Real-time early warning and monitoring system for settlement deformation of highway bridge and application method of real-time early warning and monitoring system

Abstract

The invention discloses a settlement deformation real-time early warning and monitoring system for a highway bridge and an application method, and relates to the technical field of road and bridge engineering monitoring, and the system comprises a data collection module which is used for collecting the data information of the highway bridge in real time; the remote control terminal is in communication connection with the data acquisition module; the plurality of sub-control terminals are arranged at preset positions of a road or a bridge at preset intervals and are in communication connection with the remote control terminal; the executing mechanisms are arranged at preset positions of the road or the bridge at preset intervals and are used for carrying out flow dividing, flow limiting and flow cutting-off processing on the road or the bridge based on the corresponding sub-control terminals; and the communication module is used for realizing data interaction. According to the settlement deformation real-time early warning and monitoring system of the highway bridge and the application method, compared with the prior art, passive early warning is changed into active intervention, so that when disasters occur, the on-off of the highway bridge is controlled, the occurrence of the disasters is reduced, and the system and the application method are more effective.

Description

Technical Field

[0001] The present invention relates to the technical field of road and bridge engineering monitoring, and more specifically, to a real-time early warning monitoring system for settlement and deformation of highway bridges and an application method thereof. Background Art

[0002] As an important transportation infrastructure, the safety and stability of highway bridges are directly related to the smoothness of transportation and the safety of people's lives and property. However, due to the influence of geological conditions, traffic loads, natural disasters and other factors, highway bridges often undergo settlement and deformation during use. If they are not monitored and warned in time, serious safety accidents may occur. Therefore, it is particularly important to develop a monitoring system that can monitor the settlement and deformation of highway bridges in real time and provide early warning.

[0003] In the prior art, there are also systems for real-time monitoring and early warning of the settlement and deformation of highway bridges, but all systems only monitor and do not perform any passive processing. When geological disasters occur on highway bridges, it is impossible to effectively manage the road section in real time, and only rely on management personnel to carry out on-site management. This passive method is suitable in urban areas, but in highways or non-urban areas, the management agencies cannot intervene in time, causing secondary disasters. Therefore, the current monitoring and early warning system can only serve as a database for road and bridge maintenance and does not have other functions. Summary of the invention

[0004] An object of the present invention is to solve at least the above problems or disadvantages and to provide at least the advantages which will be described hereinafter.

[0005] In order to achieve these purposes and other advantages of the present invention, a real-time early warning monitoring system for settlement deformation of a highway bridge is provided, comprising: A data acquisition module for real-time acquisition of data information of highway bridges; A remote control terminal in communication with the data acquisition module; A plurality of sub-control terminals are arranged at predetermined distances at preset positions on a road or bridge and are communicatively connected to the remote control terminal; An actuator is arranged at a preset position of a road or bridge at a predetermined interval, and performs flow diversion, flow limiting, and flow interruption processing on the road or bridge based on a corresponding sub-control terminal; A communication module used to realize data interaction among the data acquisition module, remote control terminal, sub-control terminals, and actuators.

[0006] Preferably, the remote control terminal includes: A classification and summary module for classifying and obtaining the settlement deformation of various parts of the highway bridge from the data information received in real time, and transmitting the settlement deformation to the data analysis and early warning module; The data analysis and early warning module compares the settlement deformation received in real time with the preset value, judges the settlement deformation rate of each part of the highway bridge based on the comparison result, and then determines whether to perform corresponding early warning operations.

[0007] Preferably, the actuator is configured to include: A plurality of multi-section telescopic bollards for lateral flow restriction arranged on the road surface or bridge; A video acquisition mechanism I and / or a radar arranged at a predetermined position in front of the oncoming surface of each multi-stage telescopic bollard; Wherein, each multi-stage telescopic flow-facing surface is provided with a warning light and / or a guiding symbol; Each multi-section telescopic bollard is in communication connection with a corresponding sub-control terminal, and at least two circles of elastic elements are arranged between the sections of each multi-section telescopic bollard.

[0008] Preferably, a longitudinal installation slot is provided at a relative position of the uppermost section of the multi-section telescopic device, and a guide rod is connected to each installation slot by a hinge, and a plurality of warning flags are provided on the guide rod; Wherein, arc-shaped guiding parts are arranged at both ends of the guide rod at positions contacting with the mounting groove; The guide rod is also provided with a luminous strip.

[0009] Preferably, a winding mechanism installation cavity is provided above the installation groove, and the output end of the winding mechanism is connected to the installation ring at the end of the guide rod through a flexible connecting line.

[0010] Preferably, the data acquisition module includes a sensor group arranged at a key position of the highway bridge for acquiring data information related to the settlement deformation, vibration frequency, geological conditions, and meteorological environment of the highway bridge, and a video acquisition mechanism II for acquiring the traffic flow or the status of the highway bridge protection mechanism; The key positions include: roadbed, both ends of the bridge, bridge joints, and center positions of each bridge section.

[0011] Preferably, the data analysis and early warning module analyzes the settlement and deformation of the highway bridge based on a deep learning network; The deep learning network includes: Convolutional neural network models for image and visual information processing; A long short-term memory network model used to process data information.

[0012] An application method, which is applied to a real-time early warning monitoring system for settlement deformation of a highway bridge, comprises: Step 1: Arrange data acquisition modules at key locations of highway bridges; Step 2: The remote control terminal receives data and image information sent back by the sensor group and the video acquisition mechanism II in real time through the communication module, and predicts the data information and video information respectively through the convolutional neural network model and the long short-term memory network model integrated in the remote control terminal, so as to trigger an alarm when the predicted values ​​of the two exceed the preset thresholds, and send corresponding warning and / or control information to the sub-control terminal of the corresponding node at the same time; Step 3: The sub-controller controls the actuator to perform corresponding actions based on the received control information, so as to divert, limit or cut off the flow of the road or bridge; When the road is cut off, the sub-control terminal receives the data information sent back by the video acquisition mechanism I and / or the radar in real time through the communication module to obtain the current vehicle speed. When the speed is greater than the predetermined value, the actuator is controlled to withdraw and release the cut-off. Based on the received warning information, the sub-control terminal sends corresponding warning text messages to the maintenance personnel and traffic management personnel of each section through the sub-control terminal.

[0013] Preferably, the flow diversion, flow limiting and flow interruption processing refers to the realization of the working state of the multi-stage telescopic bollard in the control actuator in the case of a two-way single channel or a two-way multi-channel; Among them, when the multi-section telescopic road bollard is in working state, the guide rod is in an open state without restraint, so that the warning flag on the guide rod can be unfolded to provide long-distance warning to drivers.

[0014] Preferably, after the road bollard in the multi-stage telescopic road bollard rises to the highest position and the guide rod is unfolded, the sub-control end puts the winding mechanism into a working state, and pulls the guide rod to change the angle by switching between forward and reverse rotation, thereby giving an early warning to the driver.

[0015] The present invention includes at least the following beneficial effects: First, the present invention provides a real-time early warning monitoring system for highway bridge settlement and deformation, which, compared with the prior art, changes passive early warning into active intervention, so as to control the opening and closing of highway bridges when disasters occur, so as to reduce the occurrence of disasters, which is more effective.

[0016] Secondly, compared with the existing road bollards, the actuator of the present invention adopts a multi-section flexible connection method, which combines prevention and control, has stronger functionality and is more effective.

[0017] Thirdly, the present invention adopts diverse and dynamic early warning methods, making its early warning and prompting more powerful.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a principle block diagram of the real-time early warning monitoring system for settlement and deformation of a highway bridge of the present invention; Figure 2 It is a structural block diagram of the execution mechanism of the present invention; Among them, there are data acquisition module 1, sensor group 10, video acquisition mechanism II 11, remote control terminal 2, classification and summary module 20, data analysis and early warning module 21, sub-control terminal 3, actuator 4, multi-section telescopic road bollard 40, video acquisition mechanism I and / or radar 41, warning lights and / or guide symbols 401, installation groove 402, guide rod 403, warning flag 404, arc-shaped guide part 405, luminous strip 406, winding mechanism 407, flexible connecting line 408, installation ring 409, and two circles of elastic elements 410. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0021] The present invention provides a real-time early warning monitoring system for the settlement and deformation of a highway bridge, which can monitor the settlement and deformation of the highway bridge in real time and issue an early warning in time when an abnormal situation is found, so as to ensure the safe operation of the highway bridge. Specifically, Figure 1 As shown in the figure, the real-time early warning monitoring system for highway bridge settlement and deformation includes: A data acquisition module 1 for real-time data information collection of highway bridges, wherein the data information includes vibration frequency, traffic volume, and settlement deformation at various locations of the highway bridge. In certain cases, temperature values ​​may also be obtained as required. The data acquisition module 1 includes a sensor group 10 arranged at key positions of the highway bridge to obtain data information related to settlement deformation, vibration frequency, geological conditions, and meteorological environment of the highway bridge, and a video acquisition mechanism II 11 for obtaining traffic volume or the status of the highway bridge protection mechanism; the key positions include: roadbed, both ends of the bridge, the bridge junction, and the center of each section of the bridge (in actual applications, the number of monitoring points should be reasonably arranged according to factors such as the length, width, and structural characteristics of the highway bridge, and arranging a sufficient number of monitoring points can improve the accuracy and reliability of the monitoring results). At the same time, according to the actual situation and monitoring requirements of the highway bridge, the data acquisition frequency of the sensor is set to ensure the real-time and accuracy of the data. In actual applications, the number of monitoring points should be reasonably arranged according to factors such as the length, width, and structural characteristics of the highway bridge, and arranging a sufficient number of monitoring points can improve the accuracy and reliability of the monitoring results; Specifically, the usual road monitoring scope mainly includes: 1. Roadbed settlement monitoring: covers all roadbed sections where settlement may occur, including sections at different heights, different foundation conditions, and different structural parts. The monitoring contents include roadbed surface settlement, roadbed body settlement, base settlement, etc.

[0022] 2. Road deformation monitoring: Monitor the overall deformation of the road surface, including cracks, potholes, ruts, etc. This can be done by setting road deformation monitoring points and using high-precision sensors for real-time monitoring.

[0023] 3. Road drainage system monitoring: Monitor the operating status of the road drainage system, including drainage pipes, drainage ditches, etc., and evaluate its impact on road settlement and deformation by monitoring the flow, water level and other parameters of the drainage system.

[0024] The usual scope of bridge monitoring mainly includes: 1. Monitoring of the main structure of the bridge: Monitoring the settlement and deformation of the main structures of the bridge, such as the piers, bridge decks, and bridge towers. Monitoring points can be set at key locations of the bridge and high-precision sensors can be used for real-time monitoring.

[0025] 2. Bridge foundation monitoring: Monitor the stability of the bridge foundation, including pile foundation, pier, etc., and evaluate its impact on the overall safety of the bridge by monitoring parameters such as the foundation's settlement deformation and horizontal displacement.

[0026] 3. Monitoring of bridge ancillary facilities: monitor the integrity and stability of the bridge's guardrails, lighting facilities, traffic signs and other ancillary facilities. Although these facilities do not directly affect the structural safety of the bridge, they have an important impact on its function and safety.

[0027] At the same time, according to the actual application needs, the monitoring scope can also be expanded as follows: 1. Geological condition monitoring: monitor the geological conditions in the area where the highway bridge is located, including stratum distribution, groundwater level, etc. Changes in these geological conditions may have an important impact on the settlement and deformation of the highway bridge. For example, soft soil foundations are more prone to settlement and deformation, so they need to be monitored more closely.

[0028] 2. Traffic load monitoring: The size and frequency of traffic loads will also affect the settlement and deformation of highway bridges. Therefore, the impact of traffic loads needs to be considered during the monitoring process, and the monitoring plan needs to be adjusted and optimized according to actual conditions. The traffic load conditions of highway bridges, including vehicle flow, vehicle type distribution, etc., are monitored, and their impact on the settlement and deformation of highway bridges is evaluated by monitoring changes in traffic loads.

[0029] 3. Environmental factor monitoring: monitor the environmental factors in the area where the highway bridge is located, such as temperature, humidity, wind force, etc. Changes in these environmental factors may have a certain impact on the settlement and deformation of the highway bridge. Data acquisition module 1 can be extended to multiple aspects of roads and bridges, covering the main structure, foundation, ancillary facilities, geological conditions, traffic loads and environmental factors. Through comprehensive and detailed real-time monitoring, settlement and deformation problems of highway bridges can be discovered in time, providing strong support for later decision-making and maintenance.

[0030] A remote control terminal 2 in communication with the data acquisition module 1, the remote control terminal 2 comprising: The classification and summary module 20 is used to classify the settlement deformation of each part of the highway bridge from the data information received in real time, and transmit the settlement deformation to the data analysis and early warning module. The classification and summary module 20 is used to receive data information in real time, and generate analysis signals such as the settlement deformation of each part of the highway bridge in the data information and transmit them to the data analysis and early warning module 21. Data analysis is to classify the collected data according to the settlement deformation, vibration frequency, vehicle flow and other categories; data summary is to summarize the classified data to the central processing system for subsequent analysis and processing. At the same time, the vibration frequency, vehicle flow and settlement deformation of the highway bridge are used to generate processing signals and transmit them to the data processing module; The settlement deformation received in real time is compared with the preset value, and the settlement deformation rate of each part of the highway bridge is judged based on the comparison result, and then the data analysis and early warning module 21 is determined whether to perform the corresponding early warning operation. The data analysis is to start the analysis operation after receiving the analysis signal in real time, obtain the settlement deformation Qi (i=1...n) of each part of the highway bridge, and compare it with the preset value. The early warning means that when Qi transmitted by the data analysis module is received in real time, it is compared with the preset value; at the same time, relevant data from other modules are received and compared with the preset value. When an abnormal situation is found, a patrol signal and a high-risk signal are generated; The data analysis includes: 1. Settlement deformation analysis: Receive and analyze settlement and deformation data of various locations on highway bridges in real time.

[0031] The settlement deformation data is compared with the preset settlement deformation threshold.

[0032] If the settlement deformation exceeds the threshold, the early warning mechanism will be triggered.

[0033] 2. Vibration frequency analysis: Real-time monitoring of the vibration frequency of highway bridges.

[0034] The vibration frequency is compared to a preset vibration frequency threshold.

[0035] If the vibration frequency is abnormal, further analysis will be conducted and combined with the settlement deformation data to determine the safety of the highway bridge.

[0036] 3. Traffic flow analysis: Monitor and record traffic flow on highway bridges in real time.

[0037] The traffic volume is compared with a preset traffic volume threshold.

[0038] If the traffic volume is too large, the bearing capacity of the highway bridge will be evaluated in combination with the settlement deformation data and vibration frequency data.

[0039] 4. Video information analysis Use video collection agencies to conduct video surveillance on road sections that are prone to potential safety hazards; Real-time analysis of the collected video data information to determine whether there are landslides, falling rocks, car accidents, road collapse and other disasters; If there is, analyze the disaster situation to determine the treatment method, and determine which early warning mechanism to trigger based on the treatment method; Multiple sub-control terminals 3 are arranged at preset positions of roads or bridges at predetermined intervals, and are connected to the remote control terminal 2 in communication. Each sub-control terminal 3 corresponds to each other at the preset distances of the roads or bridges. Each sub-control terminal 3 is used to receive the inspection signal and the high-risk signal generated by the early warning monitoring module, and control the working state of the corresponding actuator through the communication module. Each sub-control terminal 3 can be regarded as a control node, which can control the working state of a group of actuators or multiple groups of actuators. When controlling multiple groups, they can be controlled separately or linked according to the code. Controlling the corresponding structure to perform the corresponding operation through one sub-control terminal belongs to the existing technology, so the technology will not be elaborated in detail here. An actuator 4 is set at a preset position of a road or bridge at a predetermined interval, and performs flow diversion, flow limiting, and flow interruption processing on the road or bridge based on the corresponding sub-control terminal, and is used to implement different blocking measures on the road according to the early warning situation; The communication module 5 is used to realize data interaction among the data acquisition module 1, the remote control terminal 2, each sub-control terminal 3, and the actuator. It is used to realize data communication among various parts of the system. At the same time, the warning signal can be used as the warning input of the map navigation software to automatically avoid prompts through navigation. The warning information of the patrol signal and the high-risk signal can also be sent in real time to the mobile phones of the patrol personnel, maintenance personnel, and traffic management personnel for display, so that relevant personnel can grasp the settlement and deformation of the highway bridge in real time. The communication mode of the communication module 5 can be selected as wired or wireless according to needs, and 5G\4G or ray transmission mode can be selected.

[0040] The present invention can realize real-time monitoring and early warning of the settlement and deformation of highway bridges through the above monitoring system, and provide a strong guarantee for the safe operation of highway bridges.

[0041] Furthermore, the remote control terminal 2 also has visualization. Through data visualization technology, the settlement deformation data can be presented in the form of charts, curves, etc., so that relevant personnel can intuitively understand the settlement deformation of the highway bridge. Of course, the remote control terminal 2 also has data storage and query functions. Data storage refers to storing all collected data and analysis results in a storage module for subsequent query and analysis. Data query provides a data query function, allowing relevant personnel to query historical data and analysis results as needed; Furthermore, if Figure 1-Figure 2 As shown, the actuator 4 is configured to include: A plurality of multi-section telescopic road posts 40 for lateral flow restriction are arranged on the road surface or bridge. The structure of the telescopic road posts is the existing technology, and the present solution is only to innovatively change the middle column into a multi-section structure. In the actual layout, multiple rows of multi-section telescopic road posts 40 should be arranged in front of each monitoring point. The length of the multi-section telescopic road posts 40 can be increased in sequence according to the flow surface, and should be arranged in a straight line section, with the front section being shorter and the rear section being longer. The reason is to give visual layered prompts. When the vehicle can reasonably change lanes or slow down, the vehicle is directly intercepted or diverted. If the vehicle speed exceeds the predetermined range, the front section of the road post is withdrawn to prevent the vehicle from hitting the road post and causing secondary disasters. The video acquisition mechanism I and / or radar 41 are arranged at a predetermined position in front of the oncoming surface of each multi-stage telescopic road bollard 40. The speed of the vehicle during the day, night and foggy day is collected by the video acquisition mechanism I and / or radar to determine whether the road bollard needs to be withdrawn. At the same time, the distance between the radar, the video acquisition mechanism I and the frontmost road bollard should match the withdrawal time of the road bollard, that is, the withdrawal time of the road bollard can be completed in case of speeding. Among them, each multi-stage telescopic oncoming surface is provided with a warning light and / or a guide symbol 401, which is used to remind the driver to increase the prominence, and the warning light can be used for reminders in foggy days; Each multi-section telescopic road bollard 40 is communicatively connected with the corresponding sub-control terminal 3. At least two circles of elastic elements 410 are arranged between each multi-section telescopic section. Through the arrangement of the elastic elements, the cutting force of the top of the road bollard on the vehicle is reduced, so as to reduce damage when other emergency situations occur and withdrawal is impossible. The combination of prevention and treatment has stronger functionality.

[0042] Furthermore, a longitudinal installation slot 402 is provided at a relative position of the uppermost section of the multi-section telescopic device, and a guide rod 403 is connected to each installation slot 402 by means of a hinge (not shown). A plurality of warning flags 404 are provided on the guide rod 403. Through the hinge connection, the guide rod 403 is directly unfolded to both ends by the guidance of gravity when the restraint is lost, and is arranged horizontally in space to increase the prompting interval. The function of the warning flag 404 is to expand the visual influence and to remind in a more prominent way. Wherein, both ends of the guide rod 403 are provided with arc-shaped guide parts 405 at the positions in contact with the mounting grooves. The arc-shaped guide parts 405 can be more freely unfolded when the road bollard is extended to reduce interference, and when the road bollard is retracted, the arc-shaped guide parts 405 can guide the guide rod 403 to be retracted inwards, while reducing interference with other structures, and making the operation smoother; The guide rod 403 is also provided with a luminous strip 406 for providing lateral interval prompts at night.

[0043] Furthermore, a winding mechanism 407 installation cavity is provided above the installation groove 402, and the output end of the winding mechanism is connected to the installation ring 409 at the end of the guide rod 403 through a flexible connecting line 408. In actual applications, the spatial position of the guide rod 403 (i.e., moving up and down to form a change in the angle in space) can be changed through the rewinding or unwinding operation of the winding mechanism, which reminds the driver in a dynamic form, making it more eye-catching.

[0044] Furthermore, the data analysis and early warning module 21 analyzes the settlement and deformation of the highway bridge based on a deep learning network; The deep learning network includes: Convolutional neural network model for processing images and visual information. Convolutional neural network model CNN performs well in image processing and feature extraction. Although bridge settlement data is usually time series data, settlement data can be converted into image form (such as settlement curve graph), and CNN can be used for feature extraction and pattern recognition. Therefore, LSTM can be combined for comprehensive application, or they can be directly applied separately. The convolutional neural network model is used to analyze video images, and the road or bridge pavement information is judged based on the on-site collected images to determine whether there is a disaster, or whether the cracks on the pavement or bridge deck are consistent with the judgment of LSTM. Various data can also be analyzed and predicted independently, and then the prediction results of multiple models can be combined through Boosting model fusion technology to improve the final prediction accuracy; Long short-term memory network model used to process data information. In bridge settlement monitoring, the long short-term memory network model LSTM can analyze historical settlement data and predict future settlement trends. When bridge settlement data presents time series characteristics and needs to predict future settlement trends, LSTM is a good choice; In the specific implementation, based on the dynamics of settlement deformation, this scheme uses a deep learning network to analyze the settlement deformation of highway bridges. The dynamics of settlement deformation mainly include: 1. Real-time changes: The settlement and deformation of highway bridges is a continuous and slow process, which is affected by many factors, such as geological conditions, traffic loads, environmental factors, etc. Therefore, the settlement and deformation will change over time, and the monitoring system needs to capture these changes in real time.

[0045] 2. Non-uniformity: The settlement deformation of highway bridges is often non-uniform, that is, the settlement deformation at different locations and different time periods may be different. The monitoring system needs to be able to accurately measure and record these non-uniform settlement deformations so as to promptly discover and deal with potential safety hazards.

[0046] The settlement and deformation monitored by the real-time early warning monitoring system for road and bridge settlement and deformation is not a fixed value, but a dynamic process. These monitoring systems provide accurate and reliable data support to relevant departments through high-precision measurement, real-time monitoring and early warning, data analysis and visualization, etc., which helps to ensure the safe and long-term stable operation of highway bridges.

[0047] Deep learning is a machine learning method that attempts to simulate the neural network structure in the human brain to solve complex problems. The core of deep learning is to build a network structure of multiple layers of nonlinear processing units (i.e. neurons). These networks can automatically extract features from raw data and learn. The ways to build and train deep learning models include: 1. Data preparation: Collect historical data of bridge settlement and deformation, including time, settlement, etc. Preprocess the data, such as denoising and normalization.

[0048] 2. Model design: Select an appropriate deep learning model architecture based on the nature of the problem and the characteristics of the data. For bridge settlement deformation prediction, the long short-term memory network LSTM is a good choice because it can handle long-term dependencies in time series data.

[0049] 3. Model training: Input the prepared data into the model and optimize the model parameters through an iterative process to minimize the gap between the predicted value and the actual value. The training process usually uses a gradient descent optimization algorithm, which includes a back-propagation algorithm to calculate the gradient of the parameters.

[0050] 4. Effect verification: evaluate the accuracy of the model by comparing the predicted value with the actual value. You can use indicators such as mean square error (MSE) and root mean square error (RMSE) to quantify the performance of the model. If the performance of the model is poor, improve the accuracy by adjusting the model architecture, hyperparameters, or increasing the amount of data.

[0051] Using deep learning models for prediction means using the trained model to predict new input data to obtain the predicted value of bridge settlement and deformation, so as to formulate maintenance plans or take other measures based on the predicted value to prevent potential safety problems.

[0052] The monitoring refers to integrating the deep learning model into the bridge settlement and deformation monitoring system, receiving sensor data in real time and making predictions. If the predicted value exceeds a preset threshold, an alarm is triggered or other response measures are taken.

[0053] An application method, which is applied to a real-time early warning monitoring system for settlement deformation of a highway bridge, comprises: Step 1: Arrange data acquisition modules at key locations of highway bridges; Step 2: The remote control terminal receives data and image information sent back by the sensor group and the video acquisition mechanism II in real time through the communication module, and predicts the data information and video information respectively through the convolutional neural network model and the long short-term memory network model integrated in the remote control terminal, so as to trigger an alarm when the predicted values ​​of both exceed the preset thresholds, and send corresponding warning and / or control information to the sub-control terminal of the corresponding node. At the same time, when applied, the long short-term memory network model can also be converted into corresponding picture information, and learned through the convolutional neural network model to achieve comprehensive prediction; Step three, based on the received control information, the sub-control end controls the actuator to perform corresponding actions to divert, limit or interrupt the flow of the road or bridge. The diverting, limiting or interrupting flow refers to the working state of the multi-stage telescopic bollards in the control actuator in the case of a two-way single channel or a two-way multi-channel. That is, if a disaster occurs in a certain channel, the bollards can be extended to allow the flow of traffic to the channel and guide vehicles to other channels. The limiting flow refers to the distance between the bollards to limit the entry of large vehicles when the highway bridge is in a bad condition, which plays a role in limiting the flow. The interrupting flow refers to the blockade of the entire road section when the highway bridge is in a high-risk state, and the release process will be carried out after the danger is handled. At the same time, the corresponding warning information can be exchanged with the vehicle navigation through GPS, so that it can remind the driver more effectively. At the same time, it can also be communicated with the traffic management department to issue a significant and early warning on the LCD signboard of the corresponding section. Among them, when the multi-stage telescopic bollard is in working state, the guide rod is in an open state without restraint, so that the warning flag on the guide rod can be unfolded to give long-distance warning to drivers. In this way, a visual large-scale warning is formed to ensure that drivers can make corresponding speed and lane changes; When the road is cut off, the sub-control end receives the data information sent back by the video acquisition mechanism I and / or the radar in real time through the communication module to obtain the speed of the current vehicle. When its speed is greater than the preset value, the actuator is controlled to withdraw to release the cut-off. It is used to temporarily withdraw the front road pile when the speed of the moving vehicle exceeds the preset value and there is no deceleration, hoping that the driver can take emergency measures in the later stage to prevent it from hitting the road pile and causing secondary disasters. When the vehicle passes and there are no other vehicles driving in quickly, it returns to the original restricted state. At the same time, when a road pile is withdrawn, its working state can be sent back to the back end through the data return function, and the working state of other road piles can be switched through manual control or automatic control. In the specific implementation, the back-end operator can take emergency measures according to the corresponding situation to avoid secondary disasters. Based on the received warning information, the sub-control terminal sends corresponding warning text messages to maintenance personnel and traffic management personnel of each section through the sub-control terminal. The warning text messages can notify the relevant maintenance personnel to carry out corresponding patrol maintenance, reduce the mechanical and repeated patrols of maintenance personnel, and improve the inspection efficiency. At the same time, the warning text messages can also notify the traffic management department to carry out traffic management and supervision in the corresponding disaster-affected areas; Among them, after the road bollard in the multi-stage telescopic road bollard rises to the highest position and the guide rod is unfolded, the sub-control end puts the winding mechanism into a working state, and pulls the guide rod to change the angle by switching between forward and reverse rotation, thereby warning the driver. In this way, this patent adds a dynamic warning function compared to the existing static warning, thereby improving the significance of the warning.

[0054] The above solution is only an illustration of a preferred embodiment, but is not limited thereto. When implementing the present invention, appropriate replacement or modification can be made according to user needs.

[0055] Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and the embodiments. It can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily realized. Therefore, without departing from the general concept defined by the claims and equivalent scope, the present invention is not limited to the specific details and the illustrations shown and described here.

Claims

1. A real-time early warning monitoring system for settlement and deformation of a highway bridge, characterized in that: include: A data acquisition module for real-time acquisition of data information of highway bridges; A remote control terminal in communication with the data acquisition module; A plurality of sub-control terminals are arranged at predetermined distances at preset positions on a road or bridge and are communicatively connected to the remote control terminal; An actuator is arranged at a preset position of a road or bridge at a predetermined interval, and performs flow diversion, flow limiting, and flow interruption processing on the road or bridge based on a corresponding sub-control terminal; A communication module used to realize data interaction among the data acquisition module, remote control terminal, sub-control terminals, and actuators.

2. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 1, characterized in that: The remote control terminal comprises: A classification and summary module for classifying and obtaining the settlement deformation of various parts of the highway bridge from the data information received in real time, and transmitting the settlement deformation to the data analysis and early warning module; The data analysis and early warning module compares the settlement deformation received in real time with the preset value, judges the settlement deformation rate of each part of the highway bridge based on the comparison result, and then determines whether to perform corresponding early warning operations.

3. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 1, characterized in that: The actuator is configured to include: A plurality of multi-section telescopic bollards for lateral flow restriction arranged on the road surface or bridge; A video acquisition mechanism I and / or a radar arranged at a predetermined position in front of the oncoming surface of each multi-stage telescopic bollard; Wherein, each multi-stage telescopic flow-facing surface is provided with a warning light and / or a guiding symbol; Each multi-section telescopic bollard is in communication connection with a corresponding sub-control terminal, and at least two circles of elastic elements are arranged between the sections of each multi-section telescopic bollard.

4. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 3, characterized in that: A longitudinal installation slot is provided at the relative position of the uppermost section of the multi-section telescopic device, and a guide rod is connected to each installation slot by a hinge, and a plurality of warning flags are provided on the guide rod; Wherein, arc-shaped guiding parts are arranged at both ends of the guide rod at positions contacting with the mounting groove; The guide rod is also provided with a luminous strip.

5. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 4, characterized in that: A winding mechanism installation cavity is arranged above the installation groove, and the output end of the winding mechanism is connected to the installation ring at the end of the guide rod through a flexible connecting line.

6. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 1, characterized in that: The data acquisition module includes a sensor group arranged at a key position of the highway bridge to obtain data information related to the settlement deformation, vibration frequency, geological conditions, and meteorological environment of the highway bridge, and a video acquisition mechanism II for obtaining the traffic flow or the status of the highway bridge protection mechanism; The key positions include: roadbed, both ends of the bridge, bridge joints, and center positions of each bridge section.

7. The real-time early warning monitoring system for settlement and deformation of a highway bridge according to claim 1, characterized in that: The data analysis and early warning module analyzes the settlement and deformation of highway bridges based on a deep learning network; The deep learning network includes: Convolutional neural network models for image and visual information processing; A long short-term memory network model used to process data information.

8. An application method, applied to the real-time early warning monitoring system for settlement deformation of a highway bridge as claimed in any one of claims 1 to 7, characterized in that: include: Step 1: Arrange data acquisition modules at key locations of highway bridges; Step 2: The remote control terminal receives data and image information sent back by the sensor group and the video acquisition mechanism II in real time through the communication module, and predicts the data information and video information respectively through the convolutional neural network model and the long short-term memory network model integrated in the remote control terminal, so as to trigger an alarm when the predicted values ​​of the two exceed the preset thresholds, and send corresponding warning and / or control information to the sub-control terminal of the corresponding node at the same time; Step 3: The sub-controller controls the actuator to perform corresponding actions based on the received control information, so as to divert, limit or cut off the flow of the road or bridge; When the road is cut off, the sub-control terminal receives the data information sent back by the video acquisition mechanism I and / or the radar in real time through the communication module to obtain the current vehicle speed. When the speed is greater than the predetermined value, the actuator is controlled to withdraw and release the cut-off. Based on the received warning information, the sub-control terminal sends corresponding warning text messages to the maintenance personnel and traffic management personnel of each section through the sub-control terminal.

9. The application method according to claim 8, characterized in that: The flow diversion, flow limiting and flow interruption processing are achieved by controlling the working state of the multi-stage telescopic bollard in the actuator in the case of a two-way single channel or a two-way multi-channel; Among them, when the multi-section telescopic road bollard is in working state, the guide rod is in an open state without restraint, so that the warning flag on the guide rod can be unfolded to provide long-distance warning to drivers.

10. The application method according to claim 8, characterized in that: When the bollard in the multi-stage telescopic bollard rises to the highest position and the guide rod is unfolded, the sub-control end puts the winding mechanism into working state, and pulls the guide rod to change the angle by switching between forward and reverse rotation, so as to warn the driver.

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