An intelligent monitoring and collecting station for a bridge health monitoring system

Abstract

The application discloses an intelligent monitoring and collecting station for a bridge health monitoring system, and the intelligent monitoring and collecting station is used for centrally managing front-end equipment of the bridge health monitoring system and comprises the following: a collecting device used for collecting data of external sensors of the bridge health monitoring system and sending the collected data to a data processing server; the data processing server is used for pre-processing data, performing feature extraction and data correlation on the pre-processed data; a monitoring and alarming device is used for monitoring use environment data in the intelligent monitoring and collecting station, sending the monitored use environment data to the data processing server, and performing an alarming operation and starting a protection measure after receiving an alarm instruction sent by the data processing server; and a data transmission device is used for collecting instructions of the bridge health monitoring system, transmitting processed data and working state data of the intelligent monitoring and collecting station to the bridge health monitoring system according to the instructions.

Description

Technical Field

[0001] This invention relates to the field of bridge health monitoring technology, and more specifically, to an intelligent monitoring and data acquisition station for a bridge health monitoring system. Background Technology

[0002] The types of sensors and data acquisition equipment required for bridge structural health monitoring are numerous and complex, presenting three major challenges in practical application:

[0003] (1) There are many types of equipment, and the installation locations are scattered. Finally, they are all collected in the back-end data center. The front end lacks centralized management, and the field data collectors and lines are chaotic, resulting in high maintenance costs.

[0004] (2) Different types of equipment work independently, making it difficult to generate time-synchronized resonance data for analysis. However, bridge structural stability analysis is a comprehensive and complex discipline. Data from different monitoring points have certain correlations and mutual exclusions. If they are simply listed and combined, resonance data cannot be formed, and the actual effectiveness of the data itself will be greatly reduced.

[0005] (3) Due to the large number of monitoring points, the amount of raw data is very large, which puts great pressure on data transmission, storage and processing. Summary of the Invention

[0006] To address at least one of the problems described in the background section, the present invention provides an intelligent monitoring and data acquisition station for a bridge health monitoring system.

[0007] This invention provides an intelligent monitoring and data acquisition station for a bridge health monitoring system. The intelligent monitoring and data acquisition station is used for centralized management of the front-end equipment of the bridge health monitoring system. The intelligent monitoring and data acquisition station includes:

[0008] Acquisition device, data processing server, monitoring and alarm device, and data transmission device; among which

[0009] The data acquisition device is used to collect data from the external sensors of the bridge health monitoring system and send the collected data to the data processing server.

[0010] The data processing server, as a front-end data processor, is used to preprocess the data collected by the acquisition device, extract features from the preprocessed data and perform data association, and then store and transmit the associated data.

[0011] The monitoring and alarm device is used to monitor the usage environment data in the intelligent monitoring and data acquisition station, and sends the monitored usage environment data to the data processing server. After receiving the alarm command sent by the data processing server, it performs the alarm operation and activates the protection measures.

[0012] The data transmission device is used to receive instructions from the bridge health monitoring system and transmit the processed data and working status data from the intelligent monitoring and acquisition station to the bridge health monitoring system according to the instructions.

[0013] Optionally, different types of data acquisition devices can be configured according to application requirements.

[0014] Optionally, the intelligent monitoring and acquisition station for the bridge health monitoring system also includes a power supply unit to provide power to the various modules of the intelligent monitoring and acquisition station.

[0015] Optionally, the data processing server includes: a data transceiver module, a status diagnosis module, a working mode configuration module, a data calculation module, a data preprocessing module, and a data association and reconstruction module, wherein...

[0016] The data transceiver module performs data transmission and reception operations with the acquisition device, monitoring and alarm device, and data transmission device.

[0017] The status diagnosis module is used to comprehensively judge whether the working environment and equipment status of the bridge health monitoring system are normal based on the usage environment data obtained from the monitoring and alarm device; when abnormal, it uploads the status activation protection signal to the bridge health monitoring system; when normal, it determines whether the working mode needs to be configured. If the working mode needs to be configured, it sends a configuration command to the working mode configuration module; if the working mode does not need to be configured, it sends a calculation command to the data calculation module.

[0018] The working mode configuration module is used to configure the working mode according to the configuration instructions;

[0019] The data processing module is used to process the data received from the acquisition device according to the processing instructions, and send the processed data to the data preprocessing module.

[0020] The data preprocessing module is used to preprocess the solved data and send the preprocessed data to the data association and reconstruction module;

[0021] The data association and reconstruction module is used to associate and reconstruct the preprocessed data, and then send the associated and reconstructed data to the data transceiver module.

[0022] Optionally, the data preprocessing module performs preprocessing operations on the solved data, including:

[0023] If there are omissions in the calculated data, the omissions will be compensated.

[0024] If there are anomalies in the calculated data, the abnormal data will be corrected.

[0025] If the calculated data contains noise, noise reduction processing is performed on the noisy data.

[0026] Optionally, the data association and reconstruction module performs data association operations on the preprocessed data, including:

[0027] Based on the overall bridge monitoring content, the sensor data in the preprocessed data are classified and associated with preset parameters.

[0028] The structural stress and corresponding structural temperature data in the preprocessed data are synchronized and bound in time, temperature compensation correction is performed, and they are merged into a set of data.

[0029] The load data and camera data in the preprocessed data are synchronized and bound in time. The load data is used as a trigger to bind with the license plate information to obtain traffic flow data. The equivalent axle data is calculated accordingly. At the same time, the deflection data corresponding to the time period when the vehicle passes is extracted as the response data of the bridge under load and packaged into a set of vehicle information data.

[0030] According to the requirements of different systems, all data and monitoring points are divided;

[0031] The design-related temperature and humidity data, wind force data, stress data, cable force data, vibration data, equivalent shaft data, deflection data, settlement data, and displacement data are packaged and associated as a set of data and sent to the first external system for transmission.

[0032] The relevant temperature and humidity data, load data, traffic flow data, crack data, bearing aging data, displacement data, and settlement data are packaged and associated as another set of data, and then sent to a second external system.

[0033] Optionally, the data association and reconstruction module performs data reconstruction operations on the preprocessed data, including:

[0034] Based on sensor characteristics and data features, a time interval is set as the frequency base point for data sampling and reconstruction.

[0035] Extract raw sensor data from before and after the frequency base point, mark the start and end timestamps of the data, and obtain the target data segment.

[0036] Calculate the feature values ​​of the target data segment, where the feature values ​​include: mean, maximum, minimum, variance, and standard deviation;

[0037] Add start and end timestamps to the feature values ​​and package them into reconstructed feature data.

[0038] The reconstructed feature data replaces the original sensor data and is uploaded to the bridge health monitoring system.

[0039] Optionally, the data processing module is specifically used to: process the data received from the acquisition device according to the correspondence between the sensor signal and the monitored physical quantity.

[0040] Optionally, the working mode configuration module is specifically used to: configure self-test parameters, configure the working parameters of the acquisition device, and configure data processing parameters.

[0041] Optionally, the environmental data obtained from the monitoring and alarm device includes: intelligent power distribution unit status data, temperature sensor data, air conditioner status and temperature data, access control status data, water immersion sensor data, and smoke sensor data.

[0042] This invention centrally manages and collects various health monitoring signals from the front end, improving integration and intelligent management. It is compatible with the data requirements of different systems simultaneously, avoiding redundant construction and resource waste of front-end equipment. By unifying signal acquisition and data processing, it improves data correlation and more realistically reproduces the on-site situation. The invention possesses front-end data processing capabilities, selectable operating modes, reduces transmission, back-end storage, and computational pressure, and improves system stability and reliability. Attached Figure Description

[0043] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures:

[0044] Figure 1 This is an overall framework diagram of an intelligent monitoring and data acquisition station for a bridge health monitoring system provided in an exemplary embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the structure of a data processing server provided in an exemplary embodiment of the present invention;

[0046] Figure 3 This is an overall workflow diagram of a data processing server provided in an exemplary embodiment of the present invention;

[0047] Figure 4 This is a flowchart illustrating the comprehensive diagnostic working status provided by an exemplary embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the workflow and processing method of the data preprocessing module provided in an exemplary embodiment of the present invention. Detailed Implementation

[0049] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It is obvious that the described embodiments are merely some embodiments of the present invention, and not all embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0050] It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0051] Figure 1 This invention illustrates the overall framework of an intelligent monitoring and data acquisition station for a bridge health monitoring system provided by this invention. The intelligent monitoring and data acquisition station proposed in this invention is used for centralized management of the front-end equipment of the bridge health monitoring system, such as… Figure 1 As shown, the intelligent monitoring and acquisition station for the bridge health monitoring system includes: an acquisition device, a data processing server, a monitoring and alarm device, and a data transmission device. The acquisition device collects data from external sensors of the bridge health monitoring system and sends the collected data to the data processing server. The data processing server, acting as a front-end data processor, preprocesses the data collected by the acquisition device, extracts features from the preprocessed data, performs data association, and stores and transmits the associated data. The monitoring and alarm device monitors the environmental data within the intelligent monitoring and acquisition station, sends the monitored environmental data to the data processing server, and initiates alarm operations and protective measures upon receiving alarm commands from the data processing server. The data transmission device receives commands from the bridge health monitoring system and, based on these commands, transmits the processed data and operating status data from the intelligent monitoring and acquisition station to the bridge health monitoring system.

[0052] In this embodiment of the invention, the acquisition device is used to configure different acquisition devices according to actual application requirements, to acquire signals from external sensors, and to realize analog-to-digital conversion or interface conversion, such as the acquisition of voltage signals, current signals, frequency signals, etc.

[0053] The data processing server acts as a front-end data processor, preprocessing the collected data, including removing abnormal data, filling in missing data, and processing noisy data; it then performs feature extraction and correlation analysis on the preprocessed data, and finally stores and transmits the correlated data.

[0054] The monitoring and alarm device can use air conditioners, temperature sensors, smart access control systems, water immersion sensors, smoke sensors, and smart power distribution units to monitor the environmental conditions within the data acquisition station. It transmits the data to a data processing server, triggering an alarm and activating protective measures when any monitored value exceeds a threshold. The environmental data acquired from the monitoring and alarm device includes: smart power distribution unit status data, temperature sensor data, air conditioner status and temperature data, access control system status data, water immersion sensor data, and smoke sensor data.

[0055] The data transmission device is a data transceiver device, consisting of a switch and a gateway. It receives instructions from the back-end data center and transmits the processed data and working status data to the data center (bridge health monitoring system).

[0056] Optionally, the intelligent monitoring and acquisition station for the bridge health monitoring system also includes a power supply unit to provide power to the various modules of the intelligent monitoring and acquisition station.

[0057] In embodiments of the present invention, such as Figure 1 As shown, the intelligent monitoring and data acquisition station also includes a power supply unit to provide power to the various modules of the intelligent monitoring and data acquisition station.

[0058] Optionally, such as Figure 2 As shown, the data processing server includes: a data transceiver module, a status diagnosis module, a working mode configuration module, a data calculation module, a data preprocessing module, and a data association and reconstruction module. It transmits and receives data with the acquisition device, monitoring and alarm device, and data transmission device through the data transceiver module.

[0059] In this embodiment of the invention, the working mode configuration module is specifically used to: configure self-test parameters, configure the working parameters of the acquisition device, and configure data processing parameters. The data calculation module is specifically used to: calculate the data received from the acquisition device based on the correspondence between sensor signals and monitored physical quantities.

[0060] In this embodiment of the invention, combined with Figure 2 and Figure 3 As shown, the status diagnosis module is used to comprehensively judge whether the working environment and equipment status of the bridge health monitoring system are normal based on the usage environment data obtained from the monitoring and alarm device; when abnormal, it uploads a status activation protection signal to the bridge health monitoring system; when normal, it determines whether a working mode needs to be configured. If a working mode needs to be configured, it sends a configuration command to the working mode configuration module; if not, it sends a calculation command to the data calculation module. The working mode configuration module is used to configure the working mode according to the configuration command. The data calculation module is used to calculate the data received from the acquisition device according to the calculation command and send the calculated data to the data preprocessing module. The data preprocessing module is used to preprocess the calculated data and send the preprocessed data to the data association and reconstruction module. The data association and reconstruction module is used to associate and reconstruct the preprocessed data and send the associated and reconstructed data to the data transceiver module.

[0061] In this embodiment of the invention, the data received from the acquisition device includes intelligent power distribution unit status data, temperature sensor data, air conditioner status and temperature data, access control status data, water immersion sensor data, and smoke sensor data. By combining the data from various sensors, the system's operating environment and equipment status are comprehensively determined. The method and workflow for determining the status are as follows: Figure 4 As shown.

[0062] Optionally, the data preprocessing module performs preprocessing operations on the solved data, including: filling in the missing data if there are omissions in the solved data; correcting the abnormal data if there are anomalies in the solved data; and reducing the noise in the solved data if there is noise.

[0063] In this embodiment of the invention, the data preprocessing module mainly identifies and repairs data quality, addressing issues such as data omissions, data anomalies, and data noise. The specific workflow and processing methods are as follows: Figure 5 As shown.

[0064] The specific methods for compensating for missing data are as follows:

[0065] When analyzing a set of collected data, some values ​​were found to be empty. Depending on the data relationship type, the following methods can be used to handle the missing data:

[0066] (1) The correspondence between data and physical quantities is linear, or the relationship between adjacent data is linear, such as monitoring quantities such as stress, temperature, deformation, settlement, and cracks. The missing values ​​can be filled by linear interpolation.

[0067] The reasonable value for each collected data point is calculated using the corresponding time. That is, the time t for the missing data value is known. i The data values ​​are collected sequentially at times t1, t2, t3, ... . Referring to the known collected data values, the missing data values ​​are determined. The steps are as follows:

[0068] 1. Search for t i The time between the two closest data collection values.

[0069] 2.t r The corresponding data value collected at that time is λ r .

[0070] 3. Interpolation calculation.

[0071] If a match with t is found i The closest data collection time is t. a t b The corresponding data value is λ a , λ b The linear interpolation formula is as follows:

[0072]

[0073] The missing data values ​​were calculated using the linear interpolation formula:

[0074]

[0075] (2) For data and physical quantities where the correspondence is non-linear, or where adjacent data have a non-linear relationship, such as frequency, acceleration, and vibration monitoring quantities, Lagrange interpolation can be used to fill in missing values. Similarly, the reasonable value for each collected data value is calculated using the time corresponding to that data value. The steps are as follows:

[0076] 1. Given the coordinates of n points (x1, y1), (x2, y2), ..., (x... n ,y n Find an n-1 degree polynomial passing through these points.

[0077] 2. Assume the (n-1)th degree polynomial is:

[0078] y = a0 + a1x + a2x 3 +...a n-1 x n-1

[0079] 3. Substituting the n points into the polynomial yields:

[0080]

[0081] 4. The Lagrange interpolation polynomial can be easily solved as follows:

[0082]

[0083] 5. The above formula can also be written as:

[0084]

[0085] In practice, the Lagrange interpolation method with cubic polynomials is used to find the four data collection values ​​and their times that are closest to the missing value. Generally, these are the first two and the last two data points. Then, the missing value is calculated by substituting them into the above formula.

[0086] Furthermore, the specific methods for identifying and correcting anomalous data are as follows:

[0087] Outliers in bridge monitoring data refer to monitoring values ​​that clearly do not conform to the stress state of the bridge, generally caused by some kind of interference or equipment malfunction. The data identification steps are as follows:

[0088] 1. Smooth the adjacent data in this time period, calculate the difference between the monitored value and the smoothed value, calculate the mean and standard deviation, compare the largest difference with the mean, and then use statistical methods to remove outliers to identify abnormal data. This invention uses the 3σ criterion.

[0089] 2. Statistical theory shows that the probability of a measurement deviation exceeding 3σ is less than 1%. Therefore, measurements with deviations exceeding 3σ can be considered as abnormal data caused by other factors or errors.

[0090] The specific correction method is as follows:

[0091] Such data is usually processed by removing and then replenishing it; the replenishment method is described above in the section on replenishing missing data.

[0092] The specific data noise reduction methods are as follows:

[0093] In monitoring and measurement (especially dynamic data monitoring), the data obtained is often a superposition of the desired true values ​​and various interferences or error noise. Random noise has a global distribution in the time domain, meaning it exists everywhere throughout the entire observation period. To eliminate noise components from the actual observation data and retain the true measurement values, the noisy data needs to be smoothed. Smoothing is widely used in data analysis research. It can reduce the impact of statistical errors in measurements, especially in situations where it is impossible to obtain an average value through repeated measurements, and in measurement segments where there are abrupt changes in value, such as finding peaks, peak values, or inflection points.

[0094] When smoothing a set of measurement data (xi, yi), (i = 0, 1, 2, ..., n), the goal is to obtain a set of smoothed data (xi, yi). The focus is not on directly calculating the linear parameters of the fitted polynomial, but rather on obtaining... The smoothing calculation expression is given. This invention employs a five-point quadratic smoothing method. The derivation of the formula involves fitting the data of the first two and last two measurement points (a total of five points) to the curve using a quadratic trinomial. Then, the fitted value of the measurement point to be smoothed on the fitted curve is taken as the smoothed value.

[0095] Optionally, the data association and reconstruction module performs data reconstruction on the preprocessed data, including: setting a time interval as the frequency base point for data sampling and reconstruction based on sensor characteristics and data features; extracting the original sensor data within a time period before and after the frequency base point, marking the start and end timestamps of the data to obtain the target data segment; calculating the feature values ​​of the target data segment, wherein the feature values ​​include: average value, maximum value, minimum value, variance, and standard deviation; adding the start and end timestamps of the data to the feature values, packaging them into reconstructed feature data; replacing the original sensor data with the reconstructed feature data, and uploading it to the bridge health monitoring system.

[0096] In this embodiment of the invention, the bridge health monitoring system, in order to restore the true condition of the bridge under its service state, has a large number of monitoring points and a high sampling rate, resulting in a very large amount of raw data, which puts great pressure on data transmission, storage, and processing. In actual operation, data with regularity, repetition, and low rate of change can be sampled, feature extracted, stored, and transmitted over a period of time. This can significantly reduce the amount of raw data, alleviate the pressure on system transmission and storage, and improve system stability and reliability. Specifically, apart from the data collected for high-frequency dynamic monitoring, most static data collected, under normal bridge conditions, will not show significant data fluctuations within a certain time range. Examples include: temperature and humidity data; accelerometers for monitoring impact and vibration; inclinometers for monitoring rotation angle; stress sensors for monitoring strain and stress; displacement sensors for monitoring cracks, settlement, and support displacement; and static deflection data. The above data can be reconstructed and replaced according to the following methods and steps:

[0097] 1. Based on sensor characteristics and data features, set a time interval as the frequency base point for data sampling and reconstruction, taking ten minutes as an example;

[0098] 2. Extract the raw data five minutes before and after the above frequency base point, and mark the start and end timestamps;

[0099] 3. Calculate the characteristic values ​​such as the mean, maximum, minimum, variance, and standard deviation of the above data segment;

[0100] 4. Add the start and end timestamps of the data to the feature values ​​described in step 3, and package them into reconstructed feature data;

[0101] 5. Replace the original sensor data with the data generated in step four and upload it to the data processing center.

[0102] Therefore, processing data according to the above steps will not affect the analysis and observation of daily monitoring data, but will greatly reduce the occupation of transmission bandwidth and storage resources, avoiding the waste of resources that would put great pressure on the system.

[0103] Optionally, the data association and reconstruction module performs data association operations on the preprocessed data, including: classifying and pre-setting associations for sensor data in the preprocessed data according to the overall bridge monitoring content; synchronizing and binding the structural stress and corresponding structural temperature data in the preprocessed data in time, performing temperature compensation correction, and merging them into a set of data; synchronizing and binding the load data and camera data in the preprocessed data in time, using the load data as a trigger, binding it with license plate information to obtain traffic flow data, and calculating equivalent axle count data accordingly, while simultaneously extracting the time period corresponding to vehicle passage. Deflection data, as the bridge's response data under load, is packaged into a set of vehicle information data. According to the requirements of different systems, all data and monitoring points are divided. Related temperature and humidity data, wind force data, stress data, cable force data, vibration data, equivalent axle load data, deflection data, settlement data, and displacement data are packaged and correlated as a single set of data and sent to the first external system. Related temperature and humidity data, load data, traffic flow data, crack data, bearing aging data, displacement data, and settlement data are packaged and correlated as another set of data and sent to the second external system.

[0104] In this embodiment of the invention, on the one hand, bridge management and operation involves many departments, including design departments, maintenance departments and owner departments. Due to the differences in monitoring objectives and implementing entities, the same bridge may be analyzed by different systems for different monitoring data. However, the front-end collectors and sensors may overlap. In order to improve equipment utilization and the level of intelligent management of front-end equipment, this system adopts the above-mentioned intelligent monitoring and collection station as the overall manager of front-end equipment. For different customer needs, relevant data association and management can be carried out.

[0105] On the other hand, some bridge structural parameters are inherently correlated and require correlation processing. For example, structural stress and structural temperature are correlated data, and stress is also affected by temperature, requiring temperature compensation calculations. Bridge dynamic deflection is the impact of vehicles passing over the bridge, and related cameras record information about passing vehicles. Therefore, when processing related data, it is necessary to correlate and bind the data for joint processing and analysis.

[0106] To address the above requirements, the data association and management process can be carried out according to the following methods and steps:

[0107] 1. Based on the overall monitoring content of the bridge, the sensor data are classified and pre-correlated, including temperature and humidity data, wind force data, stress data, cable force data, vibration data, load data, deflection data, crack data, settlement data, displacement data, etc.

[0108] 2. Synchronize and bind the structural stress and corresponding structural temperature data over time, perform temperature compensation correction, and merge them into a single set of data;

[0109] 3. Synchronize and bind the load data and camera data in time. Using the load data as a trigger, bind it with the license plate information to obtain traffic flow data, and calculate the equivalent axle data accordingly. At the same time, extract the deflection data corresponding to the time period when the vehicle passes as the response data of the bridge under load, and package them together into a set of vehicle information data.

[0110] 4. Divide all data and monitoring points according to the requirements of different systems;

[0111] 5. Design-related temperature and humidity data, wind force data, stress data, cable force data, vibration data, equivalent shaft data, deflection data, settlement data, and displacement data are packaged and associated as a set of data and sent to the design department's system.

[0112] 6. The operation and maintenance related data such as temperature and humidity, load data, traffic flow data, crack data, bearing aging data, displacement data, and settlement data are packaged and associated as another set of data and sent to the operation and maintenance department system.

[0113] Therefore, by following the above steps to process data, it is possible to simultaneously meet the data requirements of different systems and avoid redundant construction of front-end equipment and waste of resources.

[0114] In summary, the present invention has the following beneficial effects:

[0115] (1) Centralize the management of various health monitoring signals at the front end and collect data to improve the integration and intelligent management level;

[0116] (2) It can simultaneously meet the data requirements of different systems, avoiding redundant construction of front-end equipment and waste of resources;

[0117] (3) Unify signal acquisition and data processing to improve data correlation and more realistically restore the scene;

[0118] (4) It has front-end data processing capabilities, selectable working modes, reduces transmission, back-end storage and computing pressure, and improves system stability and reliability.

[0119] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0120] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments, since they largely correspond to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0121] The block diagrams of devices, systems, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, systems, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0122] The methods and apparatus of the present invention may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the methods is for illustrative purposes only, and the steps of the methods of the present invention are not limited to the order specifically described above unless otherwise specifically stated. Furthermore, in some embodiments, the present invention may also be implemented as a program recorded on a recording medium, the program comprising machine-readable instructions for implementing the methods according to the present invention. Thus, the present invention also covers recording media storing programs for performing the methods according to the present invention.

[0123] It should also be noted that in the systems, apparatus, and methods of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered equivalents of the present invention. The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0124] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. An intelligent monitoring and data acquisition station for a bridge health monitoring system, characterized in that, The intelligent monitoring and data acquisition station is used for centralized management of the front-end equipment of the bridge health monitoring system. The intelligent monitoring and data acquisition station includes: a data acquisition device, a data processing server, a monitoring and alarm device, and a data transmission device; wherein... The data acquisition device is used to collect data from the external sensors of the bridge health monitoring system and send the collected data to the data processing server. The data processing server, as a front-end data processor, is used to preprocess the data collected by the acquisition device, extract features from the preprocessed data and perform data association, and then store and transmit the associated data. The monitoring and alarm device is used to monitor the usage environment data in the intelligent monitoring and data acquisition station, and sends the monitored usage environment data to the data processing server. After receiving the alarm command sent by the data processing server, it performs the alarm operation and activates the protection measures. The data transmission device is used to receive instructions from the bridge health monitoring system and transmit the data processed by the intelligent monitoring and acquisition station and the working status data to the bridge health monitoring system according to the instructions. The data processing server includes a data association and reconstruction module, used to perform data association and reconstruction on the preprocessed data; The data association operation on the preprocessed data includes: Based on the overall bridge monitoring content, the sensor data in the preprocessed data are classified and associated with preset parameters. The structural stress and corresponding structural temperature data in the preprocessed data are synchronized and bound in time, temperature compensation correction is performed, and they are merged into a set of data. The load data and camera data in the preprocessed data are synchronized and bound in time. The load data is used as a trigger to bind with the license plate information to obtain traffic flow data. The equivalent axle data is calculated accordingly. At the same time, the deflection data corresponding to the time period when the vehicle passes is extracted as the response data of the bridge under load and packaged into a set of vehicle information data. According to the requirements of different systems, all data and monitoring points are divided; The design-related temperature and humidity data, wind force data, stress data, cable force data, vibration data, equivalent shaft data, deflection data, settlement data, and displacement data are packaged and associated as a set of data and sent to the first external system for transmission. The relevant temperature and humidity data, load data, traffic flow data, crack data, bearing aging data, displacement data, and settlement data are packaged and associated as another set of data, and then sent to a second external system.

2. The intelligent monitoring and data acquisition station according to claim 1, characterized in that, Configure different types of data acquisition devices according to application requirements.

3. The intelligent monitoring and data acquisition station according to claim 1, characterized in that, It also includes a power supply unit to provide power to the various modules of the intelligent monitoring and data acquisition station.

4. The intelligent monitoring and data acquisition station according to claim 1, characterized in that, The data processing server includes: a data transceiver module, a status diagnosis module, a working mode configuration module, a data calculation module, a data preprocessing module, and a data association and reconstruction module, wherein... The data transceiver module performs data transmission and reception operations with the acquisition device, monitoring and alarm device, and data transmission device. The status diagnosis module is used to comprehensively judge whether the working environment and equipment status of the bridge health monitoring system are normal based on the usage environment data obtained from the monitoring and alarm device; when abnormal, it uploads the status activation protection signal to the bridge health monitoring system; when normal, it determines whether the working mode needs to be configured. If the working mode needs to be configured, it sends a configuration command to the working mode configuration module; if the working mode does not need to be configured, it sends a calculation command to the data calculation module. The working mode configuration module is used to configure the working mode according to the configuration instructions; The data processing module is used to process the data received from the acquisition device according to the processing instructions, and send the processed data to the data preprocessing module. The data preprocessing module is used to preprocess the solved data and send the preprocessed data to the data association and reconstruction module; The data association and reconstruction module is also used to send the associated and reconstructed data to the data transceiver module.

5. The intelligent monitoring and data acquisition station according to claim 4, characterized in that, The data preprocessing module performs preprocessing operations on the solved data, including: If there are omissions in the calculated data, the omissions will be compensated. If there are anomalies in the calculated data, the abnormal data will be corrected. If the calculated data contains noise, noise reduction processing is performed on the noisy data.

6. The intelligent monitoring and data acquisition station according to claim 4, characterized in that, The data association and reconstruction module performs data reconstruction operations on the preprocessed data, including: Based on sensor characteristics and data features, a time interval is set as the frequency base point for data sampling and reconstruction. Extract raw sensor data from before and after the frequency base point, mark the start and end timestamps of the data, and obtain the target data segment. Calculate the feature values ​​of the target data segment, where the feature values ​​include: mean, maximum, minimum, variance, and standard deviation; Add start and end timestamps to the feature values ​​and package them into reconstructed feature data. The reconstructed feature data replaces the original sensor data and is uploaded to the bridge health monitoring system.

7. The intelligent monitoring and data acquisition station according to claim 4, characterized in that, The data processing module is specifically used to process the data received from the acquisition device based on the correspondence between sensor signals and monitored physical quantities.

8. The intelligent monitoring and data acquisition station according to claim 4, characterized in that, The working mode configuration module is specifically used for: configuring self-test parameters, configuring the working parameters of the acquisition device, and configuring data processing parameters.

9. The intelligent monitoring and data acquisition station according to claim 4, characterized in that, The environmental data obtained from the monitoring and alarm device includes: intelligent power distribution unit status data, temperature sensor data, air conditioner status and temperature data, access control status data, water immersion sensor data, and smoke sensor data.

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