Bridge construction real-time management method and system

Abstract

The invention discloses a real-time management method and system for bridge construction, and relates to the technical field of bridge construction management, and the method comprises the steps: carrying out the patrol of a construction site of a bridge based on a preset period through a multispectral imaging system and a red-green-blue depth camera carried by an unmanned plane, real-time image data and real-time point cloud data of the construction area are collected; analyzing the real-time image data to generate a real-time three-dimensional model of the bridge structure; acquiring real-time environment data collected by a field sensor; determining construction progress information and a potential problem list of the bridge according to a preset construction standard, the real-time three-dimensional model and the real-time environment data; by means of an augmented reality algorithm, the construction progress information and the potential problem list are displayed on a real-time video stream of the construction site in an overlapped mode, and an attention area corresponding to the potential problem list is marked on the real-time video stream; and according to the construction progress information and the potential problem list, adjusting a construction plan and a resource allocation condition of the construction site.

Description

Technical Field

[0001] The present application relates to the technical field of bridge construction management, and in particular to a real-time management method and system for bridge construction. Background Art

[0002] With the acceleration of urbanization, the construction and maintenance of bridges as important transportation infrastructure are becoming increasingly important. Bridges are important channels connecting the two sides of natural obstacles such as rivers, valleys, and seas. They greatly shorten geographical distances, improve transportation efficiency, and promote economic exchanges and personnel exchanges between regions. The construction of bridges is often accompanied by the development of regional economies. It can not only promote the prosperity of the local economy, but also drive economic growth in surrounding areas and form new economic belts and industrial clusters. In natural disasters or emergencies, bridges, as important transportation lines, play an irreplaceable role in the transportation of relief supplies and the evacuation of personnel.

[0003] However, the existing bridge construction management mainly relies on manual work, which has the following problems: manual management makes it difficult to achieve real-time monitoring of the construction site, and it is impossible to grasp the project progress and on-site conditions in a timely manner, which is not conducive to timely adjustment of the construction plan.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a real-time management method and system for bridge construction to solve the above technical problems.

[0006] The present application provides a real-time management method for bridge construction, comprising: using a multispectral imaging system and a red, green, and blue depth camera carried by an unmanned aerial vehicle to patrol the construction site of the bridge based on a preset period to collect real-time image data and real-time point cloud data of the construction area; analyzing the real-time image data to generate a real-time three-dimensional model of the bridge structure; acquiring real-time environmental data collected by on-site sensors; determining the construction progress information and a list of potential problems of the bridge based on preset construction standards, the real-time three-dimensional model, and the real-time environmental data; using an augmented reality algorithm to superimpose and display the construction progress information and the list of potential problems on a real-time video stream of the construction site, and marking an attention area corresponding to the list of potential problems on the real-time video stream; and adjusting the construction plan and resource allocation of the construction site based on the construction progress information and the list of potential problems.

[0007] The present application provides a real-time management system for bridge construction, comprising: a construction area image collection module, which is used to use a multispectral imaging system and a red, green and blue depth camera carried by an unmanned aerial vehicle to patrol the construction site of the bridge based on a preset period to collect real-time image data and real-time point cloud data of the construction area; a real-time three-dimensional model generation module, which is used to analyze the real-time image data and generate a real-time three-dimensional model of the bridge structure; a progress and potential problem determination module, which is used to obtain real-time environmental data collected by on-site sensors; according to preset construction standards, the real-time three-dimensional model and the real-time environmental data, determine the construction progress information and potential problem list of the bridge; a real-time video stream annotation display module, which is used to use an augmented reality algorithm to superimpose and display the construction progress information and the potential problem list on the real-time video stream of the construction site, and annotate the attention area corresponding to the potential problem list on the real-time video stream; a construction plan and resource allocation adjustment unit, which is used to adjust the construction plan and resource allocation of the construction site according to the construction progress information and the potential problem list.

[0008] Based on the embodiments provided in the present application, a multispectral imaging system and a red, green, and blue depth camera carried by a drone are used to inspect the construction site of a bridge based on a preset period to collect real-time image data and real-time point cloud data of the construction area; the real-time image data is analyzed to generate a real-time three-dimensional model of the bridge structure; real-time environmental data collected by on-site sensors is acquired; the construction progress information and a list of potential problems of the bridge are determined based on preset construction standards, the real-time three-dimensional model, and the real-time environmental data; the construction progress information and the list of potential problems are superimposed and displayed on the real-time video stream of the construction site using an augmented reality algorithm, and the attention area corresponding to the list of potential problems is marked on the real-time video stream; the construction plan and resource allocation of the construction site are adjusted based on the construction progress information and the list of potential problems. Thereby, real-time monitoring of the bridge construction site is realized, and management efficiency is improved. Compared with traditional manual management, on-site data can be collected and processed faster, and errors and omissions caused by human factors can be reduced; real-time tracking of project progress can ensure that project managers can understand the construction status in a timely manner, so as to more effectively control the project progress; dynamic adjustment of resource allocation according to real-time data ensures the optimal utilization of resources, reduces waste and reduces costs; by collecting and analyzing a large amount of construction site data, scientific decision-making support is provided to project managers, making the adjustment of construction plans more reasonable; real-time monitoring and data analysis help to discover problems in the construction process in a timely manner, so as to take corresponding corrective measures to ensure that the project quality meets the standards; by optimizing construction plans and resource allocation, the present invention helps to reduce the impact on the environment, such as reducing noise and dust pollution, in line with the requirements of sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0010] Figure 1 is a flow chart of an optional real-time management method for bridge construction according to an embodiment of the present application;

[0011] Figure 2 It is a structural diagram of an optional real-time management system for bridge construction according to an embodiment of the present application.

[0012] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0014] Alternatively, if Figure 1 As shown, the present application provides a real-time management method for bridge construction, which is characterized by comprising:

[0015] S101, using the multispectral imaging system and red, green and blue depth cameras carried by the drone, inspects the bridge construction site based on a preset cycle to collect real-time image data and real-time point cloud data of the construction area;

[0016] S102, analyzing the real-time image data to generate a real-time three-dimensional model of the bridge structure;

[0017] S103, obtaining real-time environmental data collected by on-site sensors; determining the construction progress information and potential problem list of the bridge according to the preset construction standards, the real-time three-dimensional model and the real-time environmental data;

[0018] S104, using an augmented reality algorithm, superimposing the construction progress information and the potential problem list on the real-time video stream of the construction site, and marking the attention area corresponding to the potential problem list on the real-time video stream;

[0019] This step can assist project managers in making decisions.

[0020] S105, adjusting the construction plan and resource allocation of the construction site based on the construction progress information and the list of potential problems.

[0021] Based on the embodiments provided in the present application, a multispectral imaging system and a red, green, and blue depth camera carried by a drone are used to inspect the construction site of a bridge based on a preset period to collect real-time image data and real-time point cloud data of the construction area; the real-time image data is analyzed to generate a real-time three-dimensional model of the bridge structure; real-time environmental data collected by on-site sensors is acquired; the construction progress information and a list of potential problems of the bridge are determined based on preset construction standards, the real-time three-dimensional model, and the real-time environmental data; the construction progress information and the list of potential problems are superimposed and displayed on the real-time video stream of the construction site using an augmented reality algorithm, and the attention area corresponding to the list of potential problems is marked on the real-time video stream; the construction plan and resource allocation of the construction site are adjusted based on the construction progress information and the list of potential problems. Thereby, real-time monitoring of the bridge construction site is realized, and management efficiency is improved. Compared with traditional manual management, on-site data can be collected and processed faster, and errors and omissions caused by human factors can be reduced; real-time tracking of project progress can ensure that project managers can understand the construction status in a timely manner, so as to more effectively control the project progress; dynamic adjustment of resource allocation according to real-time data ensures the optimal utilization of resources, reduces waste and reduces costs; by collecting and analyzing a large amount of construction site data, scientific decision-making support is provided to project managers, making the adjustment of construction plans more reasonable; real-time monitoring and data analysis help to discover problems in the construction process in a timely manner, so as to take corresponding corrective measures to ensure that the project quality meets the standards; by optimizing construction plans and resource allocation, the present invention helps to reduce the impact on the environment, such as reducing noise and dust pollution, in line with the requirements of sustainable development.

[0022] Alternatively, if Figure 2 As shown, the present application provides a real-time management system for bridge construction, comprising:

[0023] The construction area image collection module 201 is used to inspect the construction site of the bridge based on a preset period using a multispectral imaging system and a red, green and blue depth camera carried by the drone to collect real-time image data and real-time point cloud data of the construction area;

[0024] A real-time three-dimensional model generation module 202 is used to analyze the real-time image data and generate a real-time three-dimensional model of the bridge structure;

[0025] The progress and potential problem determination module 203 is used to obtain real-time environmental data collected by on-site sensors; determine the construction progress information and potential problem list of the bridge according to the preset construction standards, the real-time three-dimensional model and the real-time environmental data;

[0026] A real-time video stream annotation display module 204 is used to use an augmented reality algorithm to overlay the construction progress information and the potential problem list on the real-time video stream of the construction site, and to annotate the attention area corresponding to the potential problem list on the real-time video stream;

[0027] The construction plan and resource allocation adjustment unit 205 is used to adjust the construction plan and resource allocation of the construction site according to the construction progress information and the potential problem list.

[0028] Furthermore, the real-time 3D model generation module analyzes the real-time image data to generate a real-time 3D model of the bridge structure, and is configured as follows:

[0029] Using an adaptive feature extraction algorithm, the feature extraction parameters are adjusted according to different parts of the bridge structure to adapt to different lighting and texture conditions; the different parts of the bridge structure include piers, beams, support systems, abutments, and pier foundations;

[0030] Based on the feature extraction parameters corresponding to different parts of the bridge structure, feature point matching and 3D reconstruction are performed on the real-time image data of the construction area to generate a real-time 3D model;

[0031] Using design data from the BIM model of the bridge, corrections were made to the live 3D model; this ensured that the model was not only visually accurate, but also structurally and engineering accurate.

[0032] Assign progress codes to each construction phase and each section of the bridge structure, and associate the progress codes with the corresponding sections of the real-time 3D model;

[0033] New construction parts are identified in real time based on real-time image data of the construction area, and the real-time 3D model is updated.

[0034] Furthermore, the progress and potential problem determination module determines the construction progress information and the potential problem list of the bridge according to the preset construction standards, the real-time three-dimensional model and the real-time environmental data, and is configured as follows:

[0035] Determine the construction progress information of the bridge based on the preset construction standards, real-time 3D model and the BIM model of the bridge;

[0036] Identify a list of potential issues based on preset construction standards, real-time 3D models, real-time environmental data, construction progress information, and real-time image data of the construction area;

[0037] The construction progress information includes the completion degree of each construction stage and the construction progress deviation measurement. The construction progress information of the bridge is determined based on the preset construction standards, the real-time 3D model and the BIM model of the bridge, including:

[0038] Extract structural dimension features, material usage features, and construction stage features from real-time 3D models and BIM models;

[0039] Using a deep feature crossover network and a crossover depth control parameter α, deep crossover is performed on the structural size features, material usage features, and construction stage features to extract high-order crossover features; wherein the crossover depth control parameter α is used to control the depth of feature crossover;

[0040] The xDeepFM model is trained using high-order cross features extracted from the deep feature cross network. The xDeepFM model is used to learn the relationship between the construction progress and the high-order cross features to predict the completion of each construction stage. The xDeepFM model includes a deep part and a linear part. The deep part is used to capture the nonlinear relationship between the high-order cross features, and the linear part is used to capture the linear relationship between the high-order cross features. The xDeepFM model predicts the completion of each construction stage based on the following formula:

[0041] T adjusted,i =T planned,i ×f(P predicted,i )

[0042] T adjusted,i is the adjusted construction time of the ith construction stage, which includes foundation construction, pier construction, beam installation, bearing installation, abutment construction, ancillary construction, decoration construction, and completion acceptance. planned,i is the planned construction time of the i-th construction phase; P predicted,i is the completion degree of the i-th construction stage predicted by the xDeepFM model; f(P predicted,i ) is a function used to adjust the construction time according to the predicted completion;

[0043] The degree of completion of each construction stage predicted by the xDeepFM model is compared with the BIM model and the preset construction standards to obtain the measurement characteristics of the construction progress deviation.

[0044] Further, the potential problem list includes parts that need to be repaired or repaired and erroneous dependencies between construction stages; the real-time environmental data includes temperature, humidity and wind speed; the potential problem list is determined based on the preset construction standards, the real-time three-dimensional model, the real-time environmental data, the construction progress information and the real-time image data of the construction area, and is configured as follows:

[0045] Construct a weighted graph, where the nodes of the weighted graph represent the construction stages, the edges of the weighted graph represent the relationship between the construction stages, and the weight of each edge is determined based on the completion-adjusted construction time predicted by the xDeepFM model;

[0046] Use a graph convolutional network to analyze the nodes and edges in a weighted graph, learn the topological structure and features of the nodes, and identify the relationships between construction stages;

[0047] Use the Bellman - Ford algorithm to calculate the shortest paths from a starting node to all other nodes; among them, the Bellman - Ford algorithm is used to handle negative - weighted edges in a weighted graph; among them, the starting node is used to represent the start of construction; all other nodes represent each construction stage;

[0048] Among them, negative - weighted edges represent a reduction in the normal construction time due to delays or other factors.

[0049] According to the relationships between construction stages identified by the graph convolutional network, use the Bellman - Ford algorithm to detect whether there is a negative - weighted cycle in the weighted graph; among them, a negative - weighted cycle means that in the context of the construction schedule, a certain construction stage erroneously depends on the completion of another stage after it;

[0050] If the Bellman - Ford algorithm detects a negative - weighted cycle, feedback the negative - weighted cycle to the project managers and engineers of the bridge; among them, the project managers and engineers of the bridge, in response to the received negative - weighted cycle, check the dependency relationships between construction stages;

[0051] It should be noted that the Bellman - Ford algorithm is a single - source shortest - path algorithm. It can handle the case where the graph contains negative - weighted edges and can detect whether there is a negative - weighted cycle in the graph. In construction schedule management, a negative - weighted cycle indicates a logical error or an incorrect dependency relationship between construction stages.

[0052] Specific steps for detecting a negative - weighted cycle: Initialization: Initialize the distances from the source point to all other vertices to infinity, and set the distance of the source point itself to 0; Relaxation operation: Perform ∣V∣ - 1 iterations on all edges in the graph. In each iteration, try to update the shortest paths between all vertices. If a shorter path can be obtained through a certain edge, update that path. Here, ∣V∣ represents the number of vertices in the graph; Detecting a negative - weighted cycle: After completing ∣V∣ - 1 iterations, perform an additional check. If in this check, the distance of any vertex can still be shortened, that is, there exists an edge (u, v) such that d[u]+w(u, v)<d[v] holds, then there must be a negative - weighted cycle in the graph. This check is completed by trying to relax all edges again. If there is an update, there is a negative - weighted cycle.

[0053] Through this method, we can specifically identify the problem of negative weight cycles in the construction schedule and clearly describe the application of the Bellman-Ford algorithm in detecting negative weight cycles in technical language. This helps project managers and engineers to check and adjust the relationship between construction stages and ensure the rationality and accuracy of the construction schedule.

[0054] Use deep learning algorithms to identify the stacking of construction materials, the operating status of construction equipment, and bridge structural defects in real-time image data;

[0055] The real-time 3D model of the bridge is updated based on the stacking of construction materials, the operating status of construction equipment and the structural defects of the bridge identified in the real-time image data, and the abnormal parts that need to be repaired or repaired are marked according to the real-time environmental data and construction progress information.

[0056] Furthermore, the learning formula of the graph convolutional network is:

[0057]

[0058] Among them, H (l) is the node feature representation of the lth layer, It is the adjacency matrix A plus the identity matrix of the same dimension as A; A is the adjacency matrix constructed based on the dependencies between construction stages and the construction time weights; yes The degree matrix, W (l) and b (l) are the weight and bias of the lth layer respectively; is the activation function.

[0059] Furthermore, the real-time video stream annotation display module uses an augmented reality algorithm to mark the attention area corresponding to the potential problem list on the real-time video stream, and is configured as follows:

[0060] According to the list of potential problems, the deep learning-based point cloud segmentation technology is used to perform semantic segmentation on the real-time point cloud data of the bridge using random sampling and local feature aggregation network models to extract the areas of interest corresponding to the list of potential problems.

[0061] The area of ​​interest is mapped back to the real-time video stream of the construction site through reverse projection; the accuracy evaluation formula of reverse projection is:

[0062]

[0063] Where Prec is the accuracy of the evaluated reverse projection; p j represents the points in the region of interest corresponding to the list of potential problems; q jk Represents point p jThe projection point on the real-time video stream of the construction site, j represents the index of the point in the area of ​​interest corresponding to the potential problem list; k represents the index of the point in the reverse projection p j The index of the point on the real-time video stream of the corresponding construction site; d j It's point p j The distance to the plane of the red, green and blue depth cameras; M is the total number of points in the area of ​​interest corresponding to the potential problem list. This formula measures the average distance of a 3D point to its projection point in the 2D image, while taking into account the distance from the point to the camera plane to adapt to the technical parameters of the real-time video stream of the construction site.

[0064] Furthermore, the construction plan and resource allocation adjustment unit adjusts the construction plan and resource allocation of the construction site according to the construction progress information and the potential problem list, and is configured to:

[0065] Adjust labor allocation based on construction progress information;

[0066] Adjust the construction schedule and the supply plan of construction equipment and construction materials based on the list of potential problems.

[0067] Furthermore, the real-time management system for bridge construction also includes:

[0068] Feedback interface for bridge project managers and engineers to input feedback to confirm or deny the adjusted construction plan and resource allocation.

[0069] Further, the multispectral imaging system includes an infrared thermal imaging camera and a high-resolution visible light camera;

[0070] Pre-set construction standards include construction quality standards, construction safety standards and environmental impact assessment standards.

[0071] It should be noted that in the present application, the embodiments implemented on the real-time management system for bridge construction can be cross-referenced with the embodiments implemented on the real-time management method for bridge construction, and the present application will not describe them one by one.

[0072] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A real-time management method for bridge construction, characterized in that: include: Using the multispectral imaging system and red, green and blue depth cameras carried by drones, the bridge construction site is inspected based on a preset cycle to collect real-time image data and real-time point cloud data of the construction area; Analyzing the real-time image data to generate a real-time three-dimensional model of the bridge structure; Acquire real-time environmental data collected by on-site sensors; determine the construction progress information and potential problem list of the bridge according to preset construction standards, the real-time three-dimensional model and the real-time environmental data; Using an augmented reality algorithm, the construction progress information and the list of potential problems are superimposed and displayed on a real-time video stream of the construction site, and an attention area corresponding to the list of potential problems is marked on the real-time video stream; The construction plan and resource allocation of the construction site are adjusted according to the construction progress information and the list of potential problems.

2. A real-time management system for bridge construction, the system implementing the method according to claim 1, characterized in that: include: The construction area image collection module is used to inspect the construction site of the bridge based on a preset period using the multispectral imaging system and red, green and blue depth cameras carried by the drone to collect real-time image data and real-time point cloud data of the construction area; A real-time three-dimensional model generation module, used for analyzing the real-time image data to generate a real-time three-dimensional model of the bridge structure; A progress and potential problem determination module is used to obtain real-time environmental data collected by on-site sensors; determine the construction progress information and potential problem list of the bridge according to preset construction standards, the real-time three-dimensional model and the real-time environmental data; A real-time video stream annotation display module, used to use an augmented reality algorithm to overlay the construction progress information and the potential problem list on the real-time video stream of the construction site, and to annotate the attention area corresponding to the potential problem list on the real-time video stream; The construction plan and resource allocation adjustment unit is used to adjust the construction plan and resource allocation of the construction site according to the construction progress information and the potential problem list.

3. The real-time management system for bridge construction according to claim 2 is characterized in that: The real-time 3D model generation module analyzes the real-time image data to generate a real-time 3D model of the bridge structure, and is configured as follows: Using an adaptive feature extraction algorithm, the feature extraction parameters are adjusted according to different parts of the bridge structure to adapt to different lighting and texture conditions; wherein the different parts of the bridge structure include piers, beams, support systems, abutments, and pier foundations; Based on the feature extraction parameters corresponding to different parts of the bridge structure, feature point matching and three-dimensional reconstruction are performed on the real-time image data of the construction area to generate the real-time three-dimensional model.

4. The real-time management system for bridge construction according to claim 3 is characterized in that: The real-time 3D model generation module analyzes the real-time image data to generate a real-time 3D model of the bridge structure, and is configured as follows: Correcting the real-time three-dimensional model using design data of the BIM model of the bridge; assigning a progress code to each construction phase and each portion of the bridge structure, and associating the progress code with a corresponding portion of the real-time three-dimensional model; A new construction part is identified in real time according to the real-time image data of the construction area, and the real-time three-dimensional model is updated.

5. The real-time management system for bridge construction according to claim 4 is characterized in that: The progress and potential problem determination module determines the construction progress information and the potential problem list of the bridge according to the preset construction standard, the real-time three-dimensional model and the real-time environmental data, and is configured as follows: Determining the construction progress information of the bridge according to the preset construction standard, the real-time three-dimensional model and the BIM model of the bridge; The potential problem list is determined based on the preset construction standards, the real-time three-dimensional model, the real-time environmental data, the construction progress information and the real-time image data of the construction area.

6. The real-time management system for bridge construction according to claim 5 is characterized in that ,, the construction progress information includes the completion degree of each construction stage and the construction progress deviation metric; the construction progress information of the bridge is determined according to the preset construction standard, the real-time three-dimensional model and the BIM model of the bridge, including: Extracting structural dimension features, material usage features, and construction stage features from the real-time three-dimensional model and the BIM model; Using a deep feature crossover network and a crossover depth control parameter α, the structural size feature, the material usage feature and the construction stage feature are deeply crossovered to extract high-order crossover features; wherein the crossover depth control parameter α is used to control the depth of feature crossover.

7. The real-time management system for bridge construction according to claim 6 is characterized in that ,, the construction progress information includes the degree of completion of each construction stage and the construction progress deviation metric; the construction progress information of the bridge is determined according to the preset construction standard, the real-time three-dimensional model and the BIM model of the bridge, including: using the high-order cross features extracted by the deep feature cross network to train the xDeepFM model; wherein the xDeepFM model is used to learn the relationship between the construction progress and the high-order cross features to predict the degree of completion of each construction stage; the xDeepFM model includes a deep part and a linear part, the deep part is used to capture the nonlinear relationship between the high-order cross features, and the linear part is used to capture the linear relationship between the high-order cross features; The degree of completion of each construction stage predicted by the xDeepFM model is compared with the BIM model and the preset construction standard to obtain construction progress deviation measurement characteristics.

8. The real-time management system for bridge construction according to claim 2, characterized in that: The bridge construction real-time management system also includes: A feedback interface is used for project managers and engineers of the bridge to input feedback information to confirm or deny the adjusted construction plan and resource allocation.

9. The real-time management system for bridge construction according to claim 2, characterized in that: The multispectral imaging system includes an infrared thermal imaging camera and a high-resolution visible light camera; The preset construction standards include construction quality standards, construction safety standards and environmental impact assessment standards.

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