the lifting construction modeling data to generate a lifting equipment twin model, wherein the controllable input variables of the lifting equipment twin model are structural parameters, weather conditions and installation positions, and the simulation results include the lifting parameters of the target lifting equipment under different weather conditions; based on the lifting equipment twin model, the steel component structural parameters and steel component installation positions in the basic installation information corresponding to the first type of installation node are used to perform simulation training on the lifting parameter changes under dynamic changes in weather conditions to establish the first dynamic model.

[0021] Specifically, the target lifting equipment for installing the ultra-long-span steel grid roof, such as tower cranes, crawler cranes, or truck cranes, is identified by matching the structural parameters of the components in the target structure with the actual construction site conditions, such as lifting radius, height, and load. Then, based on construction drawings, equipment manuals, and site surveys, the required lifting construction modeling data is collected. This includes, but is not limited to, equipment parameters, workspace constraints, construction site layout, component installation locations, and path planning requirements. Equipment parameters such as arm length, maximum lifting capacity, and turning radius are also collected. The collected data is cleaned to remove redundant, erroneous, or incomplete information, and standardized to ensure consistency in format, units, and accuracy across different sources. Next, using 3D modeling software or digital twin modeling tools, such as SolidWorks or Autodesk, a 3D dynamic model consistent with the actual equipment is constructed based on the collected lifting construction modeling data, generating a lifting equipment twin model. During this construction process, it is important to ensure that the lifting equipment twin model accurately reflects the actual structure and function of the lifting equipment. Based on the lifting operation's requirements, the controllable input variables for the lifting equipment twin model are determined. These include component structural parameters (such as weight and size), construction site weather conditions (such as wind speed, direction, and temperature), and the spatial coordinates of the component installation location. After inputting simulation data under different weather conditions, the lifting equipment twin model can output corresponding lifting parameters, such as the lifting path, boom extension length, and lifting speed.

[0022] After constructing a twin model of the lifting equipment, the system combines the foundation installation information corresponding to the first-type installation node with the foundation installation information, performing detailed simulation training on the lifting process of steel components corresponding to the support construction method under dynamic weather conditions to establish a first dynamic model. The model configuration associated with the first-type installation node is extracted from the twin model, including the structural parameters and installation location coordinates of the node component. This is used as input variables, and dynamic weather data such as wind speed and direction are superimposed. Multiple rounds of simulation calculations and parameter training are performed to identify the component's lifting parameters under various weather conditions, thereby generating a lightweight first dynamic model that closely matches the first-type installation node. While retaining the core features of the twin model, the first dynamic model performs detailed simulation of the steel components corresponding to the first-type installation node, enabling efficient and accurate node-level intelligent lifting operations. Since the first-type installation node is a node type for the support construction method, it only requires lifting the component to the corresponding installation location. Using digital simulation technology to simulate weather changes, the first dynamic model automatically matches the corresponding lifting parameters based on real-time weather conditions, enabling intelligent control decisions, improving industrial control construction safety and the installation efficiency of steel structure grid roofs.

[0023] Furthermore, digital modeling is performed using the hoisting construction modeling data to generate a twin model of the hoisting equipment, including: configuring weather simulation boundary constraints; according to the weather simulation boundary constraints, digital simulation of structural parameters, weather conditions and installation positions is performed using the hoisting construction modeling data to generate a twin model of the hoisting equipment.

[0024] Specifically, after constructing a simulation model and inputting relevant data, the model will perform simulation operations based on the modeled data. However, in extremely severe weather, no matter how the lifting parameters are adjusted, the lifting position cannot be stabilized and balanced. Therefore, to avoid model deviations that are theoretically feasible but practically unworkable, before generating the lifting equipment twin model, weather simulation boundary constraints are first set based on historical meteorological data, construction safety standards, and the extreme operating conditions specified in the equipment manual. Weather simulation boundary constraints refer to the weather condition limit values ​​set in the simulation system to constrain the range of environmental inputs during the simulation process. For example, the maximum wind speed cannot exceed 8m / s and / or the angle between the wind direction and the lifting path cannot exceed 75° and / or rain or snow or the temperature cannot exceed -10°C. When weather variables such as wind speed, wind direction, and temperature exceed these boundary values, the current weather is deemed unsuitable for construction, and the lifting simulation is terminated or marked as infeasible to ensure construction safety. Then, according to the set weather simulation boundary constraints, digital simulations of structural parameters, weather conditions, and installation positions are combined with the lifting construction modeling data to perform multi-scenario simulation calculations to generate the lifting equipment twin model. During simulation, the twin model of the lifting equipment automatically checks whether the weather input is within the bounds of the constraints. If it is within the workable range, the simulation output is generated normally, resulting in the simulation result. If it is within the extreme range, a warning message is output indicating that the construction is not feasible, indicating that the operation cannot be carried out under these weather conditions. This boundary constraint mechanism ensures accurate and reliable simulation results, thereby improving the reliability of the simulation model and ensuring construction safety.

[0025] Based on the basic installation information, cantilever parameter simulation modeling in the dynamic cantilever stage and stable cantilever parameter simulation modeling in the static cantilever stage are performed for the second type of installation node to construct a second dynamic model.

[0026] Furthermore, based on the basic installation information, cantilever parameter simulation modeling of the dynamic cantilever stage and stable cantilever parameter simulation modeling of the static cantilever stage are performed for the second type of installation node to construct a second dynamic model, including: determining a cantilever construction platform for ultra-long span steel structure grid roof, and collecting construction modeling data of the dynamic cantilever stage in combination with the component structure parameters and steel component installation position in the basic installation information of the second type of installation node to construct a dynamic cantilever stage digital model; extracting the connection points and connection methods of the corresponding steel components and other structures in the basic installation information of the second type of installation node; based on the connection points and connection methods of the corresponding steel components and other structures, performing connection influence offset analysis of the steel components under static conditions to generate a static cantilever stage digital model; and constructing the second dynamic model with the dynamic cantilever stage digital model and the static cantilever stage digital model.

[0027] Specifically, the second type of installation node refers to a node constructed using a cantilever construction method. This type of node has no supporting structure underneath during installation and requires extension and connection to pre-existing structures. The construction difficulty and risk are far greater than those of the first type of supported nodes. Based on this, a cantilever construction platform was identified for the construction of ultra-long span steel grid roofs. The cantilever construction platform is the structural support system relied upon for cantilever operations, such as a temporary support, climbing platform, or a mounting system to existing components. Next, the component structural parameters and steel component installation positions are obtained from the foundation installation information of the second type of installation node. Construction modeling data for the dynamic cantilever phase is collected based on the cantilever construction platform and the foundation installation information of the second type of installation node. The dynamic cantilever phase refers to the process where the component is hoisted to the target location but not yet fixed. The structure is in an unstable, temporarily suspended state that is highly affected by wind. Similar to the construction method for the first dynamic model, a digital model for the dynamic cantilever phase is constructed. Once the component is positioned at the target node, it must be welded or bolted together, entering the static cantilever phase. The static cantilever phase refers to the phase where the component is positioned and the connection operation is performed. Based on the foundational installation information of the second-type installation node, the connection points and connection methods (e.g., welding, bolting, or combined connections) between the corresponding steel components and other structures are determined. Based on these connection points and connection methods, a static connection effect offset analysis is performed on the steel components to assess the impact of the connection process on their static stability. This connection effect offset analysis involves modeling and simulating the displacement, rotation, and stress concentration that may occur during the connection process. Potential displacement errors or angular deviations are calculated, and compensation measures are pre-set in the model to offset these errors. These include setting pre-offsets during hoisting and force balance adjustments during the connection process to ensure that the construction position remains stable, generating a digital model for the static cantilever stage. Furthermore, the dynamic cantilever stage digital model and the static cantilever stage digital model obtained for the two stages of the second-type installation node are integrated into a single model, forming a second dynamic model. The outputs of the dynamic and static cantilever stage digital models are used together as parameters for the two sequential stages. This second dynamic model allows for a comprehensive assessment of the overall stability and safety of ultra-long span steel grid roof structures during cantilever construction. Furthermore, through connection effect offset simulation, the position of connected components is ensured to remain stable, enabling precise control.

[0028] Furthermore, based on the connection points and connection methods between the corresponding steel components and other structures, a connection influence offset analysis is performed on the steel components in static state to generate a digital model of the static cantilever stage, including: analyzing the influence of external forces on the steel components based on the connection points and connection methods to generate position offsets under the influence of external forces; based on the position offsets, performing a synchronous offset simulation of the connection influence on the steel components in static state to generate the digital model of the static cantilever stage, wherein the synchronous offset simulation of the connection influence includes a simulation of adjusting cantilever parameters.

[0029] Specifically, since the steel components are not yet completely stabilized and fixed in the cantilever stage, the connection points may be subjected to combined loads such as wind loads, bending moments caused by the component's own weight, and conductive forces from adjacent components during welding or bolt tightening, resulting in small displacements, rotations, or deformations of the components. Therefore, the external force influences on the steel components during the cantilever connection process are analyzed based on the connection points and connection methods. Based on the connection point positions and mechanical properties, combined with weather simulation data, static simulation calculations are performed to generate the position offset of the components under the influence of external forces. This position offset is the error caused by the connection of the components without any compensation. Then, based on the position offset, a synchronous offset simulation of the connection influence under static conditions of the steel components is performed. Using the optimization algorithm in the simulation software, compensation measures are automatically found for a new round of simulation. The above steps are repeated, and the model is continuously iterated and optimized until the error converges to an acceptable range, thereby generating the digital model of the static cantilever stage. The synchronous offset simulation of the connection influence includes the adjustment simulation of the cantilever parameters, that is, the adjustment process includes adjusting the design parameters of the cantilever construction platform, such as the cantilever length and cantilever angle. Performing a synchronous offset simulation of the connection impact of the steel component under static conditions based on the position offset can ensure the connection accuracy and structural stability of the second type of installation node, and improve the intelligence, safety and control accuracy of industrial control.

[0030] Furthermore, the influence of external force on the steel component is analyzed based on the connection points and the connection mode, and the position offset under the influence of the external force is generated, including: training an external force application prediction model based on historical external force application monitoring data, analyzing the connection points and the connection mode, and generating the external force application result; based on the structural information of the steel component, performing position offset analysis under the influence of external force with the external force application result, and generating the position offset.

[0031] Specifically, the team first retrieves external force monitoring data from previous ultra-long-span steel grid roof installation projects or from the history of this project, including data on wind loads, torque generated by component deadweight at different angles, force transmission between adjacent components, and dynamic disturbances during the connection process. A force prediction model is then trained based on this external force monitoring data. For example, samples of external force data monitored in historical projects are collected, such as the correspondence between wind speed and component swing amplitude, and the node force conditions under different connection configurations. This data is then cleaned and feature extracted, using the external force monitoring data and steel component structural information as input features and node force values ​​as output labels. Machine learning algorithms, such as neural network models, are then used for model training to develop an external force prediction model capable of predicting external force results under new connection scenarios and environmental conditions. The external force prediction model then calculates the current external force result, which represents the distribution of the primary external forces actually acting on the component under this connection configuration, including direction, magnitude, and point of application. Combined with the results of the external force application, the structural information of the steel member is used to perform a position offset analysis under the influence of the external force. For example, the external force application results and the structural information of the target steel member are input into a finite element simulation tool to construct a force calculation scenario. Subsequently, through analytical calculation, the response of the member under the action of the external force is evaluated to obtain its displacement, rotation angle, or overall posture change in space, thereby outputting the position offset of the member. By obtaining the position offset of the steel member in the cantilever stage, a quantitative basis is provided for the subsequent connection influence offset analysis, thereby ensuring that the second dynamic model has high-precision connection error control capabilities and comprehensively improving the construction safety and precision controllability of the cantilever node.

[0032] The installation of the ongoing target installation node is controlled by using the first dynamic model or the second dynamic model.

[0033] Specifically, after the construction of the first or second dynamic model is completed, the intelligent industrial control phase of the actual construction phase will begin. This involves real-time control, guidance, and feedback of the current construction node through the constructed first or second dynamic model. Before the lifting or connection operation, the installation node type is identified. If it is a supporting node, that is, a first-type installation node, the first dynamic model is loaded. If it is a cantilevered node, a second-type installation node, the second dynamic model is loaded, ensuring the safety, accuracy, and adaptability of the entire installation process.

[0034] Furthermore, before using the first dynamic model or the second dynamic model to control the installation of the ongoing target installation node, it includes: constructing a construction feasibility judgment mechanism based on weather simulation boundary constraints; collecting weather forecast information for the preset construction time zone, inputting it into the construction feasibility judgment mechanism for judgment, and issuing an installation feasibility control instruction if the judgment is passed.

[0035] Furthermore, before controlling the installation of the ongoing target installation node using the first dynamic model or the second dynamic model, the method further includes: issuing an installation unfeasible warning message if the determination fails.

[0036] Specifically, before applying the first or second dynamic model to the target installation node for construction control, a construction feasibility assessment based on weather simulation is first performed to ensure that construction operations are carried out under safe and controllable environmental conditions. A construction feasibility assessment mechanism, based on pre-set weather simulation boundary constraints, analyzes and evaluates weather conditions during the upcoming construction period to determine whether to initiate lifting or connection operations at that node. Weather forecast data for the planned construction period is collected and fed into the construction feasibility assessment mechanism for comparison and analysis. The input weather data is then verified against the boundary constraints to generate a determination result. If all weather conditions are within the safety boundary, construction is deemed feasible for the current working condition, and an installation feasibility control instruction is automatically issued, activating the corresponding dynamic model and entering the installation control process. However, if any indicator exceeds the limit, such as high wind speed, unfavorable wind direction, or low air temperature for high-altitude operations, construction is deemed unfeasible. An installation feasibility warning message is immediately issued, notifying construction personnel to suspend the installation work plan. Through the discrimination mechanism, a transition has been achieved from passively waiting for weather changes to actively predicting construction conditions. This not only improves the scientific nature of operational decisions, but also provides a safety pre-emptive guarantee for the industrial control decisions of the overall intelligent installation, ensuring that installation operations are carried out in a safe and controllable environment, and ensuring safe and reliable construction.

[0037] Example 2, based on the same inventive concept as the intelligent installation control method for the ultra-long span steel structure grid roof in the previous embodiment, Figure 2 As shown, the present application provides an intelligent installation control system for an ultra-long span steel structure grid roof, wherein the system includes: The installation node determination module 11 is used to read and analyze the installation process of the super-long span steel structure grid roof to determine the first type of installation node and the second type of installation node, wherein the first type of installation node is the node corresponding to the support construction method, and the second type of installation node is the installation node corresponding to the cantilever construction method; the information collection module 12 is used to collect the basic installation information of the steel components for the first type of installation node and the second type of installation node respectively; the first dynamic model construction module 13 is used to perform simulation modeling of the hoisting parameters of the first type of installation node to cope with dynamic weather changes based on the basic installation information, and construct a first dynamic model; the second dynamic model construction module 14 is used to perform simulation modeling of the cantilever parameters in the dynamic cantilever stage and the stable cantilever parameters in the static cantilever stage for the second type of installation node based on the basic installation information, and construct a second dynamic model; the installation control module 15 is used to control the installation of the ongoing target installation node with the first dynamic model or the second dynamic model.

[0038] Furthermore, the information collection module 12 is further configured to perform the following steps: the basic installation information of any steel component includes the structural parameters of the steel component, the installation position of the steel component, the connection points with other structures, and the connection methods.

[0039] Furthermore, the first dynamic model construction module 13 is also used to perform the following steps: determine the target lifting equipment used for the installation of the super-long span steel structure grid roof, and collect lifting construction modeling data; perform digital modeling with the lifting construction modeling data to generate a lifting equipment twin model, wherein the controllable input variables of the lifting equipment twin model are structural parameters, weather conditions and installation positions, and the simulation results include the lifting parameters of the target lifting equipment under different weather conditions; based on the lifting equipment twin model, the steel component structural parameters and steel component installation positions in the basic installation information corresponding to the first type of installation node are used to perform simulation training on the lifting parameter changes under dynamic changes in weather conditions to establish the first dynamic model.

[0040] Furthermore, the first dynamic model construction module 13 is also used to perform the following steps: configuring weather simulation boundary constraints; according to the weather simulation boundary constraints, using the hoisting construction modeling data to perform digital simulation under structural parameters, weather conditions and installation positions to generate the hoisting equipment twin model.

[0041] Furthermore, the second dynamic model construction module 14 is also used to perform the following steps: determine the cantilever construction platform for the super-long span steel structure grid roof, and collect construction modeling data for the dynamic cantilever stage in combination with the component structure parameters and steel component installation position in the basic installation information of the second type of installation node, and construct a dynamic cantilever stage digital model; extract the connection points and connection methods of the corresponding steel components and other structures in the basic installation information of the second type of installation node; based on the connection points and connection methods of the corresponding steel components and other structures, perform a connection influence offset analysis of the steel components under static conditions to generate a static cantilever stage digital model; and construct the second dynamic model with the dynamic cantilever stage digital model and the static cantilever stage digital model.

[0042] Furthermore, the second dynamic model construction module 14 is also used to perform the following steps: based on the connection points and connection methods, the external force influence analysis is performed on the steel member to generate the position offset under the influence of the external force; based on the position offset, the connection influence synchronous offset simulation is performed on the steel member under static state to generate the digital model of the static cantilever stage, wherein the connection influence synchronous offset simulation includes the adjustment simulation of the cantilever parameters.

[0043] Furthermore, the second dynamic model construction module 14 is also used to perform the following steps: training an external force application prediction model based on historical external force application monitoring data, analyzing the connection points and connection methods, and generating external force application results; based on the steel component structure information, performing position offset analysis under the influence of external force using the external force application results to generate the position offset amount.

[0044] Furthermore, the installation control module 15 is also used to perform the following steps: constructing a construction feasibility judgment mechanism based on weather simulation boundary constraints; collecting weather forecast information for the preset construction time zone, inputting it into the construction feasibility judgment mechanism for judgment, and issuing an installation feasibility control instruction if the judgment is passed.

[0045] Furthermore, the installation control module 15 is further configured to execute the following steps: if the determination fails, issuing an installation unfeasible warning message.

[0046] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0047] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, to the extent such modifications and variations fall within the scope of the present application and its equivalents, the present application is intended to include such modifications and variations.

Claims

1. An intelligent installation control method for an ultra-long span steel structure grid roof, characterized in that: include: Read and analyze the installation process of the ultra-long span steel structure grid roof to determine the first type of installation nodes and the second type of installation nodes, where the first type of installation nodes are nodes corresponding to the support construction method, and the second type of installation nodes are installation nodes corresponding to the cantilever construction method; Collecting basic installation information of steel components for the first type of installation nodes and the second type of installation nodes respectively; Based on the basic installation information, simulation modeling of the hoisting parameters responding to dynamic weather changes for the first type of installation node is performed to construct a first dynamic model; Based on the basic installation information, performing cantilever parameter simulation modeling in a dynamic cantilever stage and stable cantilever parameter simulation modeling in a static cantilever stage for the second type of installation node to construct a second dynamic model; The installation of the ongoing target installation node is controlled by using the first dynamic model or the second dynamic model.

2. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 1, characterized in that: The basic installation information of any steel component includes the structural parameters of the steel component, the installation position of the steel component, the connection points with other structures, and the connection methods.

3. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 1 is characterized in that: Performing simulation modeling of hoisting parameters responding to dynamic weather changes for the first type of installation node based on the basic installation information to construct a first dynamic model includes: Determine the target hoisting equipment for the installation of ultra-long span steel structure grid roofs and collect data for hoisting construction modeling; Performing digital modeling with the hoisting construction modeling data to generate a twin model of the hoisting equipment, wherein the controllable input variables of the twin model of the hoisting equipment are structural parameters, weather conditions, and installation location, and the simulation results include the hoisting parameters of the target hoisting equipment under different weather conditions; Based on the twin model of the lifting equipment, simulation training of lifting parameter changes under dynamic changes in weather conditions is performed using the steel component structural parameters and steel component installation positions in the basic installation information corresponding to the first type of installation node to establish the first dynamic model.

4. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 3 is characterized in that: Perform digital modeling based on the hoisting construction modeling data to generate a twin model of the hoisting equipment, including: Configure weather simulation boundary constraints; According to the weather simulation boundary constraints, digital simulation of structural parameters, weather conditions and installation positions is performed using the hoisting construction modeling data to generate a twin model of the hoisting equipment.

5. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 1 is characterized in that: Based on the basic installation information, cantilever parameter simulation modeling is performed for the second type of installation node in a dynamic cantilever stage and stable cantilever parameter simulation modeling is performed in a static cantilever stage to construct a second dynamic model, including: Determine the cantilever construction platform for the ultra-long span steel structure grid roof, and collect construction modeling data for the dynamic cantilever stage based on the component structural parameters and steel component installation positions in the foundation installation information of the second type of installation node to build a digital model for the dynamic cantilever stage. Extracting connection points and connection methods of corresponding steel components and other structures from the foundation installation information of the second type of installation node; Based on the connection points and connection methods of the corresponding steel components and other structures, the connection effect offset analysis of the steel components under static conditions is carried out to generate a digital model of the static cantilever stage; The second dynamic model is constructed using the dynamic cantilever stage digital model and the static cantilever stage digital model.

6. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 5, characterized in that: Based on the connection points and connection methods of the corresponding steel components and other structures, the static connection effect offset analysis of the steel components is carried out to generate a digital model of the static cantilever stage, including: Analyzing the influence of external forces on the steel components based on the connection points and connection methods, and generating position offsets under the influence of external forces; Based on the position offset, a synchronous offset simulation of the connection effect of the steel member in a static state is performed to generate the digital model of the static cantilever stage, wherein the synchronous offset simulation of the connection effect includes an adjustment simulation of the cantilever parameters.

7. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 6, characterized in that: Based on the connection points and connection methods, the influence of external forces on the steel components is analyzed to generate position offsets under the influence of external forces, including: An external force application prediction model is trained based on historical external force application monitoring data, the connection points and connection methods are analyzed, and an external force application result is generated; Based on the structural information of the steel component, the position offset analysis under the influence of the external force is performed using the external force application result to generate the position offset amount.

8. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 4, characterized in that: Before controlling the installation of the target installation node in progress using the first dynamic model or the second dynamic model, the method includes: Construct a construction feasibility judgment mechanism based on weather simulation boundary constraints; The weather forecast information of the preset construction time zone is collected and input into the construction feasibility judgment mechanism for judgment. If the judgment is passed, an installation feasibility control instruction is issued.

9. The intelligent installation control method for the ultra-long span steel structure grid roof according to claim 8, characterized in that: If the judgment fails, an early warning message indicating that the installation is not feasible will be issued.

10. The intelligent installation control system for ultra-long span steel structure grid roof is characterized by: The steps for implementing the intelligent installation control method of the ultra-long span steel structure grid roof according to any one of claims 1 to 9 include: An installation node determination module is used to read and analyze the installation process of the ultra-long and long-span steel structure grid roof to determine the first type of installation node and the second type of installation node, wherein the first type of installation node is a node corresponding to the support construction method, and the second type of installation node is an installation node corresponding to the cantilever construction method; An information collection module, configured to collect basic installation information of steel components for each of the first type of installation nodes and the second type of installation nodes; A first dynamic model building module is configured to perform simulation modeling of hoisting parameters responding to dynamic weather changes for the first type of installation node based on the basic installation information, thereby building a first dynamic model; A second dynamic model construction module is configured to perform cantilever parameter simulation modeling in a dynamic cantilevering stage and stable cantilever parameter simulation modeling in a static cantilevering stage for the second type of installation node based on the basic installation information, so as to construct a second dynamic model; The installation control module is used to control the installation of the ongoing target installation node using the first dynamic model or the second dynamic model.

Images