A prefabrication, assembly and precise positioning system for foundation template platforms based on BIM technology

Abstract

This invention relates to the technical field of pedestal formwork platform assembly technology, and discloses a prefabricated assembly and precise positioning system for pedestal formwork platforms based on BIM technology. By introducing BIM technology, digital design and standardized production of pedestal formwork are achieved, improving the quality and consistency of the formwork. Furthermore, by combining high-precision positioning technology with an intelligent calibration system, the accuracy of formwork installation is improved, rework and material waste are reduced, construction efficiency is increased, and the development of the building construction industry towards intelligence and digitization is further promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of assembling a pedestal formwork platform, and in particular to a pedestal formwork platform prefabrication assembly and precise positioning system based on BIM technology. Background Art

[0002] In construction projects, the foundation pile is a crucial component of the foundation structure, and its construction quality is directly related to the structural safety of the entire building. Traditional foundation pile construction typically involves on-site custom formwork, manual installation, and measurement and positioning. Formwork production often relies on manual labor, and the size and shape of the formwork must be adjusted according to specific site conditions. This method is not only inefficient, but also difficult to ensure formwork quality, making it prone to deviations, resulting in uneven concrete pouring or foundation pile shapes that do not meet design requirements.

[0003] Furthermore, traditional formwork installation relies on manual measurement and installation. Even with the use of tools like laser rangefinders, it's still difficult to ensure absolutely precise formwork positioning. Especially in large-scale foundation projects, formwork misalignment or installation errors can cause the concrete structure to shift, leading to significant quality risks. This construction method not only easily leads to extensive rework, wasting manpower, material resources, and time, but also increases the overall cost of the project.

[0004] With the digital transformation of the construction industry, how to improve the accuracy and efficiency of prefabrication and installation of pedestal formwork has become an issue that needs to be urgently addressed. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a prefabrication, assembly and precise positioning system for a pedestal formwork platform based on BIM technology to solve the problems of low accuracy and efficiency in the prefabrication and assembly process of traditional pedestal formwork.

[0006] The present invention discloses a prefabrication, assembly and precise positioning system for a cap formwork platform based on BIM technology, which includes a BIM model unit, a formwork prefabrication unit, an on-site assembly unit, a high-precision positioning unit and an intelligent calibration unit; wherein,

[0007] The BIM model unit is used to create a three-dimensional digital model of the cap formwork platform, which includes the size, shape, material properties and installation design position of each module of the cap formwork;

[0008] The template prefabrication unit is used to manufacture the template based on the three-dimensional digital model;

[0009] The on-site assembly unit is used to assemble the cap formwork platform according to the three-dimensional digital model at the construction site;

[0010] The high-precision positioning unit is used to locate the position of the template in real time and obtain the real-time positioning data of the template during the assembly process;

[0011] The intelligent calibration unit is used to calculate a calibration path based on the real-time positioning data and the template's installation design position data in the 3D digital model when an error occurs between the real-time positioning data and the template's installation design position data in the 3D digital model, and feed the calibration path back to the on-site assembly unit;

[0012] The on-site assembly unit is also used to adjust the assembly position of the template according to the calibration path provided by the intelligent calibration unit when assembling the base template platform.

[0013] Furthermore, the BIM model unit includes a template design subunit and an installation path planning subunit;

[0014] Among them, the template design subunit is used to generate a three-dimensional digital model of the foundation template platform according to the target building structure data;

[0015] The installation path planning subunit is used to generate an initial installation path of the template according to the installation design position of the template.

[0016] Furthermore, the calculation of the calibration path based on the real-time positioning data and the installation design position data of the template in the three-dimensional digital model includes:

[0017] Calculating the displacement error and angle error of the template based on the real-time positioning data collected by the high-precision positioning unit and the installation design position data of the template in the three-dimensional digital model, and using them as first error data;

[0018] Collecting environmental data of the current construction site, including the location of obstacles around the template, available space, and the location of construction equipment;

[0019] Perform data alignment operations on the current construction site's environmental data and the 3D digital model;

[0020] A calibration path is generated by a fast random tree algorithm based on the first error data, environmental data of the current construction site, the current position, and the target position; wherein the target position is determined based on the initial installation path and the first error data, and the target position includes any position in the initial installation path except the starting installation position.

[0021] Furthermore, each first error data is recorded to form second error data that changes with time;

[0022] An error trend prediction model is constructed based on historical error data and historical error change trends through a time series analysis algorithm;

[0023] Obtaining the timestamp of the latest first error data, and obtaining error data within a preset time period from the second error data to the time corresponding to the current timestamp as third error data;

[0024] Predicting an error change trend based on the third error data using an error trend prediction model as a first error change trend;

[0025] An error region of the template is determined according to the first error variation trend and the initial installation path, and a target position is determined according to the determined error region and the first error variation trend.

[0026] Furthermore, the process of generating a calibration path by a fast random algorithm includes:

[0027] The current position of the template is used as the starting node of the path tree, and candidate nodes are randomly generated based on the environmental data of the current construction phenomenon, and the candidate nodes are located within the available space;

[0028] Select the node closest to the candidate node from the existing nodes in the path tree, calculate the path from the node to the candidate node, and expand the path tree to use the candidate node that meets the requirements as the new node of the path tree;

[0029] After the path tree is expanded, the path cost from the starting node to the new node is calculated, and the path with the smallest path cost is selected from multiple expanded paths as the current optimal path;

[0030] When it is determined that there is a new node whose distance from the target position is within a preset distance, the optimal path from the starting node to the new node is used as the calibration path.

[0031] Furthermore, in the process of calculating the path cost from the starting node to the new node, the gradient of the path cost is calculated by the gradient descent method, the node positions in the path are fine-tuned to reduce the path cost, and the reduced path cost is used as the final path cost calculation result.

[0032] Furthermore, while the on-site assembly unit adjusts the assembly position of the template according to the calibration path provided by the intelligent calibration unit, it continues to monitor the construction site environment. When the environment at the construction site changes, the candidate nodes are regenerated starting from the path area affected by the environmental change through a fast random algorithm, the path tree is dynamically updated, and the original calibration path is locally optimized and adjusted.

[0033] Furthermore, the on-site assembly unit assembles the pedestal formwork platform according to the three-dimensional digital model at the construction site, including assembling according to the picture displayed by comparing the actual installation condition of the formwork with the three-dimensional digital model of the pedestal formwork platform.

[0034] Furthermore, the display screen of the actual installation status of the template is obtained based on the AR device, specifically including:

[0035] The camera of the AR device collects the installation image of the template, and the processing unit of the AR device performs data processing and analysis operations based on the collected installation image data and the real-time positioning data collected by the high-precision positioning unit to obtain the three-dimensional coordinates of the template, and AR display is performed based on the three-dimensional coordinates of the template.

[0036] Furthermore, the comparative display screen also includes an initial installation path and a calibration path.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] This invention, through the introduction of BIM technology, enables digital design and standardized production of foundation formwork, improving the quality and consistency of the formwork. Furthermore, by combining high-precision positioning technology with an intelligent calibration system, it improves formwork installation accuracy, reduces rework and material waste, and increases construction efficiency, further promoting the intelligent and digital development of the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute part of the economic application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0040] Figure 1 This is a structural schematic diagram of a pedestal template platform prefabrication assembly and precise positioning system based on BIM technology disclosed in Example 1 of the present invention. DETAILED DESCRIPTION

[0041] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] Example 1

[0043] The present invention discloses a prefabricated assembly and precise positioning system for the pedestal template platform based on BIM technology. Figure 1 , Figure 1 This is a structural diagram of a prefabrication, assembly and precise positioning system for a cap formwork platform based on BIM technology disclosed in an embodiment of the present invention. The system includes a BIM model unit, a formwork prefabrication unit, an on-site assembly unit, a high-precision positioning unit and an intelligent calibration unit; wherein,

[0044] The BIM model unit is used to create a three-dimensional digital model of the pedestal formwork platform, which includes the size, shape, material properties and installation design position of each module of the pedestal formwork.

[0045] Among them, the BIM model unit includes a template design subunit and an installation path planning subunit.

[0046] The template design subunit is used to generate a three-dimensional digital model of the foundation template platform based on the target building structure data.

[0047] Specifically, the formwork design subunit's primary function is to create a 3D digital model of the formwork platform based on the specific requirements of the construction project. This 3D digital model includes the dimensions, shape, material properties, and specific installation design locations of each formwork module. Based on the architectural design's structural data, such as building dimensions and the specifications of the formwork platform, a digital model of each formwork module is automatically generated. BIM modeling software is then used to create a 3D digital model, including but not limited to the formwork's dimensions, shape, material type, and strength. This generated 3D digital model serves as the core data reference for the entire construction process, and subsequent prefabrication and assembly will be based on this model.

[0048] The installation path planning subunit is used to generate an initial installation path of the template according to the installation design position of the template.

[0049] Specifically, the template installation path is generated through a path planning algorithm, which takes into account factors such as the feasibility of transportation, the size of the template, and the layout of the construction site.

[0050] The formwork prefabrication unit is used to manufacture formwork based on a three-dimensional digital model.

[0051] Specifically, the formwork prefabrication unit reads the formwork geometry data (size, shape, etc.) from the 3D digital model and transmits this data to the prefabrication factory's processing equipment (such as CNC machine tools and laser cutting machines). This data allows for standardized, modular production based on the defined formwork specifications. Furthermore, quality inspections are performed during the formwork production process to ensure the accuracy and consistency of each formwork module.

[0052] The on-site assembly unit is used to assemble the slab formwork platform according to the three-dimensional digital model at the construction site.

[0053] The high-precision positioning unit is used to locate the position of the template in real time and obtain real-time positioning data of the template during the assembly process.

[0054] Specifically, the positioning technology in the embodiments of the present invention includes but is not limited to high-precision positioning technologies such as laser scanning, GPS, total station, or RTK, and thereby achieves real-time monitoring of the three-dimensional position and angle of the template.

[0055] The intelligent calibration unit is used to calculate a calibration path based on the real-time positioning data and the installation design position data of the template in the three-dimensional digital model when an error occurs between the real-time positioning data and the installation design position data of the template in the three-dimensional digital model, and feed the calibration path back to the on-site assembly unit.

[0056] As a preferred embodiment of the present invention, an error threshold is preset, and the calculation of the calibration path is performed only when it is determined in real time that the error between the real-time positioning data and the installation design position data of the template in the three-dimensional digital model is greater than the preset error threshold.

[0057] The on-site assembly unit is also used to adjust the assembly position of the template according to the calibration path provided by the intelligent calibration unit when assembling the base template platform.

[0058] Specifically, the on-site assembly unit is responsible for assembling the template at the construction site based on the information in the three-dimensional digital model and the initial installation path, and adjusting the template position according to the calibration path provided by the intelligent calibration unit. In this embodiment of the present invention, the execution of the assembly operation includes but is not limited to execution by mechanical automated assembly equipment.

[0059] Furthermore, calculating the calibration path based on the real-time positioning data and the installation design position data of the template in the three-dimensional digital model includes:

[0060] Calculating the displacement error and angle error of the template based on the real-time positioning data collected by the high-precision positioning unit and the installation design position data of the template in the three-dimensional digital model, and using them as first error data;

[0061] Collecting environmental data of the current construction site, including the location of obstacles around the template, available space, and the location of construction equipment;

[0062] Perform data alignment operations on the current construction site's environmental data and the 3D digital model;

[0063] A calibration path is generated by a fast random tree algorithm based on the first error data, environmental data of the current construction site, the current position, and the target position; wherein the target position is determined based on the initial installation path and the first error data, and the target position includes any position in the initial installation path except the starting installation position.

[0064] Specifically, the real-time positioning data collected by the high-precision positioning unit can include the template's current position and attitude data, namely the template's X, Y, and Z coordinates in three-dimensional space, as well as its rotation angles (such as pitch, roll, and yaw). The template's design installation position data within the three-dimensional digital model can include the template's intended installation position and angle within the architectural design. The linear distance error between the current position and the design installation position is calculated, as is the rotational error between the current angle and the design angle.

[0065] LiDAR and other equipment are used to locate obstacles on the construction site, including other equipment, support structures, and temporary facilities. These obstacles are also identified, along with free space available for formwork movement and adjustment. The system also locates heavy machinery, lifting equipment, and other construction equipment that could affect formwork installation. Finally, sensor fusion algorithms are used to integrate these diverse environmental data into a unified 3D map.

[0066] The real-time collected construction site environment data is aligned with the 3D digital model in the BIM model. This step is performed because the coordinate system defined in the BIM model is different from the coordinate system of the actual construction environment, so coordinate conversion is required so that the two can work in the same reference frame. Specifically, homogeneous coordinate transformation, rotation matrix, or other coordinate transformation methods are used to convert the construction site environment data to the same coordinate system as the BIM model, ensuring that each obstacle and spatial element can be correctly mapped to the position in the BIM model, thereby ensuring the accuracy of subsequent path planning.

[0067] Furthermore, each first error data is recorded to form second error data that changes with time;

[0068] An error trend prediction model is constructed based on historical error data and historical error change trends through a time series analysis algorithm;

[0069] Obtaining the timestamp of the latest first error data, and obtaining error data within a preset time period from the second error data to the time corresponding to the current timestamp as third error data;

[0070] Predicting an error change trend based on the third error data using an error trend prediction model as a first error change trend;

[0071] An error region of the template is determined according to the first error variation trend and the initial installation path, and a target position is determined according to the determined error region and the first error variation trend.

[0072] Specifically, each time the first error data is acquired, it is recorded in a time series data structure, and the timestamp of each error value is saved. The first error data refers to the error data between the current position of the template and the designed installation position, including but not limited to displacement error and angle error. The second error data is the accumulation of the first error data, that is, a sequence of error data that changes over time. Each time the error data is updated, the system will add the new data to the second error data to form a continuous historical error data. The historical error data used here to construct the error trend prediction model can include the second error data.

[0073] Each time the latest first error data is collected, the current timestamp is recorded. This timestamp represents the time point of the current error data. The preset time period can be the past 10 minutes or the past N time steps, which is not specifically limited in the embodiment of the present invention.

[0074] In an embodiment of the present invention, the error area refers to the spatial range where the template may shift in the future. The size and shape of this area will be affected by the error change trend. If the error is predicted to be large in the future, the error area will be larger; if the error tends to be stable, the error area will be smaller. Based on the first error change trend, the system can predict the offset direction and possible offset amplitude of the template, and determine the error area of the template by combining these prediction results with the initial installation path. The target position refers to the position to which the template needs to be moved. The system will adjust the target position according to the position of the error area and the initial installation path. For example, if the current error change trend is to the left, the target position is set to the right side of the installation path. Specifically, the error change trend represents the error caused by uncontrollable physical factors, that is, the error will still exist during the calibration process, such as installation errors caused by factors such as terrain, mechanized equipment performing installation, and manual installation.

[0075] Furthermore, the process of generating a calibration path by a fast random algorithm includes:

[0076] The current position of the template is used as the starting node of the path tree, and candidate nodes are randomly generated based on the environmental data of the current construction phenomenon, and the candidate nodes are located within the available space;

[0077] Select the node closest to the candidate node from the existing nodes in the path tree, calculate the path from the node to the candidate node, and expand the path tree to use the candidate node that meets the requirements as the new node of the path tree;

[0078] After the path tree is expanded, the path cost from the starting node to the new node is calculated, and the path with the smallest path cost is selected from multiple expanded paths as the current optimal path;

[0079] When it is determined that there is a new node whose distance from the target position is within a preset distance, the optimal path from the starting node to the new node is used as the calibration path.

[0080] Specifically, path cost is a measure of path quality. In the embodiment of the present invention, the factors considered include the following:

[0081] Path length: The shorter the path, the lower the cost.

[0082] Obstacle avoidance capability: Whether the path effectively avoids obstacles. Paths close to obstacles have higher costs, while paths with a greater safety distance have lower costs.

[0083] Path smoothness: Sharp turns or large angle changes in the path will also increase the cost, and smoother paths have lower costs.

[0084] Furthermore, in the process of calculating the path cost from the starting node to the new node, the gradient of the path cost is calculated by the gradient descent method, the node positions in the path are fine-tuned to reduce the path cost, and the reduced path cost is used as the final path cost calculation result.

[0085] Specifically, this step is the process of fine-tuning the nodes in the initial path cost, taking the initial path cost as the starting point of optimization.

[0086] The gradient represents the derivative of a function, reflecting the rate of change of the function at a specific point. In the path optimization of this embodiment, gradient calculation is used to determine how to adjust the node positions along the path to reduce the path cost. By calculating the gradient of the path cost relative to the node positions, the direction in which the cost decreases most rapidly is found.

[0087] In each iteration, the position of the node in the path is adjusted according to the size and direction of the gradient. The update rule of the gradient descent method is as follows:

[0088]

[0089] in, is the new position of the node, α is the learning rate, which controls the step size of each update. is the gradient of the path cost with respect to the node position.

[0090] By iteratively updating the position of each node in the path, the path cost is gradually reduced until a local optimum is reached or the cost converges. After each node position update, the adjusted path cost is recalculated, and the new path cost is lower than the previous cost. The gradient descent process continues iteratively until the path cost decreases below a set threshold, indicating that the path has reached a local optimum.

[0091] The process of optimizing path costs through gradient descent can effectively improve the quality of the paths generated by the fast randomized algorithm (RRT). By gradually adjusting the node positions in the path, the path cost is reduced, ultimately generating a smoother, safer, and more efficient path.

[0092] Furthermore, while the on-site assembly unit adjusts the assembly position of the template according to the calibration path provided by the intelligent calibration unit, it continues to monitor the construction site environment. When the environment at the construction site changes, the candidate nodes are regenerated starting from the path area affected by the environmental change through a fast random algorithm, the path tree is dynamically updated, and the original calibration path is locally optimized and adjusted.

[0093] Example 2

[0094] Based on the first embodiment, the second embodiment also includes the following contents.

[0095] Furthermore, the on-site assembly unit assembles the pedestal formwork platform according to the three-dimensional digital model at the construction site, including assembling according to the picture displayed by comparing the actual installation condition of the formwork with the three-dimensional digital model of the pedestal formwork platform.

[0096] Furthermore, the display of the actual installation status of the template is obtained based on the AR device, specifically including:

[0097] The camera of the AR device collects the installation image of the template, and the processing unit of the AR device performs data processing and analysis operations based on the collected installation image data and the real-time positioning data collected by the high-precision positioning unit to obtain the three-dimensional coordinates of the template, and AR display is performed based on the three-dimensional coordinates of the template.

[0098] Furthermore, the comparative display screen also includes an initial installation path and a calibration path.

[0099] Specifically, at the construction site, the on-site assembly unit needs to guide the assembly of the formwork based on the 3D digital model in the BIM model. In this example, the actual installation of the formwork is displayed using AR equipment and compared with the BIM model, allowing construction workers to see in real time the differences between the installation progress and the design requirements.

[0100] The AR device's camera can capture real-time images of the formwork being installed at the construction site and transmit this image data to the device's processing unit, which combines it with real-time positioning data to accurately calculate the formwork's three-dimensional coordinates. This real-time positioning data, in addition to the positioning data collected by the high-precision positioning unit, can also include positioning tag technologies such as RFID, which are identified by the AR device's camera to determine the formwork's location.

[0101] Through augmented reality technology, the real scene and the virtual BIM model, that is, the three-dimensional digital model, can be superimposed on the AR device. The current installation position of the template is presented in the form of a 3D model in AR and compared with the designed position in the BIM model.

[0102] The comparison display immediately shows the difference between the actual template installation and the design. Especially when the initial installation path is superimposed with the calibration path, it can be determined what adjustments need to be made to the template to ensure the template installation accuracy, thereby reducing rework and construction delays.

[0103] Finally, it should be noted that the embodiment of the present invention discloses a prefabricated assembly and precise positioning system for a pedestal formwork platform based on BIM technology, which discloses only a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A prefabrication, assembly and precise positioning system for the cap formwork platform based on BIM technology, characterized in that: The system comprises: The BIM model unit is used to create a three-dimensional digital model of the cap formwork platform, which includes the size, shape, material properties and installation design position of each module of the cap formwork; The template prefabrication unit is used to manufacture the template based on the three-dimensional digital model; The on-site assembly unit is used to assemble the cap formwork platform according to the three-dimensional digital model at the construction site; The high-precision positioning unit is used to locate the position of the template in real time and obtain the real-time positioning data of the template during the assembly process; The intelligent calibration unit is used to generate a calibration path based on the first error data, environmental data of the current construction site, the current position and the target position by using a fast random tree algorithm when a first error data appears between the real-time positioning data and the installation design position data of the template in the three-dimensional digital model, and feed the calibration path back to the on-site assembly unit; The on-site assembly unit is further used to adjust the assembly position of the template according to the calibration path provided by the intelligent calibration unit when assembling the base template platform; The process of determining the target location includes: Recording each first error data to form second error data that changes over time, obtaining the timestamp of the latest first error data, and obtaining error data within a preset time period from the second error data to the time corresponding to the current timestamp as third error data; An error trend prediction model is constructed based on historical error data and historical error change trends through a time series analysis algorithm; The error change trend is predicted based on the third error data through the error trend prediction model as the first error change trend; the error area of the template is determined according to the first error change trend and the initial installation path, and the target position is determined according to the determined error area and the first error change trend.

2. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 1 is characterized in that: The BIM model unit includes a template design subunit and an installation path planning subunit; Among them, the template design subunit is used to generate a three-dimensional digital model of the foundation template platform according to the target building structure data; The installation path planning subunit is used to generate an initial installation path of the template according to the installation design position of the template.

3. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 2 is characterized in that: The first error data includes displacement error and angle error; the environmental data includes the location of obstacles around the template, available space, and the location of construction equipment; Before generating a calibration path based on the first error data, the environmental data of the current construction site, the current position and the target position by using a fast random tree algorithm, an operation of aligning the environmental data of the current construction site with the three-dimensional digital model data is performed.

4. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 3 is characterized in that: The process of generating a calibration path using a fast random algorithm includes: The current position of the template is used as the starting node of the path tree, and candidate nodes are randomly generated based on the environmental data of the current construction phenomenon, and the candidate nodes are located within the available space; Select the node closest to the candidate node from the existing nodes in the path tree, calculate the path from the node to the candidate node, and expand the path tree to use the candidate node that meets the requirements as the new node of the path tree; After the path tree is expanded, the path cost from the starting node to the new node is calculated, and the path with the smallest path cost is selected from multiple expanded paths as the current optimal path; When it is determined that there is a new node whose distance from the target position is within a preset distance, the optimal path from the starting node to the new node is used as the calibration path.

5. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 4 is characterized in that: In the process of calculating the path cost from the starting node to the new node, the gradient of the path cost is calculated by the gradient descent method, the node position in the path is fine-tuned to reduce the path cost, and the reduced path cost is used as the final path cost calculation result.

6. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 5 is characterized in that: While the on-site assembly unit adjusts the assembly position of the template according to the calibration path provided by the intelligent calibration unit, it continues to monitor the construction site environment. When the construction site environment changes, a fast random algorithm is used to regenerate candidate nodes starting from the path area affected by the environmental change, dynamically update the path tree, and locally optimize and adjust the original calibration path.

7. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 1 is characterized in that: The on-site assembly unit assembles the foundation template platform according to the three-dimensional digital model at the construction site, including assembling according to the actual installation situation of the template and the picture displayed by comparing the three-dimensional digital model of the foundation template platform.

8. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 7 is characterized in that: The display of the actual installation status of the template is obtained based on the AR device, specifically including: The camera of the AR device collects the installation image of the template, and the processing unit of the AR device performs data processing and analysis operations based on the collected installation image data and the real-time positioning data collected by the high-precision positioning unit to obtain the three-dimensional coordinates of the template, and AR display is performed based on the three-dimensional coordinates of the template.

9. The BIM-based prefabrication, assembly and precise positioning system for the cap formwork platform according to claim 8 is characterized in that: The comparative display screen also includes an initial installation path and a calibration path.

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