Intelligent monitoring and early warning system and method for collapse of urban roads, bridges and tunnels

Abstract

The invention discloses an intelligent monitoring and early warning system and method for collapse of urban roads, bridges and tunnels. The intelligent monitoring and early warning system comprises a data acquisition module, a data transmission module, a data processing module, a risk assessment module, an early warning and response module, a user interaction interface, an electric power guarantee module and a system diagnosis and maintenance module. The data acquisition module acquires multi-dimensional data related to structural health through various sensors and detection equipment, and transmits the data to the data processing module through the data transmission module. The data processing module combines a machine learning algorithm and a physical model to analyze data, assesses the health condition of the infrastructure in real time and predicts potential risks. And the risk assessment module generates early warning information according to the data, and automatically starts an emergency response process through the early warning and response module. And the power guarantee module ensures that the system continuously operates in a special environment. The method has the advantages of high-precision prediction, timely response and intelligent decision support, the structure collapse risk can be effectively reduced, and the emergency response efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of multi-modal information comprehensive monitoring of urban road, bridge and tunnel collapse, and in particular to an intelligent monitoring and early warning system and method for urban road, bridge and tunnel collapse. Background Art

[0002] With the acceleration of urbanization, the construction and maintenance of urban infrastructure such as roads, bridges and tunnels has become an important task in urban management. Due to the influence of multiple factors such as the natural environment, population density, and traffic load, the safety of these infrastructures faces great challenges. Especially under sudden disaster conditions such as earthquakes and extreme weather, the risk of infrastructure collapse, subsidence or damage increases significantly. Once a collapse accident occurs, it will not only pose a serious threat to public safety, but also cause traffic paralysis, casualties and huge economic losses. Therefore, how to achieve intelligent monitoring and early warning of infrastructure such as roads, bridges, and tunnels, and take effective countermeasures before potential risks occur, has become an urgent need for today's urban management and infrastructure protection.

[0003] At present, the monitoring of most urban roads, bridges and tunnels still relies on traditional manual inspections and fixed sensor monitoring methods. Although manual inspections can find some obvious structural problems, they are often restricted by the heavy workload, long cycle and low inspection frequency of inspectors, and there may be omissions or delays, resulting in problems not being discovered in time. The fixed sensor monitoring method mainly obtains data through sensors installed in certain key locations. This method is limited by the fixed installation location of the equipment, making it difficult to fully monitor the status of the entire infrastructure, and the data collection and analysis process is relatively simple, which cannot reflect the changing environment and structural health in real time. Traditional monitoring technology usually relies on simple physical quantity monitoring (such as displacement, stress, etc.), and cannot fully consider the combined effects of multiple factors such as environmental changes, construction impacts, load changes, etc., so it is difficult to make accurate risk assessments and early warnings under complex conditions. Summary of the invention

[0004] In order to overcome the shortcomings and deficiencies of the prior art, the purpose of the present invention is to provide an intelligent monitoring and early warning system and method for urban road, bridge and tunnel collapse.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An intelligent monitoring and early warning system for urban road, bridge and tunnel collapse, the system comprising:

[0007] Data acquisition module, used to collect multi-dimensional monitoring data of roads, bridges and tunnels, including ground displacement, geological changes, vibration, temperature and humidity, air pressure, acceleration, stress and strain, radar images and acoustic signals;

[0008] A data transmission module is used to transmit the collected data to a data processing center, the transmission is done by wire or wireless, and supports large data transmission and remote access;

[0009] The data processing module uses machine learning algorithms combined with physical models to process data from the data acquisition module in real time, analyze the health status of current roads, bridges, and tunnels, and establish and update structural health models;

[0010] The risk assessment module is used to calculate and predict the potential risk of structural instability or collapse based on the processed data, and issue corresponding risk alerts based on preset thresholds or self-learning algorithms;

[0011] The early warning and response module automatically generates early warning reports based on the early warning information from the risk assessment module, sends them to relevant managers and emergency response units, and initiates necessary emergency response measures;

[0012] The user interaction interface is used to display real-time monitoring results, historical data, risk assessment results and early warning information, and supports multi-dimensional analysis and report generation, which facilitates managers and engineers at all levels to analyze, make decisions and take follow-up measures;

[0013] The power guarantee module is used to ensure that the system can continue to operate under special circumstances. Through the intelligent battery and emergency power management module, it ensures the continuous monitoring and data transmission of key nodes.

[0014] The system diagnosis and maintenance module is used to perform self-diagnosis, health checks and regular maintenance of the system to ensure the stable operation of each module, especially to prevent failures in extreme weather or complex environments.

[0015] Furthermore, the data acquisition module works through sensors and detection equipment, the sensors include displacement sensors, stress sensors, temperature and humidity sensors, accelerometers, radar sensors, vibration sensors, and acoustic sensors, and the detection equipment is geological radar equipment, acoustic imaging equipment, video monitoring equipment, and automated drone inspection equipment.

[0016] Furthermore, the data processing module combines machine learning models to conduct in-depth analysis of historical data and real-time data, and uses regression analysis, neural networks, and support vector machine methods to establish a multivariate risk assessment model based on big data, and can continuously update the model according to newly collected data to improve prediction accuracy.

[0017] Furthermore, the risk assessment module adopts an algorithm model based on real-time data and historical data, combined with the actual structural characteristics, geological conditions, and climate influencing factors of roads, bridges, and tunnels, to calculate and predict the potential collapse risks in the next 24 hours, 48 ​​hours, and longer periods of time, and issue different levels of warning information according to the risk level.

[0018] Furthermore, the warning and response module supports linkage with other systems such as the city emergency command system, traffic management system, and meteorological warning system, and can automatically initiate corresponding emergency response processes according to different warning levels, such as traffic control, personnel evacuation, and post-disaster rescue.

[0019] Furthermore, the user interaction interface supports multi-language display, can customize the operation panel according to the needs of different users, provides graphical data visualization display, supports historical data playback, trend analysis, and risk prediction simulation functions, so that users can make accurate decisions.

[0020] Furthermore, the power guarantee module also includes multiple power supplement modules such as solar panels, wind turbines, and energy storage batteries to ensure the long-term stable operation of the system when the power supply is interrupted, and can adjust the power supplement method according to weather changes.

[0021] On the other hand, the present invention provides an intelligent monitoring and early warning method for urban road, bridge and tunnel collapse, the method comprising the following steps:

[0022] Step S1: deploy multiple monitoring sensors in the target area to collect various data related to the health of roads, bridges, and tunnel structures in real time;

[0023] Step S2: The collected real-time data is transmitted to the data processing module, and the data is denoised, cleaned and integrated;

[0024] Step S3: Using machine learning algorithms to train historical data and establish a multivariate health assessment model;

[0025] Step S4: Evaluate the health status of the current structure based on the real-time data and the health assessment model, and predict the possible collapse risk in combination with the physical modeling method;

[0026] Step S5: Generate warning information based on the risk assessment results, issue corresponding warning signals according to preset thresholds and risk levels, and automatically generate risk reports; start the warning response process, notify relevant personnel and start the emergency plan.

[0027] Furthermore, the machine learning algorithms used in step S3 include regression analysis, decision tree, neural network, and support vector machine methods, which can be optimized according to different types of structures, different environmental conditions, and different types of disasters.

[0028] Furthermore, the warning response process in step S5 includes multiple emergency response measures such as traffic control, personnel evacuation, and post-disaster rescue, and automatically adjusts the response strategy according to the warning level and specific location.

[0029] Beneficial effects:

[0030] The present invention proposes an intelligent monitoring and early warning system and method for urban road, bridge and tunnel collapse. The present invention integrates a variety of advanced monitoring technologies, and collects multi-dimensional monitoring data, including ground displacement, geological changes, vibration, temperature and humidity, air pressure, acceleration, stress and strain, radar images and acoustic signals, etc., to monitor the structural health of roads, bridges and tunnels in real time from multiple angles. By adopting a variety of sensors such as displacement sensors, stress sensors, accelerometers, radar sensors, as well as geological radar equipment, acoustic imaging equipment, automated drone inspection equipment and other detection equipment, the comprehensiveness and accuracy of data collection are further improved. In addition, the data acquisition module transmits all monitoring data to the data processing center through an efficient data transmission module, realizing large data transmission and remote access, and ensuring the instant processing and feedback of monitoring data. Secondly, the innovation of the data processing module lies in the combination of machine learning algorithms and physical models, and the use of deep learning methods to jointly analyze historical data and real-time data. This method can automatically discover potential risk patterns and abnormal situations from a large amount of data, establish a multivariate risk assessment model based on big data, and continuously optimize it with the support of new data, thereby improving the accuracy of prediction and dynamic adjustment capabilities. The risk assessment module combines real-time data with historical data, comprehensively considers structural characteristics, geological conditions, climate impacts and other factors, accurately predicts potential collapse risks, can give early warnings, and issue corresponding alarm information according to different levels of risks. The present invention also has a strong response capability. The early warning and response module can automatically generate a detailed early warning report based on the early warning information issued by the risk assessment module, and send it to relevant managers and emergency response units in a timely manner. In addition, the system can also be linked with multiple systems such as the urban emergency command system, traffic management system, and meteorological early warning system to automatically start emergency response processes at different levels, such as traffic control, personnel evacuation, and post-disaster rescue, to ensure the rapid initiation of emergency measures and effectively respond to potential risks. The user interaction interface design has a graphical, intuitive multi-dimensional data display and analysis function, can support display in different languages, and can customize the operation panel according to different user needs. The interface can not only display real-time monitoring results, but also support historical data playback, trend analysis, risk prediction simulation and other functions, so that managers and engineers can make accurate decisions based on data and model results to ensure timely and effective handling of possible safety hazards. The system also has a strong autonomous guarantee capability. In terms of power guarantee modules, the system not only supports traditional power supplementation methods, but also combines multiple power supplementation modules such as solar panels, wind turbines, and energy storage batteries. Even in special weather or power outages, it can still ensure continuous monitoring and data transmission of key nodes. In addition, the system self-diagnosis and maintenance module can perform regular health checks and maintenance to ensure the stable operation of the system in complex environments, greatly improving the reliability of the system.The present invention not only improves the safety of road, bridge and tunnel structures, but also provides powerful data support and decision-making basis for the management of urban infrastructure through innovative designs such as multi-dimensional real-time monitoring, in-depth analysis of machine learning models, accurate risk assessment, and intelligent emergency response mechanisms. The system has the advantages of high-precision prediction, timely warning, and flexible response, which makes it have significant advantages and application prospects in preventing collapse accidents, reducing casualties and property losses, and improving emergency response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A module composition diagram of an intelligent monitoring and early warning system for urban road, bridge and tunnel collapse provided by the present invention;

[0032] Figure 2 A schematic flow chart of an intelligent monitoring and early warning method for urban road, bridge and tunnel collapse provided by the present invention. DETAILED DESCRIPTION

[0033] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present application is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0034] like Figure 1 As shown, an intelligent monitoring and early warning system for urban road, bridge and tunnel collapse includes:

[0035] Data acquisition module, used to collect multi-dimensional monitoring data of roads, bridges and tunnels, including ground displacement, geological changes, vibration, temperature and humidity, air pressure, acceleration, stress and strain, radar images and acoustic signals;

[0036] Specifically, the data acquisition module is one of the core parts of the system, which is mainly responsible for obtaining multi-dimensional monitoring data from infrastructure such as roads, bridges and tunnels. These data include ground displacement, geological changes, vibration, temperature and humidity, air pressure, acceleration, stress and strain, radar images and acoustic signals. Each type of data reflects the health status of the infrastructure under different conditions. For example, displacement sensors monitor the displacement changes of roads or bridges to help identify possible settlement or misalignment; vibration sensors are used to detect vibration intensity to identify whether there are structural abnormalities; and temperature, humidity and air pressure sensors can monitor the impact of environmental changes on the structure. Radar images and acoustic signals are used to detect abnormal changes in deep structures. Through these sensors and devices, the data acquisition module ensures real-time and comprehensive acquisition of infrastructure health data to support subsequent analysis and evaluation.

[0037] The data transmission module is used to transmit the collected data to the data processing center. The transmission adopts wired or wireless mode and supports large data transmission and remote access.

[0038] Specifically, the data transmission module is responsible for safely and reliably transmitting the collected data to the data processing center for analysis and processing. According to actual needs, data transmission can be carried out via wired (such as optical fiber, cable) or wireless (such as Wi-Fi, 4G / 5G, satellite communication). This flexible transmission method enables the system to operate efficiently in different environments and support infrastructure monitoring of different scales and regions. Due to the large amount of data, especially data collected in real time, this module also supports big data transmission, ensuring that data can be uploaded and accessed remotely efficiently and stably, so that relevant personnel can obtain the latest monitoring information at any time and respond quickly.

[0039] The data processing module uses machine learning algorithms combined with physical models to process data from the data acquisition module in real time, analyze the health status of current roads, bridges, and tunnels, and establish and update structural health models;

[0040] Specifically, the data processing module is the "brain" of the entire system, and its role is to analyze and process various collected data. This module uses a combination of machine learning algorithms and physical models to conduct in-depth analysis of data in real time to help determine the health status of structures such as roads, bridges, and tunnels. Through big data analysis, the system can learn potential patterns in historical data, assess infrastructure risks, and promptly identify possible hidden dangers. In addition, the data processing module can dynamically update the structural health model based on real-time data and physical models to ensure the accuracy and timeliness of monitoring results, thereby improving the accuracy and response speed of early warnings.

[0041] The risk assessment module is used to calculate and predict the potential risk of structural instability or collapse based on the processed data, and issue corresponding risk alerts based on preset thresholds or self-learning algorithms;

[0042] Specifically, the risk assessment module uses the information analyzed by the data processing module to predict the potential risk of structural instability or collapse. This module predicts potential risk situations through algorithm calculations and models based on the characteristics of different structures (such as materials, dimensions, geographical environment, etc.) and the real-time data collected. The risk assessment module uses a preset risk threshold or self-learning algorithm to automatically adjust the risk assessment model based on data feedback to improve the ability to identify abnormal situations. By analyzing potential risks, the module can provide data support for emergency response, ensuring that relevant managers can make timely decisions and reduce the probability of accidents.

[0043] The early warning and response module automatically generates early warning reports based on the early warning information from the risk assessment module, sends them to relevant managers and emergency response units, and initiates necessary emergency response measures;

[0044] Specifically, the role of the early warning and response module is to automatically generate risk warning reports based on the early warning information given by the risk assessment module, and notify relevant managers and emergency response units through the system. According to the risk level, the module can initiate different levels of response measures, such as local traffic control, evacuation of personnel, mobilization of rescue forces, etc. The key to this module is rapid response, ensuring that once a potential crisis is discovered, the system can immediately take appropriate measures to reduce casualties and property losses. The early warning and response module is linked with the city's emergency management system, traffic management system, etc. to provide more accurate early warning information and response instructions, forming a unified and coordinated emergency response system.

[0045] The user interaction interface is used to display real-time monitoring results, historical data, risk assessment results and early warning information, and supports multi-dimensional analysis and report generation, which facilitates managers and engineers at all levels to analyze, make decisions and take follow-up measures;

[0046] Specifically, the user interface is the front end of the system, which is mainly used to display real-time monitoring data, historical data, risk assessment results, and early warning information to managers and engineers. Through the intuitive graphical interface, users can clearly view the health status of the infrastructure and conduct multi-dimensional analysis. The system supports custom panels and multi-language displays to ensure that the needs of different users are met. The interface not only provides real-time data display, but also includes trend analysis and risk prediction simulation functions to help users make accurate decision analysis. Users can also generate reports and records through the interface to facilitate subsequent management and decision support.

[0047] The power guarantee module is used to ensure that the system can continue to operate under special circumstances. Through the intelligent battery and emergency power management module, it ensures the continuous monitoring and data transmission of key nodes.

[0048] Specifically, the power guarantee module ensures the continuous and stable operation of the system in special environments, especially during power outages or extreme weather conditions. The module uses smart batteries and emergency power management modules to provide continuous power support for the system. The smart battery automatically adjusts charging and discharging according to the power status to ensure that the continuous monitoring and data transmission of key nodes will not be affected. In addition, the power guarantee module also integrates green energy solutions such as solar panels and wind turbines, which can automatically switch power replenishment methods according to weather changes, thereby ensuring that the system is always available under any circumstances and reducing the risk of data loss or system downtime due to power failures.

[0049] The system diagnosis and maintenance module is used to perform self-diagnosis, health checks and regular maintenance of the system to ensure the stable operation of each module, especially to prevent failures in extreme weather or complex environments.

[0050] Specifically, the system diagnosis and maintenance module is responsible for self-detection, health checks and regular maintenance of the entire monitoring system. The module can monitor hardware devices and software systems in real time, automatically diagnose potential faults or anomalies, and issue maintenance notifications to managers. Regular health checks can ensure the normal operation of all modules, especially in extreme weather or complex environments, to avoid equipment damage or data collection interruptions. In addition, the system diagnosis and maintenance module also has a self-repair function, which can automatically repair minor faults, minimize human intervention, and improve system stability and reliability.

[0051] These modules work closely together to ensure the efficient operation of the safety monitoring and early warning systems for urban roads, bridges and tunnels, effectively improving the safety management level of infrastructure and providing timely early warning and response measures when potential dangers occur.

[0052] Furthermore, the data acquisition module works through sensors and detection equipment. The sensors include displacement sensors, stress sensors, temperature and humidity sensors, accelerometers, radar sensors, vibration sensors, and acoustic sensors. The detection equipment includes geological radar equipment, acoustic imaging equipment, video monitoring equipment, and automated drone inspection equipment.

[0053] Specifically, the data acquisition module works together through a variety of sensors and detection equipment to ensure comprehensive and accurate acquisition of monitoring data. Specifically, sensors include displacement sensors, stress sensors, temperature and humidity sensors, accelerometers, radar sensors, vibration sensors and acoustic sensors. Displacement sensors can monitor the displacement of infrastructure in real time to help identify potential settlement or misalignment problems; stress sensors are used to detect stress changes in structures such as bridges or tunnels to determine whether there is an overload phenomenon; temperature and humidity sensors can monitor changes in temperature and humidity in the environment to determine the impact of environmental factors on the structure; accelerometers are used to detect vibration frequencies and analyze possible vibration anomalies. Radar sensors and vibration sensors can perform non-destructive testing of underground or internal structures to detect potential cracks or other problems. Acoustic sensors can monitor abnormal sounds emitted by equipment and capture subtle changes in structures. In addition, detection equipment includes geological radar equipment, acoustic imaging equipment, video monitoring equipment, and automated drone inspection equipment. Geological radar and acoustic imaging equipment can penetrate the ground or inside structures to identify structural hazards and provide deeper monitoring; video surveillance equipment enables visual monitoring and real-time viewing of possible anomalies; automated drone inspection equipment uses drones to conduct regular inspections of large bridges, tunnels, etc., and collect data from hard-to-reach locations, thereby comprehensively monitoring the condition of infrastructure.

[0054] Furthermore, the data processing module combines machine learning models to conduct in-depth analysis of historical data and real-time data, and uses regression analysis, neural networks, and support vector machine methods to establish a multivariate risk assessment model based on big data. The model can be continuously updated according to newly collected data to improve prediction accuracy.

[0055] Specifically, the data processing module conducts in-depth analysis of historical data and real-time data by combining machine learning models to generate an efficient risk assessment model. This module uses a variety of machine learning methods such as regression analysis, neural networks, and support vector machines (SVM) to process the collected data. Regression analysis methods help to reveal the relationship between data and determine how different monitoring indicators affect structural health; neural networks can learn potential patterns from complex nonlinear data and predict possible future risks; support vector machine methods are suitable for classification problems and can help classify different risk levels. Through these machine learning models, the data processing module establishes a multivariate risk assessment model based on big data, which can comprehensively analyze various influencing factors, including historical health data of the structure, real-time monitoring data, etc. Based on this model, the system can continuously update and optimize according to newly collected data, thereby improving the accuracy of predictions and ensuring that the prediction and assessment of potential risks remain real-time and accurate.

[0056] Furthermore, the risk assessment module uses an algorithm model based on real-time data and historical data, combined with the actual structural characteristics, geological conditions, and climate influencing factors of roads, bridges, and tunnels, to calculate and predict potential collapse risks in the next 24 hours, 48 ​​hours, and longer periods of time, and issue different levels of warning information based on the risk level.

[0057] Specifically, the risk assessment module uses an algorithm model based on real-time data and historical data, combined with the actual structural characteristics, geological conditions and climate factors of infrastructure such as roads, bridges and tunnels, to calculate and predict potential structural risks in the future. Specifically, the algorithm model comprehensively considers the physical characteristics of the structure, such as size, material, design specifications, etc., as well as the impact of its geological environment (such as soil type, groundwater level, etc.) and climatic conditions (such as temperature, humidity, precipitation, etc.) on its stability. Through this multi-factor comprehensive analysis, the system can predict the health status of the structure in the next 24 hours, 48 ​​hours or even longer, and assess whether there is a risk of collapse or instability. In addition, based on the comparative analysis of real-time data and historical data, the system can dynamically adjust the risk assessment results and issue different levels of warning information according to the risk level. If the risk level is high, the system will issue an emergency warning and initiate corresponding emergency response measures to ensure that potential structural problems are handled in a timely manner and prevent major accidents.

[0058] Furthermore, the early warning and response module supports linkage with other systems such as the city emergency command system, traffic management system, and meteorological early warning system, and can automatically initiate corresponding emergency response processes according to different early warning levels, such as traffic control, personnel evacuation, and post-disaster rescue.

[0059] Specifically, the early warning and response module not only relies on a single monitoring system, but can be linked with multiple related systems, including the city emergency command system, traffic management system and meteorological early warning system. Through collaboration with these systems, the system can automatically start the corresponding emergency response process according to different early warning levels. For example, when there is a potential risk of collapse of roads or bridges, the early warning system will be linked with the traffic management system to start traffic control to prevent accidents; when major risks occur, the system will automatically notify relevant emergency response units (such as firefighting, medical rescue, etc.) to carry out personnel evacuation and post-disaster rescue work. At the same time, through linkage with the meteorological early warning system, the system can predict risk changes in combination with weather factors and take measures in advance under extreme weather conditions. Through the automated and intelligent linkage mechanism, this module can greatly improve the response efficiency, reduce human delays and response time, and ensure that safety emergency work is carried out in a timely manner.

[0060] Furthermore, the user interaction interface supports multi-language display, can customize the operation panel according to the needs of different users, provides graphical data visualization, supports historical data playback, trend analysis, and risk prediction simulation functions, so that users can make accurate decisions.

[0061] Specifically, the user interaction interface design fully considers the needs of different users and supports multi-language display to ensure that the system can provide services to users around the world. According to the needs of different users, the interface can customize the operation panel so that each user can quickly access the data and functions they care about. The interface uses graphical data visualization to make complex monitoring data easier to understand and operate. Users can view structural health status, historical data, risk assessment results and other information in real time through the interactive interface. They can also perform trend analysis and view simulated predictions of future risk changes to help users identify potential problems in advance. In addition, the system also supports historical data playback. Users can understand the changing trends of infrastructure by replaying historical data and conduct comprehensive analysis. Through this interface, managers and engineers can make accurate decisions quickly and take necessary measures in a timely manner to ensure the safety of infrastructure.

[0062] Furthermore, the power guarantee module also includes multiple power supplement modules such as solar panels, wind turbines, and energy storage batteries to ensure the long-term stable operation of the system when the power supply is interrupted, and can adjust the power supplement method according to weather changes.

[0063] Specifically, the power guarantee module design adopts multiple power supplement methods, including solar panels, wind turbines, energy storage batteries and other power modules to ensure that the system can continue to operate stably in special environments such as power supply interruptions or extreme weather. As green energy solutions, solar panels and wind turbines can automatically provide electricity when environmental conditions are suitable, reduce dependence on external power grids, and reduce energy costs. At the same time, the energy storage battery module can store excess electricity and provide backup power when the power supply is interrupted. The system can intelligently switch power supplement methods according to weather changes to ensure a continuous and stable power source and avoid interruptions or failures of the system due to power problems at critical moments. The power guarantee module ensures the continuous operation of the system in various environments through the design of multiple power supply methods, improves the reliability and stability of the system, and ensures long-term and stable monitoring and data transmission.

[0064] like Figure 2 As shown, a method for intelligent monitoring and early warning of urban road, bridge and tunnel collapse comprises the following steps:

[0065] Step S1: deploy multiple monitoring sensors in the target area to collect various data related to the health of roads, bridges, and tunnel structures in real time;

[0066] Step S2: The collected real-time data is transmitted to the data processing module, and the data is denoised, cleaned and integrated;

[0067] Step S3: Using machine learning algorithms to train historical data and establish a multivariate health assessment model;

[0068] Step S4: Evaluate the health status of the current structure based on the real-time data and the health assessment model, and predict the possible collapse risk in combination with the physical modeling method;

[0069] Step S5: Generate warning information based on the risk assessment results, issue corresponding warning signals according to preset thresholds and risk levels, and automatically generate risk reports; start the warning response process, notify relevant personnel and start the emergency plan.

[0070] A comprehensive intelligent monitoring and early warning method for urban road, bridge and tunnel collapse, with high intelligence and accuracy. First, by deploying multiple monitoring sensors to collect multi-dimensional data related to structural health in real time, such as displacement, stress, vibration, etc., to ensure comprehensive monitoring of infrastructure. Then, the collected real-time data is denoised, cleaned and integrated to ensure the accuracy and reliability of the data. By combining machine learning algorithms with physical modeling methods, historical data and real-time data are used together to establish a multivariate health assessment model, which can not only assess the current structural health status, but also predict possible future collapse risks. Based on these assessment results, the system can generate early warning signals in a timely manner, initiate emergency response processes, notify relevant personnel and take emergency measures to prevent accidents.

[0071] The innovation of this method is that it combines the deep integration of real-time data collection, machine learning and physical modeling, providing a dynamic and intelligent risk assessment and early warning mechanism. Compared with traditional monitoring methods, it can not only more accurately assess the health status of infrastructure, but also predict potential risks in advance before they occur, greatly improving the efficiency of disaster prevention. At the same time, the system can automatically generate early warning reports and initiate joint emergency responses to ensure that potential risks can be dealt with quickly and effectively in emergency situations. The advantage of this system is that it is highly integrated and intelligent, and can provide comprehensive protection in terms of real-time, accuracy and emergency response capabilities, effectively reducing casualties and property losses.

[0072] Furthermore, the machine learning algorithms used in step S3 include regression analysis, decision tree, neural network, and support vector machine methods, which can be optimized according to different types of structures, different environmental conditions, and different types of disasters.

[0073] Furthermore, the early warning response process in step S5 includes multiple emergency response measures such as traffic control, personnel evacuation, and post-disaster rescue, and automatically adjusts the response strategy according to the early warning level and specific location.

[0074] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and early warning system for urban road, bridge and tunnel collapse, characterized in that: include: Data acquisition module, used to collect multi-dimensional monitoring data of roads, bridges and tunnels, including ground displacement, geological changes, vibration, temperature and humidity, air pressure, acceleration, stress and strain, radar images and acoustic signals; A data transmission module is used to transmit the collected data to a data processing center, the transmission is done by wire or wireless, and supports large data transmission and remote access; The data processing module uses machine learning algorithms combined with physical models to process data from the data acquisition module in real time, analyze the health status of current roads, bridges, and tunnels, and establish and update structural health models; The risk assessment module is used to calculate and predict the potential risk of structural instability or collapse based on the processed data, and issue corresponding risk alerts based on preset thresholds or self-learning algorithms; The early warning and response module automatically generates early warning reports based on the early warning information from the risk assessment module, sends them to relevant managers and emergency response units, and initiates necessary emergency response measures; The user interaction interface is used to display real-time monitoring results, historical data, risk assessment results and early warning information, and supports multi-dimensional analysis and report generation, which facilitates managers and engineers at all levels to analyze, make decisions and take follow-up measures; The power guarantee module is used to ensure that the system can continue to operate under special circumstances. Through the intelligent battery and emergency power management module, it ensures the continuous monitoring and data transmission of key nodes. The system diagnosis and maintenance module is used to perform self-diagnosis, health checks and regular maintenance of the system to ensure the stable operation of each module, especially to prevent failures in extreme weather or complex environments.

2. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The data acquisition module works through sensors and detection equipment. The sensors include displacement sensors, stress sensors, temperature and humidity sensors, accelerometers, radar sensors, vibration sensors, and acoustic sensors. The detection equipment is geological radar equipment, acoustic imaging equipment, video monitoring equipment, and automated drone inspection equipment.

3. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The data processing module combines machine learning models to conduct in-depth analysis of historical data and real-time data, and uses regression analysis, neural networks, and support vector machine methods to establish a multivariate risk assessment model based on big data. The model can be continuously updated according to newly collected data to improve prediction accuracy.

4. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The risk assessment module uses an algorithm model based on real-time data and historical data, combined with the actual structural characteristics, geological conditions, and climate influencing factors of roads, bridges, and tunnels, to calculate and predict potential collapse risks in the next 24 hours, 48 ​​hours, and longer periods of time, and issue different levels of warning information based on the risk level.

5. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The warning and response module supports linkage with other systems such as the city emergency command system, traffic management system, and meteorological warning system, and can automatically initiate corresponding emergency response processes according to different warning levels, such as traffic control, personnel evacuation, and post-disaster rescue.

6. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The user interaction interface supports multi-language display, can customize the operation panel according to the needs of different users, provides graphical data visualization display, and supports historical data playback, trend analysis, and risk prediction simulation functions, so that users can make accurate decisions.

7. The intelligent monitoring and early warning system for urban road, bridge and tunnel collapse according to claim 1 is characterized in that: The power guarantee module also includes multiple power supplement modules such as solar panels, wind turbines, and energy storage batteries to ensure the long-term stable operation of the system when the power supply is interrupted, and can adjust the power supplement method according to weather changes.

8. An intelligent monitoring and early warning method for urban road, bridge and tunnel collapse, characterized in that: The following steps are involved: Step S1: deploy multiple monitoring sensors in the target area to collect various data related to the health of roads, bridges, and tunnel structures in real time; Step S2: The collected real-time data is transmitted to the data processing module, and the data is denoised, cleaned and integrated; Step S3: Using machine learning algorithms to train historical data and establish a multivariate health assessment model; Step S4: Evaluate the health status of the current structure based on the real-time data and the health assessment model, and predict the possible collapse risk in combination with the physical modeling method; Step S5: Generate warning information based on the risk assessment results, issue corresponding warning signals according to preset thresholds and risk levels, and automatically generate risk reports; Initiate the early warning response process, notify relevant personnel and activate the emergency plan.

9. The intelligent monitoring and early warning method for urban road, bridge and tunnel collapse according to claim 8 is characterized in that: The machine learning algorithms used in step S3 include regression analysis, decision tree, neural network, and support vector machine methods, which can be optimized according to different types of structures, different environmental conditions, and different types of disasters.

10. The intelligent monitoring and early warning method for urban road, bridge and tunnel collapse according to claim 8 is characterized in that: The warning response process in step S5 includes multiple emergency response measures such as traffic control, personnel evacuation, and post-disaster rescue, and automatically adjusts the response strategy according to the warning level and specific location.

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