Municipal engineering construction progress intelligent management platform based on BIM

Abstract

The invention discloses a BIM-based municipal engineering construction progress intelligent management platform, and the platform comprises a data collection and sensing module which is used for collecting multi-source data of a construction site in real time through an Internet of Things sensor, an image collection device and a manual input terminal; the BIM digital twin model module is used for storing and managing a BIM model of municipal engineering; the multi-source data fusion and processing module adopts space optimization Huffman coding to realize model lightweight, and performs evidence storage on key progress data and payment node data through a block chain evidence storage mechanism; the progress intelligent analysis and decision module analyzes the deviation between the actual progress and the planned progress based on a deep learning network; and the visual interaction and early warning module is used for dynamically displaying the construction progress, the deviation analysis result and the early warning information on the project monitoring platform and supporting mobile terminal offline interaction. The invention belongs to the technical field of intelligent management platforms, and particularly relates to a BIM-based municipal engineering construction progress intelligent management platform.

Description

Technical Field

[0001] This invention belongs to the field of intelligent management platform technology, specifically referring to a BIM-based intelligent management platform for the construction progress of municipal engineering projects. Background Technology

[0002] Municipal engineering construction is characterized by complex environments, numerous participants, and tight schedules. Traditional progress management methods mainly rely on manual inspections, two-dimensional drawings, and static plans, which have the following shortcomings:

[0003] Data silos and collaboration difficulties: Data silos among project participants and the lack of a unified information sharing platform lead to delayed and error-prone information transmission. Progress data is not shared among the construction, supervision, and owner parties, making efficient collaborative management difficult.

[0004] Lack of real-time performance and accuracy: Traditional progress updates rely on manual recording and periodic reporting, which results in data lag and makes it difficult to reflect the actual progress of the construction site in a timely manner. When deviations occur in the progress, they are often discovered too late, missing the best time for adjustment.

[0005] Low visualization: Traditional progress representation methods such as Gantt charts and bar charts are not intuitive and cannot clearly express complex construction procedures and spatial relationships, leading to misunderstandings of the progress status among managers. Summary of the Invention

[0006] The technical problem that this invention aims to solve is that traditional progress management methods suffer from data isolation and difficulty in collaboration, lack of real-time performance and accuracy, and low level of visualization.

[0007] To achieve the above functions, the technical solution adopted by the present invention is as follows: a BIM-based intelligent management platform for municipal engineering construction progress, including a data acquisition and sensing module, used to collect multi-source data from the construction site in real time through IoT sensors, image acquisition devices and manual input terminals, including progress data, resource data, environmental data and equipment status data;

[0008] The BIM digital twin model module stores and manages the BIM model of municipal engineering projects. It converts the planned schedule language and the actual schedule language into a subset of codes for each component through the model component coding system, and dynamically updates the model status based on the real-time data from the data acquisition and perception module.

[0009] The multi-source data fusion and processing module uses spatially optimized Huffman coding to achieve model lightweighting, and uses a blockchain notarization mechanism to notarize key progress data and payment node data.

[0010] The intelligent progress analysis and decision-making module analyzes the deviation between the actual progress and the planned progress based on deep learning networks, optimizes the construction path using constrained genetic algorithms, and generates progress adjustment plans and resource allocation suggestions.

[0011] The visualization, interaction, and early warning module is used to dynamically display construction progress, deviation analysis results, and early warning information on the project monitoring platform, and supports offline interaction on mobile devices.

[0012] Preferably, the data acquisition and sensing module includes: multiple CCD image acquisition devices distributed at the construction site for real-time acquisition of image information of the bridge structure; an Internet of Things sensor network, including personnel positioning tags, equipment status sensors and material RFID tags; and a manual progress reporting terminal for receiving manually input progress confirmation information.

[0013] Preferably, the BIM digital twin model module converts the standard planned schedule language or standard actual schedule language into a subset of component codes through a model component coding system. The construction management digital twin system controls the display effect of each corresponding model component on the complete construction BIM model for each component coding subset and its complement, thereby obtaining a planned construction schedule model or an actual construction schedule model.

[0014] Preferably, the multi-source data fusion and processing module adopts a three-chain blockchain system architecture, including the collaborative realization of intelligent association between engineering quantities and payment nodes through the main chain, the computational side chain, and the audit side chain.

[0015] Preferably, the progress intelligent analysis and decision-making module includes a progress deviation analysis unit, which updates the actual construction progress identified by the image recognition module to the planned construction progress and analyzes the deviation between the actual construction progress and the planned construction progress; a resource conflict detection unit, which detects conflicts when multiple processes compete for the same resource based on a resource balancing algorithm; and a prediction and early warning unit, which uses a bidirectional long short-term memory network to analyze equipment status and progress trends and generate early warning information.

[0016] Preferably, the progress intelligent analysis and decision module is also used to control the display effect of each model component associated with each measuring point in the planned construction progress model or the actual construction progress model according to the threshold range in which the measuring point monitoring data is located, so as to visualize the measuring point monitoring data.

[0017] Preferably, the visualization interaction and early warning module supports 4D dynamic simulation, associates the Revit model with the task, intuitively displays the impact of a certain area's lag on the overall progress, and draws the actual progress front line on the Gantt chart using the front line analysis method. When the deviation exceeds the threshold, an early warning is activated.

[0018] Preferably, the platform also includes a collaborative management module, which is used to obtain the information of subordinate construction units at the current time through the construction party information management module, obtain the current time account information of equipment and materials through the material management module, update this information to the BIM model, and mark and highlight special construction processes that are prone to construction errors.

[0019] The beneficial effects achieved by adopting the above structure in this invention are as follows:

[0020] 1. By using IoT sensors and image recognition technology, the construction progress can be automatically collected and updated in real time, overcoming the lag problem of traditional manual inspection.

[0021] 2. Based on the BIM digital twin model, a 4D dynamic comparison between the planned progress and the actual progress is realized, intuitively displaying the progress deviation and potential impact;

[0022] 3. Employ deep learning networks and genetic algorithms to analyze and predict progress data, providing scientific progress adjustment plans and resource optimization suggestions. Attached Figure Description

[0023] Figure 1 This is an overall system diagram of the present invention. Detailed Implementation

[0024] like Figure 1 As shown, the present invention proposes a BIM-based intelligent management platform for municipal engineering construction progress, which includes a data acquisition and sensing module. This module consists of two parts: hardware and software. The hardware includes multiple CCD image acquisition devices distributed on the construction site, Internet of Things sensors (such as personnel positioning tags, equipment status sensors, material RFID tags, etc.), and manual input terminals. The software part includes image recognition algorithms, data preprocessing programs, and data transmission interfaces.

[0025] The image acquisition device connects to the server via wired or wireless network to collect image information of the construction site in real time. The image recognition module identifies the actual construction progress of the bridge based on the collected image information. The Internet of Things (IoT) sensors collect data on personnel, equipment, materials, and environment in real time, forming a comprehensive construction site perception network.

[0026] BIM Digital Twin Model Module: This module is based on the BIM model construction of municipal engineering projects. It adopts a model component coding system to uniformly identify and manage model components. By converting the standard planned schedule language or standard actual schedule language into a component coding subset, the system controls the display effect of each corresponding model component on the complete construction BIM model for each component coding subset and its complement, thereby obtaining the planned construction schedule model or the actual construction schedule model.

[0027] This module also controls the display effect of each model component associated with each measuring point in the planned construction progress model or the actual construction progress model based on the threshold range of the measuring point monitoring data, making the measuring point monitoring data visual.

[0028] Multi-source data fusion and processing module: This module integrates BIM model, IoT sensor data and blockchain evidence storage mechanism using multi-source data fusion technology. It achieves model lightweighting through spatial optimization Huffman coding, reducing data storage and transmission overhead. At the same time, it adopts a three-chain blockchain system architecture, which realizes intelligent association between engineering quantity and payment node through the collaboration of main chain, calculation side chain and audit side chain, ensuring the authenticity and immutability of key data.

[0029] Intelligent progress analysis and decision-making module: This module is the core intelligent part of the platform and includes multiple analysis units: Progress deviation analysis unit: Updates the actual construction progress identified by the image recognition module to the planned construction progress and analyzes the deviation between the actual construction progress and the planned construction progress;

[0030] Resource conflict detection unit: Based on the resource balancing algorithm, it detects conflicts when multiple processes compete for the same resource, and dynamically adjusts resources using the "earliest start time priority" or "shortest duration priority" strategy.

[0031] Prediction and early warning unit: It uses a bidirectional long short-term memory network to analyze the status and progress trend of equipment, generate early warning information, and establish a hierarchical early warning mechanism with red, yellow and blue thresholds: a blue warning is triggered after 3 days (team self-check), a yellow warning is triggered after 7 days (project manager intervention), and a red warning is triggered after 15 days (company-level resource allocation).

[0032] Visualization and Interaction & Early Warning Module: This module dynamically displays construction progress, deviation analysis results, and early warning information on the project monitoring platform (such as smartphones, tablets, or desktop computers). It supports 4D dynamic simulation, associates Revit models with tasks, and intuitively displays the impact of a certain area's lag on the overall progress. At the same time, it supports offline interaction on mobile devices, enabling basic data query and entry operations to be performed even at construction sites with poor network signals through data-model separation technology.

[0033] Workflow:

[0034] S1 Data Acquisition Phase: Multi-source data from the construction site is collected in real time through IoT sensors, image acquisition devices, and manual input terminals. The image acquisition devices collect image information from the construction site, the image recognition module identifies the actual construction progress based on the image information, and the IoT sensors collect data on personnel, equipment, materials, and the environment to form a comprehensive construction site perception network.

[0035] S2 Digital Twin Model Construction Phase: Based on the BIM model of municipal engineering, the standard planned schedule language or standard actual schedule language is converted into a subset of component codes through the model component coding system. The system controls the display effect of each model component on the complete construction BIM model for each component coding subset and its complement, thereby obtaining the planned construction schedule model or the actual construction schedule model.

[0036] S3 Data Processing and Fusion Stage: Multi-source data fusion technology is used to integrate BIM models, IoT sensor data and blockchain evidence storage mechanisms. Spatial optimization Huffman coding is used to achieve model lightweighting. A three-chain blockchain system is used to realize intelligent association between engineering quantities and payment nodes.

[0037] S4 Intelligent Analysis and Decision-Making Phase: Based on deep learning network analysis, the deviation between actual progress and planned progress is analyzed, the construction path is optimized using constrained genetic algorithm, the progress deviation analysis unit calculates the progress deviation, the resource conflict detection unit detects resource conflicts, and the prediction and early warning unit generates early warning information and adjustment suggestions.

[0038] S5 Visualization, Interaction and Early Warning Phase: Dynamically display construction progress, deviation analysis results and early warning information on the project monitoring platform. Supports 4D dynamic simulation and offline interaction on mobile devices. When the current frontline analysis method detects that the progress deviation exceeds the threshold, the hierarchical early warning machine is activated.

[0039] Example:

[0040] The following uses a municipal bridge project as an example to illustrate the specific implementation of the present invention:

[0041] In this municipal bridge project, the BIM-based intelligent management platform for municipal engineering construction progress of this invention was deployed. First, multiple CCD cameras and IoT sensors were deployed at the construction site to collect real-time image information, personnel location, equipment status and material arrival data. At the same time, a BIM model of the bridge was established and the model components were uniformly identified through a model component coding system.

[0042] During construction, the image recognition module identifies the actual construction progress based on the image information collected by the camera and updates it to the BIM digital twin model. The multi-source data fusion and processing module uses blockchain technology to store key progress data and payment node data to ensure the authenticity and immutability of the data.

[0043] The progress intelligent analysis and decision-making module analyzes the deviation between the actual progress and the planned progress based on deep learning networks. When the main tower construction progress is detected to be 5% behind schedule, the system triggers a yellow warning and optimizes the construction path through a constrained genetic algorithm, proposing resource adjustment suggestions to increase night shift construction. At the same time, the system detects that multiple processes are competing for tower crane resources and adopts the "earliest start time priority" strategy to dynamically adjust resources.

[0044] The visualization, interaction, and early warning module dynamically displays construction progress, deviation analysis results, and early warning information on the project monitoring platform. All project participants can view the progress status in real time through the web and mobile terminals and make collaborative decisions. Through the implementation of the platform, the project successfully controlled the progress deviation within 3%, reduced the schedule delays caused by resource conflicts, and improved the overall management efficiency of the project.

[0045] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A BIM-based intelligent management platform for municipal engineering construction progress, comprising a data acquisition and sensing module, used to collect multi-source data from the construction site in real time through IoT sensors, image acquisition devices and manual input terminals, including progress data, resource data, environmental data and equipment status data; The BIM digital twin model module stores and manages the BIM model of municipal engineering projects. It converts the planned schedule language and the actual schedule language into a subset of codes for each component through the model component coding system, and dynamically updates the model status based on the real-time data from the data acquisition and perception module. The multi-source data fusion and processing module uses spatially optimized Huffman coding to achieve model lightweighting, and uses a blockchain notarization mechanism to notarize key progress data and payment node data. The intelligent progress analysis and decision-making module analyzes the deviation between the actual progress and the planned progress based on deep learning networks, optimizes the construction path using constrained genetic algorithms, and generates progress adjustment plans and resource allocation suggestions. The visualization, interaction, and early warning module is used to dynamically display construction progress, deviation analysis results, and early warning information on the project monitoring platform, and supports offline interaction on mobile devices.

2. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The data acquisition and sensing module includes: multiple CCD image acquisition devices distributed at the construction site, used to acquire image information of the bridge structure in real time; Internet of Things (IoT) sensor networks include personnel location tags, equipment status sensors, and material RFID tags; The manual progress verification terminal is used to receive progress confirmation information manually entered.

3. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The BIM digital twin model module converts the standard planned schedule language or standard actual schedule language into a subset of component codes through a model component coding system. The construction management digital twin system controls the display effect of each corresponding model component on the complete construction BIM model based on the component code subset and its complement, thereby obtaining the planned construction schedule model or the actual construction schedule model.

4. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The multi-source data fusion and processing module adopts a three-chain blockchain system architecture, which includes the main chain, the computational side chain, and the audit side chain to achieve intelligent association between engineering quantities and payment nodes.

5. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The intelligent progress analysis and decision-making module includes a progress deviation analysis unit, which updates the actual construction progress identified by the image recognition module to the planned construction progress and analyzes the deviation between the actual construction progress and the planned construction progress. The resource conflict detection unit detects conflicts when multiple processes compete for the same resource based on a resource balancing algorithm. The prediction and early warning unit uses a bidirectional long short-term memory network to analyze the equipment status and progress trends, and generates early warning information.

6. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The progress intelligent analysis and decision-making module is also used to control the display effect of each model component associated with each measuring point in the planned construction progress model or the actual construction progress model according to the threshold range of the measuring point monitoring data, so as to visualize the measuring point monitoring data.

7. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The visualization interaction and early warning module supports 4D dynamic simulation, associates Revit models with tasks, intuitively displays the impact of a certain area's lag on the overall progress, and draws the actual progress front line on the Gantt chart using the front line analysis method. When the deviation exceeds the threshold, an early warning is activated.

8. The BIM-based intelligent management platform for municipal engineering construction progress according to claim 1, characterized in that: The platform also includes a collaborative management module, which is used to obtain information about subordinate construction units at the current time through the construction party information management module, obtain the current time account information of equipment and materials through the materials management module, update this information to the BIM model, and mark and highlight special construction processes that are prone to construction errors.

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