Fabricated bridge structure health monitoring method and system

Abstract

The invention discloses a fabricated bridge structure health monitoring method and system, and relates to the technical field of structure health monitoring. Vibration, strain and temperature data are collected at evaluation nodes of a support, a beam end, a bridge pier and a bridge abutment, network addresses are distributed, and a mapping table is constructed, so that each piece of data can be accurately associated with a specific part of a bridge. Features of the vibration data are extracted, temperature change and strain data are analyzed, maximum and minimum values are recorded, the data and tolerance range are compared to judge abnormity, and marks are recorded if at least three items of the same evaluation node are abnormal. And establishing a data comparison logic, comparing an evaluation node with abnormal data with two evaluation nodes which are the same in position and closest to each other, if the data are abnormal and change trends are different within 24 hours, performing early warning, and if the data are abnormal and change trends are consistent within 24 hours, retaining the data. The method can accurately position the problem position, comprehensively reflect the structure state, monitor whether the assembly type bridge structure is healthy or not in real time, give an early warning in time, and reduce false alarms.

Description

Technical Field

[0001] The present invention relates to the technical field of structural health monitoring, and in particular to a method and system for monitoring the structural health of an assembled bridge. Background Art

[0002] As a key component of transportation infrastructure, bridges play an indispensable role in promoting economic development and ensuring the flow of people and materials. With the continuous increase in traffic volume, the increase in the service life of bridges, and the influence of various natural and human factors, the safety and reliability of bridge structures are facing severe challenges. Ensuring the health of bridges and timely discovering and identifying potential damage are of vital importance to preventing bridge accidents, reducing maintenance costs, and extending the service life of bridges.

[0003] In the prior art, the publication number CN112861826A discloses a bridge damage identification method, which identifies the bridge damage according to the structural form of the bridge, that is, the small-section multi-girder system and the large-section single-girder system, and incorporates the vehicle load sequence samples into the analysis system, so as to understand the changes in the structural performance of the bridge under various possible damage conditions. However, this method not only cannot collect field data in real time, but also is based on structural mechanics, using limited data to establish finite element models and simulation analysis, and cannot more comprehensively reflect the health status of the prefabricated bridge structure.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method and system for monitoring the health of an assembled bridge structure to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for health monitoring of assembled bridge structures, comprising the following specific steps:

[0008] S1: Collect the status data of each evaluation node with and without vehicles passing through when the bridge is healthy, the status data includes vibration, strain and temperature data, assign a unique network address to each type of data of each evaluation node, and build a mapping table between network addresses and node locations;

[0009] S2: Extract the state of the vibration data of the healthy bridge parameters, calculate the frequency, amplitude and phase of the vibration, analyze the temperature and strain data, record the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration, and construct a state feature reference table;

[0010] S3: Real-time acquisition of status data of each evaluation node of the bridge to be monitored with and without vehicles passing by, calculation of the frequency, amplitude and phase of the vibration, and construction of a status feature data set;

[0011] S4: Preset a tolerance range. When the data of the state feature data set exceeds or falls below the preset tolerance range of the state feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked.

[0012] S5: Establish data comparison logic. When at least three data items of an evaluation node are abnormal, record its location based on the mapping table between network address and node location, and compare the data with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, an alert is issued. If the trend is consistent, retain the abnormal situation and do not issue an alert.

[0013] Furthermore, the middle positions of the four sides of each support are used as independent evaluation nodes, the lower surface of each segmental beam is used as an independent evaluation node, the two mutually perpendicular side positions at the ends of each segmental beam are used as independent evaluation nodes, the four side positions of each pier are used as independent evaluation nodes, and the three side positions of each abutment are used as independent evaluation nodes.

[0014] Further, the time series vibration data from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node is selected, calibrated as the vibration time series data in the state of a vehicle passing by, and fast Fourier transform is performed to obtain frequency domain data X(k), wherein k=0, 1, ..., N-1, N is the data length in the state of a vehicle passing by, and is also the number of time series vibration data participating in the fast Fourier transform calculation. When N<2048, the value of N is the number of time series vibration data actually collected. When N≥2048, 2048 data points are intercepted for fast Fourier transform, and the data length of N is 2048 at this time;

[0015] Construct the frequency resolution formula based on frequency domain data:

[0016]

[0017] In the formula, f s is the sampling frequency;

[0018] Calculate the frequency of the vibration data based on the frequency resolution:

[0019] f(k)=kΔf

[0020] According to the frequency domain data X(k), the amplitude calculation formula is constructed:

[0021]

[0022] Where Re(X(k)) is the real part of A(k), Im(X(k)) is the imaginary part of A(k);

[0023] Construct the phase calculation formula:

[0024]

[0025] Select the time series vibration data from the time when the vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node, mark it as the vibration time series data in the state of vehicle passing, and process it through fast Fourier transform to obtain frequency domain data Y(z), where z=0, 1, ..., H-1, H is the data length in the state of no vehicle passing, and is also the number of time series vibration data involved in fast Fourier transform calculation. When H<2048, the value of H is the number of time series vibration data actually collected. When H≥2048, 2048 data points are intercepted for fast Fourier transform, and the data length of H is 2048 at this time;

[0026] Construct the frequency resolution formula based on frequency domain data:

[0027]

[0028] In the formula, f s is the sampling frequency;

[0029] Calculate the frequency of the vibration data based on the frequency resolution:

[0030] f′(z)=zΔf

[0031] According to the frequency domain data y(z), the amplitude calculation formula is constructed:

[0032]

[0033] Where Re(Y(z)) is the real part of Y(z), Im(Y(z)) is the imaginary part of Y(z);

[0034] Construct the phase calculation formula:

[0035]

[0036] Furthermore, the frequency, amplitude, phase, temperature and strain data of each evaluation node with and without vehicles passing by are collected for one week, and the data in these two states are recorded separately. The maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data in each state with and without vehicles passing by are found, and a state feature data set is constructed.

[0037] Furthermore, the tolerance range is set to 10%. When the data is between 0.9 times the minimum value and 1.1 times the maximum value, the data of the bridge vibration frequency, vibration amplitude, vibration phase, temperature and strain are considered to be in a normal state. Otherwise, the data is considered to be abnormal. When there are at least three abnormal data at the same evaluation node, they are recorded and marked.

[0038] Furthermore, it is assumed that the acquired frequency, amplitude, phase, temperature and strain data exceeding 10% of the specified maximum value, or falling below 10% of the specified minimum value, are considered normal fluctuations and are not considered data abnormalities;

[0039] Set the maximum value of the vibration frequency data when no vehicle passes by to f max1 , the minimum value of the vibration frequency data when no vehicle passes is f min1 , the real-time collected vibration frequency data is f real , when 0.9f min1 <f real <1.1f max1 When , the data of bridge vibration frequency is considered to be in normal state;

[0040] Set the maximum value A of the vibration amplitude data when no vehicle passes by max1 , the minimum value of the vibration amplitude data when no vehicle passes by A min1 , the real-time collected vibration amplitude data is A real , when 0.9A min1 real <1.1A max1 When , the data of bridge vibration amplitude is considered to be in normal state;

[0041] Set the maximum value of the phase data when no vehicle passes by Minimum value of phase data when no vehicle passes The real-time collected vibration phase data is when When , the data of bridge vibration phase is considered to be in normal state;

[0042] Set the maximum value S of the strain data when no vehicle passes by max1 , the minimum value S of the strain data when no vehicle passes min1 , the real-time collected strain data is S real , when 0.9S min1 real <1.1S max1 When , the bridge strain data is considered to be in a normal state;

[0043] Set the maximum value T of the temperature data when no vehicle passes by max1 , the minimum value T of the temperature data when no vehicle passes min1 , the real-time collected temperature data is T​​teal , when 0.9T min1 <T real <1.1T max1 , it is considered that the bridge temperature data is in normal state.

[0044] Furthermore, it is assumed that the acquired frequency, amplitude, phase, temperature and strain data exceeding 10% of the specified maximum value, or falling below 10% of the specified minimum value, are considered normal fluctuations and are not considered data abnormalities;

[0045] Set the maximum value f of the vibration frequency data when a vehicle passes by max2 , the minimum value f of the vibration frequency data when a vehicle passes by min2 , the real-time collected vibration frequency data is f real , when 0.9f min2 <f real <1.1f max2 When , the data of bridge vibration frequency is considered to be in normal state;

[0046] Set the maximum value A of the vibration amplitude data when a vehicle passes by max2 , the minimum value A of the vibration amplitude data when a vehicle passes by min2 , the real-time collected vibration amplitude data is A real , when 0.9A min2 real <1.1A max2 When , the data of bridge vibration phase is considered to be in normal state;

[0047] Set the maximum value of the phase data when a vehicle passes by Minimum value of phase data when a vehicle passes by The real-time collected vibration phase data is when When , the data of bridge vibration phase is considered to be in normal state;

[0048] Set the maximum value S of the strain data when a vehicle passes by max2 , the minimum value S of the strain data when a vehicle passes by min2 , the real-time collected strain data is S real , when 0.9S min2 real <1.1S max2 When , the bridge strain data is considered to be in a normal state;

[0049] Set the maximum value T of the temperature data when a vehicle passes by max2 , the minimum value T of the temperature data when a vehicle passes by min2 , the real-time collected temperature data is T real , when 0.9T min2 ​​<T real <1.1T max2 When it is <1.1T, it is considered that the data of the bridge strain is in a normal state.

[0050] Furthermore, it is preset that the data change rates of each evaluation node at the same position and the evaluation node closest to it are R j and R k respectively, and the data change trend threshold is M1. When any at least three items of data of a certain evaluation node are abnormal, the data of the i-th data-abnormal evaluation node and the data of the evaluation node at the same position and closest to it are obtained, and a change rate formula for adjacent time-point data is established:

[0051]

[0052] In the formula, R i represents the change rate of the data of the i-th evaluation node, Δt represents the time interval, and the data corresponding to the t moment is D i . Based on this formula, R j and R k at the same time and with the same time interval are calculated again, and a data change trend formula is established:

[0053] M = max(|R i -R j |, |R i -R j |, |R j -R k |)

[0054] When M≥M1, it is considered that the change trends are inconsistent;

[0055] When M<M1, it is considered that the change trends are basically consistent.

[0056] Furthermore, it is set that for the evaluation node at the bearing, the evaluation nodes at the same position and closest to it are the evaluation nodes on the same side of the adjacent left and right bearings. If it is the first bearing, the evaluation nodes on the same side of the two adjacent bearings on its right are the evaluation nodes at the same position and closest to it. If it is the last bearing, the evaluation nodes on the same side of the two adjacent bearings on its left are the evaluation nodes at the same position and closest to it;

[0057] It is set that for the evaluation node at the beam end, the evaluation nodes at the same position and closest to it are the evaluation nodes at the same side position of the next and the previous beam segments. If it is the first beam end, the evaluation nodes at the same side position of the two adjacent beam segments below it are the evaluation nodes at the same position and closest to it. If it is the last beam end, the evaluation nodes at the same side position of the two adjacent beam segments above it are the evaluation nodes at the same position and closest to it;

[0058] For the evaluation nodes of the bridge piers, the evaluation nodes on the same side of the adjacent bridge piers are set to be the closest and have the same position. If it is the first bridge pier, the same side of the two adjacent bridge piers on its right side are the evaluation nodes with the closest distance and the same position. If it is the last bridge pier, the same side of the two adjacent bridge piers on its left side are the evaluation nodes with the closest distance and the same position.

[0059] When there are multiple abutments, the evaluation nodes with the closest distance and the same position are set to be the evaluation nodes on the same side of the adjacent abutments. If it is the first abutment, the evaluation nodes with the closest distance and the same position are set to be the same sides of the two adjacent abutments on its right side. If it is the last abutment, the evaluation nodes with the closest distance and the same position are set to be the same sides of the two adjacent abutments on its left side. When there are only two abutments, the evaluation nodes with the closest distance and the same position are the left and right side evaluation nodes.

[0060] In addition, a prefabricated bridge structure health monitoring device is provided, and the device is used to perform the above-mentioned prefabricated bridge structure health monitoring method, comprising:

[0061] Data acquisition module: collects the state data of each evaluation node when the bridge is healthy with and without vehicles passing through. The state data includes vibration, strain and temperature data. Each type of data of each evaluation node is assigned a unique network address, and a mapping table between network address and node position is constructed. Data processing module: extracts the state of the vibration data of the parameters when the bridge is healthy, calculates the frequency, amplitude and phase of the vibration, analyzes the temperature and strain data at the same time, records the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration, and constructs a state feature reference table. State feature module: obtains the state of each evaluation node of the bridge to be monitored with and without vehicles passing through in real time. The status data of the vehicle passing by is used to calculate the frequency, amplitude and phase of the vibration, and to build a status feature data set. The abnormality judgment module: pre-sets the tolerance range. When the data of the status feature data set exceeds or falls below the preset tolerance range of the status feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked. The early warning module: establishes data comparison logic. When at least three data items of an evaluation node are abnormal, its location is recorded according to the mapping table between the network address and the node location, and the data is compared with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, an early warning is issued. If the trend is consistent, the abnormal situation is retained without an early warning.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention not only collects vibration data of each evaluation node, but also includes strain and temperature data, which can more comprehensively reflect the health status of the bridge under different states. At the same time, through the constructed mapping table of network address and node position, the location where the problem may occur can be accurately located, and the tolerance range is preset. When the data in the state characteristic data set exceeds or falls below the preset range of the reference table, and at least three data of the same evaluation node are abnormal, they are recorded and marked. This multi-data collaborative judgment method can effectively reduce the misjudgment caused by individual data fluctuations or measurement errors. The evaluation node where at least three data abnormalities appear is automatically compared with the same type of data of the evaluation node with the same position and the nearest distance. If the data change trend is different, and the abnormal situation continues to exist for more than 20 hours in the 24-hour interval, an early warning is issued. If the data change trend is basically the same, the abnormal situation is retained and no early warning is issued. The judgment is made by combining multiple data sources, which can better exclude false abnormalities caused by factors such as environmental interference, reduce unnecessary early warning information, and retain abnormal situations without issuing early warnings, so that these data can be retained for subsequent in-depth analysis. Although these data may not represent structural damage at present, they may reflect some potential structural changes or external environmental influences that have not yet developed into damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0065] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0066] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0067] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0068] Example:

[0069] See also Figure 1 , the present invention provides a technical solution:

[0070] See also Figure 1 , the present invention provides a technical solution:

[0071] A method for health monitoring of assembled bridge structures, comprising the following specific steps:

[0072] S1: Collect the status data of each evaluation node with and without vehicles passing through when the bridge is healthy, the status data includes vibration, strain and temperature data, assign a unique network address to each type of data of each evaluation node, and build a mapping table between network addresses and node locations;

[0073] In step S1, detailed structural design drawings are obtained, including the overall layout of the bridge, the support structure, the beam end connection, and the structural drawings of the piers and abutments. Professional technicians are then organized to conduct in-depth research on the drawings to understand the structural form of the bridge, the size and material of each component, and the stress characteristics, to avoid failures or damage to the bridge during installation. According to the marking and analysis of the drawings, the vibration sensors, strain sensors, and temperature sensors are installed in the following locations:

[0074] Support: Install the sensor in the middle of the four sides of each support;

[0075] Beam end: Install sensors on the lower surface of each segment beam and on two mutually perpendicular sides of the segment beam;

[0076] Piers: Install sensors on the sides of each pier;

[0077] Abutment: Install sensors on the three sides of the abutment.

[0078] Assume that a bridge has n supports, and the middle positions of the four sides of each support are used as independent evaluation nodes, and all supports provide a total of 4n evaluation nodes;

[0079] Assume that a bridge has m segmental beams, the lower surface of each segmental beam is used as an independent evaluation node, and the two mutually perpendicular side positions of the segmental beam are used as independent evaluation nodes. All beams provide a total of 3m evaluation nodes;

[0080] Assume that a bridge has p piers, and the four side positions of each pier are used as independent evaluation nodes, and all piers provide 4p evaluation nodes in total;

[0081] Assume that a bridge has q abutments, and the three side positions of each abutment are used as independent evaluation nodes. All abutments provide a total of 4q evaluation nodes.

[0082] In summary, in a prefabricated bridge with multiple bearings, abutments, beams and piers, the total number of evaluation nodes is:

[0083] Q=4n+3m+4p+4q

[0084] The bearing plays a key role in transferring load and adjusting deformation in the bridge. The beam end is the direct area of ​​vehicle load, and the area near the pier top is the area where the pier is subjected to concentrated force. The abutment is connected to the roadbed, and its stability is crucial to the overall safety of the bridge. Therefore, by installing these three types of sensors at each important part such as the bearing, beam end, pier and abutment, the vibration, strain, temperature and other parameters of the bridge structure can be fully monitored, providing timely structural status assessment and early warning;

[0085] Afterwards, the data acquisition module starts to collect data, collecting vibration, strain and temperature data of each evaluation node with and without vehicles passing by, so as to accumulate sufficiently rich and representative data sets to provide sufficient data support for subsequent precise analysis. The collection frequency is once per minute and the collection continues for 1 week.

[0086] By using the data acquisition module to establish a mapping table between the network address and the bridge location, a one-to-one correspondence between the network address and the actual bridge structure location is established, so that when abnormalities occur in subsequent sensor data, the mapping table between the network address and the bridge location can be used to intuitively present and accurately locate the specific part of the bridge.

[0087] S2: Extract the state of the vibration data of the healthy bridge parameters, calculate the frequency, amplitude and phase of the vibration, analyze the temperature and strain data, record the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration, and build a state feature reference table;

[0088] Wherein, in step S2, the vibration, temperature and strain data of each evaluation node with and without vehicles passing by are collected for one week;

[0089] Specifically, the time series vibration data from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node is selected, calibrated as the vibration time series data in the state of a vehicle passing by, and processed by the fast Fourier transform of the data processing module to obtain the frequency domain data X(k), wherein k=0, 1, ..., N-1, N is the data length in the state of a vehicle passing by, and is also the number of time series vibration data participating in the fast Fourier transform calculation. When N<2048, the value of N is the number of time series vibration data actually collected. When N≥2048, 2048 data points are intercepted for fast Fourier transform, and the data length of N is 2048 at this time;

[0090] Construct the frequency resolution formula based on frequency domain data:

[0091]

[0092] In the formula, f s is the sampling frequency. Setting it to 1000Hz means that 1000 data points can be collected per second, and instantaneous changes can also be well recorded.

[0093] When the time without a vehicle passing by does not exceed 2.048 seconds, the data length is:

[0094] N=f s ×t1

[0095] Where t1 is the time period from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node; when the time when a vehicle passes by exceeds 2.048 seconds, N is set to 2048;

[0096] Calculate the frequency of the vibration data based on the frequency resolution:

[0097] f(k)=kΔf

[0098] According to the frequency domain data X(k), the amplitude calculation formula is constructed:

[0099]

[0100] Where Re(X(k)) is the real part of A(k), Im(X(k)) is the imaginary part of A(k);

[0101] Construct the phase calculation formula:

[0102]

[0103] Select the time series vibration data from the time when a vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node, mark it as the vibration time series data in the state of no vehicle passing, and process it through the fast Fourier transform of the data processing module to obtain the frequency domain data Y(z), where z=0, 1, ..., H-1, H is the data length in the state of no vehicle passing, and is also the number of time series vibration data involved in the fast Fourier transform calculation. When H<2048, the value of H is the number of time series vibration data actually collected. When H≥2048, 2048 data points are intercepted for fast Fourier transform, and the data length of H is 2048 at this time;

[0104] Construct the frequency resolution formula based on frequency domain data:

[0105]

[0106] In the formula, f sis the sampling frequency. Setting it to 1000Hz means that 1000 data points can be collected per second, and instantaneous changes can also be well recorded.

[0107] When the time without a vehicle passing by does not exceed 2.048 seconds, the data length is:

[0108] H=f s ×t2

[0109] Where t2 is the time period from when a vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node.

[0110] When the time without a vehicle passing exceeds 2.048 seconds, H takes the value of 2048;

[0111] Calculate the frequency of the vibration data based on the frequency resolution:

[0112] f′(z)=zΔf

[0113] According to the frequency domain data y(z), the amplitude calculation formula is constructed:

[0114]

[0115] Where Re(Y(z)) is the real part of Y(z), Im(Y(z)) is the imaginary part of Y(z);

[0116] Construct the phase calculation formula:

[0117]

[0118] At the same time, the data processing module continuously monitors the temperature sensor and strain sensor data at each evaluation node of the bridge, records the maximum and minimum values ​​of each type of data for a week, and constructs a state feature reference table for the maximum and minimum results of the frequency, amplitude, phase, temperature and strain data of the vibration data in each evaluation node to facilitate subsequent comparison with the state feature data set, thereby monitoring the health status of the bridge structure.

[0119] S3: Real-time acquisition of status data of each evaluation node of the bridge to be monitored with and without vehicles passing by, calculation of the frequency, amplitude and phase of the vibration, and construction of a status feature data set;

[0120] Among them, in step S3, the state feature module is used to monitor the time when there is a vehicle passing and when there is no vehicle passing, and the state data of each evaluation node to be monitored with and without a vehicle passing is obtained in real time, and the vibration data is divided into different time periods with and without a vehicle passing, and the data processing module is used to process the data through the fast Fourier transform. The specific processing steps are as follows:

[0121] Select the time series vibration data from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node, calibrate it as the vibration time series data when a vehicle passes by, and sort it out through the data processing module to obtain the frequency domain data X(k), where k=0, 1, ..., N-1, N is the data length when a vehicle passes by, and is also the number of time series vibration data participating in the fast Fourier transform calculation. When N<2048, the value of N is the number of time series vibration data actually collected. When N≥2048, intercept 2048 data points for fast Fourier transform, and at this time, the data length of N is 2048;

[0122] Construct the frequency resolution formula based on frequency domain data:

[0123]

[0124] In the formula, f s is the sampling frequency, set to 1000Hz;

[0125] When the time without a vehicle passing by does not exceed 2.048 seconds, the data length is:

[0126] N=f s ×t1

[0127] Where t1 is the time period from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node; when the time when a vehicle passes by exceeds 2.048 seconds, N is set to 2048;

[0128] Calculate the frequency of the vibration data based on the frequency resolution:

[0129] f(k)=kΔf

[0130] According to the frequency domain data X(k), the amplitude calculation formula is constructed:

[0131]

[0132] Where Re(X(k)) is the real part of A(k), Im(X(k)) is the imaginary part of A(k);

[0133] Construct the phase calculation formula:

[0134]

[0135] Select the time series vibration data from the time when a vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node, calibrate it as the vibration time series data in the state of no vehicle passing, and sort it out through the data processing module to obtain the frequency domain data Y(z), where z=0, 1, ..., H-1, H is the data length in the state of no vehicle passing, and is also the number of time series vibration data involved in the fast Fourier transform calculation. When H<2048, the value of H is the number of time series vibration data actually collected. When H≥2048, intercept 2048 data points for fast Fourier transform, and the data length of H is 2048 at this time;

[0136] Construct the frequency resolution formula based on frequency domain data:

[0137]

[0138] In the formula, f s is the sampling frequency, set to 1000Hz;

[0139] When the time without a vehicle passing by does not exceed 2.048 seconds, the data length is:

[0140] H=f s ×t2

[0141] Where t2 is the time period from when a vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node.

[0142] When the time without a vehicle passing exceeds 2.048 seconds, H takes the value of 2048;

[0143] Calculate the frequency of the vibration data based on the frequency resolution:

[0144] f′(z)=zΔf

[0145] According to the frequency domain data y(z), the amplitude calculation formula is constructed:

[0146]

[0147] Where Re(Y(z)) is the real part of Y(z), Im(Y(z)) is the imaginary part of Y(z);

[0148] Construct the phase calculation formula:

[0149]

[0150] The frequency, amplitude, phase, temperature and strain data of the vibration data in each evaluation node are obtained and integrated with timestamps in the state with and without vehicles passing by to construct a state feature data set.

[0151] Integrating different types of data into a state feature data set facilitates subsequent overall analysis and comparison with the previously constructed state feature reference table, making it easy to find out whether the data exceeds the normal range.

[0152] S4: Preset a tolerance range. When the data of the state feature data set exceeds or falls below the preset tolerance range of the state feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked.

[0153] Among them, in step S4, the tolerance range is set by the abnormal judgment module, that is, the acquired frequency, amplitude, phase, temperature and strain data exceeding 10% of the specified maximum value, or being lower than 10% of the specified minimum value are regarded as normal fluctuations, and the data are not considered abnormal. The reason for setting the tolerance range is that in the actual data collection process, whether it is the accuracy limitation of the sensor itself or the interference of the complex environmental factors in which the bridge is located, it will inevitably lead to certain errors in the measurement data.

[0154] Through long-term monitoring tests and data collection and analysis of bridges of different types and sizes lasting one week, it was found that setting the tolerance range at around 10% can effectively avoid misjudgments while maintaining sufficient sensitivity to possible structural abnormalities, which is more in line with the actual operating status of the bridge.

[0155] The state of a bridge will be different when there are vehicles passing by and when there are no vehicles passing by. Therefore, when establishing the automatic comparison logic of the maximum and minimum values ​​of the data, by analyzing the data collected continuously for one week, the maximum and minimum values ​​of the vibration frequency, amplitude, phase, strain, and temperature data when there are no vehicles passing by are found; the maximum and minimum values ​​of the vibration frequency, amplitude, phase, strain, and temperature data when there are vehicles passing by are found, and a comparison formula is constructed to determine whether the data is in a normal state.

[0156] For situations where there is no vehicle passing by:

[0157] Set the maximum value of the vibration frequency data when no vehicle passes by to f max1 , the minimum value of the vibration frequency data when no vehicle passes is f min1 , the real-time collected vibration frequency data is f real , when 0.9f min1 <f real <1.1f max1 When , the data of bridge vibration frequency is considered to be in normal state;

[0158] Set the maximum value A of the vibration amplitude data when no vehicle passes by max1 , the minimum value of the vibration amplitude data when no vehicle passes by A min1 , the real-time collected vibration amplitude data is Areal , when 0.9A min1 real <1.1A max1 When , the data of bridge vibration amplitude is considered to be in normal state;

[0159] Set the maximum value of the phase data when no vehicle passes by Minimum value of phase data when no vehicle passes The real-time collected vibration phase data is when When , the data of bridge vibration phase is considered to be in normal state;

[0160] Set the maximum value S of the strain data when no vehicle passes by max1 , the minimum value S of the strain data when no vehicle passes min1 , the real-time collected strain data is S real , when 0.9S min1 real <1.1S max1 When , the bridge strain data is considered to be in a normal state;

[0161] Set the maximum value T of the temperature data when no vehicle passes by max1 , the minimum value T of the temperature data when no vehicle passes min1 , the real-time collected temperature data is T real , when 0.9T min1 <T real <1.1T max1 , it is considered that the bridge temperature data is in normal state.

[0162] For vehicles passing by:

[0163] Set the maximum value f of the vibration frequency data when a vehicle passes by max2 , the minimum value f of the vibration frequency data when a vehicle passes by min2 , the real-time collected vibration frequency data is f real , when 0.9f min2 <f real <1.1f max2 When , the data of bridge vibration frequency is considered to be in normal state;

[0164] Set the maximum value A of the vibration amplitude data when a vehicle passes by max2 , the minimum value A of the vibration amplitude data when a vehicle passes by min2 , the real-time collected vibration amplitude data is A real , when 0.9A min2 real <1.1A max2 , the data of the bridge vibration phase is considered to be in normal state.​​​

[0165] Set the maximum value of the phase data when a vehicle passes by Minimum value of phase data when a vehicle passes by The real-time collected vibration phase data is when , the data of the bridge vibration phase is considered to be in normal state.

[0166] Set the maximum value S of the strain data when a vehicle passes by max2 , the minimum value S of the strain data when a vehicle passes by min2 , the real-time collected strain data is S real , when 0.9S min2 real <1.1S max2 When , the bridge strain data is considered to be in a normal state;

[0167] Set the maximum value T of the temperature data when a vehicle passes by max2 , the minimum value T of the temperature data when a vehicle passes by min2 , the real-time collected temperature data is T real , when 0.9T min2 <T real <1.1T max2 When , the bridge strain data is considered to be in a normal state.

[0168] When any three of the vibration frequency, amplitude, phase, strain, and temperature data are abnormal at the same time, the abnormality is recorded and marked so that the comparison logic can be triggered later. By comparing the data under these different states, the changes in the bridge structure under the vehicle load can be discovered in a timely manner, and it helps to find out whether there are potential problems in the no-load state. For example, if the temperature data rises abnormally when a vehicle passes by, it may mean that the bridge structure has generated additional friction or stress concentration due to the vehicle load, which may affect the durability of the bridge. Abnormal vibration amplitude and frequency may indicate that the bridge structure is loose or damaged. Abnormal strain data may indicate that the stress state of the bridge has changed. Comprehensive comparison of multiple data can provide a more comprehensive understanding of the operating status of the bridge and avoid misjudgment caused by abnormal single data.

[0169] S5: Establish data comparison logic. When at least three data items of an evaluation node are abnormal, record its location according to the mapping table between network address and bridge location, and compare data with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, issue an alarm. If the trend is consistent, keep the abnormal situation without warning.

[0170] ​Among them, in step S4, the warning module monitors the data of each sensor in real time. When at least three pieces of data among the data collected by the sensors of the same evaluation node are determined to be abnormal, the subsequent comparison logic is triggered. The data change trend threshold is preset as M1 = 0.3. The warning module automatically obtains the sensor data of the i-th data abnormal evaluation node and the sensor data of its two adjacent evaluation nodes, and establishes a change rate formula for the data at adjacent time points:

[0171]

[0172] In the formula, R i represents the change rate of the i-th sensor, Δt represents the time interval, and the data corresponding to the t moment is D i . According to this formula, the R j and R k at the same time and the same time interval are calculated again, and a data change trend formula is established:

[0173] M = max(|R i - R j |, |R i - R k |, |R j - R k |)

[0174] When M ≥ M1, it is considered that the change trend is inconsistent;

[0175] When M < M1, it is considered that the change trend is basically consistent;

[0176] For example, for vibration frequency data, if the change rate is large, it means that the vibration frequency has changed greatly in a short time, which may imply that there is a sudden abnormal situation in the bridge structure, such as being subjected to strong impact or vibration. Selecting M ≥ 0.3 as the judgment standard for inconsistent change trend and M < 0.3 as the judgment standard for basically consistent change trend is determined based on actual experience and analysis of bridge data changes. This threshold of 0.3 helps to balance the risk tolerance and false alarm rate. If the threshold is set too low, such as 0.1, the warning module may be too sensitive and issue alarms of inconsistent change trend frequently. On the contrary, if the threshold is set too high, such as 0.5, some abnormal situations that are not very serious but still need attention may be missed. And in actual applications, this judgment standard can be adjusted and optimized according to specific situations.

[0177] The early warning module compares and analyzes the data. When it is found that at least three data items of a certain evaluation node are judged to be abnormal, the abnormal start time is recorded according to the timestamp. This can not only help technicians accurately trace when the abnormal situation began, but also accurately monitor and record the duration of the three abnormal states and the time of data change trends for up to 24 hours from that time point. According to the location recorded in the network address and bridge location mapping table, the specific abnormal location of the bridge is provided. When the three data items are judged to be abnormal and the data change trends are different for more than 20 hours, the early warning mechanism is triggered. The early warning module automatically generates early warning information and pushes it to relevant personnel through SMS and APP, so that relevant personnel can go to the specific bridge location where the abnormality occurs to detect the bridge status in time; if the three data items are judged to be abnormal and the data change trends are the same for more than 20 hours, the abnormal state is retained and stored, and can be viewed at any time through SMS and APP without issuing an early warning, so that relevant personnel can pay more attention to these places where abnormalities have occurred, and conduct more targeted inspections and monitoring in daily inspections.

[0178] During bridge monitoring, weather changes may indeed cause data fluctuations. For example, a sharp change in temperature may cause bridge materials to expand and contract, thereby affecting strain and vibration data. However, such changes caused by weather factors are usually relatively synchronized and coordinated, that is, the change trends of multiple data are basically the same. By requiring that the three data items be judged as abnormal and have different change trends for more than 20 hours before triggering an early warning, most of the data anomalies caused by common external factors such as weather can be effectively filtered out, avoiding false warnings caused by these short-term, non-structural problems. If the three data items are abnormal and have different change trends, this may mean that there are uncoordinated changes in the bridge structure, such as local structural damage, loose components, etc. In this case, the problem is more likely to be caused by defects or damage in the bridge structure itself, rather than normal fluctuations in the external environment, avoiding the problem of triggering early warnings for all data anomalies.

[0179] See also Figure 2 The present invention also provides a prefabricated bridge structure health monitoring device, which is used to execute the prefabricated bridge structure health monitoring method mentioned above.

[0180] Data collection module: collects the status data of each evaluation node with and without vehicles passing through when the bridge is healthy, including vibration, strain and temperature data, assigns a unique network address to each type of data of each evaluation node, and constructs a mapping table between network addresses and node locations;

[0181] Data processing module: extract the state of the vibration data of the bridge parameters when they are healthy, calculate the frequency, amplitude and phase of the vibration, analyze the temperature and strain data at the same time, record the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration respectively, and build a state feature reference table;

[0182] State feature module: It obtains the state data of each evaluation node of the bridge to be monitored in real time, including whether there are vehicles passing by or not, calculates the frequency, amplitude and phase of the vibration, and constructs a state feature data set;

[0183] Abnormal judgment module: pre-set the tolerance range. When the data of the state feature data set exceeds or falls below the preset tolerance range of the state feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked.

[0184] Early warning module: Establish data comparison logic. When at least three data of an evaluation node are abnormal, its location is recorded according to the mapping table of network address and node location, and the data is compared with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, an early warning will be issued. If the trend is consistent, the abnormal situation will be retained without an early warning. The above formulas are all dimensioned and calculated numerically. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0185] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0186] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0187] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A method for monitoring the health of an assembled bridge structure, characterized in that: The specific steps include: S1: Collect the status data of each evaluation node with and without vehicles passing through when the bridge is healthy, the status data includes vibration, strain and temperature data, assign a unique network address to each type of data of each evaluation node, and build a mapping table between network addresses and node locations; S2: Extract the state of the vibration data of the healthy bridge parameters, calculate the frequency, amplitude and phase of the vibration, analyze the temperature and strain data, record the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration, and construct a state feature reference table; S3: Real-time acquisition of status data of each evaluation node of the bridge to be monitored with and without vehicles passing by, calculation of the frequency, amplitude and phase of the vibration, and construction of a status feature data set; S4: Preset a tolerance range. When the data of the state feature data set exceeds or falls below the preset tolerance range of the state feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked. S5: Establish data comparison logic. When at least three data items of an evaluation node are abnormal, record its location based on the mapping table between network address and node location, and compare the data with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, an alert is issued. If the trend is consistent, retain the abnormal situation and do not issue an alert.

2. A method for monitoring the health of an assembled bridge structure according to claim 1, characterized in that: In step S1, the evaluation node includes: The middle positions of the four sides of each support are used as independent evaluation nodes, the lower surface of each segmental beam is used as an independent evaluation node, the two mutually perpendicular side positions at the ends of each segmental beam are used as independent evaluation nodes, the four side positions of each pier are used as independent evaluation nodes, and the three side positions of each abutment are used as independent evaluation nodes.

3. The method for health monitoring of a prefabricated bridge structure according to claim 1, characterized in that: In step S2, extracting the state of vibration data includes the following steps: Select the time series vibration data from when a vehicle arrives at a certain evaluation node to when it completely leaves the same evaluation node, calibrate it as the vibration time series data in the state of a vehicle passing by, and perform fast Fourier transform to obtain frequency domain data X(k), where k=0, 1, ..., N-1, N is the data length in the state of a vehicle passing by, and is also the number of time series vibration data participating in the fast Fourier transform calculation. When N<2048, the value of N is the number of time series vibration data actually collected. When N≥2048, intercept 2048 data points for fast Fourier transform, and at this time the data length of N is 2048; Construct the frequency resolution formula based on frequency domain data: In the formula, f s is the sampling frequency; Calculate the frequency of the vibration data based on the frequency resolution: f(k)=kΔf According to the frequency domain data X(k), the amplitude calculation formula is constructed: Where Re(X(k)) is the real part of A(k), Im(X(k)) is the imaginary part of A(k); Construct the phase calculation formula: Select the time-series vibration data from the moment the vehicle leaves the evaluation node until the next vehicle arrives at the evaluation node, calibrate it as the vibration time-series data in the state of vehicle passing by, and process it through fast Fourier transform to obtain the frequency-domain data Y(z), where z = 0, 1, …, H - 1, and H is the data length in the state of no vehicle passing by, and also the number of time-series vibration data participating in the fast Fourier transform calculation. When H < 2048, the value of H is the number of actually collected time-series vibration data. When H ≥ 2048, intercept 2048 data points for fast Fourier transform, and at this time, the data length of H takes the value of 2048; Construct a frequency resolution formula based on the frequency-domain data: In the formula, f s is the sampling frequency; Calculate the frequency of the vibration data according to the frequency resolution: f ′ (z)=zΔf Construct an amplitude calculation formula based on the frequency-domain data y(z): In the formula, Re(Y(z)) is the real part of Y(z), and Im(Y(z)) is the imaginary part of Y(z); Construct a phase calculation formula:

4. A method for monitoring the health of an assembled bridge structure according to claim 1, characterized in that: In step S2, constructing the state feature reference table includes the following steps: Collect the frequency, amplitude, phase, temperature, and strain data when there is a vehicle passing by and when there is no vehicle passing by at each evaluation node for one week, record the data in these two states respectively, find the maximum and minimum values of the frequency, amplitude, phase, temperature, and strain data in each state when there is a vehicle passing by and when there is no vehicle passing by, and construct a state feature data set.

5. The method for health monitoring of a prefabricated bridge structure according to claim 1, characterized in that: In step S4, when at least three data items are abnormal at the same evaluation node of the bridge, recording and marking the abnormal situation includes the following steps: Set the tolerance range to 10%. When the data is between 0.9 times the minimum value and 1.1 times the maximum value, it is considered that the data of the bridge vibration frequency, vibration amplitude, vibration phase, temperature, and strain are in a normal state. Otherwise, it is considered that the data is abnormal. When at least three data items are abnormal at the same evaluation node, record and mark them.

6. A method for monitoring the health of an assembled bridge structure according to claim 4, characterized in that: When there is no vehicle passing by, the method for judging that the data is in a normal state between 0.9 times the minimum value and 1.1 times the maximum value is: Set that if the obtained frequency, amplitude, phase, temperature, and strain data exceed 10% of the specified maximum value or are lower than 10% of the specified minimum value, they are all regarded as normal fluctuations and the data is not considered abnormal; Set the maximum value of the vibration frequency data when no vehicle passes by to f max1 , the minimum value of the vibration frequency data when no vehicle passes is f min1 , the real-time collected vibration frequency data is f real , when 0.9f min1 <f real <1.1f max1 When , the data of bridge vibration frequency is considered to be in normal state; Set the maximum value A of the vibration amplitude data when no vehicle passes by max1 , the minimum value of the vibration amplitude data when no vehicle passes by A min1 , the real-time collected vibration amplitude data is A real , when 0.9A min1 real <1.1A max1 When , the data of bridge vibration amplitude is considered to be in normal state;​ Set the maximum value of the phase data when no vehicle passes by Minimum value of phase data when no vehicle passes The real-time collected vibration phase data is when When , the data of bridge vibration phase is considered to be in normal state; Set the maximum value S of the strain data when no vehicle passes by max1 , the minimum value S of the strain data when no vehicle passes min1 , the real-time collected strain data is S real , when 0.9S min1 real <1.1S max1 When , the bridge strain data is considered to be in a normal state;​ Set the maximum value T of the temperature data when no vehicle passes by max1 , the minimum value T of the temperature data when no vehicle passes min1 , the real-time collected temperature data is T real , when 0.9T min1 <T real <1.1T max1 , it is considered that the bridge temperature data is in normal state.

7. A method for monitoring the health of an assembled bridge structure according to claim 4, characterized in that: When there is a vehicle passing by, the method for judging that the data is in a normal state between 0.9 times the minimum value and 1.1 times the maximum value is: Set that if the obtained frequency, amplitude, phase, temperature, and strain data exceed 10% of the specified maximum value or are lower than 10% of the specified minimum value, they are all regarded as normal fluctuations and the data is not considered abnormal; Set the maximum value f of the vibration frequency data when a vehicle passes by max2 , the minimum value f of the vibration frequency data when a vehicle passes by min2 , the real-time collected vibration frequency data is f real , when 0.9f min2 <f real <1.1f max2 When , the data of bridge vibration frequency is considered to be in normal state; Set the maximum value A of the vibration amplitude data when a vehicle passes by max2 , the minimum value A of the vibration amplitude data when a vehicle passes by min2 , the real-time collected vibration amplitude data is A real , when 0.9A min2 real <1.1A max2 When , the data of bridge vibration phase is considered to be in normal state;​ Set the maximum value of the phase data when a vehicle passes by Minimum value of phase data when a vehicle passes by The real-time collected vibration phase data is when When , the data of bridge vibration phase is considered to be in normal state; Set the maximum value S of the strain data when a vehicle passes by max2 , the minimum value S of the strain data when a vehicle passes by min2 , the real-time collected strain data is S real , when 0.9S mim2 real <1.1S max2 When , the bridge strain data is considered to be in a normal state;​ Set the maximum value t of the temperature data when a vehicle passes by max2 , the minimum value T of the temperature data when a vehicle passes by min2 , the real-time collected temperature data is T real , when 0.9T min2 <T real <1.1T max2 When , the bridge strain data is considered to be in a normal state.

8. The method for health monitoring of a prefabricated bridge structure according to claim 1, characterized in that: In step S5, establishing the data comparison logic includes the following steps: It is pre-set that the data change rates of each evaluation node with the same position and the closest distance are R j and R k , the data change trend threshold is M1. When at least three data anomalies occur in a certain evaluation node, the data of the i-th data anomaly evaluation node and the data of the evaluation node with the same position and the closest distance are obtained to establish the change rate formula of the data at adjacent time points: In the formula, R i represents the rate of change of the data of the i-th evaluation node, Δt represents the time interval, and the data corresponding to time t is D i , and then calculate the R at the same time and time interval based on this formula j and R k , establish the data change trend formula: M=max(|R i -R j |,|R i -R k |,|R j -R k |) When M ≥ M1, it is considered that the change trends are inconsistent; When M < M1, it is considered that the change trends are basically the same.

9. A method for monitoring the health of an assembled bridge structure according to claim 8, characterized in that: It is pre-set that the change rates of each evaluation node with the same position and the closest evaluation node are R j and R k The following steps are involved: Set that for the evaluation node at the bearing, the ones with the closest distance and the same position are the evaluation nodes on the same side of the adjacent left and right two bearings. If it is the first bearing, the same sides of the two adjacent bearings on its right are the evaluation nodes with the closest distance and the same position. If it is the last bearing, the same sides of the two adjacent bearings on its left are the evaluation nodes with the closest distance and the same position; For the beam end evaluation nodes, the closest and identically located evaluation nodes are those located on the same side of the next and previous segment beams. If it is the first beam end, the same side of the two adjacent segment beams below it is the closest and identically located evaluation node. If it is the last beam end, the same side of the two adjacent segment beams above it is the closest and identically located evaluation node. For the evaluation nodes of the bridge piers, the evaluation nodes on the same side of the adjacent bridge piers are set to be the closest and have the same position. If it is the first bridge pier, the same side of the two adjacent bridge piers on its right side are the evaluation nodes with the closest distance and the same position. If it is the last bridge pier, the same side of the two adjacent bridge piers on its left side are the evaluation nodes with the closest distance and the same position. When there are multiple abutments, the evaluation nodes with the closest distance and the same position are set to be the evaluation nodes on the same side of the adjacent abutments. If it is the first abutment, the evaluation nodes with the closest distance and the same position are set to be the same sides of the two adjacent abutments on its right side. If it is the last abutment, the evaluation nodes with the closest distance and the same position are set to be the same sides of the two adjacent abutments on its left side. When there are only two abutments, the evaluation nodes with the closest distance and the same position are the left and right side evaluation nodes.

10. An assembled bridge structure health monitoring device, characterized in that: The device is used to execute a method for health monitoring of a prefabricated bridge structure according to any one of claims 1 to 9, comprising: Data collection module: collects the status data of each evaluation node with and without vehicles passing through when the bridge is healthy, including vibration, strain and temperature data, assigns a unique network address to each type of data of each evaluation node, and constructs a mapping table between network addresses and node locations; Data processing module: extract the state of the vibration data of the bridge parameters when they are healthy, calculate the frequency, amplitude and phase of the vibration, analyze the temperature and strain data at the same time, record the maximum and minimum values ​​of the frequency, amplitude, phase, temperature and strain data of the vibration respectively, and build a state feature reference table; State feature module: It obtains the state data of each evaluation node of the bridge to be monitored in real time, including whether there are vehicles passing by or not, calculates the frequency, amplitude and phase of the vibration, and constructs a state feature data set; Abnormal judgment module: pre-set the tolerance range. When the data of the state feature data set exceeds or falls below the preset tolerance range of the state feature reference table, the data is considered abnormal. When at least three data items of the same evaluation node are abnormal, the abnormal situation is recorded and marked. Early warning module: Establish data comparison logic. When at least three data items of an evaluation node are abnormal, its location is recorded according to the mapping table between network address and node location, and the data is compared with the nearest evaluation node at the same location. If the change trend is different and the abnormality lasts for 24 hours, an early warning is issued. If the trend is consistent, the abnormal situation is retained without an early warning.

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