Sluice traffic bridge deformation monitoring method, system, equipment and medium

Abstract

The invention relates to a sluice traffic bridge deformation monitoring method, system and device and a medium, and relates to the technical field of bridge monitoring, and the method comprises the steps: building a sampling frequency model based on the driving condition of a bridge floor and the flowing water condition of a bridge bottom, so as to change the sampling frequency of a micrometer and a strain gauge; according to the third-level monitoring management benchmark, whether a sampling frequency model is adopted to change the sampling frequency of the micrometer and the strain gauge or not is judged; according to the set sampling frequency, acquiring the displacement change of the micrometer and the stress change of the strain gauge at each measurement site in real time; and performing data analysis and processing on the displacement change and the stress change through an M-Cloud monitoring production management system to obtain a displacement value and a stress value. The state of the sluice traffic bridge can be fed back in a targeted manner by monitoring the surrounding condition of the sluice traffic bridge, the deformation trend of the sluice traffic bridge is predicted, the safety of the sluice traffic bridge is improved, and the safety of embankment flood control is ensured.

Description

Technical Field

[0001] The present application relates to the technical field of bridge detection, and in particular to a method, system, equipment and medium for monitoring deformation of a sluice traffic bridge. Background Art

[0002] Since many social vehicles travel on the sluice bridge, when the bridge is deformed too much, the bridge deck will be in a fatigue state. In serious cases, there will be dangers of structural damage, bridge deck shedding, and bridge collapse, which will seriously affect the flood control safety of the embankment. Therefore, it is necessary to implement deformation monitoring and analysis on the sluice bridge to effectively realize the health monitoring and operation of the bridge.

[0003] At present, with the development of the Internet of Things, big data and artificial intelligence technologies, some intelligent monitoring technologies have begun to be applied to the safety monitoring of water gate traffic bridges. For example, by installing a sensor network, the displacement, stress, vibration and other parameters of the bridge can be monitored in real time. Combined with intelligent analysis algorithms, abnormal changes in the bridge structure can be discovered in time and potential safety risks can be predicted.

[0004] The shortcomings of the existing technology are that the existing technical means for monitoring the water conditions of the sluice bridge are relatively limited, especially under extreme weather conditions such as floods. How to timely feedback and predict the deformation of the sluice bridge to ensure the safety of the sluice bridge and the flood control safety of the embankment is still an urgent problem to be solved. In addition, the existing monitoring system often uses a fixed time interval to adjust the monitoring frequency, which may miss key monitoring data and affect the accuracy and timeliness of monitoring. Summary of the invention

[0005] The first purpose of this application is to provide a deformation monitoring method for a sluice traffic bridge, which can provide targeted feedback on the status of the sluice traffic bridge by monitoring the surrounding conditions of the sluice traffic bridge, and predict the deformation trend of the sluice traffic bridge, thereby improving the safety of the sluice traffic bridge and ensuring the safety of embankment flood control.

[0006] In the first aspect, the present application provides a method for monitoring deformation of a sluice bridge, which adopts the following technical solution: Establish a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and strain gauge; According to the three-level monitoring and management benchmark, determine whether to use the sampling frequency model to change the sampling frequency of the micrometer and the strain gauge. If so, change the sampling frequency of the micrometer and the strain gauge according to the sampling frequency model. If not, change the sampling frequency of the micrometer and the strain gauge according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified in the three-level monitoring and management benchmark. According to the set sampling frequency, the displacement change of the micrometer and the stress change of the strain gauge at each measuring location are obtained in real time; Performing data analysis and processing on the displacement changes and stress changes through the M-Cloud monitoring production management system to obtain displacement values ​​and stress values; Determine whether the displacement value at the current measurement location exceeds the safe displacement threshold. If so, issue a displacement abnormality warning. Determine whether the stress value at the current measurement location exceeds the safe strain threshold. If so, issue a strain abnormality warning.

[0007] By adopting the above technical solution, the sampling frequency of the micrometer and strain gauge is set according to the driving conditions on the bridge deck and the water flow conditions under the bridge. Taking into account the impact of the surrounding environment on the bridge, and under the constraints of the preset three-level monitoring management benchmark, the sampling frequency of the micrometer and strain gauge is dynamically adjusted. When the bridge is prone to excessive deformation, the micrometer and strain gauge can increase the number of monitoring of displacement changes and stress changes in a targeted manner, reduce the omission of key monitoring nodes, and make the M-Cloud monitoring production management system more reliable in processing data on displacement changes and stress changes, thereby being able to more accurately predict the deformation trend of the sluice traffic bridge, improve the safety of the sluice traffic bridge, and ensure the safety of the embankment flood control.

[0008] In a preferred example, the present application can be further configured as follows: the step of establishing a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and the strain gauge includes: Get the current water level, water flow speed and vehicle flow; Substitute the water level, water flow velocity and vehicle flow into a preset formula to obtain the sampling frequency model, wherein the preset formula is: F = aH + bV + cT + d; In the formula, F is the sampling frequency; H is the water level; V is the water flow velocity; T is the vehicle flow; a is the linear influence coefficient of the water level on the sampling frequency; b is the linear influence coefficient of the water flow velocity on the sampling frequency; c is the linear influence coefficient of the vehicle flow on the sampling frequency; d is a constant term, representing the basic value of the sampling frequency when H, V, and T are all zero.

[0009] By adopting the above technical scheme, the water level, water flow velocity and vehicle volume are positively correlated with the sampling frequency. The sampling frequency is adjusted according to three situations around the sluice traffic bridge to target the situation where the risk of deformation of the sluice traffic bridge is higher under the influence of the surrounding environment.

[0010] In a preferred example, the present application can be further configured as follows: According to the three - level monitoring management benchmark, it is judged whether to adopt the sampling frequency model to change the sampling frequencies of the micrometer and the strain gauge. If so, the sampling frequencies of the micrometer and the strain gauge are changed according to the sampling frequency model. If not, according to the first - level enhanced sampling frequency or the second - level enhanced sampling frequency specified by the three - level monitoring management benchmark, the steps of changing the sampling frequencies of the micrometer and the strain gauge include: Establish a three - level monitoring management benchmark, where the three - level monitoring management benchmark includes the allowable cumulative displacement value Uo; integrate the displacement values at several time nodes into the measured cumulative displacement value Un and import it into the three - level monitoring management benchmark for comparison with the allowable cumulative displacement value Uo; If Uo < Un / 3, it is regarded as entering the three - level management stage. When in the three - level management stage, the sampling frequencies of the micrometer and the strain gauge are adjusted using the sampling frequency model; If Un / 3 ≤ Uo ≤ Un2 / 3, it is regarded as entering the second - level management stage. When in the second - level management stage, an alarm signal for appropriately strengthening the support is generated, and the sampling frequencies of the micrometer and the strain gauge are adjusted using the second - level enhanced sampling frequency. If Uo > Un2 / 3, it is regarded as entering the first - level management stage. When in the first - level management stage, an alarm signal for strengthening the support or taking maintenance measures is generated, and the sampling frequencies of the micrometer and the strain gauge are adjusted using the first - level enhanced sampling frequency.

[0011] By adopting the above - mentioned technical solution, according to the three - level monitoring management benchmark, the magnitude of the displacement value automatically adjusts the monitoring strategy according to each different management stage, realizing refined management according to the displacement change of the sluice traffic bridge, and then accurately monitoring the state of the sluice traffic bridge. Corresponding alarm signals are generated at different management stages, which can timely remind the management personnel to take appropriate strengthening support measures or maintenance measures.

[0012] In a preferred example, the present application can be further configured as follows: After the step of analyzing and processing the displacement change and stress change through the M - Cloud monitoring production management system to obtain the displacement value and stress value, it further includes: drawing scatter plots of several of the displacement values and several of the stress values; According to the data distribution condition of the scatter plot, perform regression analysis on several of the displacement values and several of the stress values in the scatter plot to predict the maximum displacement value and maximum stress value at the next time node.

[0013] By adopting the above technical solution, drawing a scatter plot of displacement values ​​and stress values ​​can intuitively display the distribution of monitoring data, provide a clear visual reference for subsequent regression analysis, and help managers quickly identify data patterns and anomalies, improve the efficiency and accuracy of data analysis, and predict the maximum displacement value and maximum stress value at the next time node through regression analysis, so as to predict the potential deformation trend of the sluice traffic bridge in advance.

[0014] In a preferred example, the present application may be further configured as follows: in the step of acquiring the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency, the step of acquiring the stress change includes: The stress changes at the mid-span section, 1 / 4 span section, and 3 / 4 span section of each bridge are obtained according to the sampling frequency calculated by the sampling frequency model, or the first-level enhanced sampling frequency, or the second-level enhanced sampling frequency.

[0015] By adopting the above-mentioned technical solution, the stress changes in key parts such as the mid-span section, 1 / 4 span section, and 3 / 4 span section are monitored in focus. These parts are usually areas of stress concentration and deformation sensitivity in the bridge structure. By focusing on monitoring key parts, the structural safety and stability of the sluice traffic bridge can be evaluated more accurately.

[0016] In a preferred example, the present application may be further configured as follows: in the step of acquiring the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency, the step of acquiring the displacement change includes: The displacement change is obtained through the capacitance change between the moving grid and the receiving electrode of two capacitive grid micrometers installed on the bridge, wherein the capacitance change is proportional to the displacement change of the bridge.

[0017] By adopting the above technical solution, the capacitive gate micrometer obtains displacement data by detecting the capacitance change between the moving gate and the receiving electrode, and measures the displacement change of the sluice traffic bridge in real time and with high precision, accurately reflecting the tiny displacement change of the bridge, and providing a reliable basis for subsequent data analysis and prediction.

[0018] In a preferred example, the present application may be further configured as follows: after the steps of determining whether the displacement value at the current measurement location exceeds the safe displacement threshold, and if so, issuing a displacement abnormality warning, and determining whether the stress value at the current measurement location exceeds the safe strain threshold, and if so, issuing a strain abnormality warning, the method may further include: The production management system is monitored through M-Cloud, which adopts a distributed architecture and displays real-time dynamic data and graphs of the displacement and stress values ​​at the same time; If the displacement value at a certain measurement location exceeds the safe displacement threshold, and / or the stress value at a certain measurement location exceeds the safe strain threshold, an early warning SMS message of abnormal displacement and / or abnormal strain at the measurement location is sent to a predetermined mobile phone.

[0019] By adopting the above technical solutions and a distributed architecture, the M-Cloud monitoring and production management system can simultaneously display the dynamic data and graphs of displacement and stress values ​​in real time. When the displacement value exceeds the safe displacement threshold or the stress value exceeds the safe strain threshold, it can promptly detect potential problems and deformation trends of the bridge and automatically send early warning text messages to the designated mobile phones, ensuring that relevant personnel can respond quickly and take necessary preventive measures to effectively improve the safety and flood control capabilities of the sluice traffic bridge.

[0020] In the second aspect, the present application provides a water gate traffic bridge deformation monitoring system, which adopts the following technical solution: A water gate traffic bridge deformation monitoring system, comprising: Model building module: The model building module is used to build a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and the strain gauge; Sampling frequency determination module: The sampling frequency determination module is used to determine whether to use the sampling frequency model to change the sampling frequency of the micrometer and the strain gauge according to the three-level monitoring management benchmark. If it is used, the sampling frequency of the micrometer and the strain gauge is changed according to the sampling frequency model. If it is not used, the sampling frequency of the micrometer and the strain gauge is changed according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified in the three-level monitoring management benchmark; Data acquisition module: the data acquisition module is used to obtain the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency; Data analysis module: The data analysis module is used to perform data analysis and processing on the displacement change and stress change through the M-Cloud monitoring production management system to obtain displacement values ​​and stress values; Abnormal warning module: The abnormal warning module is used to determine whether the displacement value of the current measurement location exceeds the safe displacement threshold. If so, a displacement abnormality warning is issued. It is also used to determine whether the stress value of the current measurement location exceeds the safe strain threshold. If so, a strain abnormality warning is issued.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for monitoring deformation of a sluice bridge are implemented.

[0022] In a fourth aspect, the present application provides a computer storage medium, the following technical solution: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for monitoring deformation of a sluice traffic bridge are implemented.

[0023] In summary, this application has the following beneficial technical effects: This application establishes a sampling frequency model based on micrometers and strain gauges according to the traffic conditions on the bridge deck and the water flow conditions under the bridge. Under the constraints of the three-level monitoring and management benchmark, it adds consideration of the impact of the surrounding environment on the deformation of the bridge, and dynamically adjusts the sampling frequencies of the micrometers and strain gauges. When the sluice traffic bridge is at a critical node where deformation may occur, it can increase the monitoring frequency of displacement changes and stress changes in a targeted manner, thereby reducing the risk of missing key monitoring points. The displacement changes and stress changes are analyzed through the M-Cloud monitoring production management system, and the deformation trend of the sluice traffic bridge is predicted, thereby improving the safety of the sluice traffic bridge and strengthening the flood control safety of the embankment. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of a method for monitoring deformation of a sluice traffic bridge in one embodiment of the present application.

[0025] Figure 2 It is a sub-step flow chart of step S1 in one embodiment of the present application.

[0026] Figure 3 It is a sub-step flow chart of step S2 in one embodiment of the present application.

[0027] Figure 4 This is a flowchart of steps added after step S4 in one of the embodiments of the present application.

[0028] Figure 5 This is a sub-step flowchart of obtaining stress changes in step S3 in one embodiment of the present application.

[0029] Figure 6 This is a sub-step flowchart of obtaining the displacement change in step S3 in one embodiment of the present application.

[0030] Figure 7 This is a flowchart of steps added after step S5 in one of the embodiments of the present application.

[0031] Figure 8 It is a structural schematic diagram of a water gate traffic bridge deformation monitoring system according to one embodiment of the present application.

[0032] Fig. 9It is a principle block diagram of an electronic device in one embodiment of the present application.

[0033] Figure numerals: 1. Model building module; 2. Sampling frequency determination module; 3. Data acquisition module; 4. Data analysis module; 5. Abnormal warning module. DETAILED DESCRIPTION

[0034] The following is combined with Figure 1-9 This application is described in further detail.

[0035] refer to Figure 1 , a method for monitoring deformation of a sluice bridge, specifically comprising: S1. Establish a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and strain gauge.

[0036] Specifically, when the traffic volume on the bridge deck is large, and / or the water flow under the bridge is turbulent and the water level rises, more intensive and detailed displacement change and stress change data can be obtained, thereby improving the accuracy and efficiency of monitoring, while reducing data collection during non-critical periods, reducing energy consumption and data processing costs.

[0037] S2. According to the three-level monitoring and management benchmark, determine whether to use the sampling frequency model to change the sampling frequency of the micrometer and strain gauge. If adopted, change the sampling frequency of the micrometer and strain gauge according to the sampling frequency model. If not adopted, change the sampling frequency of the micrometer and strain gauge according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified in the three-level monitoring and management benchmark.

[0038] Specifically, the three-level monitoring and management benchmark is divided into the first-level management stage, the second-level management stage and the third-level management stage. When in the first-level management stage, the micrometer and strain gauge normally change the sampling frequency according to the sampling frequency model, while in the second-level management stage and the third-level management stage, the displacement changes and stress changes at this time are beyond the control of the sampling monitoring model. Therefore, it is necessary to strengthen the support and monitor the displacement and stress changes of the bridge through a higher enhanced sampling frequency. The first-level enhanced sampling frequency is greater than the second-level enhanced sampling frequency.

[0039] S3. According to the set sampling frequency, the displacement change of the micrometer and the stress change of the strain gauge at each measurement location are obtained in real time.

[0040] S4. Use the M-Cloud monitoring production management system to analyze and process the displacement changes and stress changes to obtain the displacement values ​​and stress values.

[0041] S5. Determine whether the displacement value at the current measurement location exceeds the safe displacement threshold. If so, issue a displacement abnormality warning. Determine whether the stress value at the current measurement location exceeds the safe strain threshold. If so, issue a strain abnormality warning.

[0042] In this embodiment, the sampling frequencies of the micrometer and strain gauge are changed based on the traffic conditions of the bridge, the water flow conditions under the bridge, and the possible impact of the surrounding environment on the bridge. Under the constraints of the three-level monitoring management benchmark, the sampling frequency is dynamically adjusted. When the bridge faces the risk of deformation, the monitoring frequency of displacement changes and stress changes is increased to reduce the occurrence of key monitoring nodes being missed.

[0043] refer to Figure 2 Further, in one embodiment, step S1 is refined into the following sub-steps: S10, obtaining the current water level, water flow velocity and vehicle flow.

[0044] Specifically, it can be monitored through various sensors, or image recognition of the current water level, water flow speed and vehicle volume can be performed through video surveillance, and then the corresponding data can be obtained using machine learning and other methods.

[0045] S11. Substitute the water level, water flow velocity and vehicle flow into a preset formula to obtain a sampling frequency model, wherein the preset formula is: F = aH + bV + cT + d; In the formula, F is the sampling frequency; H is the water level; V is the water flow velocity; T is the vehicle flow; a is the linear influence coefficient of the water level on the sampling frequency; b is the linear influence coefficient of the water flow velocity on the sampling frequency; c is the linear influence coefficient of the vehicle flow on the sampling frequency; d is a constant term, representing the basic value of the sampling frequency when H, V, and T are all zero.

[0046] In this embodiment, the water level, water flow velocity and vehicle volume are all positively correlated with the sampling frequency, that is, the higher the water level, or the faster the water flow velocity, or the greater the vehicle volume, the greater the possibility of bridge deformation and structural damage, and the higher the data sampling frequency of the micrometer and strain gauge required, and the displacement change and stress change data obtained are more critical and reliable, so as to address situations where the risk of bridge deformation is higher under the influence of the surrounding environment.

[0047] In addition, reference Figure 3 Further, in one embodiment, step S2 is refined into the following sub-steps: S20. Establish a three-level monitoring and management benchmark, which includes an allowable cumulative displacement value Uo; integrate the displacement values ​​of several time nodes into the measured cumulative displacement value Un, import it into the three-level monitoring and management benchmark, and compare it with the allowable cumulative displacement value Uo.

[0048] S21. If Uo < Un / 3, it is regarded as entering the third-level management stage. When in the third-level management stage, the sampling frequencies of the micrometer and the strain gauge are adjusted using the sampling frequency model.

[0049] S22. If Un / 3 ≤ Uo ≤ Un2 / 3, it is regarded as entering the second-level management stage. When in the second-level management stage, an alarm signal for appropriately strengthening the support is generated, and the sampling frequencies of the micrometer and the strain gauge are adjusted using the second-level enhanced sampling frequency.

[0050] S23. If Uo > Un2 / 3, it is regarded as entering the first-level management stage. When in the first-level management stage, an alarm signal for strengthening the support or taking maintenance measures is generated, and the sampling frequencies of the micrometer and the strain gauge are adjusted using the first-level enhanced sampling frequency.

[0051] In this embodiment, according to the third-level monitoring management criterion, the monitoring frequency can be selected according to the management stage where the monitoring result is located: In the third-level management stage, the monitoring frequency is adjusted according to the sampling frequency model. At this time, the bridge is in a normal state, the displacement change is small, and the deformation risk is also small; in the second-level management stage, attention should be paid to increasing the monitoring frequency, and the micrometer is monitored using the preset higher-frequency second-level enhanced sampling frequency; in the first-level management stage, attention should be paid closely to the deformation situation of the bridge, and the micrometer and the strain gauge need to use the first-level enhanced sampling frequency with the highest sampling frequency to strengthen the monitoring.

[0052] Through the third-level monitoring management criterion, the displacement change of the sluice traffic bridge is managed in a refined manner, and then the accurate monitoring of the state of the sluice traffic bridge is realized. The corresponding alarm signals generated in different management stages can timely remind the management personnel to take appropriate strengthening support measures or maintenance measures.

[0053] In addition, referring to Figure 4 , further, in one embodiment, after step S4, there are further steps S40 and S41: S40. Draw scatter plots of a number of displacement values and a number of stress values.

[0054] Specifically, the scatter plot can use the displacement value and the stress value as points on the horizontal axis of the coordinate axis, and the time node as the point on the vertical axis of the coordinate axis. Each point represents the displacement value and stress value data of a measurement location at a specific time point. The scatter plot can visually display the distribution of the displacement value and the stress value, help the management personnel use the visual scatter plot to quickly identify patterns, trends and abnormal points in the data, and can initially judge the linear relationship or non-linear relationship of the data, providing an intuitive basis for subsequent regression analysis.

[0055] S41. According to the data distribution of the scatter plot, a regression analysis is performed on a number of displacement values ​​and a number of stress values ​​in the scatter plot to predict the maximum displacement value and the maximum stress value at the next time node.

[0056] Specifically, regression analysis methods, such as linear regression, polynomial regression or nonlinear regression, are used to model the relationship between displacement values ​​and stress values, and regression analysis is performed based on the modeling. The best mathematical model is fitted based on historical data to predict the displacement and stress values ​​of each measuring location at future time nodes, with special attention paid to the predicted maximum displacement and maximum stress values ​​to assess the potential risk of deformation of the bridge structure.

[0057] In this embodiment, drawing a scatter plot of displacement values ​​and stress values ​​can show the distribution of monitoring data, thereby helping managers to quickly identify patterns and anomalies in the data, thereby improving the efficiency and accuracy of data analysis. At the same time, by predicting the maximum displacement value and maximum stress value of each measurement location at the next time node through regression analysis, possible deformation trends of the sluice traffic bridge can be discovered in advance.

[0058] In addition, reference Figure 5 Further, in one embodiment, in step S3, the step of obtaining stress changes is refined into the following sub-steps: S30. Obtain stress changes at the mid-span section, 1 / 4 span section, and 3 / 4 span section of each bridge according to the sampling frequency calculated by the sampling frequency model, or the first-level enhanced sampling frequency, or the second-level enhanced sampling frequency.

[0059] In this embodiment, the stress distribution of the bridge is not uniform. The mid-span section of the bridge usually bears the largest bending moment and shear force, while the 1 / 4 span and 3 / 4 span sections may experience large torque and shear force changes. Therefore, these sections are the places where the stress in the bridge structure is most concentrated and complex, and have a key impact on the overall safety and stability of the bridge. By focusing on monitoring the key parts of the bridge, the structural safety and stability of the sluice traffic bridge can be more accurately evaluated.

[0060] In addition, reference Figure 6 Further, in one embodiment, in step S3, the step of obtaining the displacement change is refined into the following sub-steps: S31. Obtain displacement change through the capacitance change between the moving grid and the receiving electrode of two capacitive grid micrometers installed on the bridge, wherein the capacitance change is proportional to the displacement change of the bridge.

[0061] In this embodiment, the CW-141 micrometer is used to monitor the displacement of the bridge deck of the traffic bridge, and two micrometers are arranged on each traffic bridge for synchronous observation. The CW-141 micrometer is a precision capacitive sensor, whose accuracy is not affected by temperature and is better than that of an inductive sensor. It has an IP67 waterproof rating, a non-contact plate sensor, and a guide structure. It can measure repeatedly up to 10 million times and is suitable for vertical displacement monitoring between multiple points in various industrial production, construction engineering, mechanical structures, etc., and for long-term high-frequency monitoring.

[0062] Specifically, the capacitive grid micrometer obtains displacement data by detecting the capacitance change between the moving grid and the receiving electrode, realizes real-time, high-precision displacement change measurement of the sluice traffic bridge, accurately reflects the small displacement changes of the bridge, and provides a reliable basis for subsequent data analysis and prediction. In this embodiment, the main part of the micrometer system is installed on one side of the bridge deck, and the bottom is fixed with a steel frame structure extending from the pier foundation. The PLC sensor communication module is used to automatically measure the unit to collect data, and is connected to the M-Cloud monitoring production management system in the server through 4G or 5G communication, thereby realizing automated data observation.

[0063] In addition, reference Figure 7 Further, in one embodiment, after step S5, steps S50 and S51 are added: S50, through M-Cloud monitoring production management system, adopts distributed architecture, and displays real-time dynamic data and graphs of displacement and stress values ​​at the same time.

[0064] S51. If the displacement value of a certain measurement location exceeds the safe displacement threshold, and / or the stress value of a certain measurement location exceeds the safe strain threshold, a warning SMS message of abnormal displacement and / or abnormal strain of the measurement location is sent to a predetermined mobile phone.

[0065] In this embodiment, a distributed architecture is adopted, and the M-Cloud monitoring production management system can simultaneously display the dynamic data and graphs of displacement values ​​and stress values ​​in real time, ensuring the real-time synchronization and rapid update of displacement values ​​and strain value data at multiple points, and maintaining the real-time and accuracy of the data even with high data volumes.

[0066] When the displacement value of the bridge exceeds the set safe displacement threshold, or the stress value exceeds the safe strain threshold, the potential hidden dangers and deformation trends of the bridge can be quickly identified.

[0067] By automatically sending alarm information to preset mobile phones, relevant personnel can be informed of abnormal conditions in real time, so as to quickly initiate corresponding emergency plans and implement necessary safety precautions, thereby improving the safety of the sluice traffic bridge, while enhancing the ability to resist flood risks and ensuring the stability and reliability of the bridge under extreme conditions.

[0068] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0069] The embodiment of the present application also provides a water gate traffic bridge deformation monitoring system, and the water gate traffic bridge deformation monitoring system corresponds one to one with the water gate traffic bridge deformation monitoring method in the embodiment.

[0070] refer to Figure 8 A water gate traffic bridge deformation monitoring system includes: a model building module 1, a sampling frequency determination module 2, a data acquisition module 3, a data analysis module 4, and an abnormal warning module 5. The detailed description of each functional module is as follows: Model building module 1: Model building module 1 is used to establish a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and the strain gauge; Sampling frequency determination module 2: Sampling frequency determination module 2 is used to determine whether to use the sampling frequency model to change the sampling frequency of the micrometer and the strain gauge according to the three-level monitoring management benchmark. If it is used, the sampling frequency of the micrometer and the strain gauge is changed according to the sampling frequency model. If it is not used, the sampling frequency of the micrometer and the strain gauge is changed according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified in the three-level monitoring management benchmark; Data acquisition module 3: Data acquisition module 3 is used to obtain the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency; Data analysis module 4: Data analysis module 4 is used to perform data analysis and processing on displacement changes and stress changes through the M-Cloud monitoring production management system to obtain displacement values ​​and stress values; Abnormal warning module 5: Abnormal warning module 5 is used to determine whether the displacement value of the current measurement location exceeds the safe displacement threshold. If so, a displacement abnormality warning is issued. It is also used to determine whether the stress value of the current measurement location exceeds the safe strain threshold. If so, a strain abnormality warning is issued.

[0071] Among them, the model building module 1 dynamically adjusts the sampling frequency according to the driving conditions and water flow conditions to optimize the efficiency and accuracy of data collection; the sampling frequency determination module 2 dynamically selects whether to apply the sampling frequency model or other enhanced sampling frequencies according to the three-level monitoring management benchmark to ensure that the sampling frequencies of the micrometer and strain gauge can be flexibly adjusted under different monitoring requirements; the data acquisition module 3 obtains the displacement change and stress change data in real time according to the set sampling frequency to provide a basis for subsequent data analysis; the data analysis module 4 uses the M-Cloud monitoring production management system to conduct in-depth analysis of the collected data and extract the displacement and stress values ​​of key nodes; the abnormal warning module 5 monitors the analysis results in real time. If the displacement value or stress value exceeds the preset safety threshold, an early warning is triggered immediately, so that the sluice traffic bridge can operate safely and improve the safety of embankment flood control. This system integrates dynamic monitoring, data analysis and instant early warning to form a closed-loop monitoring and early warning system, which effectively improves the management efficiency and safety of key nodes of the sluice traffic bridge.

[0072] For the specific definition of the sluice bridge deformation monitoring system, please refer to the definition of the sluice bridge deformation monitoring method in the context, which will not be repeated here. Each module in the above-mentioned sluice bridge deformation monitoring system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. In one embodiment, an electronic device is provided, which is a user terminal. Reference Fig. 9 , the electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store a detection data table. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for monitoring deformation of a sluice traffic bridge is implemented.

[0073] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: S1. Establish a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequency of the micrometer and strain gauge.

[0074] S2. According to the three - level monitoring management criteria, determine whether to change the sampling frequencies of the micrometer and the strain gauge using the sampling frequency model. If so, change the sampling frequencies of the micrometer and the strain gauge according to the sampling frequency model. If not, change the sampling frequencies of the micrometer and the strain gauge according to the primary enhanced sampling frequency or the secondary enhanced sampling frequency specified in the three - level monitoring management criteria.

[0075] S3. According to the set sampling frequencies, obtain the displacement changes of the micrometer and the stress changes of the strain gauge at each measurement location in real - time.

[0076] S4. Through the M - Cloud monitoring production management system, perform data analysis and processing on the displacement changes and stress changes to obtain the displacement values and stress values.

[0077] S5. Determine whether the displacement value at the current measurement location exceeds the safe displacement threshold. If so, issue a displacement anomaly warning. Determine whether the stress value at the current measurement location exceeds the safe strain threshold. If so, issue a strain anomaly warning.

[0078] In one embodiment, the refined sub - steps of step S1 include: S10. Obtain the current water level height, water flow velocity, and traffic flow. S11. Substitute the water level height, water flow velocity, and traffic flow into a preset formula to obtain the sampling frequency model. Among them, the preset formula is: F = aH + bV + cT + d; In the formula, F is the sampling frequency; H is the water level height; V is the water flow velocity; T is the traffic flow; a is the linear influence coefficient of the water level height on the sampling frequency; b is the linear influence coefficient of the water flow velocity on the sampling frequency; c is the linear influence coefficient of the traffic flow on the sampling frequency; d is the constant term, representing the sampling frequency base value when H, V, and T are all zero.

[0079] In one embodiment, the refined sub - steps of step S2 include: S21. If Uo < Un / 3, it is considered to enter the three - level management stage. When in the three - level management stage, use the sampling frequency model to adjust the sampling frequencies of the micrometer and the strain gauge.

[0080] S22. If Un / 3 ≤ Uo ≤ Un2 / 3, it is considered to enter the two - level management stage. When in the two - level management stage, generate an alarm signal for appropriately strengthening the support, and use the secondary enhanced sampling frequency to adjust the sampling frequencies of the micrometer and the strain gauge.

[0081] S23. If Uo>Un2 / 3, it is regarded as entering the first-level management stage. When in the first-level management stage, an alarm signal for strengthening support or taking maintenance measures is generated, and the sampling frequency of the micrometer and strain gauge is adjusted using the first-level enhanced sampling frequency.

[0082] In one embodiment, the steps added after step S4 include: S40, drawing a scatter plot of several displacement values ​​and several stress values.

[0083] S41. According to the data distribution of the scatter plot, a regression analysis is performed on a number of displacement values ​​and a number of stress values ​​in the scatter plot to predict the maximum displacement value and the maximum stress value at the next time node.

[0084] In one embodiment, in step S3, the sub-steps of refining the stress change acquisition step include: S30. Obtain stress changes at the mid-span section, 1 / 4 span section, and 3 / 4 span section of each bridge according to the sampling frequency calculated by the sampling frequency model, or the first-level enhanced sampling frequency, or the second-level enhanced sampling frequency.

[0085] In one embodiment, in step S3, the sub-steps of refining the stress change acquisition step include: S31. Obtain displacement change through the capacitance change between the moving grid and the receiving electrode of two capacitive grid micrometers installed on the bridge, wherein the capacitance change is proportional to the displacement change of the bridge.

[0086] In one embodiment, the steps added after step S5 include: S50, through M-Cloud monitoring production management system, adopts distributed architecture, and displays real-time dynamic data and graphs of displacement and stress values ​​at the same time.

[0087] S51. If the displacement value of a certain measurement location exceeds the safe displacement threshold, and / or the stress value of a certain measurement location exceeds the safe strain threshold, a warning SMS message of abnormal displacement and / or abnormal strain of the measurement location is sent to a predetermined mobile phone.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0089] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A method for monitoring deformation of a sluice bridge, characterized in that: Including: Establish a sampling frequency model based on the driving conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequencies of the micrometer and the strain gauge; According to the three-level monitoring management criteria, determine whether to use the sampling frequency model to change the sampling frequencies of the micrometer and the strain gauge. If so, change the sampling frequencies of the micrometer and the strain gauge according to the sampling frequency model. If not, change the sampling frequencies of the micrometer and the strain gauge according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified by the three-level monitoring management criteria; According to the set sampling frequency, obtain the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time; Perform data analysis and processing on the displacement change and the stress change through the M-Cloud monitoring production management system to obtain the displacement value and the stress value; Judge whether the displacement value at the current measurement location exceeds the safe displacement threshold. If so, issue a displacement anomaly warning. Judge whether the stress value at the current measurement location exceeds the safe strain threshold. If so, issue a strain anomaly warning.

2. The method according to claim 1, characterized in that The step of establishing a sampling frequency model based on the driving conditions on the bridge deck and the water flow conditions under the bridge to change the sampling frequencies of the micrometer and the strain gauge includes: Obtain the current water level height, water flow velocity, and traffic flow; Substitute the water level height, water flow velocity, and traffic flow into a preset formula to obtain the sampling frequency model, where the preset formula is: F = aH + bV + cT + d; In the formula, F is the sampling frequency; H is the water level height; V is the water flow velocity; T is the traffic flow; a is the linear influence coefficient of the water level height on the sampling frequency; b is the linear influence coefficient of the water flow velocity on the sampling frequency; c is the linear influence coefficient of the traffic flow on the sampling frequency; d is a constant term representing the basic sampling frequency value when H, V, and T are all zero.

3. The method according to claim 2, characterized in that The step of, according to the three-level monitoring management criteria, determining whether to use the sampling frequency model to change the sampling frequencies of the micrometer and the strain gauge. If so, changing the sampling frequencies of the micrometer and the strain gauge according to the sampling frequency model. If not, changing the sampling frequencies of the micrometer and the strain gauge according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified by the three-level monitoring management criteria includes: Establish three-level monitoring management criteria, where the three-level monitoring management criteria include the allowable cumulative displacement value Uo; integrate the displacement values at several time nodes into the measured cumulative displacement value Un and import it into the three-level monitoring management criteria for comparison with the allowable cumulative displacement value Uo; If Uo < Un / 3, it is regarded as entering the three-level management stage. When in the three-level management stage, use the sampling frequency model to adjust the sampling frequencies of the micrometer and the strain gauge; If the Un / 3≤Uo≤Un2 / 3, it is regarded as entering the secondary management stage. When in the secondary management stage, an alarm signal for appropriate strengthening of support is generated, and the sampling frequency of the micrometer and strain gauge is adjusted using the secondary strengthened sampling frequency; if Uo>Un2 / 3, it is regarded as entering the primary management stage. When in the primary management stage, an alarm signal for strengthening support or taking maintenance measures is generated, and the sampling frequency of the micrometer and strain gauge is adjusted using the primary strengthened sampling frequency.

4. The method according to claim 1, characterized in that: After the step of performing data analysis and processing on the displacement change and stress change by using the M-Cloud monitoring production management system to obtain the displacement value and stress value, the method further includes: Draw a scatter plot of a number of the displacement values ​​and a number of the stress values; According to the data distribution of the scatter plot, a regression analysis is performed on the displacement values ​​and the stress values ​​in the scatter plot to predict the maximum displacement value and the maximum stress value at the next time node.

5. The method according to claim 1, characterized in that In the step of acquiring the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency, the step of acquiring the stress change includes: The stress changes at the mid-span section, 1 / 4 span section, and 3 / 4 span section of each bridge are obtained according to the sampling frequency calculated by the sampling frequency model, or the first-level enhanced sampling frequency, or the second-level enhanced sampling frequency.

6. The method according to claim 1, characterized in that In the step of acquiring the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency, the step of acquiring the displacement change includes: The displacement change is obtained through the capacitance change between the moving grid and the receiving electrode of two capacitive grid micrometers installed on the bridge, wherein the capacitance change is proportional to the displacement change of the bridge.

7. The method according to claim 1, characterized in that After the steps of determining whether the displacement value at the current measurement location exceeds the safe displacement threshold, and if so, issuing a displacement abnormality warning, and determining whether the stress value at the current measurement location exceeds the safe strain threshold, and if so, issuing a strain abnormality warning, the method further includes: The production management system is monitored through M-Cloud, which adopts a distributed architecture and displays real-time dynamic data and graphs of the displacement and stress values ​​at the same time; If the displacement value at a certain measurement location exceeds the safe displacement threshold, and / or the stress value at a certain measurement location exceeds the safe strain threshold, an early warning SMS message of abnormal displacement and / or abnormal strain at the measurement location is sent to a predetermined mobile phone.

8. A water gate traffic bridge deformation monitoring system, characterized in that: include: Model building module (1): the model building module (1) is used to build a sampling frequency model based on the traffic conditions on the bridge deck and the water flow conditions under the bridge, so as to change the sampling frequency of the micrometer and the strain gauge; Sampling frequency determination module (2): the sampling frequency determination module (2) is used to determine whether to use the sampling frequency model to change the sampling frequency of the micrometer and the strain gauge according to the three-level monitoring management benchmark; if it is used, the sampling frequency of the micrometer and the strain gauge is changed according to the sampling frequency model; if it is not used, the sampling frequency of the micrometer and the strain gauge is changed according to the first-level enhanced sampling frequency or the second-level enhanced sampling frequency specified in the three-level monitoring management benchmark; Data acquisition module (3): the data acquisition module (3) is used to obtain the displacement change of the micrometer and the stress change of the strain gauge at each measurement location in real time according to the set sampling frequency; Data analysis module (4): the data analysis module (4) is used to perform data analysis and processing on the displacement change and stress change through the M-Cloud monitoring production management system to obtain displacement values ​​and stress values; Abnormal warning module (5): The abnormal warning module (5) is used to determine whether the displacement value at the current measurement location exceeds the safe displacement threshold, and if so, issue a displacement abnormality warning, and determine whether the stress value at the current measurement location exceeds the safe strain threshold, and if so, issue a strain abnormality warning.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a method for monitoring deformation of a sluice bridge as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer program is stored which can be loaded by a processor and execute the method for monitoring deformation of a sluice bridge as claimed in any one of claims 1 to 7.

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