Prefabricated pipe pile construction total factor intelligent management method and system based on digital twinning technology

Abstract

The invention provides a prefabricated pipe pile construction total factor intelligent management method and system based on the digital twinborn technology, and relates to the technical field of construction management, a three-dimensional model is established through the BIM technology, pipe pile attributes are bound, construction data are collected in real time through the Internet of Things, and a dynamic digital twinborn body is formed. Through integration of an API interface and a project management platform, an AI algorithm is used for analyzing data, resource allocation and construction plans are dynamically adjusted, instructions are optimized and pushed to the site, construction efficiency and installation precision are improved, and the problems that traditional management is low in efficiency and large in error are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of construction management, and in particular to a method and system for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology. Background Art

[0002] Currently, precast pipe pile construction management generally relies on manual record-keeping and on-site coordination, resulting in inefficient and error-prone information transfer. Data from various stages, including production, transportation, construction, and maintenance, is isolated and lacks a unified management platform, leading to information asymmetry and traceability difficulties. For example, traditional BIM models are designed only for visual display and lack deep integration with real-time construction data. This makes it impossible to dynamically reflect the full lifecycle status of precast pipe piles, hindering refined management.

[0003] Existing smart construction site systems often utilize independent modular designs. Data interoperability between systems, such as quality monitoring, progress management, and safety warnings, is poor, creating "data silos." The application of technologies like the Internet of Things and Building Information Modeling (BIM) has yet to achieve full integration, leading to delayed response times and inefficient resource allocation during construction. For example, the inventory status of prefabricated pipe piles is out of sync with the construction progress, easily leading to wasted resources and project delays.

[0004] For large, complex projects like airport terminals, where the construction environment is highly variable and the number of piles is enormous, traditional management models struggle to achieve real-time dynamic monitoring and accurate decision-making. Existing technologies lack the ability to intelligently analyze multi-dimensional data such as geological conditions, construction errors, and environmental parameters, making them unable to effectively support quality traceability, risk warning, and the achievement of green construction goals. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a full-factor intelligent management method and system for prefabricated pipe pile construction based on digital twin technology, which can accurately predict progress deviations and quality risks, significantly improve construction efficiency and installation accuracy, and solve the problems of low efficiency and large errors in traditional manual management.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A comprehensive intelligent management method for prefabricated pipe pile construction based on digital twin technology, including:

[0008] Building a 3D model of prefabricated pipe piles based on BIM technology, binding the pile attribute information to the 3D model, and using IoT devices to collect real-time construction data during the construction phase to form a dynamically updated digital twin.

[0009] Integrate the digital twin with the project management platform through a standardized API interface, and use AI algorithms to perform real-time analysis on the construction dynamic data to obtain analysis results;

[0010] Dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results;

[0011] The model parameters of the digital twin are automatically updated according to the construction dynamic data and the analysis results, and optimization instructions are pushed to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

[0012] Preferably, a three-dimensional model of prefabricated pipe piles is established based on BIM technology, the pile attribute information is bound to the three-dimensional model, and construction dynamic data during the construction phase is collected in real time through IoT devices to form a dynamically updated digital twin, including:

[0013] A three-dimensional model of prefabricated pipe piles is established based on BIM technology, and the geometric parameters and physical properties of the pipe piles are defined through the three-dimensional model; the geometric parameters include length, diameter, and cross-sectional shape; the physical properties include material strength and bending stiffness;

[0014] Associating the pipe pile attribute information with the three-dimensional model to form a static data layer; the pipe pile attribute information is static data of the entire life cycle of the pipe pile; the static data includes: production batch, quality inspection report and transportation record;

[0015] The construction dynamic data during the construction phase is collected in real time through IoT devices, and the construction dynamic data is uploaded to a cloud database to form a dynamic data layer;

[0016] The static data layer is integrated with the dynamic data layer, and the status of the pipe piles is dynamically updated in the three-dimensional model to form a digital twin with full life cycle visualization; the status of the pipe piles includes: unproduced, shipped, and constructed.

[0017] Preferably, the construction dynamic data includes: pipe pile installation position, construction load, environmental temperature and humidity, and geological monitoring data.

[0018] Preferably, the digital twin is integrated with the project management platform through a standardized API interface, and the construction dynamic data is analyzed in real time using an AI algorithm to obtain analysis results, including:

[0019] Integrate the 3D model interface in the digital twin with the project management platform through a standardized API interface to achieve real-time synchronization of construction progress data, quality acceptance results, and safety monitoring indicators;

[0020] Cleaning the construction dynamic data and marking the data source and timestamp to obtain structured analysis data;

[0021] Use historical construction data to train the initial LSTM algorithm model to obtain a trained quality risk analysis model;

[0022] The structured analysis data is input into a quality risk analysis model to generate analysis results; and the analysis results are sent to the project management platform for visualization of the analysis results.

[0023] Preferably, the analysis results include construction progress correction plans, quality hazard warnings and safety risk prompts.

[0024] Preferably, the input features of the LSTM algorithm model include: time series features X t , spatial feature G t and environmental characteristics E t ; in, is the dynamic indicator of the nth time series feature, is the spatial correlation parameter of the mth spatial feature, is the environmental parameter of the kth environmental feature.

[0025] Preferably, the dynamic forget gate f of the LSTM algorithm model t The calculation formula is: The adaptive input gate i of the LSTM algorithm model t The calculation formula is: t =σ(W i ·[t t-1 ,X t ]+U i ∈ t +b i ); The formula for updating the environment compensation cell state of the LSTM algorithm model is: The calculation formula of the multimodal output gate of the LSTM algorithm model is: in, is the geological stability attenuation factor, ΔS t is the current geological subsidence rate, β is the sensitivity coefficient, σ(·) is the Sigmoid function, U f 、U i 、U C and U o are all spatial / environmental feature weight matrices, b f 、b i 、b C and b o Both are bias terms, W f 、W i 、W C and Wo Both are input feature weight matrices, h t-1 is the hidden state of the previous time step, G t is the spatial eigenvector, ∈ t is the comprehensive index of construction error, ∈ t =‖Pile positioning deviation‖2+‖verticality deviation‖2, is the candidate cell state, C t is the current cell state, tanh(·) is the hyperbolic tangent function, γ is the environmental compensation coefficient, γ is dynamically adjusted according to the environmental characteristics, o t is the output gate activation value, h t The current hidden state.

[0026] Preferably, dynamically adjusting resource allocation and construction plans based on the digital twin and the analysis results includes:

[0027] Combining the real-time updated status of the pipe piles in the digital twin with the analysis results to predict resource demand and resource allocation priorities in the future period;

[0028] Automatically generate pipe pile production plan adjustment instructions and transportation batch optimization plans based on the resource demand and priority, and synchronize them to prefabricated pipe pile manufacturers and logistics service providers through the supply chain management system to ensure that inventory levels match construction progress;

[0029] In the digital twin, based on the GIS overlay analysis method, the pile foundation layout is optimized according to the construction dynamic data, the pipe pile installation sequence and the construction node schedule are adjusted, and a construction plan is obtained. The construction plan is then issued through the project management platform;

[0030] The pipe pile production plan adjustment instructions, the transportation batch optimization plan and the construction plan are synchronized to the three-level management nodes of the group headquarters, branches and subsidiaries, and project departments through the cloud platform, and the adjustment areas are marked in the digital twin.

[0031] Preferably, the optimization instructions include construction correction instructions, resource replenishment instructions and safety management and control instructions.

[0032] A comprehensive intelligent management method for prefabricated pipe pile construction based on digital twin technology, including:

[0033] A twin construction unit is used to build a three-dimensional model of prefabricated pipe piles based on BIM technology, bind the pipe pile attribute information to the three-dimensional model, and collect construction dynamic data in real time through IoT devices during the construction phase to form a dynamically updated digital twin;

[0034] A construction analysis unit, configured to integrate the digital twin with the project management platform through a standardized API interface, and to perform real-time analysis of the construction dynamic data using an AI algorithm to obtain analysis results;

[0035] a dynamic adjustment unit, configured to dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results;

[0036] A feedback unit is used to automatically update the model parameters of the digital twin based on the construction dynamic data and the analysis results, and push optimization instructions to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

[0037] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0038] This invention achieves dynamic monitoring of the entire lifecycle of prefabricated pipe piles, from production and transportation to construction, through the deep integration of digital twin technology and BIM models. AI algorithms analyze multi-source data in real time, accurately predicting schedule deviations and quality risks, significantly improving construction efficiency and installation accuracy, and addressing the low efficiency and high error rates of traditional manual management. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in 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 paying any creative work.

[0040] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0041] Figure 2 A flowchart for constructing a digital twin provided by an embodiment of the present invention;

[0042] Figure 3 A schematic diagram of the project management platform interface provided by an embodiment of the present invention;

[0043] Figure 4 A schematic diagram of the raw data interface for pipe pile production provided by an embodiment of the present invention;

[0044] Figure 5 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] The purpose of this invention is to provide a full-factor intelligent management method and system for prefabricated pipe pile construction based on digital twin technology, which can accurately predict progress deviations and quality risks, significantly improve construction efficiency and installation accuracy, and solve the problems of low efficiency and large errors in traditional manual management.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology, including:

[0049] Step 100: Build a 3D model of the prefabricated pipe piles based on BIM technology, bind the pile attribute information to the 3D model, and collect construction dynamic data in real time through IoT devices to form a dynamically updated digital twin.

[0050] Step 200: Integrate the digital twin with the project management platform through a standardized API interface, and use AI algorithms to perform real-time analysis on construction dynamic data to obtain analysis results;

[0051] Step 300: Dynamically adjust resource allocation and construction plans based on the digital twin and analysis results;

[0052] Step 400: Automatically update the model parameters of the digital twin based on the construction dynamic data and analysis results, and push optimization instructions to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

[0053] like Figure 2 As shown, this embodiment establishes a three-dimensional model of prefabricated pipe piles based on BIM technology, binds the pipe pile attribute information to the three-dimensional model, and collects construction dynamic data in real time during the construction phase through IoT devices to form a dynamically updated digital twin, including:

[0054] A three-dimensional model of prefabricated pipe piles is established based on BIM technology, and the geometric parameters and physical properties of the pipe piles are defined through the three-dimensional model; the geometric parameters include length, diameter, and cross-sectional shape; the physical properties include material strength and bending stiffness;

[0055] Associating the pipe pile attribute information with the three-dimensional model to form a static data layer; the pipe pile attribute information is static data of the entire life cycle of the pipe pile; the static data includes: production batch, quality inspection report and transportation record;

[0056] The construction dynamic data during the construction phase is collected in real time through IoT devices, and the construction dynamic data is uploaded to a cloud database to form a dynamic data layer;

[0057] The static data layer is integrated with the dynamic data layer, and the status of the pipe piles is dynamically updated in the three-dimensional model to form a digital twin with full life cycle visualization; the status of the pipe piles includes: unproduced, shipped, and constructed.

[0058] Preferably, the construction dynamic data includes: pipe pile installation position, construction load, environmental temperature and humidity, and geological monitoring data.

[0059] Specifically, this embodiment uses BIM technology, using Revit or Tekla, to create a 3D geometric model of prefabricated pipe piles. The model precisely defines the pile's geometric parameters, including length, diameter, and cross-sectional shape, to ensure full consistency with actual production specifications. Furthermore, the physical properties of the piles, such as the concrete grade and steel reinforcement ratio for material strength, and the elastic modulus and section moment of inertia for flexural stiffness, are defined within the BIM platform to provide basic data support for subsequent mechanical simulations. After modeling is complete, the model is exported in a universal format to ensure cross-platform compatibility.

[0060] Through the BIM platform's attribute management capabilities, static data covering the entire lifecycle of prefabricated pipe piles is linked to the 3D model. This static data includes production batch numbers, used to trace the source of raw materials; quality inspection reports, including compressive strength test results and ultrasonic flaw detection records; and transportation records, detailing the logistics company and delivery time points. This data is stored in a structured table format in a local or cloud database and linked to the corresponding pipe pile instance in the BIM model using unique identifiers such as the pipe pile ID, forming a static data layer that can be queried at any time.

[0061] IoT devices are deployed at construction sites to collect real-time construction data. This includes: Pre-embedded RFID tags and GPS positioning modules are used to obtain the real-time installation location and time of piles; stress sensors are installed at the connection between the pile driver and the piles to monitor pressure, vibration frequency, and load distribution during construction; temperature and humidity sensors are deployed in the construction area to monitor changes in air temperature and humidity; and groundwater level sensors and geological radar are used to obtain real-time data on soil bearing capacity, groundwater level, and geological settlement rate. The collected data is uploaded to a cloud database via wireless communication technologies such as LoRa or 5G networks, where it is stored and categorized by timestamp, forming a dynamic data layer.

[0062] In the cloud data processing platform, the static data layer and the dynamic data layer are integrated. The static attributes and dynamic data are matched through the unique identifier of the pipe pile, and the status label of each pipe pile in the BIM model is updated in real time, such as unproduced, shipped, or constructed. When the pipe pile is installed, its status automatically switches from shipped to constructed, and different states are distinguished by color in the BIM interface, for example, red represents unproduced and green represents constructed. Dynamic data such as installation coordinate deviation or load overlimit information are superimposed on the three-dimensional model through visual charts, and detailed construction logs can be clicked to query. The final digital twin can be accessed in real time through the web or mobile terminal, providing full-factor, full-cycle visualization support for construction management.

[0063] Preferably, the digital twin is integrated with the project management platform through a standardized API interface, and the construction dynamic data is analyzed in real time using an AI algorithm to obtain analysis results, including:

[0064] Integrate the 3D model interface in the digital twin with the project management platform through a standardized API interface to achieve real-time synchronization of construction progress data, quality acceptance results, and safety monitoring indicators;

[0065] Cleaning the construction dynamic data and marking the data source and timestamp to obtain structured analysis data;

[0066] Use historical construction data to train the initial LSTM algorithm model to obtain a trained quality risk analysis model;

[0067] The structured analysis data is input into a quality risk analysis model to generate analysis results; and the analysis results are sent to the project management platform for visualization of the analysis results.

[0068] Preferably, the analysis results include construction progress correction plans, quality hazard warnings and safety risk prompts.

[0069] Alternatively, as Figure 3As shown, the project management platform of this embodiment provides real-time monitoring and management functions of process status through the user interface. The user enters the process management interface through the navigation menu, which displays the real-time status and quantity of processes including on-site acceptance, transportation, acceptance, construction and warehousing. The process statistics on the right side intuitively present the distribution of each process in the form of a pie chart, and the user can filter and analyze by setting the start time and end time. At the same time, the system integrates the BIM model, which supports users to view the location and distribution of pipe piles in a three-dimensional environment to assist construction management. The process management module also supports QR code generation and management, which is convenient for the identification and recording of pipe piles at the construction site.

[0070] Furthermore, in this embodiment, Figure 4 A mobile terminal-based pipe pile production tracking system was demonstrated. The left interface provides basic information about the PHC prestressed pipe piles, including the unique identification of the pipe pile (component ID: 24431252), the relevant project (West Finger Corridor), the specification model (800-130-13) and related parameters (pile top elevation -8.7m). The user interface is friendly and allows users to enter detailed data by clicking on specific areas, which enhances the interactivity and usability of the system. The middle and right interfaces present detailed information on on-site acceptance and recorded data, including production date, pile maker information and manufacturer. The structured display of this information not only makes supervision and data traceability more efficient, but also ensures the transparency and traceability of information at all stages of the project. The system records and updates various data in real time, ensuring that responsible personnel can obtain and process relevant information in a timely manner during the construction process, thus realizing the digitization and informatization of production management.

[0071] Optionally, this embodiment uses RESTful API as a standardized interface protocol to connect the three-dimensional model interface of the digital twin with the project management platform. By defining a unified data exchange format, real-time two-way transmission of construction progress data, quality acceptance results and safety monitoring indicators can be achieved. For example, the installation progress information of the pipe piles in the digital twin is pushed to the Gantt chart module of the project management platform through the API. At the same time, the quality acceptance results fed back by the platform are transmitted back to the digital twin and dynamically updated in the form of tags in the BIM interface. To ensure the real-time nature of the data, the interface uses the WebSocket protocol to establish a long connection, supports data synchronization in seconds, and avoids the delay caused by the traditional polling mechanism.

[0072] Furthermore, this embodiment preprocesses the raw construction dynamic data collected by IoT devices using a data cleaning tool. First, invalid data is removed, then missing values are interpolated, and finally the data is standardized according to a unified format. For example, the installation location of the pipe piles is converted to longitude and latitude coordinates, and the load data is standardized to kilonewtons. The cleaned data is categorized by source (such as GPS, stress sensors), and timestamps are added with millisecond accuracy to form a structured analysis data set with metadata. This data set is stored in a time series database, supporting efficient query and analysis.

[0073] Furthermore, this embodiment collects historical construction data (including success cases and accident records) as a training set, covering time series characteristics (construction progress, load changes), spatial characteristics (geological parameters, pipe pile positioning deviation) and environmental characteristics (temperature and humidity, wind speed). The LSTM neural network model is built using the TensorFlow framework. The input layer receives multidimensional feature data, the hidden layer extracts temporal dependencies through long short-term memory units, and the output layer generates quality risk levels (low risk, medium risk, high risk). During the training process, the cross-validation method is used to adjust hyperparameters (such as learning rate and batch size), and the model accuracy is evaluated through the confusion matrix. The trained model is deployed on a cloud inference server to support real-time data processing.

[0074] Specifically, this embodiment inputs the structured data after real-time cleaning into the trained LSTM model, and the model outputs the quality risk prediction results (such as the risk level of pipe pile tilt in a certain area) and progress deviation analysis (such as the number of days of construction delay). The analysis results are pushed to the visualization module of the project management platform through the API and superimposed on the three-dimensional scene of the digital twin in the form of a heat map: high-risk areas are displayed as red warnings, and progress delay nodes are marked as yellow warnings. At the same time, the platform provides an interactive dashboard that supports managers to click to view detailed analysis reports (such as risk causes and recommended measures), and can issue instructions to on-site terminal equipment with one click (such as adjusting pile driver parameters). All visual data supports multi-terminal access to ensure that all parties involved in the construction have a real-time grasp of the overall status.

[0075] Preferably, the input features of the LSTM algorithm model include: time series features X t , spatial feature G t and environmental characteristics E t ; in, is the dynamic indicator of the nth time series feature, is the spatial correlation parameter of the mth spatial feature, is the environmental parameter of the kth environmental feature.

[0076] Preferably, the dynamic forget gate f of the LSTM algorithm modelt The calculation formula is: The adaptive input gate i of the LSTM algorithm model t The calculation formula is: t =σ(W i ·[h t-1 ,X t ]+U i ∈ t +b i ); The formula for updating the environment compensation cell state of the LSTM algorithm model is: The calculation formula of the multimodal output gate of the LSTM algorithm model is: in, is the geological stability attenuation factor, ΔS t is the current geological subsidence rate, β is the sensitivity coefficient, σ(·) is the Sigmoid function, U f 、U i 、U C and U o are all spatial / environmental feature weight matrices, b f 、b i 、b C and b o Both are bias terms, W f 、W i 、W C and W o Both are input feature weight matrices, h t-1 is the hidden state of the previous time step, G t is the spatial eigenvector, ∈ t is the comprehensive index of construction error, ∈ t =‖Pile positioning deviation‖2+‖verticality deviation‖2, is the candidate cell state, C t is the current cell state, tanh(·) is the hyperbolic tangent function, γ is the environmental compensation coefficient, γ is dynamically adjusted according to the environmental characteristics, o t is the output gate activation value, h t The current hidden state.

[0077] Specifically, the weight matrix and bias term are automatically learned through the model training process. The LSTM model is trained using a historical construction dataset (including time series, spatial and environmental characteristics), and the back propagation algorithm is used to optimize the loss function (such as cross entropy loss), and the parameters are gradually adjusted to minimize the prediction error. The environmental compensation coefficient γγ is dynamically calculated through environmental characteristic data (such as temperature, humidity, and wind speed). For example, it is generated through linear interpolation or a rule engine based on the relationship between the real-time data of the environmental sensor and the preset threshold. The sensitivity coefficient β is determined through engineering experience and experimental calibration (such as the grid search method), and the value range is usually 0.1 to 1.0. The comprehensive index of construction error ∈ t Construction accuracy is quantified by calculating the sum of the squares of the positioning deviation and verticality deviation measured on-site. The optimal configuration of other parameters (such as initial cell state and hidden layer dimensions) is selected based on model complexity and training data size, combined with cross-validation results.

[0078] Preferably, dynamically adjusting resource allocation and construction plans based on the digital twin and the analysis results includes:

[0079] Combining the real-time updated status of the pipe piles in the digital twin with the analysis results to predict resource demand and resource allocation priorities in the future period;

[0080] Automatically generate pipe pile production plan adjustment instructions and transportation batch optimization plans based on the resource demand and priority, and synchronize them to prefabricated pipe pile manufacturers and logistics service providers through the supply chain management system to ensure that inventory levels match construction progress;

[0081] In the digital twin, based on the GIS overlay analysis method, the pile foundation layout is optimized according to the construction dynamic data, the pipe pile installation sequence and the construction node schedule are adjusted, and a construction plan is obtained. The construction plan is then issued through the project management platform;

[0082] The pipe pile production plan adjustment instructions, the transportation batch optimization plan and the construction plan are synchronized to the three-level management nodes of the group headquarters, branches and subsidiaries, and project departments through the cloud platform, and the adjustment areas are marked in the digital twin.

[0083] Preferably, the optimization instructions include construction correction instructions, resource replenishment instructions and safety management and control instructions.

[0084] Specifically, this embodiment uses a time series prediction model (such as ARIMA) to predict resource demand in the next week, including the number of prefabricated pipe piles, construction machinery shifts, and manpower allocation, based on the real-time updated status of pipe piles in the digital twin (such as the number of unproduced, shipped, and constructed) and AI analysis results (progress deviation prediction, quality risk level). Combined with the construction critical path algorithm (such as the critical chain method), the priority of resource allocation is dynamically calculated to give priority to ensuring resource supply in high-risk or delayed areas. The prediction results are displayed on the digital twin interface through visual charts, supporting managers to adjust priority strategies in real time.

[0085] Based on predicted resource demand and priority, the supply chain management system automatically generates instructions for adjusting the pipe pile production plan (such as adding production batches and adjusting production line schedules) and optimizing transportation batches (such as optimizing logistics routes and transport vehicle scheduling). These instructions are synchronized in real time to the ERP systems of the prefabricated pipe pile manufacturer and logistics service provider via the Electronic Data Interchange (EDI) interface, ensuring that inventory levels are accurately aligned with construction schedules. For example, when pipe pile inventory in a certain area falls below a safety threshold, the system automatically triggers an emergency replenishment order and prioritizes the allocation of transportation resources to that area.

[0086] The digital twin integrates geographic information system (GIS) data (such as geological maps and underground pipeline distribution) with real-time construction dynamics data (pile installation coordinates and geological settlement rates), and utilizes spatial analysis tools (such as ArcGIS Engine) to optimize pile foundation layout. For example, if a geological radar detects insufficient bearing capacity in a certain area, the system automatically adjusts the pile installation sequence to avoid high-risk areas and reschedules the construction schedule. The optimized construction plan is distributed to on-site personnel's mobile devices via the project management platform, and the adjusted construction process is demonstrated in a 3D animation within the BIM interface.

[0087] Resource adjustment instructions, transportation plans, and construction plans are synchronized to the three-level management nodes of the group headquarters, branches, subsidiaries, and project departments through the cloud platform to ensure cross-level consistency of instructions. In the digital twin interface, the adjustment area is intuitively displayed using highlighted marks (such as red flashing areas indicating that correction is required, and green arrows indicating resource transportation paths). On-site personnel receive specific instructions (such as construction correction coordinates, safety control area ranges) through mobile terminals and feedback the execution results (such as calibrated data, reinforcement construction records), forming a "command-execution-feedback" closed loop. All operation logs and data change records are stored in the blockchain to ensure that the process is traceable and responsibilities are clear.

[0088] like Figure 5 As shown, corresponding to the above method, this embodiment also provides a method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology, including:

[0089] A twin construction unit is used to build a three-dimensional model of prefabricated pipe piles based on BIM technology, bind the pipe pile attribute information to the three-dimensional model, and collect construction dynamic data in real time through IoT devices during the construction phase to form a dynamically updated digital twin;

[0090] A construction analysis unit, configured to integrate the digital twin with the project management platform through a standardized API interface, and to perform real-time analysis of the construction dynamic data using an AI algorithm to obtain analysis results;

[0091] a dynamic adjustment unit, configured to dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results;

[0092] A feedback unit is used to automatically update the model parameters of the digital twin based on the construction dynamic data and the analysis results, and push optimization instructions to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

[0093] The beneficial effects of the present invention are as follows:

[0094] (1) This invention achieves dynamic monitoring of the entire life cycle of prefabricated pipe piles, from production and transportation to construction, through the deep integration of digital twin technology and BIM models. The AI algorithm's real-time analysis of multi-source data can accurately predict schedule deviations and quality risks, significantly improving construction efficiency and installation accuracy, and solving the problems of low efficiency and large errors in traditional manual management.

[0095] (2) The standardized API interface of this invention enables seamless integration of digital twins with project management platforms, IoT devices, and supply chain systems, connecting construction data flows with business flows. Through blockchain evidence storage and dynamic model parameter calibration, data traceability and authenticity are ensured, reducing resource waste (inventory costs reduced by 15%-20%) and communication costs (instruction execution accuracy increased by over 90%) caused by information asymmetry.

[0096] (3) The improved LSTM algorithm model of the present invention (such as the dynamic forget gate and environmental compensation mechanism) is deeply integrated with construction scene characteristics (geological changes and installation errors), and can generate risk warnings (such as excessive pile tilt and abnormal geological settlement) and correction plans in real time. Combined with GIS overlay analysis, it optimizes pile foundation layout, effectively avoids construction hazards under complex geological conditions, and improves project safety and structural reliability.

[0097] (4) This invention adapts to large-scale engineering scenarios such as airports and bridges through modular design, forming a replicable intelligent construction technology system. Functions such as paperless acceptance and multi-level collaborative management reduce reliance on manual experience, promote the digitalization and green transformation of the construction industry, and cultivate compound talents with both engineering and information technology skills, with significant social and economic benefits and industry demonstration value.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0099] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology, characterized in that: include: Building a 3D model of prefabricated pipe piles based on BIM technology, binding the pile attribute information to the 3D model, and using IoT devices to collect real-time construction data during the construction phase to form a dynamically updated digital twin. Integrate the digital twin with the project management platform through a standardized API interface, and use AI algorithms to perform real-time analysis on the construction dynamic data to obtain analysis results; Dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results; The model parameters of the digital twin are automatically updated according to the construction dynamic data and the analysis results, and optimization instructions are pushed to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

2. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 1 is characterized in that: A 3D model of prefabricated pipe piles is established based on BIM technology. The pile attribute information is bound to the 3D model. Furthermore, dynamic construction data during the construction phase is collected in real time through IoT devices to form a dynamically updated digital twin, including: A three-dimensional model of prefabricated pipe piles is established based on BIM technology, and the geometric parameters and physical properties of the pipe piles are defined through the three-dimensional model; the geometric parameters include length, diameter, and cross-sectional shape; the physical properties include material strength and bending stiffness; Associating the pipe pile attribute information with the three-dimensional model to form a static data layer; the pipe pile attribute information is static data of the entire life cycle of the pipe pile; the static data includes: production batch, quality inspection report and transportation record; The construction dynamic data during the construction phase is collected in real time through IoT devices, and the construction dynamic data is uploaded to a cloud database to form a dynamic data layer; The static data layer is integrated with the dynamic data layer, and the status of the pipe piles is dynamically updated in the three-dimensional model to form a digital twin with full life cycle visualization; the status of the pipe piles includes: unproduced, shipped, and constructed.

3. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 1 is characterized in that: The construction dynamic data includes: pipe pile installation position, construction load, environmental temperature and humidity, and geological monitoring data.

4. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 1 is characterized in that: The digital twin is integrated with the project management platform through a standardized API interface, and the construction dynamic data is analyzed in real time using AI algorithms to obtain analysis results, including: Integrate the 3D model interface in the digital twin with the project management platform through a standardized API interface to achieve real-time synchronization of construction progress data, quality acceptance results, and safety monitoring indicators; Cleaning the construction dynamic data and marking the data source and timestamp to obtain structured analysis data; Use historical construction data to train the initial LSTM algorithm model to obtain a trained quality risk analysis model; The structured analysis data is input into a quality risk analysis model to generate analysis results; and the analysis results are sent to the project management platform for visualization of the analysis results.

5. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 4 is characterized in that: The analysis results include construction progress correction plans, quality hazard warnings and safety risk reminders.

6. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 5 is characterized in that: The input features of the LSTM algorithm model include: time series features X t , spatial feature G t and environmental characteristics E t ; in, is the dynamic indicator of the nth time series feature, is the spatial correlation parameter of the mth spatial feature, is the environmental parameter of the kth environmental feature.

7. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 6 is characterized in that: The dynamic forget gate f of the LSTM algorithm model t The calculation formula is: The adaptive input gate i of the LSTM algorithm model t The calculation formula is: t =σ(W i ·[h t-1 ,X t ]+U i ∈ t +b i ); The formula for updating the environment compensation cell state of the LSTM algorithm model is: The calculation formula of the multimodal output gate of the LSTM algorithm model is: in, is the geological stability attenuation factor, ΔS t is the current geological subsidence rate, β is the sensitivity coefficient, σ(·) is the Sigmoid function, U f 、U i 、U C and U o are all spatial / environmental feature weight matrices, b f 、b i 、b C and b o Both are bias terms, W f 、W i 、W C and W o Both are input feature weight matrices, h t-1 is the hidden state of the previous time step, G t is the spatial eigenvector, ∈ t is the comprehensive index of construction error, ∈ t =‖Pile positioning deviation‖2+‖verticality deviation‖2, is the candidate cell state, C t is the current cell state, tanh(·) is the hyperbolic tangent function, γ is the environmental compensation coefficient, γ is dynamically adjusted according to the environmental characteristics, o t is the output gate activation value, h t The current hidden state.

8. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 6 is characterized in that: Dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results, including: Combining the real-time updated status of the pipe piles in the digital twin with the analysis results to predict resource demand and resource allocation priorities in the future period; Automatically generate pipe pile production plan adjustment instructions and transportation batch optimization plans based on the resource demand and priority, and synchronize them to prefabricated pipe pile manufacturers and logistics service providers through the supply chain management system to ensure that inventory levels match construction progress; In the digital twin, based on the GIS overlay analysis method, the pile foundation layout is optimized according to the construction dynamic data, the pipe pile installation sequence and the construction node schedule are adjusted, and a construction plan is obtained. The construction plan is then issued through the project management platform; The pipe pile production plan adjustment instructions, the transportation batch optimization plan and the construction plan are synchronized to the three-level management nodes of the group headquarters, branches and subsidiaries, and project departments through the cloud platform, and the adjustment areas are marked in the digital twin.

9. The method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology according to claim 6 is characterized in that: The optimization instructions include construction correction instructions, resource replenishment instructions and safety management and control instructions.

10. A method for intelligent management of all elements of prefabricated pipe pile construction based on digital twin technology, characterized in that: include: A twin construction unit is used to build a three-dimensional model of prefabricated pipe piles based on BIM technology, bind the pipe pile attribute information to the three-dimensional model, and collect construction dynamic data in real time through IoT devices during the construction phase to form a dynamically updated digital twin; A construction analysis unit, configured to integrate the digital twin with the project management platform through a standardized API interface, and to perform real-time analysis of the construction dynamic data using an AI algorithm to obtain analysis results; a dynamic adjustment unit, configured to dynamically adjust resource allocation and construction plans based on the digital twin and the analysis results; A feedback unit is used to automatically update the model parameters of the digital twin based on the construction dynamic data and the analysis results, and push optimization instructions to on-site personnel through mobile terminals to achieve continuous optimization of the construction process.

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