A Health Monitoring System and Method for Railway Bridges Based on Infrared Thermal Imaging

Abstract

This application provides a railway bridge health monitoring system and method based on infrared thermal imaging. The data acquisition device uploads the collected monitoring data to the monitoring center via a local monitoring module. The monitoring center sets a disease alarm threshold; if the difference between the average temperature of the monitored area and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential disease in the monitored area. Then, sensor data from the monitored area is acquired to determine the type of disease, and the disease level is determined based on the bridge temperature data in the monitored area. An alarm is issued and a handling plan is displayed. Simultaneously, the monitoring center performs machine learning on the monitoring data to predict potential disease risks, issue early warnings, and display handling strategies. The technical solution provided in this application comprehensively analyzes the bridge temperature data acquired by the infrared thermal imaging camera and the sensor data acquired by the sensors to determine the type and level of disease and display handling plans, thereby improving the efficiency of detection and maintenance.

Description

Technical Field

[0001] This application relates to the field of railway bridge health monitoring technology, and in particular to a railway bridge health monitoring system and method based on infrared thermal imaging. Background Technology

[0002] After railway bridges are put into use, their structures deteriorate due to natural disasters and structural aging. The significant increase in railway transport volume and the aging of bridges further increase the probability of these defects. Failure to detect and maintain these defects in a timely manner can reduce the lifespan of railway bridges and even lead to bridge collapses, seriously threatening public safety and causing damage to national property. Therefore, it is necessary to conduct performance assessments and health monitoring of railway bridge structures, and to carry out timely maintenance to ensure the safe operation of railway bridges and reduce the occurrence of accidents.

[0003] Infrared thermal imaging is a commonly used bridge inspection technique. It utilizes the working principle of infrared radiation to capture images of the bridge surface under inspection using an infrared thermal imaging camera, obtaining a thermal image that allows direct observation of temperature distribution and thus pinpointing the location of bridge defects. However, traditional infrared thermal imaging methods only identify areas of abnormal temperature on railway bridges and cannot determine specific types of defects such as displacement or tilt. Further analysis is required during railway bridge inspection and maintenance, resulting in low work efficiency. Summary of the Invention

[0004] This application provides a railway bridge health monitoring system and method based on infrared thermal imaging to solve the problem that traditional infrared thermal imaging technology cannot determine the specific type of defects when inspecting railway bridges, thereby improving the efficiency of inspection and maintenance.

[0005] On one hand, this application provides a railway bridge health monitoring system based on infrared thermal imaging, comprising: a monitoring center, several local monitoring modules, and several data acquisition devices. The monitoring center communicates bidirectionally with the local monitoring modules, and the local monitoring modules communicate bidirectionally with the data acquisition devices. The data acquisition devices include an infrared thermal imaging camera, sensors, and a weather station.

[0006] The monitoring center is configured to acquire control commands and parameter setting commands and send them to the local monitoring module. The control commands are used to control the start and stop of the data acquisition device and to control the rotation of the infrared thermal imaging camera. The parameter setting commands are used to set the address and monitoring area of ​​the data acquisition device.

[0007] The local monitoring module is configured to acquire and execute the parameter setting command and send the execution result to the monitoring center.

[0008] The local monitoring module is configured to acquire the control command and send the control command to the data acquisition device.

[0009] The data acquisition device is configured to acquire and execute the control command and collect monitoring data of the monitoring area. The monitoring data includes bridge temperature data acquired by the infrared thermal imaging camera, sensor data acquired by the sensor, and ambient temperature data acquired by the weather station.

[0010] The local monitoring module is configured to acquire the monitoring data and upload the monitoring data to the monitoring center in real time.

[0011] The monitoring center is configured to acquire the bridge temperature data and the ambient temperature data in the monitoring area when a train passes over the railway bridge, compare the bridge temperature data and the ambient temperature data, and if the bridge temperature data is abnormal, acquire the sensor data of the monitoring area, determine the type and level of the defect based on the bridge temperature data and the sensor data, issue an alarm and display a handling plan.

[0012] The monitoring center is configured to acquire the monitoring data of the monitoring area, perform machine learning on the monitoring data, predict potential hazards, issue early warnings, and display handling strategies.

[0013] In one implementation, the temperature data acquired by the infrared thermal imaging camera includes the average temperature, minimum temperature, and maximum temperature of the monitored area.

[0014] In one implementation, in the steps of comparing the bridge temperature data and the ambient temperature data, and if the bridge temperature data is abnormal, acquiring the sensor data of the monitoring area, determining the type and level of damage based on the bridge temperature data and the sensor data, issuing an alarm, and displaying a treatment plan, the monitoring center is further configured as follows:

[0015] Calculate the difference between the average temperature of the monitored area and the ambient temperature.

[0016] An alarm threshold is set. If the difference between the average temperature and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential for disease in the monitored area.

[0017] Acquire sensor data of the monitored area, and determine the type of disease based on the sensor data. The sensor data includes displacement data, tilt angle data, and vibration data.

[0018] The severity of the disease is determined based on the difference between the highest and average temperatures in the monitored area.

[0019] The monitoring center displays the disease level and type in the monitored area, issues alarms, and displays the treatment plan.

[0020] In one implementation, the monitoring center is configured to determine the disease type based on the sensor data, wherein:

[0021] Displacement threshold, tilt angle threshold and vibration threshold are set for the monitoring area according to different structures.

[0022] If the displacement data exceeds the displacement threshold, the location where the displacement data exceeds the displacement threshold is determined as a displacement defect point.

[0023] If the tilt angle data exceeds the tilt angle threshold, the location where the tilt angle data exceeds the tilt angle threshold is determined as a tilting defect point.

[0024] If the vibration data exceeds the vibration threshold, the location where the vibration data exceeds the vibration threshold is determined as a vibration fault point.

[0025] Based on the structures corresponding to the displacement fault locations, tilt fault locations, and vibration fault locations, the specific fault type is determined.

[0026] In the step of determining the disease level based on the difference between the highest temperature and the average temperature in the detection area, the monitoring center is configured as follows:

[0027] Set a first temperature threshold, a second temperature threshold, and a third temperature threshold.

[0028] Calculate the difference between the highest temperature and the average temperature in the monitored area.

[0029] If the difference between the highest temperature and the average temperature in the monitored area exceeds the first temperature threshold but does not exceed the second temperature threshold, the point with the highest temperature in the monitored area is identified as a common disease point, and the monitoring center issues a common alarm.

[0030] If the difference between the highest temperature and the average temperature in the monitored area exceeds the second temperature threshold but does not exceed the third temperature threshold, the point with the highest temperature in the monitored area is identified as a serious disease point, and the monitoring center issues a serious alarm.

[0031] If the difference between the highest temperature and the average temperature in the monitored area exceeds the third temperature threshold, the point with the highest temperature in the monitored area is identified as a dangerous disease point, and the monitoring center issues a danger alarm.

[0032] In one implementation, in the step of performing machine learning on the monitoring data to predict potential disease hazards, issuing early warnings, and displaying treatment solutions, the monitoring center is further configured as follows:

[0033] The monitoring center (1) acquires the temperature change trend of the bridge in the monitoring area and the sensor data change trend;

[0034] Extract the temperature change characteristics of the bridge at the location of the damage;

[0035] If the temperature change trend of the bridge in the monitoring area is consistent with the temperature change characteristics of the bridge at the defect location, the sensor data change characteristics of the defect location are extracted.

[0036] If the trend of the sensor data in the monitoring area is consistent with the trend of the sensor data at the disease location, it is determined that there is a potential disease in the monitoring area, an early warning is issued, and a handling strategy is displayed.

[0037] On the other hand, this application provides a railway bridge health monitoring method based on infrared thermal imaging, applied to the aforementioned railway bridge health monitoring system based on infrared thermal imaging, the steps of which include:

[0038] The monitoring center acquires control commands and parameter setting commands and sends them to the local monitoring module. The control commands are used to control the start and stop of the data acquisition device and to control the rotation of the infrared thermal imaging camera. The parameter setting commands are used to set the address and monitoring area of ​​the data acquisition device.

[0039] The local monitoring module acquires and executes the parameter setting command, and sends the execution result to the monitoring center.

[0040] The local monitoring module acquires the control command and sends the control command to the data acquisition device.

[0041] The data acquisition device acquires and executes the control command and collects monitoring data of the monitoring area. The monitoring data includes bridge temperature data acquired by the infrared thermal imaging camera, sensor data acquired by the sensor, and ambient temperature data acquired by the weather station.

[0042] The local monitoring module acquires the monitoring data and uploads it to the monitoring center in real time.

[0043] The monitoring center acquires the bridge temperature data and the ambient temperature data in the monitoring area when a train passes over the railway bridge. It compares the bridge temperature data and the ambient temperature data. If the bridge temperature data is abnormal, it acquires the sensor data of the monitoring area, determines the type and level of the damage based on the bridge temperature data and the sensor data, issues an alarm, and displays a handling plan.

[0044] The monitoring center acquires the monitoring data of the monitoring area, performs machine learning on the monitoring data, predicts potential disease hazards, issues early warnings, and displays handling strategies.

[0045] As can be seen from the above technical solutions, this application provides a railway bridge health monitoring system and method based on infrared thermal imaging. The data acquisition device uploads the collected monitoring data to the monitoring center through a local monitoring module. The monitoring center sets a disease alarm threshold. If the difference between the average temperature of the monitored area and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential disease in the monitored area. Then, sensor data of the monitored area is acquired to determine the type of disease. Based on the bridge temperature data of the monitored area, the disease level is determined, an alarm is issued, and a handling plan is displayed. Simultaneously, the monitoring center performs machine learning on the monitoring data to predict potential disease hazards, issue early warnings, and display handling strategies. The technical solution provided in this application comprehensively analyzes the bridge temperature data acquired by the infrared thermal imaging camera and the sensor data acquired by the sensor to determine the type and level of disease and display the handling plan, thereby improving the efficiency of detection and maintenance. Attached Figure Description

[0046] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the structure of a railway bridge health monitoring system based on infrared thermal imaging in an embodiment of this application;

[0048] Figure 2 This is a schematic diagram of the structure of the monitoring center and the local monitoring module in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the railway bridge health monitoring method based on infrared thermal imaging in the embodiments of this application;

[0050] Figure 4 This is a flowchart illustrating the process of determining the disease type and disease level in the embodiments of this application;

[0051] Figure 5This is a flowchart illustrating the process of determining specific disease types in the embodiments of this application;

[0052] Figure 6 This is a flowchart illustrating the process of determining the specific disease level in an embodiment of this application;

[0053] Figure 7 This is a flowchart illustrating the process of predicting potential disease hazards in the embodiments of this application. Detailed Implementation

[0054] The embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described below do not represent all embodiments consistent with this application. They are merely examples of systems and methods consistent with some aspects of this application as detailed in the claims.

[0055] When the surface temperature of an object exceeds absolute zero (approximately -273.15℃), it radiates electromagnetic waves. As temperature changes, the intensity and wavelength distribution of these electromagnetic waves also change. Electromagnetic waves with wavelengths between 0.75μm and 1000μm are called infrared radiation. Infrared thermal imaging technology uses photoelectric technology to detect specific bands of infrared signals emitted by an object's thermal radiation, converting these signals into a thermal image that can be discerned by the human eye. Different colors in the thermal image represent different temperatures, thus revealing the temperature distribution on the object's surface and allowing for further calculation of temperature values. Applying infrared thermal imaging technology to railway bridge monitoring allows for the analysis of the temperature distribution of the bridge under test, enabling assessment of its operational status.

[0056] However, traditional infrared thermal imaging detection methods can only identify the locations of abnormal temperatures on railway bridges, but cannot determine specific types of damage such as displacement or tilt. Further judgment is required during railway bridge inspection and maintenance, resulting in low work efficiency. Therefore, this application proposes a railway bridge health monitoring system and method based on infrared thermal imaging. This system combines an infrared thermal imaging camera and sensors to comprehensively analyze temperature and sensor data, determine the type and severity of damage, and improve inspection and maintenance efficiency.

[0057] The technical solution of this application will be described below with reference to specific embodiments.

[0058] See Figure 1 The railway bridge health monitoring system based on infrared thermal imaging provided in this application includes a monitoring center 1, several local monitoring modules 2, and several data acquisition devices 3. The monitoring center 1 establishes a communication connection with the local monitoring modules 2, and the local monitoring modules 2 establish a communication connection with the data acquisition devices 3. See also... Figure 2The monitoring center 1 includes a control unit 101, a command sending unit 102, a data receiving unit 103, a data processing unit 104, an alarm unit 105, and a display unit 106. The local monitoring module 2 includes a command receiving unit 201, an execution unit 202, a data acquisition unit 203, and a data sending unit 204. The data acquisition device 3 includes an infrared thermal imaging camera 301, a sensor 302, and a weather station 303. The sensor 302 includes, but is not limited to, displacement sensors, tilt sensors, and vibration sensors.

[0059] The monitoring system provided in this embodiment adopts a distributed structure. Monitoring center 1 oversees multiple railway bridges, with each bridge equipped with a local monitoring module 2. The local monitoring module 2 manages several infrared thermal imaging cameras 301, several sensors 302, and a weather station 303, among other data acquisition devices 3. Infrared thermal imaging cameras 301 of different structural types are installed at different monitoring locations. For example, PTZ cameras can be installed for larger monitoring areas such as above and below the bridge deck and piers, while bullet cameras can be installed for smaller monitoring areas such as expansion joints and supports. The infrared thermal imaging cameras 301 communicate with the local monitoring module 2 via a wired network or wireless bridge. Sensors 302 communicate with the local monitoring module 2 via analog input interfaces when close to it, and via LORA (Long Range Radio) when farther away. The weather station 303 communicates with the local monitoring module 2 via an RS485 bus or LORA.

[0060] See Figure 3 In monitoring center 1, control unit 101 is used to set control commands and parameter setting commands. Control unit 101 is connected to command sending unit 102, and sends the control commands and parameter setting commands to command sending unit 102. Command sending unit 102 is connected to command receiving unit 201 in local monitoring module 2, and sends the control commands and parameter setting commands to command receiving unit 201 via a 4G wireless network. Command receiving unit 201 is connected to execution unit 202, and sends the control commands and parameter setting commands to execution unit 202. Execution unit 202 executes the parameter setting commands, sets the address and monitoring area of ​​data acquisition device 3, and sends the execution results to monitoring center 1. Execution unit 202 is connected to data acquisition device 3, and sends the control commands to data acquisition device 3 to control the start and stop of data acquisition device 3 and the rotation of infrared thermal imaging camera 301.

[0061] The data acquisition device 3 executes the control command and collects monitoring data within the monitoring area. This monitoring data includes bridge temperature data acquired by the infrared thermal imaging camera 301, sensor data acquired by the sensor 302, and ambient temperature data acquired by the weather station 303. The data acquisition device 3 is connected to the data acquisition unit 203 of the local monitoring module 2, and the data acquisition unit is connected to the data transmission unit 204. The data acquisition unit 203 acquires the monitoring data and sends it to the data transmission unit 204. The data transmission unit 204 is connected to the data receiving unit 103 of the monitoring center 1 via a 4G wireless network, and sends the monitoring data to the data receiving unit 103 in real time. The data receiving unit 103 is connected to the data processing unit 104, and sends the monitoring data to the data processing unit 104, which processes and analyzes the monitoring data. The data processing unit 104 is connected to the alarm unit 105 and the display unit 106.

[0062] When a train passes over a railway bridge, the bridge structure vibrates. If the bridge is in good condition, all its components work closely together and synchronously without deviation. However, if a component is damaged, such as due to displacement, tilting, or torsion, it can lead to an imbalance in the coordination between components, causing friction and heat, which in turn can damage the bridge structure. By monitoring changes in the bridge's temperature and comparing them with typical changes, it is possible to determine if there are any potential defects or damage to the bridge.

[0063] Data processing unit 104 extracts bridge temperature data and ambient temperature data in the monitoring area when a train passes over the railway bridge. The bridge temperature data includes the average temperature, minimum temperature, and maximum temperature of the monitoring area. (See also...) Figure 4 The system calculates the difference between the average temperature of the monitored area and the ambient temperature, sets an alarm threshold, and compares the difference between the average temperature and the ambient temperature with the alarm threshold. If the difference exceeds the alarm threshold, the monitored area is determined to have a potential for disease. For example, if the alarm threshold is set to 10℃, and the difference between the average temperature of the monitored area and the ambient temperature is 13℃, then the monitored area is determined to have a potential for disease.

[0064] If potential defects exist in the monitored area, sensor data for that area is acquired, and the type of defect is determined based on this data. This is illustrated using displacement data acquired by a displacement sensor, tilt data acquired by a tilt sensor, and vibration data acquired by a vibration sensor as examples. See [link to documentation]. Figure 5First, displacement thresholds, tilt angle thresholds, and vibration thresholds are set based on the length of the railway bridge, the location of the monitoring area, and different structural features. The monitoring data also includes weather data acquired by meteorological station 303, such as wind force and direction. Different vibration thresholds are set for different locations on the railway bridge based on different wind directions and wind force levels. Locations where the displacement data exceeds the displacement threshold are identified as displacement fault points, locations where the tilt angle data exceeds the tilt angle threshold are identified as tilt fault points, and locations where the vibration data exceeds the vibration threshold are identified as vibration fault points.

[0065] Based on the structural information corresponding to the displacement, tilting, and vibration fault locations, a comprehensive analysis is conducted to determine the specific type of fault. For example, at the pier location, a lateral displacement of 2.3 mm, a longitudinal displacement of 1.6 mm, and an inclination angle of 89.233° were detected, indicating a potential for tilting and settlement of the pier. Regular inspections are required, and the trend of real-time monitoring data should be closely monitored. At the support location, a lateral displacement of 3.8 mm, a longitudinal displacement of 1.4 mm, and an inclination angle of 88.897° were detected, indicating a potential for obstructed movement of the support. On-site inspection is necessary to check for deformation or the presence of debris, and any debris should be removed.

[0066] In one illustrative embodiment, the monitoring data also includes image data and video data acquired by the infrared thermal imaging camera 301. If potential defects exist in the monitored area, infrared thermal images and monitoring videos of the monitored area can also be acquired to determine the specific type of defect. For example, when potential defects are detected on the bridge deck, an infrared thermal image of the bridge deck is acquired to observe whether cracks exist, and regular inspections are conducted. When potential defects are detected on bolts, monitoring videos of the bolts are acquired to observe whether deformation has occurred, and timely replacement is carried out.

[0067] After acquiring temperature data, the severity of the disease can be determined based on the difference between the highest and average temperatures in the monitored area. See also... Figure 6 The system sets a first temperature threshold, a second temperature threshold, and a third temperature threshold, and calculates the difference between the highest temperature and the average temperature in the monitoring area. If the difference between the highest temperature and the average temperature in the monitoring area exceeds the first temperature threshold but does not exceed the second temperature threshold, the point with the highest temperature in the monitoring area is identified as a common disease point. The data processing unit 104 issues a common alarm signal and sends the common alarm signal to the alarm unit 105. The alarm unit 105 receives the common alarm signal and issues a common alarm.

[0068] If the difference between the highest temperature and the average temperature in the monitored area exceeds a second temperature threshold but does not exceed a third temperature threshold, the point with the highest temperature in the monitored area is identified as a serious disease point. The data processing unit 104 issues a serious alarm signal and sends the serious alarm signal to the alarm unit 105. The alarm unit 105 receives the serious alarm signal and issues a serious alarm. If the difference between the highest temperature and the average temperature in the monitored area exceeds a third temperature threshold, the point with the highest temperature in the monitored area is identified as a dangerous disease point. The data processing unit 104 issues a dangerous alarm signal and sends the dangerous alarm signal to the alarm unit 105. The alarm unit 105 receives the dangerous alarm signal and issues a dangerous alarm.

[0069] After the above processing, the data processing unit 104 can send the monitoring results, such as the location, type, level and treatment plan of the defects in the railway bridge, to the display unit 106, and the display unit 106 displays the monitoring results.

[0070] In one illustrative embodiment, monitoring center 1 also performs machine learning on the monitoring data to predict potential hazards, issue early warnings, and display handling solutions, which will be described in detail below. See also Figure 7 The data processing unit 104 plots curves showing the changes in the highest, average, and lowest temperatures of the monitored area over time, as well as curves showing the changes in sensor data of the monitored area over time, based on the monitoring data, to obtain the temperature change trend and sensor data change trend of the monitored area. It extracts the temperature change characteristics of bridges at different types of defect locations, compares the temperature change trend of the monitored area with the temperature change characteristics of the defect locations, and if the temperature change trend of the monitored area matches the temperature change characteristics of the defect locations, it extracts the sensor data change characteristics of the defect locations. It then compares the sensor data change trend of the monitored area with the sensor data change characteristics of the defect locations, and if the temperature change trend of the monitored area matches the sensor data change characteristics of the defect locations, it determines that there is a potential defect in the monitored area.

[0071] For example, if monitoring shows a 12°C increase in temperature difference, a 2.1mm increase in lateral displacement, a 1.5mm increase in longitudinal displacement, and a 0.263° increase in tilt angle for a bridge pier within 4 months, it is determined that the pier may exceed the displacement and tilt angle thresholds after 3 months, indicating a potential for tilting and settlement. Regular inspections are necessary to monitor the trend, and reinforcement should be carried out within 3 months. If monitoring shows an 8°C increase in temperature difference for a bolt within 3 months, comparing the monitoring video of the bolt from 3 months ago with the current video reveals deformation. The bolt may break after 4 months and should be replaced within 2 months.

[0072] After processing, the data processing unit 104 issues a warning signal and sends it to the alarm unit 105. The alarm unit 105 receives the warning signal and issues a warning alarm. The data processing unit 104 sends the prediction results to the display unit 106. The prediction results include the bridge temperature change trend, the sensor data change trend, and the disease type, disease level, and treatment strategy of the disease points that are consistent with the bridge temperature change trend and sensor data change trend in the monitoring area. The display unit 106 displays the prediction results.

[0073] The railway bridge health monitoring system based on infrared thermal imaging provided in this embodiment includes multiple infrared thermal imaging cameras 301 and sensors 302, which are installed at different locations on the railway bridge. Different monitoring areas are divided by a monitoring center 1. By comprehensively processing all the monitoring areas of the same railway bridge, the monitoring data of the entire railway bridge can be obtained, including weather conditions, an infrared thermal image of the entire railway bridge, and sensor data and monitoring videos of various parts. In addition to the displacement sensor, tilt sensor, and vibration sensor provided in this embodiment, other types of sensors can be used as needed. The specific temperature, displacement, and time values ​​mentioned in this embodiment are only illustrative examples. In actual applications, the specific structure and parameters of each railway bridge are different, and a reasonable solution needs to be developed based on the actual situation.

[0074] As can be seen from the above embodiments, this application provides a railway bridge health monitoring system and method based on infrared thermal imaging. The data acquisition device 3 uploads the monitoring data to the monitoring center 1 through the local monitoring module 2. The monitoring center 1 sets a disease alarm threshold. If the difference between the average temperature of the monitoring area and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential disease in the monitoring area. Then, the sensor data of the monitoring area is acquired to determine the type of disease. Based on the bridge temperature data of the monitoring area, the disease level is determined, an alarm is issued, and a handling plan is displayed. At the same time, the monitoring center 1 performs machine learning on the monitoring data to predict potential disease hazards, issue early warnings, and display handling strategies. The technical solution provided by this application comprehensively analyzes the bridge temperature data acquired by the infrared thermal imaging camera 301, the sensor data acquired by the sensor 302, and the weather data acquired by the meteorological station 303 to determine the type and level of disease and display the handling plan, thereby improving the efficiency of detection and maintenance.

[0075] Similar parts between the embodiments provided in this application can be referred to mutually. The specific implementation methods provided above are only a few examples under the overall concept of this application and do not constitute a limitation on the scope of protection of this application. For those skilled in the art, any other implementation methods extended from the solution of this application without creative effort shall fall within the scope of protection of this application.

Claims

1. A railway bridge health monitoring system based on infrared thermal imaging, characterized in that, include: The monitoring center (1), several local monitoring modules (2) and several data acquisition devices (3) are configured to communicate bidirectionally with the local monitoring modules (2); the local monitoring modules (2) communicate bidirectionally with the data acquisition devices (3); the data acquisition devices (3) include an infrared thermal imaging camera (301), a sensor (302) and a weather station (303). The monitoring center (1) is configured to acquire control commands and parameter setting commands and send the control commands and parameter setting commands to the local monitoring module (2); the control commands are used to control the start and stop of the data acquisition device (3) and to control the rotation of the infrared thermal imaging camera (301); the parameter setting commands are used to set the address and monitoring area of ​​the data acquisition device (3); The local monitoring module (2) is configured to acquire and execute the parameter setting command and send the execution result to the monitoring center (1). The local monitoring module (2) is configured to acquire the control command and send the control command to the data acquisition device (3). The data acquisition device (3) is configured to acquire and execute the control command and acquire monitoring data of the monitoring area; the monitoring data includes bridge temperature data acquired by the infrared thermal imaging camera (301), sensor data acquired by the sensor (302), and ambient temperature data acquired by the weather station (303); The local monitoring module (2) is configured to acquire the monitoring data and upload the monitoring data to the monitoring center (1) in real time. The monitoring center (1) is configured to acquire the bridge temperature data and the ambient temperature data when a train passes over the railway bridge in the monitoring area, compare the bridge temperature data and the ambient temperature data, and if the bridge temperature data is abnormal, acquire the sensor data of the monitoring area, determine the type and level of the disease based on the bridge temperature data and the sensor data, issue an alarm and display the handling plan. The monitoring center (1) is configured to acquire the monitoring data of the monitoring area, perform machine learning on the monitoring data, predict potential disease hazards, issue early warnings and display processing strategies; The bridge temperature data acquired by the infrared thermal imaging camera (301) includes the average temperature, minimum temperature and maximum temperature of the monitored area; In the steps of comparing the bridge temperature data and the ambient temperature data, if the bridge temperature data is abnormal, acquiring the sensor data of the monitoring area, determining the type and level of the defect based on the bridge temperature data and the sensor data, issuing an alarm, and displaying the treatment plan, the monitoring center (1) is further configured as follows: Calculate the difference between the average temperature of the monitored area and the ambient temperature; An alarm threshold is set. If the difference between the average temperature and the ambient temperature exceeds the alarm threshold, it is determined that there is a potential for disease in the monitored area. Acquire sensor data of the monitoring area, and determine the type of disease based on the sensor data. The sensor data includes displacement data, tilt angle data, and vibration data. The severity of the disease is determined based on the difference between the highest and average temperatures in the monitored area. The monitoring center (1) displays the disease level and disease type of the monitored area, issues alarms and displays the treatment plan; In the steps of performing machine learning on the monitoring data to predict potential disease hazards, issuing early warnings, and displaying handling strategies, the monitoring center (1) is further configured as follows: The monitoring center (1) acquires the temperature change trend of the bridge in the monitoring area and the change trend of the sensor data; Extract the temperature change characteristics of the bridge at the location of the damage; If the temperature change trend of the bridge in the monitoring area is consistent with the temperature change characteristics of the bridge at the defect location, the sensor data change characteristics of the defect location are extracted. If the trend of the sensor data in the monitoring area is consistent with the trend of the sensor data at the disease location, it is determined that there is a potential disease in the monitoring area, an early warning is issued, and a handling strategy is displayed.

2. The railway bridge health monitoring system based on infrared thermal imaging according to claim 1, characterized in that, Based on the sensor data, the disease type is determined, and the monitoring center (1) is configured as follows: For the monitoring area, displacement threshold, tilt angle threshold, and vibration threshold are set according to different structures; If the displacement data exceeds the displacement threshold, the location where the displacement data exceeds the displacement threshold is determined as a displacement defect point; If the tilt angle data exceeds the tilt angle threshold, the location where the tilt angle data exceeds the tilt angle threshold is determined as a tilting defect point; If the vibration data exceeds the vibration threshold, the location where the vibration data exceeds the vibration threshold is determined as a vibration fault point; Based on the structures corresponding to the displacement fault locations, tilt fault locations, and vibration fault locations, the specific fault type is determined.

3. The railway bridge health monitoring system based on infrared thermal imaging according to claim 1, characterized in that, In the step of determining the disease level based on the difference between the highest temperature and the average temperature in the monitored area, the monitoring center (1) is configured as follows: Set a first temperature threshold, a second temperature threshold, and a third temperature threshold; Calculate the difference between the highest temperature and the average temperature in the monitored area; If the difference between the highest temperature and the average temperature in the monitoring area exceeds the first temperature threshold but does not exceed the second temperature threshold, the point with the highest temperature in the monitoring area is identified as a common disease point, and the monitoring center (1) issues a common alarm. If the difference between the highest temperature and the average temperature in the monitoring area exceeds the second temperature threshold but does not exceed the third temperature threshold, the point with the highest temperature in the monitoring area is identified as a serious disease point, and the monitoring center (1) issues a serious alarm. If the difference between the highest temperature and the average temperature in the monitoring area exceeds the third temperature threshold, the point with the highest temperature in the monitoring area is identified as a dangerous disease point, and the monitoring center (1) issues a danger alarm.

4. A method for monitoring the health of railway bridges based on infrared thermal imaging, applied to the railway bridge health monitoring system based on infrared thermal imaging as described in any one of claims 1 to 3, characterized in that, include: The monitoring center (1) acquires control commands and parameter setting commands and sends the control commands and parameter setting commands to the local monitoring module (2); the control commands are used to control the start and stop of the data acquisition device (3) and to control the rotation of the infrared thermal imaging camera (301); the parameter setting commands are used to set the address and monitoring area of ​​the data acquisition device (3); The local monitoring module (2) acquires and executes the parameter setting command, and sends the execution result to the monitoring center (1). The local monitoring module (2) acquires the control command and sends the control command to the data acquisition device (3). The data acquisition device (3) acquires and executes the control command and collects monitoring data of the monitoring area; the monitoring data includes bridge temperature data acquired by the infrared thermal imaging camera (301), sensor data acquired by the sensor (302), and ambient temperature data acquired by the weather station (303); The local monitoring module (2) acquires the monitoring data and uploads the monitoring data to the monitoring center (1) in real time. The monitoring center (1) acquires the bridge temperature data and the ambient temperature data of the monitoring area when a train passes over the railway bridge, compares the bridge temperature data and the ambient temperature data, and if the bridge temperature data is abnormal, acquires the sensor data of the monitoring area, determines the type and level of the disease based on the bridge temperature data and the sensor data, issues an alarm and displays the handling plan. The monitoring center (1) acquires the monitoring data of the monitoring area, performs machine learning on the monitoring data, predicts potential disease hazards, issues early warnings and displays processing strategies.

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