A dynamic monitoring method and system for the service condition of bridge piers

Abstract

This application provides a dynamic monitoring method and system for the service status of bridge piers, relating to the field of bridge inspection technology. The monitoring method includes: acquiring first acceleration sensing time-series data and second acceleration sensing time-series data of the bridge pier to be inspected; performing data preprocessing on the first and second acceleration sensing time-series data to obtain first preprocessed acceleration data and second preprocessed acceleration data; generating a cross-correlation function based on the first and second acceleration preprocessed data; determining time offset data based on the cross-correlation function; performing offset processing on the first and second acceleration preprocessed data based on the time offset data to obtain first offset acceleration data and second offset processed acceleration data; and obtaining a health status assessment result of the bridge pier to be inspected based on the first offset acceleration data and the second offset processed acceleration data.

Description

Technical Field

[0001] This application relates to the field of bridge inspection technology, and more specifically, to a dynamic monitoring method and system for the service condition of bridge piers. Background Technology

[0002] With the rapid development of highways and high-speed railways, a large number of viaducts have been built across the country. The safety of the bridge substructure (piers) is of paramount importance in bridge technology; therefore, monitoring the health status of completed bridges is a necessary measure to ensure their safety and stability. Piers subjected to impacts, erosion, and other factors are at risk of collapse, jeopardizing the safety and durability of the bridge.

[0003] Generally, bridge piers are the weakest points in small-to-medium span bridges. Most non-critical bridges require low-cost, automated observation and monitoring methods to ensure the safety of the bridge substructure. In the prior art, patent document CN115828393B discloses a bridge information management method, system, electronic equipment, and medium. Based on the structural parameters of corrugated steel web bridges, including bridge elevation data, bridge material, prestressed steel strand data, duct friction data, and bridge temperature data, an initial finite element model is established to determine the simulated stress data set for the corrugated steel web bridge. The initial finite element model is then corrected based on the difference between the simulated stress data and the corresponding measured stress data to obtain an optimized finite element model. This optimized model closely reflects the actual bridge condition, ensuring the accuracy of further evaluation data. Based on the optimized finite element model and a preset bridge load-bearing early warning model, early warning and damage assessment are performed on the corrugated steel web bridge to obtain the early warning assessment results.

[0004] However, bridge piers are usually quite heavy and have a weak response, making it difficult to implement existing monitoring methods at low cost. Moreover, existing monitoring methods require modeling and complex modal analysis, involve a large number of sensors, make it difficult to reduce monitoring costs, and have problems such as untimely feedback of monitoring results, making it difficult to achieve vehicle early warning. Summary of the Invention

[0005] The purpose of this application is to provide a dynamic monitoring method, system, electronic device, and computer-readable storage medium for the service status of bridge piers, which can achieve the technical effects of reducing bridge inspection costs and improving bridge inspection efficiency.

[0006] Firstly, this application provides a dynamic monitoring method for the service status of bridge piers, including:

[0007] Acquire the first acceleration sensor time series data and the second acceleration sensor time series data of the bridge pier to be tested, wherein the bridge pier to be tested is provided with the first acceleration sensor and the second acceleration sensor at different locations;

[0008] Data preprocessing is performed based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data;

[0009] A cross-correlation function is generated based on the first acceleration preprocessing data and the second acceleration preprocessing data;

[0010] The time offset data is determined based on the cross-correlation function;

[0011] Based on the time offset data, the first acceleration preprocessed data and the second acceleration preprocessed data are offset to obtain the first acceleration offset data and the second acceleration offset processed data.

[0012] The health status assessment results of the bridge pier to be tested are obtained based on the first acceleration offset data and the second acceleration offset processed data.

[0013] In the above implementation process, the dynamic monitoring method for the service status of bridge piers involves setting up a first acceleration sensor and a second acceleration sensor at different locations on the pier to be tested. By acquiring and processing the time series data of the first and second acceleration sensors, time offset data is determined based on a cross-correlation function. Based on the offset-processed first and second acceleration offset data, the health status of the bridge can be monitored and evaluated, and the health status assessment result of the pier to be tested can be obtained. Therefore, this dynamic monitoring method for the service status of bridge piers only requires setting up two acceleration sensors on the pier to be tested, which can monitor the health status of the pier at low cost, and can achieve the technical effects of reducing bridge inspection costs and improving bridge inspection efficiency.

[0014] Further, the step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data includes:

[0015] The first acceleration sensing time series data and the second acceleration sensing time series data are subjected to mean removal processing to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

[0016] In the above implementation process, by performing mean-reduction processing on the first acceleration sensing time series data and the second acceleration sensing time series data, the influence of low-frequency components on the phase difference calculation can be effectively reduced, thereby improving the accuracy of the health status assessment results of the bridge pier under test.

[0017] Further, in the step of generating a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data, the cross-correlation function is:

[0018] ;

[0019] in, R xy ( τ ) represents the cross-correlation function. τ Indicates the delay time. x ( t () indicates the first acceleration preprocessing data. y ( t () indicates the second acceleration preprocessing data. N express x ( t )or y ( t The total number of data points. t Represents a time series.

[0020] Further, the step of determining the time offset data based on the cross-correlation function includes:

[0021] The maximum value is calculated based on the cross-correlation function to obtain the maximum delay time data corresponding to when the cross-correlation function is at its maximum value.

[0022] Phase difference data is determined based on the maximum delay time data;

[0023] The time offset data is determined based on the phase difference data.

[0024] Further, the step of obtaining the health status assessment result of the bridge pier to be detected based on the first acceleration offset data and the second acceleration offset processed data includes:

[0025] Based on the first acceleration offset data and the second acceleration offset processed data, ratio processing is performed to obtain ratio time series data;

[0026] Based on the preset ratio threshold and the ratio time series data, a significance statistical test is performed to obtain the health status assessment results of the bridge pier to be tested.

[0027] Furthermore, before the step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data, the method further includes:

[0028] The first acceleration sensing time series data and the second acceleration sensing time series data are processed by maximum value-minimum value to obtain the first difference data and the second difference data;

[0029] If the first difference data or the second difference data is greater than the preset difference threshold, then the step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first acceleration preprocessed data and the second acceleration preprocessed data is executed.

[0030] If both the first difference data and the second difference data are less than or equal to a preset difference threshold, then the health status assessment result of the bridge pier to be tested is obtained based on the first difference data and the second difference data.

[0031] In the above implementation process, the bridge health status assessment based on the first acceleration sensing time series data and the second acceleration sensing time series data is only performed when the difference between the maximum and minimum values ​​of a certain sensor signal exceeds the preset difference threshold. If the difference between the maximum and minimum values ​​of the sensor signals does not exceed the preset difference threshold, it indicates that the health status of the bridge pier under test is good, and the health status assessment result of the bridge pier under test can be directly obtained based on the first difference data and the second difference data, that is, its health status assessment result is determined to be good.

[0032] Furthermore, after obtaining the health status assessment result of the bridge pier to be detected based on the first acceleration offset data and the second acceleration offset processed data, the method further includes:

[0033] An alarm message is generated based on the health status assessment results;

[0034] The alarm information is transmitted to the audible and visual alarm device, which then issues an alarm and reminds passing vehicles to slow down or stop based on the alarm information.

[0035] Secondly, this application provides a dynamic monitoring system for the service status of bridge piers, comprising:

[0036] The acquisition module is used to acquire the first acceleration sensor time series data and the second acceleration sensor time series data of the bridge pier to be tested, wherein the bridge pier to be tested is provided with the first acceleration sensor and the second acceleration sensor at different positions respectively.

[0037] The preprocessing module is used to perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data;

[0038] The cross-correlation module is used to generate a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data;

[0039] The offset module is used to determine time offset data based on the cross-correlation function;

[0040] The offset processing module is used to perform offset processing on the first acceleration preprocessed data and the second acceleration preprocessed data according to the time offset data to obtain the first acceleration offset data and the second acceleration offset processed data.

[0041] The evaluation module is used to obtain the health status evaluation result of the bridge pier to be detected based on the first acceleration offset data and the second acceleration offset processing data.

[0042] Furthermore, the preprocessing module is specifically used for:

[0043] The first acceleration sensing time series data and the second acceleration sensing time series data are subjected to mean removal processing to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

[0044] Furthermore, the cross-correlation module is specifically used for:

[0045] The maximum value is calculated based on the cross-correlation function to obtain the maximum delay time data corresponding to when the cross-correlation function is at its maximum value.

[0046] Phase difference data is determined based on the maximum delay time data;

[0047] The time offset data is determined based on the phase difference data.

[0048] Furthermore, the evaluation module is specifically used for:

[0049] Based on the first acceleration offset data and the second acceleration offset processed data, ratio processing is performed to obtain ratio time series data;

[0050] Based on the preset ratio threshold and the ratio time series data, a significance statistical test is performed to obtain the health status assessment results of the bridge pier to be tested.

[0051] Furthermore, the dynamic monitoring system for the service status of the bridge piers also includes a difference module, which is specifically used for:

[0052] The difference data is obtained by performing maximum-minimum value processing on the first acceleration sensing time series data and the second acceleration sensing time series data;

[0053] If the difference data is greater than a preset difference threshold, then perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data.

[0054] If the difference data is less than or equal to a preset difference threshold, the health status assessment result of the bridge pier to be tested is obtained based on the difference data.

[0055] Thirdly, this application provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method described in any of the first aspects.

[0056] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described in any of the first aspects.

[0057] Fifthly, this application provides a computer program product that, when run on a computer, causes the computer to perform the method described in any of the first aspects.

[0058] Other features and advantages disclosed in this application will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the above-described technology disclosed in this application.

[0059] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a structural schematic diagram of a bridge provided in an embodiment of this application;

[0062] Figure 2 A flowchart illustrating a dynamic monitoring method for the service status of bridge piers provided in this application embodiment;

[0063] Figure 3A flowchart illustrating another method for dynamic monitoring of bridge pier service status provided in this application embodiment;

[0064] Figure 4 A structural block diagram of the dynamic monitoring system for the service status of bridge piers provided in the embodiments of this application;

[0065] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0066] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0067] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0068] This application provides a method, system, electronic device, and computer-readable storage medium for dynamic monitoring of the service status of bridge piers, which can be applied to the health status monitoring process of bridge piers. The method involves installing a first accelerometer and a second accelerometer at different locations on the pier under test. By acquiring and processing the time-series data of the first and second accelerometers, a time offset is determined based on a cross-correlation function. Based on the offset-processed first and second accelerometer offset data, the health status of the bridge can be monitored and evaluated, obtaining the health status assessment result of the pier under test. Therefore, this method only requires two accelerometers on the pier under test, enabling low-cost health status monitoring of bridge piers and achieving the technical effects of reducing bridge inspection costs and improving bridge inspection efficiency.

[0069] Please see Figure 1 , Figure 1 This is a structural schematic diagram of a bridge provided in an embodiment of this application; as shown below. Figure 1 As shown, the bridge includes a bridge deck 10 and multiple piers 11. The bottom of the multiple piers 11 is installed on the riverbed 20, and the bridge deck 10 is supported by the multiple piers 11. One of the piers 11 is selected as the pier to be tested, and a first acceleration sensor 31 and a second acceleration sensor 32 are installed on it. The vibration information of the pier to be tested is monitored by the first acceleration sensor 31 and the second acceleration sensor 32.

[0070] Optionally, two acceleration sensors can be installed on the bridge piers most susceptible to erosion, with the sensors positioned on the opposite side of the water flow to avoid direct erosion.

[0071] For example, after the bridge pier is scourted, the mechanical state changes and the vibration of the bridge pier intensifies, making the vibration of the lower part obvious; the information processing device 33 collects data from the acceleration sensors (first acceleration sensor 31 and second acceleration sensor 32) at intervals, and by analyzing and processing the data from the acceleration sensors, it can intelligently identify whether there is a risk of excessive scour of the bridge pier.

[0072] In some implementations, a first acceleration sensor 31 and a second acceleration sensor 32 are arranged at two third-thirds points of the pier to be tested; wherein, the third-thirds point refers to one-third of the length of the pier to be tested.

[0073] For example, the sensor data collected by the first acceleration sensor 31 and the second acceleration sensor 32 are transmitted to the information processing device 33 via wired or wireless means; after processing the information, the information processing device 33 can feed back the information to the bridge monitoring system.

[0074] Optionally, once the warning conditions are met, the information can be directly sent to the traffic safety information display system near the bridge, which can be used to warn vehicles to pay attention to safety.

[0075] Optionally, the accelerometer provided in this application embodiment is sensitive to low frequencies, and the high-frequency signals caused by the impact of river water do not contribute much to the signal amplitude of the sensor; in addition, the accelerometer is waterproof and is buried on the downstream side of the bridge pier on the water-facing side of the bridge to reduce the direct scouring of the water flow.

[0076] Optionally, accelerometers can be installed on multiple piers of a single bridge; if an accelerometer is installed on only one pier, that pier can be the one with the greatest water depth. See also... Figure 2 , Figure 2 This is a flowchart illustrating a dynamic monitoring method for the service status of bridge piers provided in an embodiment of this application. The dynamic monitoring method for the service status of bridge piers includes the following steps:

[0077] S100: Acquire the first acceleration sensor time series data and the second acceleration sensor time series data of the bridge pier to be tested, wherein the bridge pier to be tested is equipped with the first acceleration sensor and the second acceleration sensor at different locations.

[0078] For example, the first accelerometer and the second accelerometer can acquire signals at a preset frequency; in this embodiment, the sampling frequency of the first accelerometer and the second accelerometer is... f s ;

[0079] S200: Perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data;

[0080] For example, preprocessing the first acceleration sensing time series data and the second acceleration sensing time series data can reduce the impact of low-frequency components on phase difference calculation.

[0081] S300: Generate a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data;

[0082] S400: Determines time offset data based on cross-correlation function;

[0083] For example, the cross-correlation function can be used to represent the similarity between the first acceleration preprocessed data and the second acceleration preprocessed data at different delay times; thus, based on this similarity, the delay time corresponding to the maximum value of the cross-correlation function can be determined to determine the time offset data.

[0084] S500: Perform offset processing on the first acceleration preprocessed data and the second acceleration preprocessed data based on the time offset data to obtain the first acceleration offset data and the second acceleration offset processed data;

[0085] S600: Obtain the health status assessment results of the bridge pier to be inspected based on the first acceleration offset data and the second acceleration offset processing data.

[0086] In some embodiments, the dynamic monitoring method for the service status of bridge piers involves installing a first acceleration sensor and a second acceleration sensor at different locations on the pier to be inspected. By acquiring and processing the time-series data of the first and second acceleration sensors, a time offset is determined based on a cross-correlation function. Finally, based on the offset-processed first and second acceleration offset data, the health status of the bridge can be monitored and evaluated, yielding a health status assessment result for the pier to be inspected. Therefore, this dynamic monitoring method for the service status of bridge piers only requires the installation of two acceleration sensors on the pier to be inspected, enabling low-cost monitoring of the pier's health status and achieving the technical effects of reducing bridge inspection costs and improving bridge inspection efficiency.

[0087] Please see Figure 3 , Figure 3 A flowchart illustrating another method for dynamic monitoring of bridge pier service status provided in this application embodiment.

[0088] For example, S200: The step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data includes:

[0089] S210: Perform mean-removal processing on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

[0090] For example, by performing mean-reduction processing on the first acceleration sensing time series data and the second acceleration sensing time series data, the influence of low-frequency components on phase difference calculation can be effectively reduced, thereby improving the accuracy of the health status assessment results of the bridge pier under test.

[0091] For example, in the step of generating a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data, the cross-correlation function is:

[0092] ;

[0093] in, R xy ( τ ) represents the cross-correlation function. τ Indicates the delay time. x ( t () indicates the first acceleration preprocessing data. y ( t () indicates the second acceleration preprocessing data. N express x ( t )or y ( t The total number of data points. t Represents a time series.

[0094] For example, S400: The step of determining time offset data based on the cross-correlation function includes:

[0095] S410: Calculate the maximum value based on the cross-correlation function to obtain the maximum delay time data corresponding to the maximum value of the cross-correlation function;

[0096] S420: Determine phase difference data based on maximum delay time data;

[0097] S430: Determine the time offset data based on the phase difference data.

[0098] For example, S600: The step of obtaining the health status assessment result of the bridge pier to be detected based on the first acceleration offset data and the second acceleration offset processing data includes:

[0099] S610: Based on the first acceleration offset data and the second acceleration offset processed data, perform ratio processing to obtain ratio time series data;

[0100] S620: Based on the preset ratio threshold and ratio time series data, a significance statistical test is performed to obtain the health status assessment results of the bridge pier to be tested.

[0101] For example, before step S200: performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data, the method further includes:

[0102] S110: Perform maximum-minimum value processing on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first difference data and the second difference data;

[0103] S120: Determine whether the first difference data or the second difference data is greater than the preset difference threshold;

[0104] If the first difference data or the second difference data is greater than the preset difference threshold, then execute S200: perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first acceleration preprocessed data and the second acceleration preprocessed data;

[0105] If both the first difference data and the second difference data are less than or equal to the preset difference threshold, then execute S130: obtain the health status assessment result of the bridge pier to be tested based on the first difference data and the second difference data.

[0106] For example, the bridge health assessment is only performed based on the first acceleration sensing time series data and the second acceleration sensing time series data when the difference between the maximum and minimum values ​​of a certain sensor signal exceeds a preset difference threshold. If the difference between the maximum and minimum values ​​of the sensor signals does not exceed the preset difference threshold, it indicates that the health status of the bridge pier under test is good, and the health status assessment result of the bridge pier under test can be directly obtained based on the first difference data and the second difference data, that is, its health status assessment result is determined to be good.

[0107] In some implementations, the maximum-minimum value processing provided in this application refers to identifying the difference between the maximum and minimum values ​​of the recorded signal in a certain observation period of the first acceleration sensing time series data, thereby obtaining the first difference data corresponding to the first acceleration sensing time series data; similarly, the second difference data corresponding to the second acceleration sensing time series data can be obtained.

[0108] For example, after obtaining the health status assessment result of the bridge pier to be inspected based on the first acceleration offset data and the second acceleration offset processed data, the method further includes:

[0109] Alarm information is generated based on the health status assessment results;

[0110] The alarm information is transmitted to the audible and visual alarm device, which then sounds an alarm and reminds passing vehicles to slow down or stop based on the alarm information.

[0111] In some implementation scenarios, combined with Figures 1 to 3 The dynamic monitoring method for the service status of bridge piers shown is illustrated below. An example of the specific implementation steps of this monitoring method is as follows:

[0112] 1. Data acquisition from two signal sensors: The first accelerometer and the second accelerometer record the signals as follows: x a ( t ), y a ( t The signal processing equipment uses a time range T as the observation period, and the number of signal samples recorded by each signal sensor within one observation period T is N.

[0113] The signal processing device monitors two signal sensors and identifies the difference between the maximum and minimum values ​​of the recorded signals within a certain observation period T. When the difference between the maximum and minimum values ​​of a signal from one of the signal sensors exceeds a preset difference threshold, the signal processing device activates the vibration difference identification module to assess the health status of the bridge based on the first acceleration sensor time series data and the second acceleration sensor time series data.

[0114] Optionally, the signals transmitted back to the bridge's monitoring system during the active observation period are saved;

[0115] 2. Data preprocessing:

[0116] To reduce the impact of low-frequency components on phase difference calculation, we can first analyze the two signals... x a ( t ), y a ( t Perform mean removal processing:

[0117] x a ( t )- x’ → x ( t );

[0118] ya ( t )- y’ → y ( t );

[0119] in, x’ express x a ( t The average value of ) y’ express y a ( t The average value of ) is used to obtain the first acceleration preprocessing data. x ( t ) and second acceleration preprocessing data y ( t );

[0120] 3. Calculate the cross-correlation function:

[0121] Cross-correlation function R xy ( τ ) indicates a signal x ( t )and y ( t Between different delay times τ The similarity is calculated using the following formula:

[0122] ;

[0123] in, R xy ( τ ) represents the cross-correlation function. τ Indicates the delay time. x ( t () indicates the first acceleration preprocessing data. y ( t () indicates the second acceleration preprocessing data. N express x ( t )or y ( t The total number of data points. t Represents a time series;

[0124] Alternatively, in practical applications, FFT (Fast Fourier Transform) can be used to efficiently calculate the cross-correlation function. R xy ( τ );

[0125] 4. Find the maximum cross-correlation:

[0126] Find the cross-correlation function R xy ( τ The delay time corresponding to the maximum value of ) τ max ;

[0127] ;

[0128] 5. Calculate the phase difference:

[0129] Phase difference Δ φ It can be obtained through the maximum cross-correlation function R xy ( τ The corresponding delay time τ max Calculations show that if the signal sampling frequency is... f s Then the phase difference Δ φ for:

[0130] ;

[0131] 6. Determine the time offset:

[0132] Based on the calculated phase difference Δ φ This allows us to determine the time offset that needs to be applied to the signal. τ p ;

[0133] The number of samples that the signal needs to be offset is: τ p × f s , τ p Positive representation y ( t The signal shifts forward. τ p A negative value indicates a signal. y ( t ) Shift backward;

[0134] Signal y ( t After offset, the two signals x ( t )and y ( t Data extraction of time-overlapping sequences, recorded as follows: x m ( t ), y m ( t ), p (t )=| x m ( t )| / | y m ( t )|;

[0135] 7. Monitoring and assessment of health status:

[0136] based on p ( t )=| x m ( t )| / | y m ( t | Monitor and assess the health status of the bridge piers to be inspected;

[0137] when y m ( t The value in | is less than 5% y m ( t ) max However, due to significant numerical calculation errors, the ratio time series data obtained by the above formula may not be accurate. p ( t Discard; among them, y m ( t ) max express y m ( t The maximum value in );

[0138] Setting ratio time series data p ( t The threshold for ) is G;

[0139] Comparison value time series data p ( t If the hypothesis that the vibration is significantly greater than G is assumed, a statistical test is performed. If the hypothesis test passes at a significance level of α = 0.05, it indicates that the vibration of the lower part of the bridge pier is large and there is a risk of overturning, and the system will issue an alarm. If the hypothesis test fails, the health status is assessed as good and the system will not respond.

[0140] The system also records the time series for this period and its calculation results. p ( t This is to facilitate tracking changes in the health status of the bridge.

[0141] Please see Figure 4 , Figure 4This is a structural block diagram of a dynamic monitoring system for the service status of bridge piers provided in an embodiment of this application. The dynamic monitoring system for the service status of bridge piers includes:

[0142] The acquisition module 100 is used to acquire the first acceleration sensing time series data and the second acceleration sensing time series data of the bridge pier to be tested, wherein the bridge pier to be tested is provided with the first acceleration sensor and the second acceleration sensor at different positions.

[0143] The preprocessing module 200 is used to perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data;

[0144] The cross-correlation module 300 is used to generate a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data;

[0145] Offset module 400 is used to determine time offset data based on cross-correlation function;

[0146] The offset processing module 500 is used to perform offset processing on the first acceleration preprocessed data and the second acceleration preprocessed data according to the time offset data to obtain the first acceleration offset data and the second acceleration offset processed data.

[0147] The evaluation module 600 is used to obtain the health status evaluation results of the bridge pier to be tested based on the first acceleration offset data and the second acceleration offset processing data.

[0148] For example, the preprocessing module 200 is specifically used for:

[0149] The first acceleration sensing time series data and the second acceleration sensing time series data are subjected to mean removal processing to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

[0150] For example, the mutual correlation module 300 is specifically used for:

[0151] The maximum value is calculated based on the cross-correlation function to obtain the maximum delay time data corresponding to the maximum value of the cross-correlation function.

[0152] Phase difference data is determined based on maximum delay time data;

[0153] The time offset data is determined based on the phase difference data.

[0154] For example, the evaluation module 600 is specifically used for:

[0155] Ratio processing is performed on the first acceleration offset data and the second acceleration offset processed data to obtain ratio time series data;

[0156] Based on the preset ratio threshold and ratio time series data, a significance statistical test was performed to obtain the health status assessment results of the bridge piers to be tested.

[0157] For example, the dynamic monitoring system for the service status of bridge piers also includes a differential module, which is specifically used for:

[0158] The difference data is obtained by performing maximum-minimum value processing on the first acceleration sensor time series data and the second acceleration sensor time series data;

[0159] If the difference data is greater than the preset difference threshold, then perform the step of data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

[0160] If the difference data is less than or equal to the preset difference threshold, the health status assessment result of the bridge pier to be tested is obtained based on the difference data.

[0161] It should be noted that the dynamic monitoring system for the service status of bridge piers provided in this application embodiment is related to... Figures 1 to 3 The method embodiments shown correspond to each other, and will not be described again here to avoid repetition.

[0162] This application also provides an electronic device, please refer to [link to application]. Figure 5 , Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of this application. The electronic device may include a processor 510, a communication interface 520, a memory 530, and at least one communication bus 540. The communication bus 540 is used to enable direct communication between these components. In this embodiment, the communication interface 520 of the electronic device is used for signaling or data communication with other node devices. The processor 510 may be an integrated circuit chip with signal processing capabilities.

[0163] The processor 510 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor, or the processor 510 can be any conventional processor.

[0164] The memory 530 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc. The memory 530 stores computer-readable instructions. When these computer-readable instructions are executed by the processor 510, the electronic device can perform the aforementioned operations. Figures 1 to 3 The various steps involved in the method implementation examples.

[0165] Alternatively, the electronic device may also include a storage controller and an input / output unit.

[0166] The memory 530, storage controller, processor 510, peripheral interface, and input / output unit are electrically connected directly or indirectly to achieve data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses 540. The processor 510 is used to execute executable modules stored in the memory 530, such as software function modules or computer programs included in electronic devices.

[0167] The input / output unit is used to provide users with the ability to create tasks and to set optional start periods or preset execution times for those tasks, thereby enabling user-server interaction. The input / output unit may be, but is not limited to, a mouse and keyboard.

[0168] Understandable. Figure 5 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 5 The more or fewer components shown, or having the same Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0169] This application also provides a storage medium storing instructions. When the instructions are run on a computer, the computer program is executed by a processor to implement the method described in the method embodiment. To avoid repetition, the method will not be described again here.

[0170] This application also provides a computer program product that, when run on a computer, causes the computer to perform the method described in the method embodiment.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0172] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0173] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0174] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0175] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0176] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for dynamic monitoring of the service condition of bridge piers, characterized in that, include: Acquire the first acceleration sensor time series data and the second acceleration sensor time series data of the bridge pier to be tested, wherein the bridge pier to be tested is provided with the first acceleration sensor and the second acceleration sensor at different locations; Data preprocessing is performed based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data; A cross-correlation function is generated based on the first acceleration preprocessing data and the second acceleration preprocessing data; The time offset data is determined based on the cross-correlation function; Based on the time offset data, the first acceleration preprocessed data and the second acceleration preprocessed data are offset to obtain the first acceleration offset data and the second acceleration offset processed data. The health status assessment results of the bridge pier to be inspected are obtained based on the first acceleration offset data and the second acceleration offset processed data; In the step of generating a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data, the cross-correlation function is: ； in, R xy ( τ ) represents the cross-correlation function. τ Indicates the delay time. x ( t () indicates the first acceleration preprocessing data. y ( t () indicates the second acceleration preprocessing data. N express x ( t )or y ( t The total number of data points. t Represents a time series; The steps for determining the time offset data based on the cross-correlation function include: The maximum value is calculated based on the cross-correlation function to obtain the maximum delay time data corresponding to when the cross-correlation function is at its maximum value. Phase difference data is determined based on the maximum delay time data; The time offset data is determined based on the phase difference data; The steps for obtaining the health status assessment results of the bridge pier under test based on the first acceleration offset data and the second acceleration offset processed data include: Based on the first acceleration offset data and the second acceleration offset processed data, ratio processing is performed to obtain ratio time series data; Based on the preset ratio threshold and the ratio time series data, a significance statistical test is performed to obtain the health status assessment results of the bridge pier to be tested.

2. The dynamic monitoring method for the service status of bridge piers according to claim 1, characterized in that, The steps of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data include: The first acceleration sensing time series data and the second acceleration sensing time series data are subjected to mean removal processing to obtain the first acceleration preprocessed data and the second acceleration preprocessed data.

3. The method for dynamic monitoring of bridge pier service status according to claim 1, characterized in that, Before the step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data, the method further includes: The first acceleration sensing time series data and the second acceleration sensing time series data are processed by maximum value-minimum value to obtain the first difference data and the second difference data; If the first difference data or the second difference data is greater than the preset difference threshold, then the step of performing data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain the first acceleration preprocessed data and the second acceleration preprocessed data is executed. If both the first difference data and the second difference data are less than or equal to a preset difference threshold, then the health status assessment result of the bridge pier to be tested is obtained based on the first difference data and the second difference data.

4. The dynamic monitoring method for the service status of bridge piers according to claim 1, characterized in that, After obtaining the health status assessment result of the bridge pier to be inspected based on the first acceleration offset data and the second acceleration offset processed data, the method further includes: An alarm message is generated based on the health status assessment results; The alarm information is transmitted to the audible and visual alarm device, which then issues an alarm and reminds passing vehicles to slow down or stop based on the alarm information.

5. A dynamic monitoring system for the service status of bridge piers, characterized in that, include: The acquisition module is used to acquire the first acceleration sensor time series data and the second acceleration sensor time series data of the bridge pier to be tested, wherein the bridge pier to be tested is provided with the first acceleration sensor and the second acceleration sensor at different positions respectively. The preprocessing module is used to perform data preprocessing based on the first acceleration sensing time series data and the second acceleration sensing time series data to obtain first acceleration preprocessed data and second acceleration preprocessed data; The cross-correlation module is used to generate a cross-correlation function based on the first acceleration preprocessing data and the second acceleration preprocessing data; The offset module is used to determine time offset data based on the cross-correlation function; The offset processing module is used to perform offset processing on the first acceleration preprocessed data and the second acceleration preprocessed data according to the time offset data to obtain the first acceleration offset data and the second acceleration offset processed data. The evaluation module is used to obtain the health status evaluation result of the bridge pier to be detected based on the first acceleration offset data and the second acceleration offset processed data; The cross-correlation function is: ； in, R xy ( τ ) represents the cross-correlation function. τ Indicates the delay time. x ( t () indicates the first acceleration preprocessing data. y ( t () indicates the second acceleration preprocessing data. N express x ( t )or y ( t The total number of data points. t Represents a time series; The cross-correlation module is specifically used for: The maximum value is calculated based on the cross-correlation function to obtain the maximum delay time data corresponding to when the cross-correlation function is at its maximum value. Phase difference data is determined based on the maximum delay time data; The time offset data is determined based on the phase difference data; The evaluation module is specifically used for: Based on the first acceleration offset data and the second acceleration offset processed data, ratio processing is performed to obtain ratio time series data; Based on the preset ratio threshold and the ratio time series data, a significance statistical test is performed to obtain the health status assessment results of the bridge pier to be tested.

6. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the dynamic monitoring method for the service status of bridge piers as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the dynamic monitoring method for the service status of bridge piers as described in any one of claims 1 to 4.

Images