Intelligent sensing type road and bridge safety monitoring method

Abstract

The invention discloses an intelligent sensing type road bridge safety monitoring method, and relates to the technical field of bridge safety monitoring. According to the intelligent sensing type road bridge safety monitoring method, bridge monitoring data of a plurality of road sections of a set bridge are obtained in real time, data analysis is carried out on the bridge monitoring data of each road section of the set bridge, and a bridge safety index set of each road section of the set bridge is obtained; comprising a bridge deck visual damage perception index, a structure risk perception index and a traffic risk perception index; the bridge safety index set of each road section of the set bridge is comprehensively analyzed to obtain the safety early warning index of the set bridge, and the preset safety early warning measures are taken for the set bridge through the safety early warning index, so that the safety risk of the bridge is comprehensively and accurately assessed, the monitoring accuracy is improved, and the safety early warning effect is improved. And potential safety hazards are identified, so that the scientificity and effectiveness of bridge management and maintenance are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge safety monitoring, and in particular to an intelligent perception-based road bridge safety monitoring method. Background Art

[0002] With the rapid development of my country's transportation infrastructure, the number and scale of road bridges continue to grow, and their operational safety issues are increasingly attracting social attention. Traditional bridge monitoring methods mostly rely on manual inspections or single-type sensors, which have problems such as discontinuous monitoring data, untimely response, and lack of early warning capabilities, making it difficult to meet the needs of modern bridge operation and maintenance. In recent years, with the continuous advancement of the Internet of Things, artificial intelligence, and multi-source sensing technology, "intelligent perception" bridge monitoring has become an important direction for industry development. Intelligent perception refers to the deployment of a variety of intelligent sensing devices, such as inclinometers and cameras, at key locations on the bridge to collect multi-dimensional data such as the bridge's structural response and traffic load in real time. At the same time, with the help of intelligent algorithms, intelligent analysis, anomaly identification, and safety assessment of monitoring data are realized, thereby achieving real-time perception and early warning of the bridge status.

[0003] The limitations of existing technologies include at least the following problems: existing technologies usually rely on a single data source, such as using only structural monitoring data or traffic flow data, which leads to one-sided monitoring information and makes it difficult to fully reflect the true safety status of the bridge. Moreover, due to the lack of multi-source data integration, existing technologies often only focus on a single perspective when assessing bridge safety, such as only focusing on structural damage or traffic load, and ignoring other important factors affecting bridge safety. As a result, it is difficult for existing technologies to fully capture the safety status of bridges and it is easy to miss potential risk factors, which in turn leads to misjudgment or neglect of safety hazards. Summary of the Invention

[0004] In response to the shortcomings of existing technologies, the present invention provides an intelligent perception-based road and bridge safety monitoring method, which solves the problem that existing technologies rely on a single data source and ignore multi-dimensional evaluation, resulting in one-sided bridge safety monitoring, making it difficult to fully capture risks, and easily leading to misjudgment of hidden dangers.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent perception-based road bridge safety monitoring method, comprising the following steps: real-time acquisition of bridge monitoring data of several sections of a set bridge, wherein the bridge monitoring data includes bridge monitoring image data, structure monitoring data, and traffic monitoring data; data analysis is performed on the bridge monitoring data of each section of the set bridge to obtain a bridge safety index set for each section of the set bridge, including a bridge deck visual damage perception index, a structure risk perception index, and a traffic risk perception index; a comprehensive analysis is performed on the bridge safety index set of each section of the set bridge to obtain a safety warning index for the set bridge, and preset safety warning measures are taken based on the safety warning index of the set bridge.

[0006] Furthermore, the bridge monitoring image data includes a pixel value and a two-dimensional coordinate of each pixel point in the bridge monitoring image.

[0007] Furthermore, the specific steps for obtaining the bridge deck visual damage perception index of each section of the set bridge are as follows: performing edge detection processing on the pixel value of each pixel point in the bridge monitoring image of each section of the set bridge to obtain several crack areas in the bridge monitoring image of each section of the set bridge; reading the two-dimensional coordinates of each pixel point in each crack area in the bridge monitoring image of each section of the set bridge, and performing comprehensive analysis to obtain a crack monitoring index set for each section of the set bridge, including a crack area index and a crack morphology index; performing comprehensive analysis on the crack monitoring index set for each section of the set bridge to obtain the bridge deck visual damage perception index for each section of the set bridge.

[0008] Furthermore, the specific steps for obtaining the crack monitoring index set for each section of the set bridge are as follows: performing a comprehensive analysis on each pixel point in each crack area in the bridge monitoring image of each section of the set bridge to obtain the crack area index of each section of the set bridge; reading the two-dimensional coordinates of each edge pixel point in each crack area in the bridge monitoring image of each section of the set bridge, and performing a comprehensive analysis to obtain the crack morphology index of each section of the set bridge.

[0009] Furthermore, the structural monitoring data includes component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value. The specific steps for obtaining the structural risk perception index of each section of the set bridge are as follows: standardizing the component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge; and comprehensively analyzing the standardized component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge to obtain the structural risk perception index of each section of the set bridge.

[0010] Furthermore, the traffic monitoring data includes a vehicle speed deviation index, a traffic flow value, a load index, and a vehicle distance value, and the specific steps for obtaining the traffic risk perception index of each section of the set bridge are as follows: normalizing the vehicle speed deviation index, traffic flow value, load index, and vehicle distance value of each section of the set bridge; and comprehensively analyzing the normalized vehicle speed deviation index, traffic flow value, load index, and vehicle distance value of each section of the set bridge to obtain the traffic risk perception index of each section of the set bridge.

[0011] Furthermore, the specific steps for taking preset safety warning measures based on the safety warning index of a set bridge are as follows: the safety warning index of the set bridge is judged and analyzed with a preset safety warning index threshold set, wherein the safety warning index threshold set includes a first safety warning index threshold, a second safety warning index threshold, and a third safety warning index threshold; and corresponding preset safety warning measures are taken based on the judgment results.

[0012] Furthermore, the specific steps for taking corresponding preset safety warning measures based on the judgment results are as follows: if the safety warning index of the set bridge is lower than or equal to the preset first safety warning index threshold, it is marked as safe, and safety monitoring measures are taken; if the safety warning index of the set bridge is higher than the preset first safety warning index threshold and lower than or equal to the preset second safety warning index threshold, it is marked as low risk, and the first safety warning measure is taken; if the safety warning index of the set bridge is higher than the preset second safety warning index threshold and lower than or equal to the preset third safety warning index threshold, it is marked as medium risk, and the second safety warning measure is taken; if the safety warning index of the set bridge is higher than the preset second safety warning index threshold, it is marked as high risk, and the third safety warning measure is taken.

[0013] The present invention has the following beneficial effects:

[0014] (1) This intelligent perception-based road bridge safety monitoring method obtains bridge monitoring image data, structural monitoring data and traffic monitoring data in real time, integrates multiple data sources, and thus ensures that the bridge can be comprehensively monitored and analyzed from multiple dimensions. It also conducts detailed analysis of the monitoring data of each section of the bridge, and obtains a set of safety indexes including the bridge deck visual damage perception index, the structural risk perception index and the traffic risk perception index. In this way, the safety risk of the bridge can be comprehensively and accurately assessed, thereby improving the accuracy of monitoring and identifying potential safety hazards, thereby effectively avoiding incomplete or delayed safety assessments caused by a single data source, and improving the scientificity and effectiveness of bridge management and maintenance.

[0015] (2) This intelligent perception-based road and bridge safety monitoring method generates an accurate safety warning index by real-time monitoring and comprehensive analysis of the safety index of each section of the bridge, and provides timely warning information for bridge management, so that it can more sensitively identify and respond to bridge safety risks, especially when early hidden dangers appear on the bridge, so that repair measures can be taken in advance, while avoiding losses and accidents. Through timely and effective safety response, the operational safety of the bridge can be improved, the service life of the bridge can be extended, and the negative impact of sudden safety incidents can be reduced.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of an intelligent perception-based road and bridge safety monitoring method of the present invention.

[0018] Figure 2 This is a flowchart of the specific steps for obtaining the bridge deck visual damage perception index of each section of a set bridge in an intelligent perception-based road and bridge safety monitoring method of the present invention. DETAILED DESCRIPTION

[0019] See also Figure 1 , an embodiment of the present invention provides a technical solution: an intelligent perception-based road bridge safety monitoring method, comprising the following steps: acquiring bridge monitoring data of several sections of a set bridge in real time, the bridge monitoring data including bridge monitoring image data, structure monitoring data, and traffic monitoring data; performing data analysis on the bridge monitoring data of each section of the set bridge respectively to obtain a bridge safety index set for each section of the set bridge, including a bridge deck visual damage perception index, a structure risk perception index, and a traffic risk perception index; performing a comprehensive analysis on the bridge safety index set of each section of the set bridge to obtain a safety warning index of the set bridge; and taking preset safety warning measures based on the safety warning index of the set bridge.

[0020] Specifically, if Figure 2 As shown, the bridge monitoring image data includes the pixel value and two-dimensional coordinates of each pixel point in the bridge monitoring image.

[0021] The specific steps for obtaining the bridge deck visual damage perception index of each section of the set bridge are as follows: performing edge detection processing on the pixel value of each pixel point in the bridge monitoring image of each section of the set bridge (i.e., Canny edge processing, detecting potential edges by grayscale gradient changes of the image, and effectively extracting small and continuous edge contours in the image through Gaussian filtering, gradient calculation, non-maximum suppression and double threshold hysteresis processing. In the processing results, areas with obvious boundary features are identified as crack area candidate areas, thereby obtaining a number of crack areas in the image of each section of the set bridge), and obtaining a number of crack areas in the bridge monitoring image of each section of the set bridge; reading the two-dimensional coordinates of each pixel point of each crack area in the bridge monitoring image of each section of the set bridge, and performing comprehensive analysis to obtain a crack monitoring index set for each section of the set bridge, including a crack area index and a crack morphology index; performing comprehensive analysis on the crack monitoring index set for each section of the set bridge to obtain the bridge deck visual damage perception index for each section of the set bridge.

[0022] The specific steps for obtaining the crack monitoring index set for each section of the set bridge are as follows: performing a comprehensive analysis on each pixel point in each crack area in the bridge monitoring image of each section of the set bridge (i.e., first performing statistical analysis and then performing summation processing) to obtain the crack area index of each section of the set bridge; reading the two-dimensional coordinates of each edge pixel point in each crack area in the bridge monitoring image of each section of the set bridge, and performing a comprehensive analysis (based on the Euclidean distance formula, calculating and analyzing to obtain the crack perimeter value, and based on the curvature calculation formula, calculating and analyzing to obtain the crack curvature value, and then weighting the crack perimeter value and the crack curvature value, and performing average processing based on the weighted processing result) to obtain the crack morphology index of each section of the set bridge.

[0023] In this embodiment, edge detection processing is performed on the pixel value of each pixel point in the bridge monitoring image, especially the Canny edge detection algorithm is used, so as to efficiently identify small and continuous crack areas in the image, and ensure that the extraction of the crack area is not only highly accurate, but also can identify tiny cracks, thereby improving the accuracy of bridge monitoring. Secondly, by performing a detailed comprehensive analysis of each pixel point in the crack area, the crack area index and crack morphology index are obtained, which can more deeply evaluate the degree of damage to the bridge, thereby helping to comprehensively and accurately evaluate the visual damage condition of the bridge and provide more detailed and scientific safety assessment data. Finally, by comprehensively analyzing the crack monitoring index set and obtaining the bridge deck visual damage perception index, a more accurate assessment of the health status of the bridge is provided, thereby improving the accuracy of safety warnings, and then ensuring that the safety of the bridge is effectively guaranteed in a timely manner, and avoiding safety accidents caused by untimely detection or inaccurate data.

[0024] Specifically, the structural monitoring data includes component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value. The specific steps for obtaining the structural risk perception index of each section of the set bridge are as follows: standardize the component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge; and comprehensively analyze the standardized component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge to obtain the structural risk perception index of each section of the set bridge.

[0025] The component inclination index is the average of the inclination angles of all bridge components in the road section, and the inclination angle of each bridge component can be obtained by an inclinometer.

[0026] The component strain index is the deformation per unit length of all bridge components in the section after being subjected to force, and the strain of each bridge component can be obtained by a vibrating wire strain gauge.

[0027] The deflection value is a representation of the vertical deformation of the bridge deck in this section, which can be obtained by a laser rangefinder.

[0028] The settlement index is the vertical displacement value of the road section and can be obtained by a laser rangefinder.

[0029] The vibration amplitude value can be obtained by an accelerometer.

[0030] In this implementation plan, the structural monitoring data of each section of the bridge is standardized to ensure that the data have consistent dimensions and comparability, so that various types of monitoring data can be effectively and comprehensively analyzed, thereby providing more scientific and accurate safety assessment results. Secondly, by comprehensively analyzing multiple structural monitoring indicators, the health status of each bridge component is comprehensively evaluated, and each monitoring indicator represents the different safety risks of the bridge under different working conditions. For example, the inclination index reflects whether the bridge component has excessive inclination, thereby improving the accuracy and comprehensiveness of the risk assessment. Finally, by obtaining the structural risk perception index of each section, a quantitative assessment basis is provided for the safety management of the bridge, making the maintenance and repair decisions of the bridge more scientific and accurate.

[0031] Specifically, the traffic monitoring data includes a vehicle speed deviation index, a traffic flow value, a load index, and a vehicle distance value, and the specific steps for obtaining the traffic risk perception index of each section of the set bridge are as follows: normalizing the vehicle speed deviation index, traffic flow value, load index, and vehicle distance value of each section of the set bridge; and comprehensively analyzing the normalized vehicle speed deviation index, traffic flow value, load index, and vehicle distance value of each section of the set bridge to obtain the traffic risk perception index of each section of the set bridge.

[0032] The vehicle speed deviation index is the average of the ratios between the driving speed of each vehicle on the road section and the bridge speed limit. The driving speed of each vehicle can be obtained through a geomagnetic sensor, and the bridge speed limit can be obtained through traffic signs stored in a database.

[0033] The traffic volume value is the number of vehicles on the road section, which can be obtained through the geomagnetic sensor.

[0034] The load index is the total weight of the vehicles on the road section, and the weight value of each vehicle can be obtained through the geomagnetic sensor.

[0035] The distance index is the average distance between adjacent vehicles on the road section. The distance between adjacent vehicles can be obtained through the geomagnetic sensor array. When each vehicle passes the geomagnetic sensor, it causes a local magnetic field disturbance, and this moment is recorded as the vehicle's passing timestamp. For two adjacent vehicles, their distance value can be obtained by calculating the product of the time interval and the speed of the preceding vehicle.

[0036] In this implementation plan, by normalizing and comprehensively analyzing multiple traffic monitoring indicators such as vehicle speed deviation index, traffic flow value, load index and vehicle distance value, the complexity of bridge traffic flow and its impact on bridge safety can be fully reflected. By comprehensively analyzing these traffic monitoring data, a more accurate bridge traffic risk assessment can be provided, and potential traffic safety problems can be discovered in a timely manner. Secondly, by acquiring and normalizing multiple traffic monitoring data in real time, the response speed of traffic risk assessment can be improved, thereby ensuring that a quick response can be made in the event of danger, and early warning measures can be implemented to avoid traffic accidents or bridge damage. Finally, by comprehensively analyzing multiple traffic monitoring data, managers can be helped to identify in advance the pressure that bridges may face in high-risk situations, thereby improving the safety and service life of bridges.

[0037] Specifically, the specific steps for taking preset safety warning measures based on the safety warning index of a set bridge are as follows: the safety warning index of the set bridge is judged and analyzed with the preset safety warning index threshold set, and the safety warning index threshold set includes a first safety warning index threshold, a second safety warning index threshold, and a third safety warning index threshold; and corresponding preset safety warning measures are taken based on the judgment results.

[0038] The specific steps for taking corresponding preset safety warning measures based on the judgment results are as follows: if the safety warning index of the set bridge is lower than or equal to the preset first safety warning index threshold, then (the set bridge) is marked as safe, and safety monitoring measures are taken, that is, continue monitoring; if the safety warning index of the set bridge is higher than the preset first safety warning index threshold and lower than or equal to the preset second safety warning index threshold, then (the set bridge) is marked as low risk, and the first safety warning measures are taken, which specifically include inspecting specific parts (based on the monitoring data, paying special attention to the weak parts of the bridge, such as cracks, deformation or structural damage, and conducting local manual inspections or adding sensors); if the safety warning index of the set bridge is higher than the preset second safety warning index threshold and lower than or equal to the preset second safety warning index threshold, then (the set bridge) is marked as low risk, and the first safety warning measures are taken, which specifically include inspecting specific parts (based on the monitoring data, paying special attention to the weak parts of the bridge, such as cracks, deformation or structural damage, and conducting local manual inspections or adding sensors); if the safety warning index of the set bridge is higher than the preset second safety warning index threshold and lower than If the safety warning index of the set bridge is greater than or equal to the preset third safety warning index threshold, (the set bridge) is marked as medium risk, and the second safety warning measures are taken, which are specifically strengthening structural inspections (comprehensive inspections of key locations such as the main load-bearing components and connection points of the bridge) and restricting traffic flow (restricting the passage of heavy vehicles or large traffic flows, implementing temporary traffic control or adjusting driving routes to reduce pressure on the bridge); if the safety warning index of the set bridge is higher than the preset second safety warning index threshold, (the set bridge) is marked as high risk, and the third safety warning measures are taken, which are specifically suspending traffic (closing the bridge and stopping the passage of all vehicles and personnel) and comprehensive reinforcement (organizing a professional team to reinforce or repair the bridge to ensure that the bridge is restored to a safe and usable state).

[0039] In this implementation plan, by setting multiple safety warning index thresholds and dividing different risk levels (safe, low risk, medium risk, high risk) according to these thresholds, the risk status of the bridge can be accurately assessed based on the real-time monitoring data of the bridge. Secondly, by making specific plans for the safety warning measures corresponding to each risk level, corresponding monitoring and repair strategies can be formulated according to different risk situations, thereby reducing the possibility of major safety accidents. Finally, through graded risk assessment and targeted warning measures, emergency resources and maintenance personnel can be rationally deployed to avoid waste of resources.

[0040] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0041] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent perception-based road and bridge safety monitoring method, characterized in that: The following steps are involved: Acquire bridge monitoring data of several sections of a set bridge in real time, wherein the bridge monitoring data includes bridge monitoring image data, structure monitoring data, and traffic monitoring data; Data analysis is performed on the bridge monitoring data of each section of the set bridge to obtain a set of bridge safety indexes for each section of the set bridge, including the bridge deck visual damage perception index, the structural risk perception index, and the traffic risk perception index; Comprehensively analyze the bridge safety index set of each section of the set bridge to obtain the safety warning index of the set bridge; And take preset safety warning measures based on the safety warning index of the set bridge.

2. The intelligent perception-based road and bridge safety monitoring method according to claim 1, characterized in that: The bridge monitoring image data includes the pixel value and two-dimensional coordinates of each pixel point in the bridge monitoring image.

3. The intelligent perception-based road and bridge safety monitoring method according to claim 2, characterized in that: The specific steps for obtaining the bridge deck visual damage perception index of each section of a given bridge are as follows: Performing edge detection processing on the pixel value of each pixel point in the bridge monitoring image of each road section of the set bridge to obtain a plurality of crack areas in the bridge monitoring image of each road section of the set bridge; Read the two-dimensional coordinates of each pixel point in each crack area in the bridge monitoring image of each section of the set bridge, and perform comprehensive analysis to obtain a crack monitoring index set for each section of the set bridge, including a crack area index and a crack morphology index; A comprehensive analysis is performed on the crack monitoring index set of each section of the set bridge to obtain the bridge deck visual damage perception index of each section of the set bridge.

4. The intelligent perception-based road and bridge safety monitoring method according to claim 3 is characterized in that: The specific steps to obtain the crack monitoring index set for each section of a given bridge are as follows: Comprehensively analyzing each pixel point of each crack area in the bridge monitoring image of each section of the set bridge to obtain the crack area index of each section of the set bridge; The two-dimensional coordinates of each edge pixel point of each crack area in the bridge monitoring image of each section of the set bridge are read, and a comprehensive analysis is performed to obtain the crack morphology index of each section of the set bridge.

5. The intelligent perception-based road and bridge safety monitoring method according to claim 1, characterized in that: The structural monitoring data includes component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value. The specific steps for obtaining the structural risk perception index of each section of the set bridge are as follows: Standardize the component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge; A comprehensive analysis is conducted on the component inclination index, component strain index, deflection value, settlement index, and vibration amplitude value of each section of the set bridge after standardization to obtain the structural risk perception index of each section of the set bridge.

6. The intelligent perception-based road and bridge safety monitoring method according to claim 1, characterized in that: The traffic monitoring data includes a vehicle speed deviation index, a traffic flow value, a load index, and a vehicle distance value, and the specific steps for obtaining the traffic risk perception index of each road section of a set bridge are as follows: Normalize the speed deviation index, traffic volume value, load index, and vehicle distance value of each road section of the set bridge; A comprehensive analysis is then conducted on the normalized speed deviation index, traffic flow value, load index, and vehicle distance value of each section of the set bridge to obtain the traffic risk perception index of each section of the set bridge.

7. The intelligent perception-based road and bridge safety monitoring method according to claim 1, characterized in that: The specific steps for taking preset safety warning measures based on the set bridge safety warning index are as follows: The safety warning index of the set bridge is judged and analyzed respectively with a preset safety warning index threshold set, wherein the safety warning index threshold set includes a first safety warning index threshold, a second safety warning index threshold, and a third safety warning index threshold; And take corresponding preset safety warning measures based on the judgment results.

8. The intelligent perception-based road and bridge safety monitoring method according to claim 7, characterized in that: The specific steps for taking corresponding preset safety warning measures based on the judgment results are as follows: If the safety warning index of a set bridge is lower than or equal to the preset first safety warning index threshold, it is marked as safe and safety monitoring measures are taken; If the safety warning index of a bridge is higher than the preset first safety warning index threshold and lower than or equal to the preset second safety warning index threshold, it is marked as low risk and the first safety warning measure is taken; If the safety warning index of a bridge is higher than the preset second safety warning index threshold and lower than or equal to the preset third safety warning index threshold, it is marked as medium risk and the second safety warning measures are taken; If the safety warning index of the set bridge is higher than the preset second safety warning index threshold, it is marked as high risk and the third safety warning measures are taken.

Images