Intelligent installation method and system for fabricated structure system

Abstract

The embodiment of the specification provides an intelligent installation method and system for a fabricated structure system, wherein the method comprises the following steps: collecting, transmitting and monitoring construction site data in real time; establishing an engineering BIM model and a construction model based on the construction site data, and generating an optimal construction sequence and task management through algorithm optimization; transmitting the optimal construction sequence and task management to an FMS robot collaborative management platform, and controlling a construction robot to perform corresponding construction operations according to the optimal construction sequence and task management through the FMS robot collaborative management platform.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of building engineering, and particularly relates to an intelligent installation method and system for a fabricated structure system. BACKGROUND

[0002] Digital twinning is to establish a corresponding digital model in a virtual space according to a physical model, which contains information such as specifications, geometric parameters, material properties, simulation data and the like of a real world model, and can perform real-time updating of the virtual model according to data fed back by a sensor. Digital twinning is a simulation process of fully utilizing a physical model, sensor updating, integration of multiple disciplines and multi-dimensional and multi-scale.

[0003] Fabricated building is an inevitable way for industrialization of future civil engineering construction, and fabricated building is divided into two parts of prefabricated component production and on-site assembly construction. Compared with traditional construction projects, the fabricated building has higher requirements for prefabricated component supply and accuracy and collaboration of the fabricated building, and therefore how to improve the installation efficiency of the fabricated structure and improve the installation method of the fabricated structure becomes a problem to be solved for promoting the development of the fabricated building. SUMMARY

[0004] The present application aims to provide an intelligent installation method and system for a fabricated structure system, and aims to solve the above problems in the prior art.

[0005] The present application provides an intelligent installation method for a fabricated structure system, comprising:

[0006] Real-time collection, transmission and monitoring of construction site data are performed;

[0007] An engineering BIM model and a construction model are established based on the construction site data, and an optimal construction sequence and task management are generated through algorithm optimization;

[0008] The optimal construction sequence and task management are transmitted to an FMS robot collaborative management platform, and a construction robot is controlled to perform corresponding construction operations according to the optimal construction sequence and task management through the FMS robot collaborative management platform.

[0009] The present application provides an intelligent installation system for a fabricated structure system, comprising:

[0010] A data collection module is configured to perform real-time collection, transmission and monitoring of construction site data;

[0011] A digital twinning module is configured to establish an engineering BIM model and a construction model based on the construction site data, and to generate an optimal construction sequence and task management through algorithm optimization;

[0012] The intelligent installation module is used for transmitting the optimal construction sequence and task management to an FMS robot collaborative management platform, and through the FMS robot collaborative management platform, the construction robot is controlled to perform corresponding construction operations according to the optimal construction sequence and task management.

[0013] By adopting the embodiment of the present application, the problems of traditional assembly type construction, such as excessive reliance on manual work, low precision and slow progress, are solved, and the installation of the assembly type structure system is comprehensively and intelligently improved, so that the installation efficiency of the assembly type structure is effectively improved, and the installation quality of the assembly type structure is improved. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present application or the prior art, the drawings needed to be used in the embodiment or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and for those skilled in the art, other drawings can also be obtained without creative labor under the premise of these drawings.

[0015] Figure 1 is a flow chart of the assembly type structure system intelligent installation method of the embodiment of the present application;

[0016] Figure 2 is a schematic diagram of the assembly type structure system intelligent installation method of the embodiment of the present application Figure 1 ;

[0017] Figure 3 is a schematic diagram of the assembly type structure system intelligent installation method of the embodiment of the present application Figure 2 ;

[0018] Figure 4 is a schematic diagram of the assembly type structure system intelligent installation system of the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to solve the above problems in the prior art, the embodiment of the present application discloses an assembly type structure system intelligent installation method and system. In the assembly type structure installation process, first, the component parameters are obtained through the RFID tag, and the corresponding BIM model and construction model are established. The construction model is optimized through the BP neural network algorithm, the optimal construction sequence and construction task management are generated, then the generated data are transmitted to the robot collaborative management platform, the construction task is issued to the robot, and the data and results in the construction process are timely synchronized in the intelligent management platform. The embodiment of the present application is suitable for the installation of the assembly type structure system.

[0020] In order for those skilled in the art to better understand the technical solutions in the one or more embodiments of the present specification, the technical solutions in the one or more embodiments of the present specification will be clearly and completely described in the following with reference to the drawings in the one or more embodiments of the present specification. Obviously, the described embodiments are only part of the embodiments of the present specification, rather than all the embodiments. Based on the one or more embodiments of the present specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present document.

[0021] Method embodiment

[0022] According to the embodiment of the present application, an intelligent installation method of a fabricated structure system is provided, Figure 1 is the flow chart of the intelligent installation method of the fabricated structure system according to the embodiment of the present application, as Figure 1 shown, the intelligent installation method of the fabricated structure system according to the embodiment of the present application specifically comprises:

[0023] Step 101, real-time collection, transmission and monitoring of construction site data; specifically, the RFID parameters of the building components are obtained by scanning the RFID tags fixed on the building components, the on-site situation is monitored by the positioning function of GPS and GIS through the cameras arranged on the construction site, the monitoring data are obtained, and the construction site situation and progress are updated through the unmanned aerial vehicle to obtain the construction site situation and progress data.

[0024] Step 102, establishing an engineering BIM model and a construction model based on the construction site data, and generating an optimal construction sequence and task management through algorithm optimization; specifically, an engineering BIM model is established, the RFID parameters of the building components are input into the engineering BIM model, and a construction model is established, the construction model is optimized by using a BP neural network algorithm, the construction installation sequence, construction collision detection, construction progress simulation and construction danger prediction are performed, the optimal installation sequence of the fabricated structure is calculated in combination with the functions of the construction model and the algorithm, construction collision is avoided, construction danger is predicted in advance, and the optimal construction sequence and task management are generated.

[0025] Step 103, transmitting the optimal construction sequence and task management to an FMS robot collaborative management platform, and controlling the construction robot to perform corresponding construction operations according to the optimal construction sequence and task management through the FMS robot collaborative management platform. Specifically,

[0026] Through the FMS robot collaborative management platform, task receiving, task splitting and robot quantity arrangement are performed, and relevant construction robots are overall arranged to perform collaborative work; path of the construction robot work is simulated and optimized to avoid path conflict of the construction robots, and to reduce the influence of environmental road conditions on the construction robot work; the construction robot work amount, work time, work efficiency and work failure are synchronously managed, and subsequent analysis and improvement are performed.

[0027] After the above processing is performed, the following processing can also be included:

[0028] Construction failure data of the construction robot during construction is sent to the FMS robot collaborative management platform, and an optimized solution is performed based on the construction failure data, and the solution is issued to the construction robot;

[0029] The monitoring data and the construction site situation and progress data during construction are synchronously transmitted to the FMS robot collaborative management platform, and subsequent data monitoring and operation and maintenance are performed through the FMS robot collaborative management platform.

[0030] The above technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0031] As shown in Figure 2 and Figure 3 The intelligent installation method of the fabricated structural system according to the embodiments of the present application can be divided into three layers: the data acquisition layer includes RFID tags, GPS, GIS, cameras and drones and other devices to realize real-time acquisition and transmission and monitoring functions of the construction site data; the digital twin function layer stores component parameters through BIM modeling, collision detection, progress model and cost prediction are performed by construction simulation software, intelligent algorithms include BP neural network and machine learning to realize optimization of construction simulation, and the intelligent installation layer realizes robot construction work through management and planning of the FMS system, and the software simulation is continuously optimized and updated.

[0032] The intelligent installation method of the fabricated structural system according to the embodiments of the present application specifically includes the following processing:

[0033] Step one: fix the RFID tag on the building component, and the component parameters can be obtained by scanning the RFID tag through the device; wherein the RFID tag includes the following parameters: for example, component ID, material type, storage location, geometric size, placement position, etc. Cameras are arranged at the construction site, the site situation is monitored by using the positioning function of GPS and GIS, and the construction site situation and progress are updated by using the drone at regular intervals.

[0034] Step Two: Establish a BIM model for the project, inputting data and parameters stored in the RFID tags of building components. Simultaneously, create a construction model and optimize it using a BP neural network algorithm to generate the optimal construction sequence and task management. This optimization of construction simulation using the BP neural network algorithm includes construction installation sequence, collision detection, construction progress simulation, and construction hazard prediction. Combining the functions of the construction model and the algorithm, the optimal installation sequence for the prefabricated structure is calculated, avoiding construction collisions, predicting construction hazards in advance, accelerating construction progress, and reducing costs.

[0035] Step 3: Transmit the generated data and tasks to the FMS robot collaborative management platform. This platform provides construction robots with functions such as task scheduling, construction path planning, and data management. After receiving instructions, the robots perform corresponding construction operations according to the path planning and task assignment. The FMS collaborative management platform implements the following functions: Task scheduling includes task reception, task splitting, and robot quantity arrangement, coordinating related construction robots for collaborative operations; Path planning simulates and optimizes the robot's operation path to avoid path conflicts between robots and reduce the impact of environmental road conditions on robot operations; Data management enables synchronous management of robot workload, operation time, operation efficiency, and operation failures for subsequent analysis and improvement.

[0036] Step 4: Construction robots will promptly report any construction faults and data encountered during construction to the FMS collaborative management platform. Then, simulation software will use algorithms to optimize and resolve the construction faults, and the solutions will be distributed to the robots for subsequent construction tasks.

[0037] Step 5: All equipment monitoring data, robot construction tasks, construction faults, and other relevant data during the construction process are synchronously transmitted to the intelligent management platform for subsequent data monitoring and operation and maintenance.

[0038] In summary, the technical solution of this invention installs RFID tag sensors and embedded terminals in prefabricated components to store specific information about the components, establishing a digital twin framework for the installation of the prefabricated structural system. Based on a traditional construction site, the digital twin model enables real-time interaction between physical and virtual model data. This provides a prefabricated intelligent installation method incorporating digital twins, IoT devices, construction robots, and intelligent algorithms, which facilitates refined management, efficient construction, ensures construction quality, and guarantees an intelligent and efficient construction process.

[0039] Specifically, the synchronization of the physical and virtual models in this embodiment of the invention involves installing RFID tags and various types of sensors in prefabricated components. RFID tags can be used to identify component information, generating unique IDs, types, materials, storage locations, production dates, and geometric dimensions for each component in the twin model. Specific construction information can be retrieved from the database via a component index. Through the development of an independent IoT platform, RFID, GIS, GPS, and cameras are used to achieve real-time data collection for engineering projects, real-time monitoring of site personnel, real-time updates of the construction process, and dynamic tracking by intelligent robots. Data collected by drones is used to create a panoramic model of the construction site, enabling integrated platform management.

[0040] Establish a digital twin framework for the installation of prefabricated structural systems, such as the frame structure system. Figure 2 The system can perform two main functions: modeling and simulation, and algorithm optimization. Modeling and simulation mainly includes simulating the construction process, collision detection, schedule simulation, cost prediction, and hazard prediction. After modeling and simulation, the system is optimized according to the algorithm until a construction plan that meets the requirements is output.

[0041] The intelligent installation method simulation based on digital twins: A corresponding BIM model is established according to the actual project. A corresponding construction model is generated in the construction simulation software based on the BIM 3D model. The BP neural network algorithm is used to simulate and optimize the construction sequence to calculate the optimal prefabricated construction and installation sequence. Then, the construction simulation is carried out in the construction simulation software to predict safety and cost issues that may exist during construction. The algorithm is optimized for the problems predicted by the simulation until the requirements are met.

[0042] Intelligent Installation of Construction Robots Based on Digital Twins: In construction robot installation, the FMS robot collaborative management system, developed based on the FleetManagement System, provides robots with functions such as task management, dynamic path planning, multi-device collaborative services, and data management by connecting to twin BIM model data and construction simulation platform data. Task Management: Task reception, breakdown, robot allocation, and collaborative operation of multiple robots and related intelligent systems reduce overall operation costs. Path Planning: Planning operation and movement paths, optimizing paths that affect robot construction, and improving robot operation efficiency and safety. Multi-device Collaborative Services: Automatically allocating robot tasks and performing intelligent path planning based on the construction tasks received by the robot, avoiding task conflicts and path collisions between devices. Data Management: Automatically calculating workload, operation time, operation efficiency, and fault status for analysis, improvement, and iterative upgrades.

[0043] The BIM model and construction simulation software platform use a backpropagation (BP) neural network algorithm to calculate a reasonable and efficient construction sequence, path planning, and collaborative operation method. The model then distributes data and tasks to the FMS (Frontline Management System) robot collaborative management system, which in turn assigns tasks to the robots. The robots then perform collaborative operations according to the corresponding construction paths and sequences. During the operation, the robots transmit data back to the FMS robot collaborative management system in real time based on the construction status. The FMS robot collaborative management system can update and optimize task allocation based on the transmitted data, achieving real-time updates and efficient synchronization between the physical model and the twin model.

[0044] The above technical solutions are illustrated with examples below.

[0045] The intelligent installation method for rapid prefabricated structural systems based on digital twins described in this invention includes digital twins, IoT devices, construction robots, and intelligent algorithms. It utilizes a computer equipped with modeling and analysis software and includes equipment such as RFID, GIS, GPS, cameras, drones, and construction robots. Specifically, it includes the following processing steps:

[0046] Step 1: Attach RFID tags to various building components. By scanning the RFID tags with smart devices, various parameters of the components can be collected, such as component ID, material type, storage location, geometric dimensions, and placement position.

[0047] Step 2: Place cameras at fixed locations on the construction site as needed, and use GPS and GIS positioning functions to monitor and provide feedback on the construction site in real time. Regularly use drones to take aerial photos of the operation, update the construction progress, monitor construction safety, and dynamically track the robots.

[0048] Step 3: Establish a BIM model based on the engineering design data, and input the data stored by the RFID sensors and the known parameters of the prefabricated components into the model to facilitate equipment management.

[0049] Step 4: Based on the established BIM model, create a corresponding construction model. Optimize the construction model using a backpropagation (BP) neural network algorithm, including construction installation sequence, collision detection, construction progress simulation, and construction hazard prediction. Combining the functions of the construction model and the algorithm, calculate the optimal installation sequence for the prefabricated structure, avoiding construction collisions, predicting construction hazards in advance, accelerating construction progress, and reducing construction costs.

[0050] Step 5: Real-time interactive feedback of the BIM model, construction model, and neural network algorithm processing results generates optimal construction sequence data and task management. The data and tasks are then transmitted to the FMS robot collaborative management platform, which provides construction robots with functions such as task scheduling, construction path planning, and data management. Task scheduling refers to the receipt and breakdown of tasks and the allocation of robot numbers, coordinating the collaborative work of related construction robots. Path planning optimizes the robot's work path, reducing the impact of the surrounding environment, avoiding path conflicts between robots, and improving work efficiency. Data management can synchronize robot workload, work duration, work efficiency, and construction faults in real time.

[0051] Step Six: After receiving the task, the construction robot performs construction work according to the path planning and task arrangement. Relevant data and construction faults during the construction period will be promptly fed back to the FMS collaborative management platform. The FMS management platform transmits relevant faults back to the simulation software, uses algorithms and other functions to optimize and resolve them in the construction software, and then sends the solution back to the FMS collaborative management platform and transmits it to the robot. The robot can then optimize the construction plan and resolve construction faults according to the task arrangement.

[0052] Step 7: During the construction process, relevant data from various equipment and software such as RFID tags, cameras, drones, and robots are synchronously transmitted to the intelligent management platform for later data monitoring and operation and maintenance, realizing the intelligent installation of the digital twin rapid prefabricated structure system.

[0053] In summary, the intelligent installation method for rapid prefabricated structural systems based on digital twins in this invention applies digital twins, the Internet of Things, RFID, BIM, construction simulation, robot collaborative management, neural network algorithms, and other methods. It utilizes intelligent devices such as computers equipped with modeling and analysis software, cameras, drones, and intelligent robots to fully leverage the advantages of various technologies and methods. This method comprehensively improves the intelligent installation of prefabricated structural systems, effectively enhancing the installation efficiency and quality of prefabricated structures.

[0054] System Implementation Examples

[0055] According to embodiments of the present invention, an intelligent installation system for prefabricated structural systems is provided. Figure 4 This is a schematic diagram of the intelligent installation system for the prefabricated structural system according to an embodiment of the present invention, such as... Figure 4 As shown, the intelligent installation system for prefabricated structural systems according to an embodiment of the present invention specifically includes:

[0056] Data acquisition module 40 is used for real-time acquisition, transmission, and monitoring of construction site data; specifically, data acquisition module 40 is used for:

[0057] The RFID parameters of the building components are obtained by scanning the RFID tags fixed on the building components. The on-site conditions are monitored by using GPS and GIS positioning functions through cameras set up at the construction site to obtain monitoring data. The construction site conditions and progress are updated by using drones to obtain construction site conditions and progress data.

[0058] The digital twin module 42 is used to establish an engineering BIM model and a construction model based on the construction site data, and to generate the optimal construction sequence and task management through algorithm optimization; the digital twin module 42 is specifically used for:

[0059] A BIM model for the project is established, and the RFID parameters of the building components are input into the BIM model. A construction model is also established. The BP neural network algorithm is used to optimize the construction model, including construction installation sequence, construction collision detection, construction progress simulation, and construction hazard prediction. Combining the functions of the construction model and the algorithm, the optimal installation sequence of the prefabricated structure is calculated to avoid construction collisions, predict construction hazards in advance, and generate the optimal construction sequence and task management.

[0060] The intelligent installation module 44 is used to transmit the optimal construction sequence and task management to the FMS robot collaborative management platform. Through the FMS robot collaborative management platform, the construction robot is controlled to perform corresponding construction operations according to the optimal construction sequence and task management. The intelligent installation module 44 specifically includes:

[0061] The FMS robot collaborative management platform enables task reception, task breakdown, and robot quantity arrangement, coordinating related construction robots for collaborative operations; it simulates and optimizes the operation paths of construction robots to avoid path conflicts between them and reduce the impact of environmental road conditions on their operations; it achieves synchronous management of the workload, operation time, operation efficiency, and operation failures of construction robots, and conducts subsequent analysis and improvement.

[0062] The intelligent installation module 44 is further used for:

[0063] The construction robot sends construction fault data during construction to the FMS robot collaborative management platform, optimizes and resolves the faults based on the construction fault data, and then distributes the solutions to the construction robot.

[0064] The monitoring data, construction site conditions, and progress data during the construction process are synchronously transmitted to the FMS robot collaborative management platform, which is then used for subsequent data monitoring and operation and maintenance.

[0065] The embodiments of the present invention are system embodiments corresponding to the above method embodiments. The specific operation of each module can be understood by referring to the description of the method embodiments, and will not be repeated here.

[0066] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent installation of a prefabricated structural system, characterized in that, include: Real-time data collection, transmission, and monitoring at the construction site; Based on the construction site data, an engineering BIM model and a construction model are established, and the optimal construction sequence and task management are generated through algorithm optimization. The optimal construction sequence and task management are transmitted to the FMS robot collaborative management platform. Through the FMS robot collaborative management platform, the construction robot is controlled to perform corresponding construction operations according to the optimal construction sequence and task management. Specifically, the real-time collection, transmission, and monitoring of construction site data includes: The RFID parameters of the building components are obtained by scanning the RFID tags fixed on the building components. The on-site conditions are monitored by using GPS and GIS positioning functions through cameras set up at the construction site to obtain monitoring data. The construction site conditions and progress are updated by using drones to obtain construction site conditions and progress data. Specifically, the process of establishing an engineering BIM model and construction model based on the construction site data, and generating the optimal construction sequence and task management includes: A BIM model for the project is established, and the RFID parameters of the building components are input into the BIM model. A construction model is also established, and the construction model is optimized using a BP neural network algorithm to generate the optimal construction sequence and task management. The optimization of the construction model using the BP neural network algorithm to generate the optimal construction sequence and task management specifically includes: The BP neural network algorithm is used to optimize the construction model, including construction and installation sequence, construction collision detection, construction progress simulation, and construction hazard prediction. By combining the functions of the construction model and the algorithm, the optimal installation sequence of the prefabricated structure is calculated to avoid construction collisions and predict construction hazards in advance. Specifically, controlling the construction robot to perform corresponding construction operations according to the optimal construction sequence and task management through the FMS robot collaborative management platform includes: The FMS robot collaborative management platform enables task reception, task breakdown, and robot quantity arrangement, coordinating related construction robots for collaborative operations; it simulates and optimizes the operation paths of construction robots to avoid path conflicts between them and reduce the impact of environmental road conditions on their operations; it achieves synchronous management of the workload, operation time, operation efficiency, and operation failures of construction robots, and conducts subsequent analysis and improvement.

2. The method according to claim 1, characterized in that, The method further includes: The construction robot sends construction fault data during construction to the FMS robot collaborative management platform, optimizes and resolves the faults based on the construction fault data, and then distributes the solutions to the construction robot. The monitoring data, construction site conditions, and progress data during the construction process are synchronously transmitted to the FMS robot collaborative management platform, which is then used for subsequent data monitoring and operation and maintenance.

3. An intelligent installation system for a prefabricated structural system, characterized in that, include: The data acquisition module is used for real-time acquisition, transmission, and monitoring of data at the construction site; The digital twin module is used to build an engineering BIM model and a construction model based on the construction site data, and to generate the optimal construction sequence and task management through algorithm optimization. The intelligent installation module is used to transmit the optimal construction sequence and task management to the FMS robot collaborative management platform, and through the FMS robot collaborative management platform, control the construction robot to perform corresponding construction operations according to the optimal construction sequence and task management. Specifically, the data acquisition module is used for: The RFID parameters of the building components are obtained by scanning the RFID tags fixed on the building components. The on-site conditions are monitored by using GPS and GIS positioning functions through cameras set up at the construction site to obtain monitoring data. The construction site conditions and progress are updated by using drones to obtain construction site conditions and progress data. The digital twin module is specifically used for: A BIM model for the project is established, and the RFID parameters of the building components are input into the BIM model. A construction model is also established. The BP neural network algorithm is used to optimize the construction model, including construction installation sequence, construction collision detection, construction progress simulation, and construction hazard prediction. Combining the functions of the construction model and the algorithm, the optimal installation sequence of the prefabricated structure is calculated to avoid construction collisions, predict construction hazards in advance, and generate the optimal construction sequence and task management. Specifically, the intelligent installation module is used for: The FMS robot collaborative management platform enables task reception, task breakdown, and robot quantity arrangement, coordinating related construction robots for collaborative operations; it simulates and optimizes the operation paths of construction robots to avoid path conflicts between them and reduce the impact of environmental road conditions on their operations; it achieves synchronous management of the workload, operation time, operation efficiency, and operation failures of construction robots, and conducts subsequent analysis and improvement.

4. The system according to claim 3, characterized in that, The intelligent installation module is further used for: The construction robot sends construction fault data during construction to the FMS robot collaborative management platform, optimizes and resolves the faults based on the construction fault data, and then distributes the solutions to the construction robot. The monitoring data, construction site conditions, and progress data during the construction process are synchronously transmitted to the FMS robot collaborative management platform, which is then used for subsequent data monitoring and operation and maintenance.

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