Bridge global monitoring, early warning and risk avoiding system

Abstract

The utility model relates to the technical field of road surface monitoring, and discloses a bridge global monitoring early warning risk avoiding system which comprises a sensor module, a camera module, a central processing module and an intelligent terminal. The sensor module and the camera module are both mounted on a bridge; the sensor module comprises at least one tension / tension sensor; the camera module comprises at least one infrared thermal imaging camera; the central processing module comprises a server; the tension / tension sensor and the infrared thermal imaging camera are connected with the server; and the server is connected with the intelligent terminal. The utility model solves the problem that natural disasters cannot be timely and accurately identified in the prior art, and has the characteristic of improving the monitoring coverage range.

Description

TECHNICAL FIELD

[0001] The utility model relates to road surface monitoring technical field more specifically, relate to a bridge global monitoring early warning risk avoidance system. BACKGROUND

[0002] Recently, rainstorm, mountain landslide, sandstorm and cold wave and low temperature rain and snow freezing weather, more and more frequent extreme weather, constantly impact highway bridge, bridge, railway and other public infrastructure, also bring very serious threat to people's life and property safety.

[0003] If the vehicle that is about to drive to the collapsed section cannot be informed or informed quickly after the collapse of the high-speed bridge (bridge section), the vehicle may fall, causing casualties and other property losses.

[0004] The prior art has a bridge safety monitoring method, which comprises a tension sensor and a displacement sensor arranged at the connection position of two-span beams, the tension sensor monitors the tension between the two-span beams on the pier, and the displacement sensor monitors the displacement between the two-span beams;The weighing area is arranged at the two ends of the cross beam, and the weighing sensor is arranged in the weighing area to monitor the weight of the vehicle entering each cross beam;The wind speed and direction sensor is arranged above the bridge, and the wind speed and direction sensor monitors the wind speed and direction that the bridge is subjected to in real time;The positioning sensor is arranged at the upper end of the pier, and the positioning sensor obtains the coordinate information and the altitude information of the upper end of the pier;The positioning sensor, the wind speed and direction sensor, the vibration sensor and the weighing sensor are connected with the bridge safety monitoring center.

[0005] However, the prior art still has the problem of not comprehensive enough monitoring, and cannot identify natural disasters in time and accurately, therefore, how to design a global monitoring early warning risk avoidance system capable of accurately identifying natural disasters is a technical problem that needs to be solved in the technical field. UTILITY MODEL CONTENT

[0006] The utility model discloses in order to solve the prior art problem that natural disasters cannot be identified in time and accurately, provides a bridge global monitoring early warning risk avoidance system, it has the characteristics of being capable of improving monitoring coverage.

[0007] In order to realize the above-mentioned utility model purposes, the technical scheme adopted is as follows:

[0008] A bridge global monitoring early warning risk avoidance system, comprising a sensor module, a camera module, a central processing module, an intelligent terminal;

[0009] The sensor module and the camera module are installed on the bridge; the sensor module comprises at least one tension sensor; the camera module comprises at least one infrared thermal imaging camera; the central processing module comprises a server; the tension sensor and the infrared thermal imaging camera are connected with the server; and the server is connected with an intelligent terminal.

[0010] Preferably, a physical signal line is further included, which is installed on the bridge guardrail; the tension sensor is connected with the server through the physical signal line.

[0011] Further, the tension sensor is installed on the bridge in any one of the following modes: being integrated into the physical signal line or being installed at the end of the physical signal line as an end point accessory of the physical signal line.

[0012] Further, the sensor module further comprises at least one vibration sensor and at least one displacement sensor; the vibration sensor and the displacement sensor are respectively connected with the server.

[0013] Further, the vibration sensor is installed on the bridge in any one of the following modes: being embedded into a key position of the bridge structure or being fixedly installed on the bridge guardrail; and the displacement sensor is installed on the bridge in any one of the following modes: being installed on the bridge at a designated position on the two sides of the bridge or on the road surface of the bridge.

[0014] Further, the vibration sensor and the displacement sensor are connected with the server in any one of the following modes: being connected through a wire or being connected wirelessly.

[0015] Further, the camera module further comprises at least one panoramic camera; the panoramic camera is installed at any one of the following positions: the two ends of the bridge, an important intersection or an open road section; and the infrared thermal imaging camera is installed at a key load-bearing position of the bridge.

[0016] Further, the infrared thermal imaging camera and the panoramic camera are connected with the server in any one of the following modes: being connected through a wire or being connected wirelessly.

[0017] Further, the central processing module further comprises a master control device and a coordinator; the sensor module and the camera module are respectively connected with the master control device; and the master control device, the coordinator and the server are sequentially connected.

[0018] Further, the central processing module further comprises a local large model module; and the local large model module is connected with the master control device.

[0019] The bridge safety monitoring system has the following beneficial effects:

[0020] The utility model discloses a kind of bridge global monitoring early warning danger avoidance systems combined with multiple advanced sensing technologies, by tension / tension sensor, infrared thermal imaging camera, this monitoring early warning danger avoidance system can real-time acquisition temperature, vibration to capture the dynamic change in monitoring area;After this monitoring early warning danger avoidance system collects sensor module, the information collected by camera module, these signal data are analyzed by server, potential disaster signs are identified in time, for example, microseismic wave before earthquake, water level change of flood or smoke diffusion of fire etc., and further identify the characteristics of natural disasters such as flame, debris flow, waterlogging etc.When perception system detects abnormal condition, server can rapidly send early warning information to intelligent terminal, help people to make emergency preparation, maximumly reduce the loss caused by disaster.This perception technology based on signal and image fusion provides more accurate and timely support for disaster prevention and response, and improves monitoring coverage. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 It is a system schematic diagram of a kind of bridge global monitoring early warning danger avoidance system of the utility model.

[0022] Figure 2 It is the specific arrangement schematic diagram of bridge global monitoring early warning danger avoidance system in embodiment 2.

[0023] Figure 3 It is the specific system schematic diagram of bridge global monitoring early warning danger avoidance system in embodiment 3.

[0024] Wherein, 1 is tension / tension sensor, 2 is infrared thermal imaging camera, 3 is server, 4 is physical signal line, 5 is vibration sensor, 6 is displacement sensor, 7 is panoramic camera. DETAILED DESCRIPTION

[0025] The utility model will be described in detail in combination with drawing and specific embodiment.

[0026] Embodiment 1

[0027] As Figure 1 Shown, a kind of bridge global monitoring early warning danger avoidance system, including sensor module, camera module, central processing module, intelligent terminal;

[0028] Sensor module, camera module are all installed on bridge;The sensor module includes at least 1 tension / tension sensor 1;The camera module includes at least 1 infrared thermal imaging camera 2;The central processing module includes server 3;Tension / tension sensor 1 and infrared thermal imaging camera 2 are connected with server 3;The server 3 is connected with intelligent terminal.

[0029] In the embodiment, the intelligent terminal includes vehicle terminal and mobile phone terminal.

[0030] Embodiment 2

[0031] As Figure 2 shown, a bridge global monitoring early warning system, comprising a sensor module, a camera module, a central processing module, an intelligent terminal;

[0032] The sensor module and the camera module are installed on the bridge; the sensor module comprises two tension sensors 1; the camera module comprises two infrared thermal imaging cameras 2; the central processing module comprises a server 3; the tension sensors 1 and the infrared thermal imaging cameras 2 are connected with the server 3; and the server 3 is connected with the intelligent terminal.

[0033] In this embodiment, high-precision and weather-resistant tension sensors 1 are selected, which can accurately measure the tension changes of the physical signal lines 4 (such as steel cables or optical fibers).

[0034] In a specific embodiment, it also comprises physical signal lines 4, which are installed on the bridge guardrails; and the tension sensors 1 are respectively connected with the server 3 through the physical signal lines 4.

[0035] In this embodiment, the two tension sensors 1 are integrated into two physical signal lines 4 and installed on the bridge through the guardrails installed at both ends of the bridge, so as to ensure that when the bridge or the road surface collapses and causes the signal line to break, the sensor can immediately perceive the sharp change of the tension.

[0036] In a specific embodiment, the sensor module further comprises at least one vibration sensor 5 and at least one displacement sensor 6; and the vibration sensor 5 and the displacement sensor 6 are respectively connected with the server 3.

[0037] In this embodiment, a vibration sensor 5 capable of monitoring low-frequency vibration and sensitive to slight changes is selected, such as an accelerometer or a ground sound detector.

[0038] In a specific embodiment, the vibration sensor 5 is installed on the bridge in any way, such as embedding the key positions under the bridge structure such as the pier and the bridge deck or fixedly installing on the bridge guardrail, to monitor the natural vibration frequency changes or abnormal vibration of the bridge, which may be a precursor of damage to the bridge structure. The displacement sensor 6 is installed on the bridge in any way, such as at both sides of the bridge or at the specified position of the bridge road surface, to detect the potential collapse risk by periodically measuring the distance changes between the fixed reference point.

[0039] In this embodiment, the vibration sensor 5 is installed on the bridge by being fixedly installed on the bridge guardrail, and the displacement sensor 6 is installed on the bridge at the specified position of the bridge road surface.

[0040] In one specific embodiment, the vibration sensor 5 and the displacement sensor 6 are connected to the server 3 by any of wired or wireless means.

[0041] In this embodiment, the vibration sensor 5 and the displacement sensor 6 are connected to the server 3 by wireless means.

[0042] In one specific embodiment, the camera module further comprises at least one panoramic camera 7; the panoramic camera 7 is installed at either end of the bridge, at an important intersection, or at an open road section; the infrared thermal imaging camera 2 is installed at a key load-bearing part of the bridge.

[0043] In one specific embodiment, the infrared thermal imaging camera 2 and the panoramic camera 7 are connected to the server 3 by any of wired or wireless means.

[0044] In this embodiment, the infrared thermal imaging camera 2 and the panoramic camera 7 are connected to the server 3 by wireless means.

[0045] In this embodiment, the panoramic camera 7 is specifically a high-definition camera with night vision function and 360° panoramic shooting capability, specifically comprising two panoramic cameras 7, which are installed at both ends of the bridge to ensure comprehensive coverage and clear capture of real-time images of the bridge and the road surface.

[0046] In this embodiment, the infrared thermal imaging camera 2 is specifically installed on the pier of the bridge for non-contact monitoring; in the monitoring, the infrared thermal imaging technology is used to identify temperature abnormalities inside the bridge structure, which may be caused by cracks, water seepage, etc.

[0047] In this embodiment, all sensors and cameras should be connected to the central control system by wired or wireless means to realize real-time transmission and processing of data. All devices should have the characteristics of waterproof, dustproof, and sunproof to adapt to the complex and changeable natural environment of the highway bridge. Combined with big data analysis and machine learning algorithms, the collected data is deeply mined to automatically identify and warn potential bridge and road collapse risks. According to the degree of abnormality of the monitored data, multiple warning thresholds are set to ensure that the alarm device is started in sufficient time before the disaster occurs, providing sufficient risk avoidance time for drivers and passengers.

[0048] Embodiment 3

[0049] More specifically, as shown in Figure 3 The central processing module further comprises a master control device and a coordinator; the sensor module and the camera module are connected to the master control device; the master control device, the coordinator, and the server 3 are connected in sequence.

[0050] The central processing module further comprises a local large model module; the local large model module is connected with the master control device.

[0051] In this embodiment, after the sensor module and the camera module collect the original data, the master control device performs filtering, denoising and other preprocessing operations on the collected original data to improve the quality of the data. The local large model module uses multi-dimensional signal fusion technology to comprehensively analyze the data of different sensors, extracts feature information related to disasters, and matches with the preset disaster model to identify potential disaster signs. After local recognition, connect the cloud large model through the server 3 to further verify the potential disaster identification. For example, the microseismic wave signal monitored by the vibration sensor can judge the possibility of earthquake occurrence; the air pressure change monitored by the air pressure sensor can predict the arrival of storm surge or typhoon. By identifying the color, shape and dynamic change of the flame in the image, the occurrence of fire can be judged; by identifying the flow velocity, turbidity and coverage of the water flow in the image, the scale and influence range of the flood can be judged.

[0052] In this embodiment, the method of identifying natural disasters through signals and images combines a variety of advanced sensing technologies and image processing algorithms, and can monitor and evaluate the occurrence of natural disasters in real time. The sensor network collects a large amount of signal data in the environment, such as temperature, humidity, vibration and air pressure, etc., and high-definition cameras are deployed to capture dynamic changes in the area. Through the analysis of these signal data, potential disaster signs can be identified in a timely manner, such as microseismic waves before an earthquake, water level changes in floods, or smoke diffusion in fires, etc. Image processing technology can analyze the pictures captured by the camera and identify the characteristics of natural disasters such as fire, mudslide, water accumulation, etc. When abnormal conditions are detected, the bridge global monitoring and early warning system can quickly send warning information to relevant departments and the public through vehicle terminals and mobile phone terminals, helping people to make emergency preparations and minimize the loss caused by disasters. This signal and image fusion-based sensing technology provides more accurate and timely support for disaster prevention and response.

[0053] Classification and grading of natural disasters is a key step in improving the effectiveness of disaster management and emergency response. First, according to the nature of the disaster, it is divided into several categories, such as earthquake, flood, typhoon, fire, mudslide, etc., in order to facilitate targeted analysis and response. Then, within each category of disaster, according to its intensity and influence range, it is classified, for example, earthquake can be divided into mild, moderate and strong disasters, flood can be divided into small, medium and large floods according to the water level and flow rate. Fault classification focuses on identifying specific problems and potential risks caused by disasters, such as infrastructure damage, traffic disruption, power interruption, etc., which helps to develop appropriate emergency response plans and resource allocation strategies. Through systematic classification and fault classification, management departments can more effectively integrate information resources, achieve accurate early warning and scientific decision-making, and improve the overall ability and efficiency of responding to natural disasters.

[0054] Traditional image processing techniques mainly rely on manually designed feature extraction algorithms, while modern techniques increasingly use deep learning algorithms. Deep learning algorithms can automatically learn and extract features from large amounts of image data without human intervention. In this application, deep learning algorithms can identify more complex and subtle disaster features, such as weak seismic wave signals and hidden fire smoke, improving the accuracy and efficiency of disaster identification.

[0055] The multi-source image fusion technology in this application fuses image data from different cameras, different angles or different time points to obtain more comprehensive and accurate disaster information. This method can make up for the limitations of single camera vision, improving the breadth and depth of monitoring.

[0056] Through the application of vehicle-to-everything (V2X) technology, this application realizes real-time warning of vehicles, significantly improving driving safety. In this system, vehicles, road infrastructure and other traffic participants are connected through a wireless communication network, enabling rapid sharing of important traffic information and environmental data. When the system detects potential hazards such as traffic congestion, accidents or adverse weather conditions, V2X technology will immediately push warning information to nearby vehicle terminals, prompting drivers to take appropriate safety measures. In addition, through intelligent analysis of the cloud platform, the system can integrate data from multiple vehicles and infrastructure, enabling large-scale risk monitoring and early warning. This collaborative governance approach not only reduces the probability of accidents, but also provides drivers with a safer and more efficient driving experience, helping to promote overall traffic safety management on expressway bridges.

[0057] Obviously, the above embodiments of the present application are merely examples for clearly illustrating the present application, and are not intended to limit the embodiments of the present application. Any modification, equivalent replacement and improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A bridge-wide monitoring, early warning, and risk avoidance system, characterized in that: Includes sensor modules, camera modules, central processing modules, and smart terminals; The sensor module and camera module are both installed on the bridge; the sensor module includes at least one tension / tension sensor (1); the camera module includes at least one infrared thermal imaging camera (2); the central processing module includes a server (3); the tension / tension sensor (1) and the infrared thermal imaging camera (2) are connected to the server (3); the server (3) is connected to a smart terminal.

2. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 1, characterized in that: It also includes a physical signal line (4), which is installed on the bridge railing; the tension / tension sensor (1) is connected to the server (3) through the physical signal line (4).

3. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 2, characterized in that: The tension / tension sensor (1) is installed on the bridge either by integrating it into the physical signal line (4) or by installing it as an end accessory of the physical signal line (4) at both ends of the bridge railing.

4. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 2, characterized in that: The sensor module further includes at least one vibration sensor (5) and at least one displacement sensor (6); the vibration sensor (5) and displacement sensor (6) are respectively connected to the server (3).

5. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 4, characterized in that: The vibration sensor (5) is installed on the bridge by either embedding it into a key part of the bridge structure or fixing it on the bridge railing; the displacement sensor (6) is installed on the bridge by either placing it on both sides of the bridge or at a designated location on the bridge surface.

6. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 4, characterized in that: The vibration sensor (5) and displacement sensor (6) are connected to the server (3) via either wired or wireless means.

7. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 1, characterized in that: The camera module also includes at least one panoramic camera (7); the panoramic camera (7) is installed at any of the two ends of the bridge, at an important intersection or in an open section; the infrared thermal imaging camera (2) is installed on a key load-bearing part of the bridge.

8. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 7, characterized in that: The infrared thermal imaging camera and panoramic camera are connected to the server via either wired or wireless means.

9. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 1, characterized in that: The central processing module also includes a main control device and a coordinator; the sensor module and camera module are respectively connected to the main control device; the main control device, coordinator, and server are connected in sequence.

10. The bridge full-area monitoring, early warning, and risk avoidance system according to claim 9, characterized in that: The central processing module also includes a local large model module; the local large model module is connected to the main control device.

Images