Bridge construction digital twin management and control system and method

Abstract

The invention discloses a digital twin management and control system and method for bridge construction. The system comprises an equipment end, an edge end and a cloud end, the equipment end is used for collecting equipment data for building a bridge, and the equipment data comprises posture data, operation state data and sensor data of a bridge erecting machine; the edge end is used for screening the received equipment data sent by the equipment end, carrying out real-time analysis processing on the screened real-time data so as to carry out real-time control on equipment, sending non-real-time data to the cloud end, carrying out analysis processing on the received non-real-time data, and sending the non-real-time data to the cloud end; and a control instruction is sent to the equipment end through the edge end so as to control equipment for building the bridge. According to the invention, the response speed and expansibility of field data are improved, and the construction efficiency and safety are guaranteed.

Description

Technical Field

[0001] The present invention relates to bridge construction technology, and in particular to a bridge construction digital twin management and control system and method. Background Art

[0002] Traditional bridge construction digital twin management and control systems generally use a centralized, monolithic architecture. This means that data collected by various sensors on the construction site is uploaded to a single central server, where all data is analyzed and calculated. Once the analysis and calculations are complete, the server issues instructions to the construction site. All system functions are packaged together and run in a single process. While this centralized, monolithic architecture offers advantages such as simple logic and easy deployment, it also presents the following challenges: 1. Due to network latency and other issues, there may be delays in uploading data from the construction site to the server and issuing instructions from the server to the construction site, which may result in the construction site not receiving instructions in a timely manner. In some cases, the management and control system needs to be able to issue real-time instructions to the construction site based on the collected data, such as collision warnings during the lifting of bridge components and instructions for the next operation of construction machinery. In these cases, if the management and control system cannot respond to the data collected on site in a timely manner, it will at best affect construction efficiency and at worst lead to safety accidents.

[0003] 2. The management and control system often cannot determine all requirements at once. With the development of bridge construction and IT information technology, new requirements will arise for the management and control system. The traditional monolithic architecture cannot be easily expanded. When new requirements arise, the entire management and control system has to be rebuilt. In addition, the conditions at the bridge construction site are complex, and different projects often have different requirements for the management and control system. The traditional monolithic architecture is very complicated to modify and update, and any changes will affect the entire system, resulting in poor adaptability of the management and control system. A new management and control system must be developed for each project, wasting manpower and resources.

[0004] Therefore, how to improve the response speed and scalability of field data is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The main purpose of the present invention is to provide a digital twin management and control system and method for bridge construction, which improves the response speed and scalability of on-site data and ensures construction efficiency and safety.

[0006] In a first aspect, the present application provides a bridge construction digital twin management and control system, which includes: Device, edge, and cloud; The device side is used to collect equipment data for bridge construction, including: attitude data, operating status data and sensor data of the bridge erection machine; The edge end is used to filter the device data sent by the device end, perform real-time analysis and processing on the filtered real-time data to control the device in real time, and send non-real-time data to the cloud end; The cloud end is used to analyze and process the received non-real-time data, and send control instructions to the device end through the edge end to control the equipment for building the bridge.

[0007] In combination with the above-mentioned first aspect, as an optional implementation method, the cloud includes: a microservice module, which is used to split the program on the cloud into multiple independent microservices, including: a data microservice module, a model microservice module and other microservice modules. The other microservice modules include: a construction organization microservice module, an intelligent control microservice module and a quality inspection microservice module.

[0008] In combination with the first aspect above, as an optional implementation method, the microservice module is also used to delete or expand microservices according to actual project requirements.

[0009] In combination with the first aspect above, as an optional implementation method, the data microservice module is used to classify, distribute, share, store and standardize the data uploaded by the edge end; The model microservice module is used to fuse the created geometric model, mechanism model and information model to form a digital twin model.

[0010] In combination with the first aspect above, as an optional implementation method, the model microservice module is also used to update the twin model status in real time based on the bidirectional mapping of physical entity data and virtual model data, so as to enable the virtual and real synchronous driving of the bridge construction process.

[0011] In conjunction with the first aspect above, as an optional implementation, the construction organization microservice module is used to dynamically schedule and optimize bridge construction resources based on historical bridge data and real-time bridge construction progress data stored in the cloud to generate a bridge construction plan; The intelligent control microservice module is used to receive device status data uploaded by the edge end and simulate the device operation trajectory in combination with the digital twin model to generate optimal control instructions; The quality inspection microservice module is used to collect quality data during the bridge construction process and compare it with the design standards of the digital twin model to evaluate the construction quality in real time.

[0012] In conjunction with the first aspect above, as an optional implementation, the edge end includes: a data screening module, which is used to screen the device data sent by the device end to determine real-time data and non-real-time data; Edge computing module, which is used to analyze and calculate real-time data to determine whether there is collision warning and equipment failure information; If it exists, the edge side will directly send control instructions to the device side to control the equipment for building the bridge in real time.

[0013] In combination with the first aspect above, as an optional implementation, the edge end further includes: a data compression module, which is used to compress non-real-time data; The data packaging module is used to package the compressed non-real-time data and send it to the cloud.

[0014] In combination with the first aspect above, as an optional implementation manner, the device end and the edge end are connected via a wireless network or an Internet of Things; The edge terminal and the cloud are connected via a wireless network or a 5G network.

[0015] In a second aspect, the present application provides a bridge construction digital twin management and control method, wherein the method comprises the steps of: Collecting equipment data for bridge construction, including: attitude data, operating status data, and sensor data of the bridge erection machine; Filter the collected device data and perform real-time analysis and processing on the filtered real-time data to control the device in real time and send non-real-time data to the cloud; The cloud is used to analyze and process the received non-real-time data, and control instructions are sent to the device side through the edge side to control the equipment for building the bridge.

[0016] The present application provides a bridge construction digital twin management and control system and method, which includes: a device end, an edge end, and a cloud end; the device end is used to collect equipment data for bridge construction, and the equipment data includes: the attitude data, operating status data, and sensor data of the bridge erection machine; the edge end is used to filter the equipment data received from the device end, and to perform real-time analysis and processing on the filtered real-time data to control the equipment in real time, and to send non-real-time data to the cloud end, which is used to analyze and process the received non-real-time data and send control instructions to the device end through the edge end to control the equipment for bridge construction. This application improves the response speed and scalability of on-site data, ensuring construction efficiency and safety.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 A schematic diagram of a bridge construction digital twin management and control system provided in an embodiment of the present application; Figure 2 This is a flow chart of a bridge construction digital twin management and control method provided in an embodiment of the present application; Figure 3 Schematic diagram of the edge-cloud architecture provided in this application embodiment Figure 4 This is a schematic diagram of the microservice module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0021] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. Some of the blocks shown in the drawings are functional entities that do not necessarily correspond to physically or logically separate entities.

[0022] The embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0023] Reference Figure 1 , Figure 1 The figure shows a schematic diagram of a bridge construction digital twin management and control system provided by the present invention, as shown in FIG. Figure 1 As shown, the system includes: The device end 101, the edge end 102 and the cloud end 103, wherein the edge end 102 includes: a data screening module 1021, an edge computing module 1022, a data compression module 1023 and a data packaging module 1024.

[0024] The cloud 103 includes: a microservice module 1030 , a data microservice module 1031 , a model microservice module 1033 , a construction organization microservice module 1034 , an intelligent control microservice module 1035 and a quality inspection microservice module 1032 .

[0025] Equipment end 101: It is used to collect equipment data for building a bridge, and the equipment data includes: posture data, operation status data and sensor data of the bridge-building machine.

[0026] Edge end 102: It is used to filter the device data sent by the device end, and perform real-time analysis and processing on the filtered real-time data to control the device in real time, and send non-real-time data to the cloud.

[0027] The cloud 103 analyzes and processes the received non-real-time data, and sends a control instruction to the device end through the edge end to control the equipment for building the bridge.

[0028] Furthermore, in a possible implementation, the cloud includes: a microservice module, which is used to split the program on the cloud into multiple independent microservices, including: a data microservice module, a model microservice module and other microservice modules, and the other microservice modules include: a construction organization microservice module, an intelligent control microservice module and a quality inspection microservice module.

[0029] Furthermore, in a possible implementation, the microservice module is also used to delete or expand microservices according to actual project requirements.

[0030] Furthermore, in a possible implementation, the data microservice module is used to classify, distribute, share, store, and standardize the data uploaded by the edge end; The model microservice module is used to fuse the created geometric model, mechanism model and information model to form a digital twin model.

[0031] Furthermore, in a possible implementation, the model microservice module is also used to update the twin model status in real time based on the bidirectional mapping of physical entity data and virtual model data, so as to achieve synchronous driving of the virtual and real aspects of the bridge construction process.

[0032] Furthermore, in a possible implementation, the construction organization microservice module is configured to dynamically schedule and optimize bridge construction resources based on historical bridge data and real-time bridge construction progress data stored in the cloud, so as to generate a bridge construction plan; The intelligent control microservice module is used to receive device status data uploaded by the edge end and simulate the device operation trajectory in combination with the digital twin model to generate optimal control instructions; The quality inspection microservice module is used to collect quality data during the bridge construction process and compare it with the design standards of the digital twin model to evaluate the construction quality in real time.

[0033] Furthermore, in a possible implementation manner, the edge end includes: A data screening module, which is used to screen the device data sent by the device end to determine real-time data and non-real-time data; Edge computing module, which is used to analyze and calculate real-time data to determine whether there is collision warning and equipment failure information; If it exists, the edge side will directly send control instructions to the device side to control the equipment for building the bridge in real time.

[0034] Furthermore, in a possible implementation manner, the edge end further includes: A data compression module, which is used to compress non-real-time data; The data packaging module is used to package the compressed non-real-time data and send it to the cloud.

[0035] Furthermore, in a possible implementation manner, the device end and the edge end are connected via a wireless network or an Internet of Things; The edge terminal and the cloud are connected via a wireless network or a 5G network.

[0036] Reference Figure 2 , Figure 2 The figure shows a flow chart of a bridge construction digital twin management and control method provided by the present invention, as shown in FIG. Figure 2 As shown, the method includes the steps of: Step S201: Collecting equipment data for building a bridge, wherein the equipment data includes: posture data, operating status data and sensor data of the bridge erection machine.

[0037] Step S202: Filter the collected device data and perform real-time analysis and processing on the filtered real-time data to control the device in real time, and send non-real-time data to the cloud.

[0038] Step S203: Analyze and process the received non-real-time data using the cloud, and send control instructions to the device end through the edge end to control the equipment for building the bridge.

[0039] It can be understood that data is first collected on the device side and then sent to the data screening module on the edge side for data screening, which is screened into data that requires real-time processing and data that does not require real-time processing. Data that requires real-time processing is directly sent to the edge computing module on the edge side for real-time data analysis and calculation. If information such as collision warning is calculated, the edge side directly sends control instructions to the device side to control the device in real time. Data that does not require real-time calculation and analysis is compressed and packaged and sent to the cloud side for further analysis and calculation. After the calculation is completed, the cloud side sends control instructions to the device side through the edge side.

[0040] In one embodiment, data processing is divided into "real-time response at the edge" and "complex computing in the cloud." Data collected by the device is first filtered by the edge. Critical, real-time data (such as collision warnings and equipment fault diagnosis) is directly processed and issued by the edge, avoiding network latency when uploading to the cloud. Non-real-time data (such as 3D model updates and construction progress analysis) is compressed and uploaded to the cloud, where it is processed using the powerful computing power of cloud computing, achieving the advantages of both low latency and high computing power.

[0041] Microservices Architecture: The edge focuses on millisecond-level real-time tasks (such as safety alerts and equipment control), while the cloud handles global optimization tasks (such as construction scheduling, model updates, and data prediction). The cloud program utilizes a microservices architecture, split into independent modules for data, models, construction organization, intelligent control, and quality inspection. Each module can be expanded or updated as needed, avoiding the "ripple effect" of traditional monolithic architectures and improving system adaptability and development efficiency.

[0042] Understandably, the edge in this application functions as a local cerebellum: for tasks requiring real-time, immediate response (such as crane collision warnings), data can be collected directly at the edge, completing the closed loop of "collection → analysis → command issuance" without uploading it to the cloud. For example, if a bridge crane's sensors detect a proximity to an obstacle, the edge calculates the collision risk in real time and immediately issues a braking command, reducing latency from seconds in the "end → cloud → end" process to milliseconds in the "end → edge → end" process, thus avoiding network latency in the cloud.

[0043] The cloud serves as the global brain: Non-real-time, computationally intensive tasks (such as updating a 3D model of an entire bridge or simulating construction progress) are handled by the cloud. These tasks are less sensitive to latency but require large-scale data storage and complex algorithms (such as BIM model rendering and machine learning optimization). The cloud's distributed computing resources can efficiently handle these tasks, freeing up computing power at the edge.

[0044] Microservice modularization improves system scalability: Cloud programs are broken down into independent microservices (such as data, model, and construction organization modules). Each module handles specific functions, avoiding the inefficiencies of traditional monolithic architectures where all functions are squeezed into the same process. For example, when new quality inspection requirements are added, only the quality inspection microservice needs to be expanded, without modifying the entire system. This shortens the development cycle from months to weeks, allowing for faster response to personalized project needs and indirectly improving overall collaboration efficiency.

[0045] In summary, the beneficial effects of this application include: (1) by adopting the edge-cloud architecture, real-time data calculation and analysis are completed at the edge, which improves the response speed of the system and ensures construction efficiency and safety; non-real-time data analysis is completed in the cloud computing center, giving full play to the powerful data processing capabilities of cloud computing (that is, real-time data is directly processed by the edge and instructions are issued (such as hoisting control), avoiding network delays (traditional systems need to be uploaded to the central server). Non-real-time data (such as three-dimensional model updates) are processed by the cloud, saving edge computing resources).

[0046] (2) The programs on the cloud computing center adopt a microservice architecture, which ensures the scalability of the programs, increases the adaptability of the system, and can save manpower and material resources.

[0047] (3) It combines the edge-cloud architecture and microservice architecture, fully leveraging the advantages of both to improve the system's performance, scalability, and responsiveness while reducing latency and bandwidth requirements.

[0048] (4) By processing key data in real time on the edge, processing complex tasks on the cloud, and dynamically mapping digital twin models, the delay problem of the traditional centralized architecture can be directly solved, and construction instructions can be issued in real time (on the edge) and global efficiency can be improved (on the cloud), ensuring safety and efficiency.

[0049] Reference Figure 3 , Figure 3 The diagram shows the edge-cloud architecture provided by the present invention. Figure 3 As shown: It includes a device end 101, which is used to collect equipment data for building a bridge. The equipment data includes: posture data, operating status data and sensor data of the bridge-building machine. It needs to be explained that the equipment for building a bridge includes the bridge-building machine.

[0050] The collected data is then sent to the edge's data screening module for screening, which separates the data into those that require real-time processing and those that do not. Data that requires real-time processing is sent directly to the edge's edge computing module for real-time data analysis and calculation. If information such as collision warnings is calculated, the edge directly issues control instructions to the device for real-time control. Data that does not require real-time analysis and calculation is sent to the cloud computing center (cloud) for further analysis and calculation after compression and packaging modules. Once the calculation is complete, the cloud computing center issues control instructions to the device through the edge.

[0051] Specifically: The end-edge cloud architecture includes the basic layer, perception layer, transport layer, edge layer, transport layer, data layer, model layer, service layer, transport layer, application layer, and user layer.

[0052] The foundational and perception layers run on the device side, the edge layer runs on the edge side, and the service, model, and data layers run in the cloud computing center (the cloud). Programs running on the device and edge side primarily perform data collection and simple real-time data analysis tasks, such as collision warning and real-time fault diagnosis.

[0053] Programs running on cloud computing centers require complex non-real-time data analysis tasks, such as updating 3D models and predicting data. These complex programs require a microservices architecture to improve their adaptability. Devices, edge devices, cloud computing centers, and clients are connected via the transport layer. Generally speaking, the management and control system utilizes an edge-cloud architecture for hardware, while the programs running on cloud computing centers utilize a microservices architecture for software. Communication between devices and edge devices typically utilizes the Internet of Things (IoT), but wireless networks are also possible. The edge devices communicate with the cloud via wireless networks or 5G networks.

[0054] It should be explained that data screening is based on project site requirements. For example, data related to safety warnings, real-time control of on-site equipment, etc. are real-time data, while data related to on-site organization management, construction scheduling, etc. that do not require real-time processing are not real-time data. For example, data that requires real-time processing (edge ​​processing) Safety: Distance data between components and surrounding equipment during lifting (collision warning); stress and tilt sensor data of construction machinery (such as cranes) (real-time diagnosis of equipment failures to prevent overturning).

[0055] Control: Real-time position and speed data of the bridge-building machine (the edge end calculates the optimal movement path in real time and issues control instructions); slump and temperature data during concrete pouring (real-time adjustment of mixing parameters to ensure pouring quality).

[0056] Data that does not require real-time processing (cloud processing): Planning: Historical construction progress data, resource consumption data (cloud-based analysis of optimal construction cycles to optimize subsequent project plans); environmental data over the entire life cycle of the bridge (such as long-term changes in temperature and humidity, for durability prediction).

[0057] Model category: 3D component scanning data (updating digital twin geometry models in the cloud to generate high-precision BIM models); long-term stress and displacement monitoring data (combining cloud-based mechanism models to simulate the aging process of bridge structures).

[0058] Reference Figure 4 , Figure 4 The diagram of the microservice module provided by the present invention is shown as follows: Figure 4 As shown: Microservice module 1030 , data microservice module 1031 , model microservice module 1033 , construction organization microservice module 1034 , intelligent control microservice module 1035 and quality inspection microservice module 1032 .

[0059] Specifically, the microservices architecture is further explained. The programs running on the cloud computing center utilize a microservices architecture, splitting them into multiple microservices: a data microservice module, a model microservice module, and other microservice modules. Each microservice module has its own database, ensuring complete control over its data, reducing coupling between microservice modules, and facilitating program expansion. The data microservice module primarily distributes and stores shared data. Data distribution refers to distributing data transmitted from the edge to each microservice module based on its intended purpose. Shared data storage refers to storing data generated by a microservice module and shared with other modules, ensuring the consistency and accuracy of shared data. The model microservice module primarily performs digital twin model updates (through bidirectional mapping between physical entity data and virtual model data, updating the twin model status in real time and supporting simultaneous virtual-physical driving of the construction process). The digital twin model is formed by integrating multi-dimensional digital models, including geometric, mechanistic, and information models. The geometric model accurately reproduces the physical form and spatial relationships of bridge components; the mechanistic model simulates component motion and force patterns based on mechanical principles; and the information model correlates construction data, equipment status, and process parameters. Other microservice modules include but are not limited to construction organization microservice module, intelligent control microservice module, quality inspection microservice module, etc., which can be deleted or expanded according to actual project needs.

[0060] It needs to be explained that the data microservice module: core function: responsible for the classification distribution, shared storage and standardized processing of data, and is the data hub of the system.

[0061] Specific uses: Data distribution: Distributes data uploaded from the edge to corresponding microservice modules based on their intended use (e.g., model updates, quality inspections). Shared storage: Centrally manages cross-module shared data (e.g., construction standards, equipment parameters) to ensure data consistency. Cleansing and standardization: Unifies the formats and units of data from different sources to improve data quality.

[0062] Model microservice module: Core function: Build and update digital twin models and provide high-precision simulation computing capabilities.

[0063] Specific Applications: Model Construction: Create geometric models (structural morphology), mechanism models (mechanical laws), and information models (construction data). Simulation: Simulate the construction process (e.g., lifting paths, stress changes) and predict long-term bridge performance. Service Output: Provide model interfaces for other modules (e.g., geometric coordinate query, mechanical property calculation).

[0064] Construction Organization Microservice Module: This module is responsible for the dynamic scheduling and optimization of construction resources (manpower, machinery, and materials). It generates construction plans (such as component lifting sequence and process connection plans) based on historical data and real-time progress data stored in the cloud. It supports the collaborative management of multiple projects and flexibly adjusts construction organization strategies based on the personalized needs of different bridge projects (such as span, terrain, and construction period), avoiding the need to redevelop traditional monolithic architectures.

[0065] Intelligent control microservice module: Receives equipment status data uploaded by the edge (such as the position of the bridge crane and stress sensor parameters), combines it with the digital twin model to simulate the equipment's operating trajectory, and generates optimal control instructions (such as lifting speed and angle adjustment). It also supports the automated control of construction machinery (such as unmanned crane path planning) and realizes a closed-loop control of "cloud-side policy formulation - real-time execution at the edge" through real-time interaction with the edge.

[0066] Quality inspection microservice module: Collects quality data during the construction process (such as concrete strength and component splicing accuracy), compares it with the design standards of the digital twin model, and evaluates construction quality in real time; establishes a quality problem knowledge base, analyzes historical quality data through machine learning, predicts potential quality risks (such as crack development trends), and provides rectification suggestions to assist managers in decision-making.

[0067] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A bridge construction digital twin management and control system, characterized by: Including device side, edge side and cloud side; The device side is used to collect equipment data for bridge construction, including: attitude data, operating status data and sensor data of the bridge erection machine; The edge end is used to filter the device data sent by the device end, perform real-time analysis and processing on the filtered real-time data to control the device in real time, and send non-real-time data to the cloud end; The cloud end is used to analyze and process the received non-real-time data, and send control instructions to the device end through the edge end to control the equipment for building the bridge.

2. The system according to claim 1, wherein: The cloud includes: The microservice module is used to split the program on the cloud into multiple independent microservices, including: a data microservice module, a model microservice module and other microservice modules. The other microservice modules include: a construction organization microservice module, an intelligent control microservice module and a quality inspection microservice module.

3. The system according to claim 2, characterized in that: The microservice module is also used to delete or expand microservices according to actual project requirements.

4. The system according to claim 2, wherein: The data microservice module is used to classify, distribute, share, store and standardize the data uploaded by the edge end; The model microservice module is used to fuse the created geometric model, mechanism model and information model to form a digital twin model.

5. The system according to claim 4, characterized in that: The model microservice module is also used to update the twin model status in real time based on the bidirectional mapping of physical entity data and virtual model data, so as to achieve synchronous driving of the virtual and real aspects of the bridge construction process.

6. The system according to claim 2, characterized in that: The construction organization microservice module is used to dynamically schedule and optimize bridge construction resources based on historical bridge data and real-time bridge construction progress data stored in the cloud to generate a bridge construction plan; The intelligent control microservice module is used to receive device status data uploaded by the edge end and simulate the device operation trajectory in combination with the digital twin model to generate optimal control instructions; The quality inspection microservice module is used to collect quality data during the bridge construction process and compare it with the design standards of the digital twin model to evaluate the construction quality in real time.

7. The system according to claim 1, wherein: The edge end includes: A data screening module, which is used to screen the device data sent by the device end to determine real-time data and non-real-time data; Edge computing module, which is used to analyze and calculate real-time data to determine whether there is collision warning and equipment failure information; If it exists, the edge side will directly send control instructions to the device side to control the equipment for building the bridge in real time.

8. The system according to claim 7, characterized in that The edge end further includes: A data compression module, which is used to compress non-real-time data; The data packaging module is used to package the compressed non-real-time data and send it to the cloud.

9. The system according to claim 1, wherein: The device end and the edge end are connected via a wireless network or an Internet of Things; The edge terminal and the cloud are connected via a wireless network or a 5G network.

10. A bridge construction digital twin management and control method using the bridge construction digital twin management and control system according to any one of claims 1 to 9, characterized in that: include: Collecting equipment data for bridge construction, including: attitude data, operating status data, and sensor data of the bridge erection machine; Filter the collected device data and perform real-time analysis and processing on the filtered real-time data to control the device in real time and send non-real-time data to the cloud; The cloud is used to analyze and process the received non-real-time data, and control instructions are sent to the device side through the edge side to control the equipment for building the bridge.

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