Ice raft impact identification and early warning method and system in cold region bridge construction process based on digital twinning

Abstract

The invention provides a cold region bridge construction process ice raft impact identification and early warning method and system based on digital twinning, and belongs to the field of bridges. The problems that in the existing bridge construction process, ice floes in the cold region are not effectively recognized and pre-warned, so that bridge piers are possibly damaged due to impact of the ice floes, and even casualties are caused are solved. The method comprises the following steps: monitoring and identifying ice floats in a water area by using a computer vision technology, and tracking moving paths of the ice floats; then, the position of the ice raft is monitored, and the position and the moving track of the ice raft are monitored in real time through a computer vision technology and a multi-target tracking algorithm; in the digital twin platform, carrying out simulation analysis on the possible moving path of the ice raft and the potential influence on the bridge according to the real-time data; and performing early warning on the impact position based on an analysis result. Potential risks caused by passing of ships in the bridge construction process can be effectively recognized and early warned, and therefore safe construction and stable operation of the bridge are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of bridge technology, and in particular to a method and system for identifying and warning of ice sheet impact during cold-region bridge construction based on digital twins. Background Art

[0002] my country has a vast territory, spanning 33 degrees of latitude from north to south and 60 degrees of longitude from east to west. It has a monsoon climate. At the same time, it is affected by the distribution of land and sea, latitude, and high mountains, and the climate in different places varies greatly. The northern region is cold in winter, with low temperatures and long frozen rivers. The number of days that the main rivers in Northeast my country are frozen is 90 to 171 days, the number of days that the main rivers in North China are frozen is 43 to 128 days, and the number of days that the main rivers in Northwest China are frozen is 70 to 176 days. Ice floes in cold regions are an important part of the bridge construction process that must be considered. The current bridge construction process lacks effective identification and early warning of ice floes, which may cause temporary construction structures and even bridge structures during construction to be damaged by ice floes, which not only affects the progress of the project, but also may cause casualties and bring serious risks. By deploying surveillance cameras in upstream sections, tracking and identifying ice floes and calculating their speed, and integrating them into the digital twin platform, ice floe collision early warning and prevention can be achieved to reduce the impact of collision risks. Summary of the invention

[0003] The technical problems to be solved by the present invention are:

[0004] In order to solve the problem that cold-region ice sheets are not effectively identified and warned during the construction of existing bridges, resulting in the collapse of temporary construction structures and even the damage of bridge piers by the impact of ice sheets, causing casualties.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] The present invention provides a method for identifying and warning ice sheet impact in a cold region bridge construction process based on digital twin, comprising the following steps:

[0007] S100, identifying ice floes in the water area, and obtaining image information of the ice floes in the water area using computer vision;

[0008] S200, based on the ice floes identified in step S100, monitoring the positions of the ice floes; using a multi-target tracking algorithm to perform real-time tracking of the ice floes to handle the situation where multiple ice floes appear at the same time, and tracking the moving paths of the multiple ice floes;

[0009] S300, synchronizing the ice row position and trajectory data monitored in step S200 to the digital twin platform for real-time data analysis and simulation;

[0010] S400, performing a model analysis on the data synchronized by the digital twin platform in step S300, and obtaining the movement path of the ice sheet and the potential impact on the bridge based on the real-time data;

[0011] S500: Based on the analysis result of step S400, a graded warning is provided for the bridge impact position.

[0012] Furthermore, in step S100, the ice blocks are identified using the YOLOv8 algorithm.

[0013] Furthermore, in step S200, based on the YOLOv8 algorithm, the Deepsort algorithm is used to track the ice floes and monitor the position and movement trajectory of the ice floes.

[0014] Furthermore, in step S500, a graded warning system is implemented according to the size and movement speed of the ice floes; a high-level warning is issued for ice floes that cause major damage or destruction to the bridge structure; and a low-level warning is issued for ice floes that cause minor damage to the bridge structure.

[0015] Furthermore, the method for determining whether it is a high-level warning is as follows:

[0016] Calculate the impact force of the ice sheet when it hits, and judge whether it poses a threat to the structure based on the magnitude of the impact force; the impact force calculation formula is:

[0017]

[0018] Where F is the impact force of the ice sheet; m is the mass of the ice sheet, and the calculation formula is:

[0019] m=ρ·A·d

[0020] Among them, ρ is the density of the ice sheet; A is the area of ​​the ice sheet, which is identified by computer vision; d is the thickness of the ice sheet, which is measured by the thickness of the ice sheet in previous years as an empirical value; v is the speed of the ice sheet, which is identified by computer vision; t is the action time, which is determined by empirical values ​​and ranges from 0.1 seconds to 0.5 seconds;

[0021] When the potential impact force brought by the speed and area of ​​the ice sheet exceeds the pre-calculated warning value, it is judged as a high-level warning ice sheet; that is, the system continuously monitors multiple ice sheets, calculates the predicted dynamic load generated by each ice sheet separately and superimposes them, and introduces a safety factor based on the calculated dynamic load. The safety factor is 1.2 to 1.5 to analyze the impact on structural safety.

[0022] Furthermore, real-time warning information is sent to managers and construction teams through the digital twin platform for timely response measures.

[0023] A digital twin-based ice impact identification and early warning system for cold-region bridge construction processes, the system having program modules corresponding to the above-mentioned steps, and executing the steps in the above-mentioned digital twin-based ice impact identification and early warning method for cold-region bridge construction processes during operation.

[0024] A computer-readable storage medium stores a computer program, wherein the computer program is configured to implement the steps of an ice rip impact identification and early warning method in a cold-region bridge construction process based on digital twins when called by a processor.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention discloses a method and system for identifying and warning ice floes impact in cold-region bridge construction process based on digital twin, which identifies ice floes in water area, monitors and identifies ice floes in water area by computer vision technology, and the key of this step is to adopt the latest image recognition algorithm; after identifying ice floes, the deepsort algorithm is used to track ice floes in real time to handle the situation that multiple ice floes appear at the same time, and track their moving paths; based on the identified ice floes, the position of ice floes is monitored, and the position and moving trajectory of ice floes are monitored in real time by the above-mentioned computer vision technology and multi-target tracking algorithm; the monitored ice floes are synchronized to the digital twin platform, and the digital twin platform combines the data in the actual environment with the model in the digital world to realize real-time data analysis and simulation; the data synchronized by the digital twin platform is modeled, and the possible moving path of ice floes and the potential impact on the bridge are simulated and analyzed in the digital twin platform according to the real-time data; the impact position is warned based on the analysis results. The present invention can effectively identify and warn the potential risks caused by the passage of ships in the process of bridge construction, thereby ensuring the safe construction and stable operation of the bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a structural schematic diagram of a method for identifying and warning of ice impact in the cold-region bridge construction process based on digital twin in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] Specific implementation plan 1: Combine Figure 1 As shown, the present invention provides a method for identifying and warning ice sheet impact in the cold region bridge construction process based on digital twin, comprising the following steps:

[0030] S100, identify ice sheets in water areas;

[0031] Using computer vision technology to monitor and identify ice sheets in the water, the key to this step is to use image recognition algorithms such as YOLOv8 to quickly and accurately identify ice sheets; YOLOv8 is used for this task due to its high efficiency and accuracy;

[0032] Specifically, we use the on-site surveillance camera to obtain image information, build an ice floe target detection dataset, use the YOLOv8 target detection training model to obtain the identified ice floes;

[0033] S200, based on the ice blocks identified in step S100, monitoring the positions of the ice blocks;

[0034] Multi-target tracking: After identifying the ice floes, the deepsort algorithm is used to track the ice floes in real time to handle the situation where multiple ice floes appear at the same time and track their movement paths. That is, based on the identified ice floes, the position of the ice floes is monitored, and the position and movement trajectory of the ice floes are monitored in real time through the above-mentioned computer vision technology and multi-target tracking algorithm. Deepsort is an efficient multi-target tracking algorithm that can handle the situation where multiple ice floes appear at the same time and track their movement paths.

[0035] S300, synchronizing the ice blocks monitored in step S200 to the digital twin platform;

[0036] The monitored ice sheet location and trajectory data are synchronized to the digital twin platform, which can use TCP local port communication; the digital twin platform combines the real-time data of ice sheet size, speed and location in the actual environment with the model in the digital twin platform to realize real-time data analysis and simulation;

[0037] S400, performing model analysis on the data synchronized by the digital twin platform in step S300;

[0038] In the digital twin platform, the movement path of ice sheets and their potential impact on bridges are simulated and analyzed based on real-time data;

[0039] S500, issuing an early warning of the impact position based on the analysis result of step S400;

[0040] A graded warning system is implemented based on the size and movement trajectory of the ice floes, as well as the analysis results of the digital twin platform. A high-level warning is issued for larger ice floes that may have a significant impact on the bridge structure; for ice floes with a smaller impact, a corresponding level of warning is issued.

[0041] For example, cameras are deployed in sections upstream (e.g., one at 4km, one at 2km, one at 1.5km, one at 1km, one at 500m, and one at the site), and the ice floes that may cause risks when impacted and their speed are identified in sections. Through continuous monitoring, the alarm is triggered from the earliest discovery (the alarm is triggered when the ice floes are discovered in the first surveillance video upstream of the river). If there are still multiple risky ice floes when approaching, the alarm is upgraded (as the ice floes flow downstream, the number of ice floes that threaten the structure is still large, and the alarm is upgraded when there is no natural decrease). While identifying, control time is reserved (designed according to the response time for ice floe impact risk control in the engineering plan to ensure that the risk is controllable, that is, how much time is needed on site to take protective measures) to control the risk.

[0042] In order to determine whether an ice sheet is harmful, it is necessary to calculate the impact force when the ice sheet hits the structure and determine whether it poses a threat to the structure based on the magnitude of the impact force. The calculation formula for the impact force of a single ice sheet is:

[0043]

[0044] Where: F is the impact force of the ice block (unit: N); m is the mass of the ice block (unit: kg), and the calculation formula is:

[0045] m=ρ·A·d

[0046] Where: ρ is the density of the ice sheet (unit: kg / m3); A is the area of ​​the ice sheet, which is identified by computer vision; d is the thickness of the ice sheet, which is measured by measuring the thickness of ice sheets in previous years as an empirical value; v is the speed of the ice sheet, which is identified by computer vision; t is the action time, which is usually determined by empirical values ​​and ranges from 0.1 seconds to 0.5 seconds;

[0047] When the potential impact force caused by the speed and area of ​​the ice sheet exceeds the warning value obtained by pre-calculation of the result structure, it is judged as an ice sheet with a large impact; that is, the system continuously monitors multiple ice sheets, calculates the predicted dynamic load generated by each ice sheet separately and superimposes them, and introduces a safety factor (usually 1.2 to 1.5) based on the calculated dynamic load to analyze the impact on the structural safety.

[0048] Preferably, real-time warning information is sent to relevant managers and construction teams through the digital twin platform, so that they can take timely measures to mitigate or avoid the potential impact of ice sheets on bridge construction.

[0049] In addition, the system monitors the structural anti-impact protection measures and continuously monitors the structural protection measures through the above-mentioned YOLOv8 target detection and IoT position sensors. If the protection measures are lost, the system will synchronize to the digital twin platform and issue an early warning.

[0050] The digital twin platform includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the memory is used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and the processor are connected via a bus.

[0051] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0052] The memory may include a read-only memory, a flash memory, and a random access memory, and provides instructions and data to the processor. A part or all of the memory may also include a nonvolatile random access memory.

[0053] It can be seen from the above that the electronic device provided in the embodiment of the present invention can implement the ice impact warning method for the cold-region bridge construction process based on digital twin as described in embodiment one by running a computer program, thereby obtaining a new method for predicting the impact caused by ice floes in the construction of bridges in cold regions.

[0054] It should be understood that the above-mentioned integrated module / unit, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method implementations when executed by the processor. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0055] Specific implementation scheme two: The present invention provides a system for identifying and warning of ice impacts in the process of cold-region bridge construction based on digital twins. The system has program modules corresponding to the above steps, and executes the steps in the above-mentioned method for identifying and warning of ice impacts in the process of cold-region bridge construction based on digital twins during operation.

[0056] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0057] Specific implementation scheme three: The present invention provides a computer-readable storage medium, which stores a computer program. The computer program is configured to implement the steps of an ice impact identification and early warning method in a cold-region bridge construction process based on digital twins when called by a processor.

[0058] The other combinations and connection relationships of this embodiment are the same as those of the first embodiment.

[0059] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for identifying and warning ice sheet impact during bridge construction in cold regions based on digital twins, characterized in that: The following steps are involved: S100, identifying ice floes in the water area, and obtaining image information of the ice floes in the water area using computer vision; S200, based on the ice floes identified in step S100, monitoring the positions of the ice floes; using a multi-target tracking algorithm to perform real-time tracking of the ice floes to handle the situation where multiple ice floes appear at the same time, and tracking the moving paths of the multiple ice floes; S300, synchronizing the ice row position and trajectory data monitored in step S200 to the digital twin platform for real-time data analysis and simulation; S400, performing a model analysis on the data synchronized by the digital twin platform in step S300, and obtaining the movement path of the ice sheet and the potential impact on the bridge based on the real-time data; S500: Based on the analysis result of step S400, a graded warning is provided for the bridge impact position.

2. The ice sheet impact identification and early warning method for cold region bridge construction process based on digital twin according to claim 1 is characterized by: In step S100, ice blocks are identified using the YOLOv8 algorithm.

3. The ice sheet impact identification and early warning method for cold region bridge construction process based on digital twin according to claim 2 is characterized by: In step S200, based on the YOLOv8 algorithm, the Deepsort algorithm is used to track the ice floes and monitor the position and movement trajectory of the ice floes.

4. The ice sheet impact identification and early warning method for cold region bridge construction process based on digital twin according to claim 3 is characterized by: In step S500, a graded warning system is implemented according to the size and movement speed of the ice floes; a high-level warning is issued for ice floes that cause major damage or destruction to the bridge structure; and a low-level warning is issued for ice floes that cause minor damage to the bridge structure.

5. The ice sheet impact identification and early warning method for cold region bridge construction process based on digital twin according to claim 4 is characterized by: The method to determine whether it is a high-level warning is: Calculate the impact force of the ice sheet when it hits, and judge whether it poses a threat to the structure based on the magnitude of the impact force; the impact force calculation formula is: Where F is the impact force of the ice sheet; m is the mass of the ice sheet, and the calculation formula is: m=ρ·A·d Among them, ρ is the density of the ice sheet; A is the area of ​​the ice sheet, which is identified by computer vision; d is the thickness of the ice sheet, which is measured by the thickness of the ice sheet in previous years as an empirical value; v is the speed of the ice sheet, which is identified by computer vision; t is the action time, which is determined by empirical values ​​and ranges from 0.1 seconds to 0.5 seconds; When the potential impact force brought by the speed and area of ​​the ice sheet exceeds the pre-calculated warning value, it is judged as a high-level warning ice sheet; that is, the system continuously monitors multiple ice sheets, calculates the predicted dynamic load generated by each ice sheet separately and superimposes them, and introduces a safety factor based on the calculated dynamic load. The safety factor is 1.2 to 1.5 to analyze the impact on structural safety.

6. The ice sheet impact identification and early warning method for cold region bridge construction process based on digital twin according to claim 5 is characterized by: Real-time warning information is sent to managers and construction teams through the digital twin platform so that they can take timely response measures.

7. The ice impact identification and early warning system for cold-region bridge construction based on digital twins is characterized by: The system has a program module corresponding to the steps of any one of claims 1 to 6 above, and executes the steps in the above-mentioned ice impact identification and early warning method in the cold-region bridge construction process based on digital twins when running.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps of the ice impact identification and early warning method for cold-region bridge construction based on digital twins according to any one of claims 1 to 6 when called by a processor.

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