Double complementary analysis real-time monitoring system for falling off of bridge body

Abstract

The invention discloses a dual complementary analysis real-time monitoring system for bridge body falling, and belongs to the technical field of bridge monitoring. Comprising a beam body falling-off dual complementation analysis module which is connected with a broken line identification module and an image identification module. A stay wire is transversely arranged at the bottom of a beam body of a target bridge, the two ends of the stay wire are arranged on abutments, and the broken wire recognition module is arranged at the end of the stay wire. The image recognition module is arranged at the bottom of the beam body; and the beam body falling dual complementary analysis module is used for comparing and analyzing the position change of the target object relative to the image features of the initial image at different time points based on the extracted image features, calculating and generating beam body displacement data, and judging whether the beam body falls or not by combining the dual complementary analysis of the broken line identification signal. According to the invention, image recognition and broken line recognition technologies are combined to form a complementary double analysis system, and the monitoring accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge monitoring, and in particular to a real-time monitoring system for bridge beam shedding with dual complementary analysis. Background Art

[0002] At present, small and medium-sized bridges are large in size and wide in area, which has also led to problems such as inadequate maintenance and management of small and medium-sized bridges; traditional bridge monitoring methods mainly rely on manual inspections or single sensor monitoring, which have problems such as low efficiency, high cost, poor real-time performance, great environmental impact, high false alarm rate, and insufficient reliability. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a real-time monitoring system for bridge beam detachment with dual complementary analysis.

[0004] The objective of the present invention is achieved through the following technical solutions: a real-time monitoring system for dual complementary analysis of bridge beam detachment, comprising a dual complementary analysis module for beam detachment, the module being connected to a wire breakage recognition module and an image recognition module; a wire is horizontally arranged at the bottom of the beam of the target bridge, both ends of the wire are arranged on piers, the wire breakage recognition module is arranged at the end of the wire; and the image recognition module is arranged at the bottom of the beam; The image recognition module is used to collect beam image data in real time through an optical lens; The broken wire identification module is used to determine whether the wire is broken according to the voltage signal value of the broken wire identification instrument; The dual complementary analysis module for beam detachment is used to compare and analyze the position changes of the target object relative to the image features of the initial image at different time points based on the extracted image features, calculate and generate beam displacement data, and combine the dual complementary analysis of the broken wire identification signal to determine whether the beam has detached.

[0005] Preferably, when collecting beam image data, the image recognition module further includes the following steps: Set the image acquisition angle and acquisition frame according to the target beam size, and collect beam image data in real time; Perform image preprocessing on the beam image data collected in real time; Generate feature descriptors through computer vision algorithms and extract real-time image features.

[0006] Preferably, the image preprocessing includes using a deep learning algorithm to grayscale, denoise, contrast enhance and correct the beam image data collected in real time.

[0007] Preferably, the computer vision algorithm is an edge detection algorithm or a feature point matching algorithm.

[0008] Preferably, when the beam shedding dual complementary analysis module performs beam shedding analysis, the following steps are further included: Collect the beam contour image and the output voltage signal of the broken wire identification instrument; The beam contour image is preprocessed, and then image registration is performed to extract key points of structural features to obtain key point features of the beam real-time image; the output voltage signal is standardized and abnormal values are removed to obtain the disconnection signal; The key point features of the real-time image of the beam are quantified into displacement features, and compared with the initial image features to analyze whether displacement has occurred and obtain the beam displacement data; the disconnection signal is quantified into characters with the disconnection state being 0 and the connection state being 1 according to the output voltage value to obtain the disconnection signal data; Align the beam displacement data and the wire break signal data in time and space; Check whether the beam displacement data and the line break signal data after time and space alignment are logically consistent, and then perform double complementary analysis to determine the state of the target beam; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has fallen off; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the connected state 1 or the beam displacement data analysis result shows that no displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has not fallen off, and the data is affected by interference, and continuous attention and inspection are carried out, and the interference source is identified to provide feedback and correction to the system.

[0009] The beneficial effects of the present invention are: 1) Combining image recognition and broken wire recognition technologies to form a complementary dual analysis system improves monitoring accuracy and reliability. The system collects and analyzes data in real time, enabling real-time monitoring of the risk of bridge beam failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is the principle block diagram of the real-time monitoring system for dual complementary analysis of bridge beam shedding; Figure 2 Schematic diagram of the image acquisition area; Figure 3 This is a schematic diagram of the module workflow; Figure 4 This is a detailed workflow diagram for the dual complementary analysis module for beam shedding. DETAILED DESCRIPTION

[0011] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0012] See Figures 1-4 The present invention provides a technical solution: a real-time monitoring system for bridge beam detachment with dual complementary analysis, comprising a beam detachment dual complementary analysis module, the beam detachment dual complementary analysis module being connected to a wire breakage recognition module and an image recognition module; a wire is horizontally arranged at the bottom of the beam of the target bridge, both ends of the wire are arranged on piers, the wire breakage recognition module is arranged at the end of the wire; the image recognition module is arranged at the bottom of the beam; The image recognition module is used to collect beam image data in real time through an optical lens; The broken wire identification module is used to determine whether the wire is broken according to the voltage signal value of the broken wire identification instrument; The dual complementary analysis module for beam detachment is used to compare and analyze the position changes of the target object relative to the image features of the initial image at different time points based on the extracted image features, calculate and generate beam displacement data, and combine the dual complementary analysis of the broken wire identification signal to determine whether the beam has detached.

[0013] In this embodiment, real-time and precise monitoring of bridge beam failure is achieved through image acquisition, preprocessing, feature extraction, wire breakage detection, and beam failure and displacement analysis. This system offers the advantages of dual security, high accuracy, and strong real-time performance. Image recognition involves installing an image recognition and analysis system on the bridge piers. The viewing angle is adjusted according to the length and width of the beam, and a capture frame is set. The system then analyzes beam displacement by comparing images of the beam bottom taken at different times. Image recognition is susceptible to environmental influences, and wire breakage detection complements this. A wire is laid horizontally at the beam bottom, with both ends attached to the piers. A wire breakage detector is installed at the end of the wire. When the beam shifts or fails, the wire is pulled, altering the signal from the wire breakage detector. The system uses a dual, complementary analysis of image recognition and the signal returned by the wire breakage detector to comprehensively determine whether the beam has failed.

[0014] Image recognition module: collects image data of the target object in real time through a high-resolution optical lens. Pre-processes the image and uses a deep learning algorithm to grayscale, denoise, contrast enhance, and correct the collected image to improve image quality. Computer vision algorithms (such as edge detection, feature point matching, etc.) are used to improve the accuracy of feature matching, generate feature descriptors, and extract key features of the target object. Broken wire identification module: determines whether the wire is broken through the voltage signal value of the broken wire identifier. Beam detachment dual complementary analysis module: Based on the extracted image features, compares and analyzes the position changes of the target object relative to the image features of the initial image at different time points, calculates and generates beam displacement data, and combines the broken wire identification signal analysis with dual complementary analysis to determine whether the beam has detached.

[0015] In some embodiments, when collecting beam image data, the image recognition module further includes the following steps: Set the image acquisition angle and acquisition frame according to the target beam size, and collect beam image data in real time; Perform image preprocessing on the beam image data collected in real time; Generate feature descriptors through computer vision algorithms and extract real-time image features.

[0016] In some embodiments, the image preprocessing includes using a deep learning algorithm to grayscale, denoise, contrast enhance and correct the beam image data collected in real time.

[0017] In some embodiments, the computer vision algorithm is an edge detection algorithm or a feature point matching algorithm.

[0018] In some embodiments, when the beam fall-off dual complementary analysis module performs beam fall-off analysis, the following steps are further included: Collect the beam contour image and the output voltage signal of the broken wire identification instrument; The beam contour image is preprocessed, and then image registration is performed to extract key points of structural features to obtain key point features of the beam real-time image; the output voltage signal is standardized and abnormal values are removed to obtain the disconnection signal; The key point features of the real-time image of the beam are quantified into displacement features, and compared with the initial image features to analyze whether displacement has occurred and obtain the beam displacement data; the disconnection signal is quantified into characters with the disconnection state being 0 and the connection state being 1 according to the output voltage value to obtain the disconnection signal data; Align the beam displacement data and the wire break signal data in time and space; Check whether the beam displacement data and the line break signal data after time and space alignment are logically consistent, and then perform double complementary analysis to determine the state of the target beam; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has fallen off; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the connected state 1 or the beam displacement data analysis result shows that no displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has not fallen off, and the data is affected by interference, and continuous attention and inspection are carried out, and the interference source is identified to provide feedback and correction to the system.

[0019] The foregoing description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein and should not be construed as excluding other embodiments. Rather, the present invention can be used in various other combinations, modifications, and environments and can be modified within the scope of the concept described herein through the above teachings or techniques or knowledge in the relevant field. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention are intended to be protected by the appended claims.

Claims

1. A real-time monitoring system for bridge beam detachment with dual complementary analysis, characterized by: It includes a beam-fall dual complementary analysis module, which is connected to a wire breakage recognition module and an image recognition module; a wire is horizontally arranged at the bottom of the beam of the target bridge, and both ends of the wire are arranged on the pier, and the wire breakage recognition module is arranged at the end of the wire; the image recognition module is arranged at the bottom of the beam; The image recognition module is used to collect beam image data in real time through an optical lens; The broken wire identification module is used to determine whether the wire is broken according to the voltage signal value of the broken wire identification instrument; The dual complementary analysis module for beam detachment is used to compare and analyze the position changes of the target object relative to the image features of the initial image at different time points based on the extracted image features, calculate and generate beam displacement data, and combine the dual complementary analysis of the broken wire identification signal to determine whether the beam has detached.

2. The bridge beam falling dual complementary analysis and real-time monitoring system according to claim 1 is characterized by: When collecting beam image data, the image recognition module further includes the following steps: Set the image acquisition angle and acquisition frame according to the target beam size, and collect beam image data in real time; Perform image preprocessing on the beam image data collected in real time; Generate feature descriptors through computer vision algorithms and extract real-time image features.

3. The bridge beam falling dual complementary analysis and real-time monitoring system according to claim 2 is characterized by: The image preprocessing includes using a deep learning algorithm to grayscale, denoise, enhance contrast and correct the beam image data collected in real time.

4. The bridge beam fall-off dual complementary analysis and real-time monitoring system according to claim 2 is characterized by: The computer vision algorithm is an edge detection algorithm or a feature point matching algorithm.

5. The bridge beam falling dual complementary analysis and real-time monitoring system according to claim 1 is characterized by: When the beam fall-off dual complementary analysis module performs beam fall-off analysis, the following steps are also included: Collect the beam contour image and the output voltage signal of the broken wire identification instrument; The beam contour image is preprocessed, and then image registration is performed to extract key points of structural features to obtain key point features of the beam real-time image; the output voltage signal is standardized and abnormal values are removed to obtain the disconnection signal; The key point features of the real-time image of the beam are quantified into displacement features, and compared with the initial image features to analyze whether displacement has occurred and obtain the beam displacement data; the disconnection signal is quantified into characters with the disconnection state being 0 and the connection state being 1 according to the output voltage value to obtain the disconnection signal data; Align the beam displacement data and the wire break signal data in time and space; Check whether the beam displacement data and the line break signal data after time and space alignment are logically consistent, and then perform double complementary analysis to determine the state of the target beam; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has fallen off; if the beam displacement data analysis result shows that displacement has occurred and the line break signal data is in the connected state 1 or the beam displacement data analysis result shows that no displacement has occurred and the line break signal data is in the disconnected state 0, then it is judged that the target beam has not fallen off, and the data is affected by interference, and continuous attention and inspection are carried out, and the interference source is identified to provide feedback and correction to the system.

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