Road and bridge deck sudden collapse early warning system and method based on Internet of Things

Abstract

The invention relates to the technical field of data processing systems for supervision or prediction, in particular to a road and bridge deck sudden collapse early warning system and method based on the Internet of Things, and the system comprises a deformation monitoring system which is used for collecting the deformation data of a road and bridge deck; the data processing system is used for processing the deformation data based on the Internet of Things so as to obtain effective data meeting a preset storage condition; and the early warning management center is used for inputting the effective data into a pre-constructed road and bridge surface collapse risk prediction model to obtain a risk assessment result and a trend prediction result of the road and bridge surface so as to carry out early warning on sudden collapse of the road and bridge surface. Therefore, the problems of low data precision, poor credibility, early warning information delay, increased safety risk and the like caused by the fact that an early warning system only depends on a single technology, is easily influenced by environmental factors and cannot comprehensively capture various abnormal signals which may predict collapse in related technologies are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing systems for monitoring or prediction purposes, and in particular to an Internet of Things-based early warning system and method for sudden collapse of a road or bridge deck. Background Art

[0002] Mountain roads account for a large proportion of the total mileage of existing roads in my country. With the frequent occurrence of geological disasters during the flood season, the road and bridge decks suddenly collapse, posing a serious threat to the safety of mountain roads. In order to respond to emergencies such as road and bridge deck collapse and ensure traffic safety, an early warning system is essential.

[0003] In related technologies, early warning systems usually use distributed fiber optic sensing technology for road deformation sensing detection, or visual sensing technology for road collapse monitoring. Specifically, distributed fiber optic sensing technology uses optical fiber as a sensing element. By applying external pressure or temperature changes on the optical fiber, it can cause changes in the phase or intensity of light in the optical fiber, thereby detecting changes in external physical quantities; visual sensing technology is a technology that projects an object image onto a photosensitive element through an optical lens and converts the optical signal into an electrical signal.

[0004] However, in the related technologies, the early warning system only relies on one of the distributed fiber optic sensing technology or the visual sensing technology, which may be affected by environmental factors and cannot fully capture the various abnormal signals that may indicate collapse. It has the problems of low accuracy and poor credibility, and cannot promptly convey the road condition information ahead to the moving vehicles, which delays the early warning time and increases the risk of accidents. It is in urgent need of improvement. Summary of the invention

[0005] The present invention provides an Internet of Things-based early warning system and method for sudden collapse of road and bridge decks, in order to solve the problem in related technologies that the early warning system relies on only a single technology, making it susceptible to environmental factors and unable to fully capture a variety of abnormal signals that may indicate collapse, resulting in low data accuracy, poor credibility and delayed early warning information, thereby increasing safety risks and other issues.

[0006] The first aspect of the present invention provides an Internet of Things-based sudden collapse warning system for road and bridge decks, including: a deformation monitoring system for collecting deformation data of road and bridge decks; a data processing system for processing the deformation data based on the Internet of Things to obtain valid data that meets preset storage conditions; and an early warning management center for inputting the valid data into a pre-built road and bridge deck collapse risk prediction model to obtain risk assessment results and trend prediction results for the road and bridge deck, so as to issue an early warning for sudden collapse of the road and bridge deck.

[0007] Through the above technical solution, the embodiment of the present invention can collect deformation data of the road and bridge deck through the deformation monitoring system, and use the Internet of Things technology to process the collected deformation data, thereby screening out valid data, providing a basis for subsequent analysis, and improving data accuracy. The early warning management center inputs valid data into the pre-built risk prediction model, which can not only evaluate the current collapse risk of the road and bridge deck, but also predict the development trend of the risk, provide a scientific basis for preventive measures, and improve vehicle driving safety.

[0008] Optionally, in one embodiment of the present invention, the deformation monitoring system includes: a distributed fiber optic monitoring module, the distributed fiber optic monitoring module is provided with a plurality of distributed fiber optic sensors to collect deformation stress data of the road bridge deck that meets preset micro conditions to obtain part of the deformation data; a sensor monitoring module, the sensor monitoring module includes at least one of an acceleration sensor, an inclination sensor, a strain sensor and an environmental sensor to collect the body data of the road bridge deck and the environmental data of the surrounding environment to obtain part of the deformation data; a visual monitoring module, the visual detection module includes at least one camera and a synthetic aperture radar interferometry measurement system to collect visual data and surface deformation data of the road bridge deck to obtain part of the deformation data.

[0009] Through the above technical solutions, the embodiment of the present invention can integrate multiple high-precision monitoring modules, can collect deformation data of road and bridge decks from multiple dimensions, and improve the accuracy and comprehensiveness of data collection.

[0010] Optionally, in one embodiment of the present invention, the data processing system includes: a data acquisition module for acquiring deformation data of the road and bridge deck; a data processing module for inputting the deformation data into a pre-built multi-source data fusion model to obtain valid data that meets preset storage conditions.

[0011] Through the above technical solution, the embodiment of the present invention can integrate multivariate data through a multi-source data fusion model, eliminate redundant information, screen out valid data that meets preset storage conditions, improve data processing efficiency, and ensure the accuracy of subsequent analysis.

[0012] Optionally, in one embodiment of the present invention, the data processing system further includes: a data transmission module, used to transmit the valid data to the early warning management center.

[0013] Through the above technical solution, the embodiment of the present invention can transmit effective data to the early warning management center, shorten the subsequent processing time of the early warning center on the data, and improve the response speed of the early warning system.

[0014] Optionally, in one embodiment of the present invention, the early warning management center includes: a road bridge deck collapse big data monitoring center, which is used to establish the road bridge deck collapse risk prediction model in combination with the historical data of the road bridge deck and the deformation data; a real-time alarm module, which is used to use the road bridge deck collapse risk prediction model to obtain the risk assessment results and the trend prediction results, and generate an early warning signal based on the risk assessment results and the trend prediction results, and send the early warning signal to all vehicles within the target range.

[0015] Through the above technical scheme, the embodiment of the present invention can establish a risk prediction model by integrating historical data and real-time deformation data to provide a scientific basis for preventive measures. The real-time alarm module instantly generates a warning signal based on the risk assessment results and trend prediction results output by the model, and sends the warning signal to all vehicles within the target range, thereby improving the accuracy and timeliness of the warning.

[0016] Optionally, in one embodiment of the present invention, the early warning management center further includes: a system security center for providing at least one service of daily maintenance of the system, data backup, model update and emergency response.

[0017] Through the above technical solutions, the embodiments of the present invention can ensure the continuous healthy operation of the system, prevent potential system failures, and ensure the reliability of the early warning function; ensure the integrity and availability of historical data through data backup; and enable the early warning model to be continuously optimized and upgraded over time through model updates, adapt to new risk characteristics, and improve the accuracy and timeliness of predictions.

[0018] The second aspect of the present invention provides an Internet of Things-based sudden collapse warning method for road and bridge decks, including: collecting deformation data of road and bridge decks; processing the deformation data based on the Internet of Things to obtain valid data that meets preset storage conditions; inputting the valid data into a pre-constructed road and bridge deck collapse risk prediction model to obtain risk assessment results and trend prediction results of the road and bridge deck to warn of sudden collapse of the road and bridge deck.

[0019] Through the above technical scheme, the embodiment of the present invention can collect deformation data of the road and bridge deck, and use the Internet of Things technology to process the collected deformation data, so as to screen out valid data, provide a basis for subsequent analysis, improve data accuracy, and input the valid data into a pre-built risk prediction model, which can not only evaluate the current collapse risk of the road and bridge deck, but also predict the development trend of the risk, provide a scientific basis for preventive measures, and improve vehicle driving safety.

[0020] Optionally, in one embodiment of the present invention, the collecting of deformation data of the road bridge deck includes: collecting deformation stress data of the road bridge deck that meets preset micro conditions to obtain part of the deformation data; collecting the body data of the road bridge deck and environmental data of the surrounding environment to obtain part of the deformation data; collecting visual data and surface deformation data of the road bridge deck to obtain part of the deformation data.

[0021] Through the above technical solutions, the embodiment of the present invention can integrate multiple high-precision monitoring modules, can collect deformation data of road and bridge decks from multiple dimensions, and improve the accuracy and comprehensiveness of data collection.

[0022] Optionally, in one embodiment of the present invention, the processing of the deformation data includes: acquiring the deformation data of the road and bridge deck; inputting the deformation data into a pre-built multi-source data fusion model to obtain the valid data that meets the preset storage conditions.

[0023] Through the above technical solution, the embodiment of the present invention can integrate multivariate data through a multi-source data fusion model, eliminate redundant information, screen out valid data that meets preset storage conditions, improve data processing efficiency, and ensure the accuracy of subsequent analysis.

[0024] Optionally, in one embodiment of the present invention, the processing of the deformation data further includes: transmitting the valid data to the early warning management center.

[0025] Through the above technical solution, the embodiment of the present invention can transmit effective data to the early warning management center, shorten the subsequent processing time of the early warning center on the data, and improve the response speed of the early warning system.

[0026] Optionally, in one embodiment of the present invention, the inputting of the valid data into a pre-constructed road bridge deck collapse risk prediction model includes: establishing the road bridge deck collapse risk prediction model in combination with the historical data of the road bridge deck and the deformation data; obtaining the risk assessment results and the trend prediction results using the road bridge deck collapse risk prediction model, and generating a warning signal based on the risk assessment results and the trend prediction results, and sending the warning signal to all vehicles within the target range.

[0027] Through the above technical scheme, the embodiment of the present invention can establish a risk prediction model by integrating historical data and real-time deformation data to provide a scientific basis for preventive measures. The real-time alarm module instantly generates a warning signal based on the risk assessment results and trend prediction results output by the model, and sends the warning signal to all vehicles within the target range, thereby improving the accuracy and timeliness of the warning.

[0028] The third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things as described in the above embodiment.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things.

[0030] A fifth aspect of the present invention provides a computer program product, including a computer program. When the computer program is executed, it is used to implement the above-mentioned method for early warning of sudden collapse of road and bridge decks based on the Internet of Things.

[0031] The embodiment of the present invention can collect deformation data of the road and bridge deck from multiple dimensions through a deformation monitoring system integrating multiple high-precision monitoring modules, thereby improving the accuracy and comprehensiveness of data collection; utilize the Internet of Things technology, integrate multivariate data through a multi-source data fusion model, eliminate redundant information, screen out valid data that meets the preset storage conditions, improve the efficiency of data processing, and ensure the accuracy of subsequent analysis; transmit the processed data to the early warning management center, shorten the subsequent early warning center's processing time for data, and improve the response speed of the early warning system; establish a risk prediction model by integrating historical data and real-time deformation data, and provide a scientific basis for preventive measures; the real-time alarm module generates an early warning signal in real time according to the risk assessment results and trend prediction results output by the model, and sends the early warning signal to all vehicles within the target range, thereby improving the accuracy and timeliness of the early warning; the system guarantee center ensures the continuous and healthy operation of the system, prevents potential system failures, and ensures the reliability of the early warning function. Thus, the problems that the early warning system in the related technology relies only on a single technology, resulting in a large impact of environmental factors, inability to fully capture a variety of abnormal signals that may indicate collapse, and inability to issue early warnings in a timely manner, increasing the risk of accidents, etc. are solved.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A schematic diagram of the structure of a sudden road and bridge deck collapse warning system based on the Internet of Things according to an embodiment of the present invention;

[0035] Figure 2A schematic diagram of a field monitoring scenario according to an embodiment of the present invention;

[0036] Figure 3 A flow chart of a method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things according to an embodiment of the present invention;

[0037] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0039] The following describes the sudden collapse warning system and method of the road and bridge deck based on the Internet of Things according to the embodiment of the present invention with reference to the accompanying drawings. In view of the related technologies mentioned in the background technology center, the warning system relies on only a single technology, which makes it susceptible to environmental factors and cannot fully capture a variety of abnormal signals that may indicate collapse, resulting in low data accuracy, poor credibility and delayed warning information, thereby increasing safety risks and other issues. The present invention provides a sudden collapse warning system of the road and bridge deck based on the Internet of Things. In this system, the deformation data of the road and bridge deck can be collected in multiple dimensions through the deformation monitoring system to ensure the real-time and integrity of the data, and the collected deformation data can be processed by using the Internet of Things technology to screen out valid data and ensure the validity and value of the data. The valid data can be input into the pre-built risk prediction model through the warning management center, which can not only evaluate the current collapse risk of the road and bridge deck, but also predict the development trend of the risk, provide a scientific basis for preventive measures, issue warning signals in time, and improve vehicle driving safety. This solves the problem in related technologies that the early warning system relies on only a single technology, making it susceptible to environmental factors and unable to fully capture a variety of abnormal signals that may indicate collapse, resulting in low data accuracy, poor credibility and delayed early warning information, thereby increasing safety risks.

[0040] Specifically, Figure 1 A schematic structural diagram of a sudden road or bridge deck collapse warning system based on the Internet of Things provided in an embodiment of the present invention.

[0041] like Figure 1 As shown, the sudden road and bridge deck collapse warning system 10 based on the Internet of Things includes: a deformation monitoring system 100, a data processing system 200 and a warning management center 300.

[0042] Specifically, the deformation monitoring system 100 is used to collect deformation data of a road or bridge deck.

[0043] It is understandable that the deformation data of the road and bridge deck include but are not limited to deformation, cracks, vibration and tilt, etc. These deformation data have an important impact on the safety of the road and bridge deck and directly reflect the health status and bearing capacity of the road and bridge deck. The deformation monitoring system collects the deformation data of the road and bridge deck and the surrounding environment in real time, obtains the original deformation trend data in real time, and provides basic information for subsequent data processing and early warning.

[0044] Optionally, in one embodiment of the present invention, the deformation monitoring system 100 includes: a distributed optical fiber monitoring module, a sensor monitoring module and a visual monitoring module.

[0045] Among them, the distributed optical fiber monitoring module is provided with a plurality of distributed optical fiber sensors to collect deformation stress data of the road and bridge deck that meets preset micro conditions to obtain part of the deformation data.

[0046] In the actual implementation process, the distributed optical fiber monitoring module uses distributed optical fiber sensors, Brillouin scattering, Rayleigh scattering and Raman scattering effects, and deploys sensors along the lanes of the road bridge deck and between the asphalt layer and the road base of the road bridge deck to achieve large-scale, high-precision strain and temperature monitoring. Distributed optical fiber sensors can provide continuous spatial distribution data and effectively identify tiny deformations and cracks. Their anti-electromagnetic interference ability makes them suitable for monitoring in complex environments, and they have long-term stability and low maintenance costs.

[0047] The sensor monitoring module may include, but is not limited to, at least one of an acceleration sensor, an inclination sensor, a strain sensor, and an environmental sensor to collect the body data of the road or bridge deck and the environmental data of the surrounding environment to obtain part of the deformation data.

[0048] In some embodiments, the sensor monitoring module can comprehensively monitor the road bridge deck and the surrounding environment by building a sensor network. The sensor network includes but is not limited to acceleration sensors, inclination sensors, strain sensors, multi-parameter environmental sensors (such as temperature and humidity sensors, wind speed and direction sensors, etc.) and quantum sensors, among which the acceleration sensors are used to detect the vibration and dynamic response of the road bridge deck, the inclination sensors are used to monitor the tilt changes of the structure, the strain sensors are used to measure the changes in structural stress, and the quantum sensors provide high-sensitivity and high-precision measurement capabilities. Multiple sensors work together to provide multi-dimensional deformation information, and realize efficient data transmission and integration through wireless sensor networks.

[0049] The visual monitoring module may include, but is not limited to, at least one camera and a synthetic aperture radar interferometry system to collect visual data and surface deformation data of the road or bridge deck to obtain part of the deformation data.

[0050] In some cases, such as Figure 2 As shown, Figure 2 It is a field monitoring scene, which includes, in addition to the deformation monitoring system 100, the data processing system 200 and the early warning management center 300: the road bridge deck body 1, the surrounding environment 2, the high-definition camera 3, and the INSAR (Interferometric Synthetic Aperture Radar) system 4. The visual monitoring module uses the high-definition camera 3 and the INSAR system 4 for monitoring. The high-definition camera 3 captures a continuous image sequence of the road bridge deck through high resolution, removes noise in the image through filtering technology (such as but not limited to Gaussian filtering and mean filtering), and enhances the image. The edge in the image is identified by algorithms such as but not limited to Canny (Canny Edge Detector) or Sobel (Sobel Edge Detector), the key feature points in the image are extracted by the scale-invariant feature transformation algorithm, the image is aligned by using the image registration technology, the specific change amount of the deformation area is calculated, and the deformation trend is analyzed through long-term monitoring data. The INSAR system 4 provides a wide range of surface deformation data through satellite remote sensing technology. Differential interferometric synthetic aperture radar technology obtains multiple SAR (Synthetic Aperture Radar) images of the same area from radar satellites or aircraft, performs geometric registration on these images to ensure accurate alignment, and generates interferograms to show phase changes. The phase is unwrapped and converted into actual displacement of the surface, deformation patterns and trends are analyzed, and the results are verified with ground observation data. The application of differential interferometric synthetic aperture radar technology can accurately monitor and evaluate surface deformation, thereby further improving the accuracy and reliability of surface deformation monitoring. Visual monitoring is combined with ground sensor data to provide comprehensive deformation monitoring information.

[0051] The embodiment of the present invention forms a comprehensive, high-precision deformation monitoring system for bridge decks and surrounding environments by combining three methods: distributed optical fiber monitoring, sensor monitoring, and visual monitoring. Distributed optical fiber monitoring technology realizes large-scale strain and temperature monitoring, can capture tiny deformations and cracks, provide continuous spatial distribution data, and improve monitoring accuracy and coverage; the sensor network comprehensively monitors the bridge deck structure, including but not limited to dynamic response, tilt transformation, etc., provides multi-dimensional deformation information, and improves the comprehensiveness and accuracy of monitoring; visual monitoring can provide intuitive, high-resolution deformation information, combined with ground sensor data, further enhance the comprehensiveness of monitoring, and improve the ability to analyze deformation trends.

[0052] In summary, the embodiments of the present invention integrate multiple monitoring technologies to form a deformation monitoring system with wide coverage, high precision and multi-dimensionality, which can not only effectively monitor the deformation of the road and bridge deck, but also provide accurate information support for the safety warning of the road and bridge deck.

[0053] The data processing system 200 is used to process the deformation data based on the Internet of Things to obtain valid data that meets the preset storage conditions.

[0054] It is understandable that the Internet of Things generates a large amount of data, and data processing based on the Internet of Things can perform efficient big data storage and analysis. Preset storage conditions refer to the policies and rules for data storage, including but not limited to storing specific data types and formats, or ensuring the integrity and accuracy of data through data cleaning and outlier monitoring.

[0055] Optionally, in one embodiment of the present invention, the data processing system 200 includes: a data acquisition module and a data processing module.

[0056] Among them, the data acquisition module is used to obtain the deformation data of the road and bridge deck.

[0057] In some cases, the data acquisition module obtains various types of monitoring data from the deformation monitoring system 100, performs preliminary processing and storage, and uses edge computing technology to process the monitoring data in real time through distributed computing nodes, thereby reducing data transmission delays and central processing pressure, and ensuring the real-time and accuracy of the data.

[0058] The data processing module is used to input the deformation data into a pre-built multi-source data fusion model to obtain valid data that meets the preset storage conditions.

[0059] In the actual implementation process, the data processing module includes imaging preprocessing and data fusion. Imaging preprocessing improves the clarity and accuracy of image data through image denoising, enhancement and correction. Data fusion technology comprehensively analyzes different types of monitoring data, uses the Bayesian estimation fusion algorithm, and uses the data of different types of sensors as information sources for iterative calculations to obtain joint posterior density and reliability index data, establish a multi-source data fusion model, realize data normalization processing, and improve the accuracy and reliability of monitoring results. The data processing module uses machine learning and deep learning algorithms such as convolutional neural networks and long short-term memory networks for in-depth analysis to identify potential deformation patterns and trends.

[0060] Optionally, in one embodiment of the present invention, the data processing system 200 further includes: a data transmission module, used to transmit valid data to the early warning management center 300.

[0061] In some embodiments, the data transmission module adopts an efficient and secure data transmission protocol and uses a 5G wireless network for data transmission to realize data transmission between the collected data and the data processing system 200 and the early warning management center 300. The 5G network has the characteristics of high bandwidth, low latency and a large number of connections, ensuring the real-time transmission and reliable delivery of monitoring data, and supporting remote monitoring and cloud processing. The data transmission module ensures the security and integrity of the data through blockchain technology to prevent tampering and loss of data during transmission.

[0062] The data acquisition module in the embodiment of the present invention can use edge computing technology and distributed computing nodes to process monitoring data in real time, thereby improving the real-time and accuracy of the data; the data processing module uses imaging preprocessing technology to improve image quality, uses data fusion technology to improve monitoring accuracy and reliability, and uses machine learning and deep learning algorithms to identify potential deformation trends; the data transmission module uses 5G networks to achieve real-time and reliable data transmission and remote monitoring cloud processing, and uses blockchain technology to improve data security and integrity, providing reliable data support for subsequent warnings.

[0063] The early warning management center 300 is used to input valid data into a pre-built road bridge deck collapse risk prediction model to obtain the risk assessment results and trend prediction results of the road bridge deck, so as to issue an early warning for sudden collapse of the road bridge deck.

[0064] It is understandable that the pre-built road and bridge deck collapse risk prediction model is a prediction model based on including but not limited to machine learning or deep learning algorithms, which outputs risk assessment results (such as including but not limited to risk level, safety factor, etc.) and trend prediction results (such as including but not limited to whether a certain risk value may be reached in the next few months, etc.) based on current data.

[0065] Optionally, in one embodiment of the present invention, the early warning management center 300 includes: a road and bridge deck collapse big data monitoring center and a real-time alarm module.

[0066] Among them, the road and bridge deck collapse big data monitoring center is used to establish a road and bridge deck collapse risk prediction model by combining the historical data and deformation data of the road and bridge deck.

[0067] In the actual implementation process, the road and bridge deck collapse big data monitoring center uses big data analysis technology, combines historical data and real-time monitoring data, establishes a road and bridge deck collapse risk prediction model, conducts risk assessment and trend forecasting, and uses machine learning algorithms such as random forests, support vector machines, and deep learning models such as convolutional neural networks and long short-term memory networks to improve the accuracy and timeliness of risk predictions. Through dynamic analysis and modeling, the health status and potential risks of road and bridge decks are evaluated in real time.

[0068] The real-time alarm module is used to obtain risk assessment results and trend prediction results using the road and bridge deck collapse risk prediction model, generate early warning signals based on the risk assessment results and trend prediction results, and send early warning signals to all vehicles within the target range.

[0069] In some embodiments, the real-time alarm module can notify the vehicles within 50 meters to 3 kilometers around the accident of the warning information through the Internet of Things linkage navigation software, and provide personalized braking and deceleration suggestions based on the danger range of the warning area and different vehicle speeds to ensure that the vehicle passes safely or avoids the accident area. The warning information is pushed in real time through, but not limited to, vehicle terminals (such as, but not limited to, smart phones, vehicle navigation systems), combined with V2X (Vehicle-to-Everything, a communication technology for vehicles to exchange data with various entities in the surrounding environment) technology to achieve information interaction between vehicles and between vehicles and infrastructure. When the linkage finds that nearby vehicles have slowed down or receive warning information, they will slow down in time to improve the response speed and coverage of the warning. The real-time alarm module also integrates an intelligent traffic management system to adjust traffic signals and traffic in real time, optimize traffic conditions in the accident area, and reduce the occurrence of secondary accidents.

[0070] Optionally, in one embodiment of the present invention, the early warning management center 300 also includes: a system security center, which is used to provide at least one service of daily maintenance, data backup, model update and emergency response of the system.

[0071] In the actual implementation process, the System Security Center is responsible for the daily maintenance, data backup and emergency response of the system to ensure the stable operation of the system and data security. The System Security Center uses cloud computing and blockchain technology to ensure the storage and transmission security of data, provide redundant backup and disaster recovery mechanisms, and improve the reliability and risk resistance of the system. The System Security Center is also responsible for regularly upgrading and maintaining monitoring equipment and software to ensure that the system is always in the best operating state.

[0072] In the embodiment of the present invention, the road and bridge deck collapse big data monitoring center establishes a risk prediction model by combining historical and real-time data through big data analysis, and uses a variety of machine learning and deep learning algorithms to achieve real-time assessment of the health status and potential risks of the road and bridge deck, thereby improving the accuracy and timeliness of risk prediction; the real-time alarm module sends a warning signal to the vehicle based on the risk prediction result, notifies the surrounding vehicles and gives deceleration suggestions through the Internet of Things linkage navigation software, relies on the on-board terminal and V2X technology to improve the response speed and coverage, and also integrates the intelligent traffic management system to optimize traffic, reduce secondary accidents, and improve vehicle driving safety; the system guarantee center ensures the stable operation of the system, uses cloud computing and blockchain technology to ensure the security of data storage and transmission, and regularly upgrades and maintains the monitoring equipment and software to keep the system in the best operating state at all times, thereby improving the reliability of system operation.

[0073] According to the Internet of Things-based sudden road and bridge deck collapse early warning system proposed in an embodiment of the present invention, the embodiment of the present invention can collect deformation data of the road and bridge deck in multiple dimensions through a deformation monitoring system to ensure the real-time and integrity of the data, and use the Internet of Things technology to process the collected deformation data to screen out valid data, and then input the valid data into a pre-built risk prediction model through an early warning management center, which can not only evaluate the current road and bridge deck collapse risk, but also predict the development trend of the risk, provide a scientific basis for preventive measures, issue early warning signals in a timely manner, and improve vehicle driving safety.

[0074] Next, the sudden road and bridge deck collapse early warning method based on the Internet of Things proposed according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0075] Figure 3 It is a flow chart of a method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things according to an embodiment of the present invention.

[0076] like Figure 3 As shown, the sudden collapse warning method of road and bridge deck based on the Internet of Things includes the following steps:

[0077] In step S301, deformation data of the road or bridge deck is collected.

[0078] Optionally, in one embodiment of the present invention, deformation data of a road or bridge deck is collected, including: collecting deformation stress data of the road or bridge deck that satisfies preset micro conditions to obtain part of the deformation data; collecting the body data of the road or bridge deck and environmental data of its surrounding environment to obtain part of the deformation data; collecting visual data and surface deformation data of the road or bridge deck to obtain part of the deformation data.

[0079] In step S302, based on the Internet of Things, the deformation data is processed to obtain valid data that meets the preset storage conditions.

[0080] Optionally, in one embodiment of the present invention, the deformation data is processed, including: acquiring the deformation data of the road or bridge deck; inputting the deformation data into a pre-built multi-source data fusion model to obtain valid data that meets preset storage conditions.

[0081] Optionally, in one embodiment of the present invention, processing the deformation data further includes: transmitting valid data to an early warning management center.

[0082] In step S303, valid data is input into a pre-built road bridge deck collapse risk prediction model to obtain the risk assessment results and trend prediction results of the road bridge deck, so as to give an early warning of sudden collapse of the road bridge deck.

[0083] Optionally, in one embodiment of the present invention, valid data is input into a pre-built road bridge deck collapse risk prediction model, including: establishing a road bridge deck collapse risk prediction model in combination with historical data and deformation data of the road bridge deck; using the road bridge deck collapse risk prediction model to obtain risk assessment results and trend prediction results, and generating a warning signal based on the risk assessment results and trend prediction results, and sending the warning signal to all vehicles within the target range.

[0084] It should be noted that the aforementioned explanation of the embodiment of the sudden road and bridge deck collapse warning system based on the Internet of Things is also applicable to the sudden road and bridge deck collapse warning method based on the Internet of Things in this embodiment, and will not be repeated here.

[0085] According to the method for early warning of sudden collapse of road and bridge deck based on Internet of Things proposed in the embodiment of the present invention, the embodiment of the present invention can collect deformation data of road and bridge deck in multiple dimensions through the deformation monitoring system to ensure the real-time and integrity of the data, and use Internet of Things technology to process the collected deformation data to screen out valid data, so as to input the valid data into a pre-built risk prediction model through the early warning management center, which can not only evaluate the current collapse risk of road and bridge deck, but also predict the development trend of risk, provide a scientific basis for preventive measures, issue early warning signals in time, and improve vehicle driving safety.

[0086] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0087] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0088] When the processor 402 executes the program, the sudden road and bridge deck collapse warning method based on the Internet of Things provided in the above embodiment is implemented.

[0089] Furthermore, the electronic device further comprises:

[0090] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0091] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0092] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0093] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0095] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0096] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things.

[0097] An embodiment of the present invention further provides a computer program, including a computer program, which, when executed by a processor, implements the above-mentioned method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0102] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0104] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0105] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A sudden collapse warning system for road and bridge decks based on the Internet of Things, characterized in that: include: Deformation monitoring system, used to collect deformation data of road and bridge decks; A data processing system, used to process the deformation data based on the Internet of Things to obtain valid data that meets preset storage conditions; The early warning management center is used to input the valid data into a pre-built road bridge deck collapse risk prediction model to obtain the risk assessment results and trend prediction results of the road bridge deck, so as to issue an early warning for sudden collapse of the road bridge deck.

2. The sudden collapse warning system for road and bridge decks based on the Internet of Things according to claim 1 is characterized in that: The deformation monitoring system comprises: A distributed optical fiber monitoring module, wherein the distributed optical fiber monitoring module is provided with a plurality of distributed optical fiber sensors to collect deformation stress data of the road and bridge deck that meets preset micro conditions to obtain part of the deformation data; A sensor monitoring module, wherein the sensor monitoring module includes at least one of an acceleration sensor, an inclination sensor, a strain sensor, and an environmental sensor to collect the body data of the road bridge deck and the environmental data of the surrounding environment to obtain part of the deformation data; A visual monitoring module includes at least one camera and a synthetic aperture radar interferometry system to collect visual data and surface deformation data of the road and bridge surface to obtain part of the deformation data.

3. The sudden collapse warning system for road and bridge decks based on the Internet of Things according to claim 1 is characterized in that: The data processing system comprises: A data acquisition module, used to obtain deformation data of the road and bridge deck; The data processing module is used to input the deformation data into a pre-built multi-source data fusion model to obtain the valid data that meets the preset storage conditions.

4. The sudden collapse warning system for road and bridge decks based on the Internet of Things according to claim 3 is characterized in that: The data processing system further comprises: A data transmission module is used to transmit the valid data to the early warning management center.

5. The sudden collapse warning system for road and bridge decks based on the Internet of Things according to claim 1 is characterized in that: The early warning management center includes: A road and bridge deck collapse big data monitoring center is used to establish a road and bridge deck collapse risk prediction model by combining the historical data of the road and bridge deck and the deformation data; A real-time alarm module is used to obtain the risk assessment result and the trend prediction result by using the road and bridge deck collapse risk prediction model, generate a warning signal based on the risk assessment result and the trend prediction result, and send the warning signal to all vehicles within the target range.

6. The sudden collapse warning system for road and bridge decks based on the Internet of Things according to claim 5 is characterized in that: The early warning management center also includes: The system assurance center is used to provide at least one service of daily system maintenance, data backup, model update and emergency response.

7. A sudden collapse warning method for road and bridge deck based on the Internet of Things, characterized in that: The following steps are involved: Collect deformation data of road and bridge decks; Based on the Internet of Things, the deformation data is processed to obtain valid data that meets preset storage conditions; The valid data is input into a pre-built road bridge deck collapse risk prediction model to obtain the risk assessment result and trend prediction result of the road bridge deck, so as to give an early warning of sudden collapse of the road bridge deck.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for early warning of sudden collapse of a road or bridge deck based on the Internet of Things as claimed in claim 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for early warning of sudden collapse of road and bridge deck based on the Internet of Things as described in claim 7.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for early warning of sudden road and bridge deck collapse based on the Internet of Things as claimed in claim 7.

Images