Bridge structure health monitoring method and system

Abstract

The invention discloses a bridge structure health monitoring method and system, and relates to the technical field of bridge detection. Comprising the following steps: arranging a sensor on a bridge, collecting bridge health data including various monitoring signals, converting the monitoring signals into electric data, and transmitting the electric data to a data processing center; a recognition model is trained through abnormal data corresponding to existing abnormal conditions, representative monitoring data are input into the trained recognition model, real-time monitoring is carried out, early warning is carried out when the abnormal data are recognized, and timely early warning is carried out when the abnormal data appear in the bridge health data, so that the labor cost is effectively saved, and the working efficiency is improved. An inaccurate result caused by non-uniform inertial thinking and subjective judgment standards of workers is avoided; and meanwhile, the data is regularly compiled into reports, and the reports in different periods are compared, so that the change trend of the bridge health data is obtained, slow change which is difficult to recognize is obtained, advanced maintenance is carried out, and the service life of the bridge is effectively prolonged.

Description

Technical Field

[0001] The present application relates to the technical field of bridge detection, and in particular to a bridge structure health monitoring method and system. Background Art

[0002] As a major and powerful infrastructure country, my country has built roads through mountains and bridges over rivers. Bridges, as an indispensable part of transportation, are used from the water surface to valleys, urban elevated areas and other areas. Compared with ordinary roads built directly on the ground, bridges need to be supported by piers or have the bridge deck suspended by steel cables. In order for the bridge to bear its own weight and the designed load weight, the bridge needs to adopt a higher structural standard than the ground. At the same time, the health status of the bridge needs to be monitored during its use to ensure the safety of life and property.

[0003] At present, the monitoring of bridges is usually done by staff members judging the health of the bridge by checking whether there are any changes in the bridge deck. However, due to the inertial thinking of the staff members, it is difficult to perceive slow changes, and they are greatly influenced by subjective consciousness. Different staff members have different judgment bases on whether the bridge is healthy, and it is difficult to reach a consensus. In addition, as time goes by, a large number of bridges will reach their design service life. Manual monitoring of each bridge requires a lot of manpower and funds, and the monitoring results are difficult to unify. Therefore, there is an urgent need for a bridge structure health monitoring method and system. Through the implementation of multiple sensor systems, representative environmental loads, dynamic traffic loads, and structural response and local damage information during the bridge operation period can be obtained. Based on the evaluation of the monitoring information, the technical safety status of the structure can be tracked regularly to provide technical support for the safe, efficient, and economical operation of the structure and the formulation of maintenance decisions. Summary of the invention

[0004] This application aims to solve the problem that the current manual safety monitoring of bridges requires a lot of manpower and funds, and the monitoring results are difficult to unify. It provides a bridge structure health monitoring method and system, which obtains bridge information through the implementation of multiple sensor systems, regularly tracks the technical safety status of the structure based on the evaluation of the monitoring information, and provides technical support for the safe, efficient and economic operation of the structure and the formulation of management and maintenance decisions. The specific technical solution is as follows: In a first aspect of the present application, a bridge structure health monitoring method is provided, comprising: Sensors are distributed at multiple locations on the bridge to collect bridge health data through the sensors. The bridge health data includes various monitoring signals, and the monitoring signals are converted into electrical data and then transmitted to a data processing center; The data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a device for screening representative monitoring data of bridge structures and components in the electrical data; The recognition model is trained by using abnormal data corresponding to existing abnormal situations, and the representative monitoring data is input into the trained recognition model for real-time monitoring, and an early warning is issued when abnormal data is identified; Statistics are collected based on the data from previous inspections and monitoring, and reports are compiled regularly. The reports include the identification of the overall internal force state of the structure, modal analysis, load-bearing capacity assessment, and comparison results with historical reports.

[0005] In one embodiment of the present application, the sensor includes an image sensor, which is used to collect images of the bridge and vehicles passing through the bridge, form traffic monitoring data and transmit it to the data processing center. The recognition model identifies abnormal events in the traffic monitoring data and records the statistics in the report.

[0006] In one embodiment of the present application, based on the comparison result between the report form and the historical report form, the location where the structural health condition has declined in the comparison result is output, and the sensor distribution is adjusted around the location where the structural health condition has declined.

[0007] In one embodiment of the present application, the data processing center obtains the corresponding relationship between different environmental data and various bridge health data according to the change of corresponding sensor data under different environmental data in the historical data; Different maintenance health data and blocking health data are set according to the monitoring level required for the bridge. When the sensor collects one or more of the bridge health data reaching the corresponding maintenance health data, the staff is notified to go for maintenance; when the sensor collects one or more of the bridge health data reaching the corresponding blocking health data, the staff is notified to block the bridge; Combined with big data to obtain data on future environmental changes, the predicted changes of corresponding sensor data are obtained. When the predicted change of the bridge health data reaches the maintenance health data, the staff is notified to perform maintenance before future environmental changes. When the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before future environmental changes. If the maintenance cannot be completed before the future environmental changes, the bridge will be blocked.

[0008] In one embodiment of the present application, when the sensor collects one or more of the bridge health data that reaches the corresponding maintenance health data or the blockade health data, the bridge health data collected by multiple sensors surrounding the sensor are simultaneously obtained. If the bridge health data collected by the surrounding sensors does not show any data change, the sensor is used as the sensor to be adopted, and temporary data of the sensor's position is obtained through data from multiple surrounding sensors.

[0009] In one embodiment of the present application, the data processing center sends the location information of the sensor to be adopted to the staff, and the staff goes to check whether the sensor to be adopted is damaged. If damaged, the data collection of the sensor to be adopted is suspended until the sensor to be adopted is repaired or replaced; if not damaged, the staff checks whether the surrounding sensors are damaged.

[0010] In one embodiment of the present application, the bridge is divided into areas according to the bridge structure, including the bridge deck, piers and railings. The bridge deck is also divided according to the bridge deck segments in the construction drawings. Data difference analysis is performed based on the distance between the sensor and the surrounding sensors and whether they are located in different areas.

[0011] In one embodiment of the present application, the data processing center sets a standard work process, and the standard work process is obtained by pre-collecting or photographing various predetermined work processes performed by staff, inputting the standard work process into the model for training, and obtaining the trained recognition model; The image sensor collects working images of the staff, and the recognition model identifies the type of work performed by the staff through the working images, and provides guidance and warnings for subsequent operations of the staff.

[0012] In one embodiment of the present application, the bridge health data displays various types of bridge health data obtained from bridge operation period monitoring through a user interface, and at the same time, users are classified and graded according to user information, different static and dynamic data contents are displayed to different users according to different classifications and grades, and different classified and graded users are granted different control permissions over the data processing center.

[0013] In a second aspect of the present application, a bridge structure health monitoring system is provided, comprising: A data acquisition module, which is equipped with sensors at multiple locations on the bridge to collect bridge health data, including various monitoring signals, and convert the monitoring signals into electrical data before transmitting them to a data processing center; Data processing module, the data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a module for screening representative monitoring data of bridge structures and components in the electrical data; The model recognition module trains the recognition model through the abnormal data corresponding to the existing abnormal situation, inputs the representative monitoring data into the trained recognition model, performs real-time monitoring, and issues an early warning when abnormal data is recognized; The report output module collects statistics based on the data of previous inspections and monitoring, and regularly compiles reports, which include the overall internal force state identification, modal analysis, load-bearing capacity assessment of the structure, and comparison results with historical reports. In one embodiment of the present application, the data acquisition module also includes an image acquisition submodule, and the sensor in the image acquisition submodule includes an image sensor, which is used to collect images of the bridge and vehicles passing through the bridge to form traffic monitoring data for transmission to the data processing center. The recognition model identifies abnormal events in the traffic monitoring data and records the statistics in the report form.

[0014] In one embodiment of the present application, it also includes a distribution adjustment module, which outputs the location where the structural health status has declined in the comparison result based on the comparison result between the report form and the historical report form, and adjusts the sensor distribution around the location where the structural health status has declined.

[0015] In one embodiment of the present application, a big data prediction module is also included, and the data processing center obtains the corresponding relationship between different environmental data and various bridge health data according to the changes in corresponding sensor data under different environmental data in the historical data; Different maintenance health data and blocking health data are set according to the monitoring level required for the bridge. When the sensor collects one or more of the bridge health data reaching the corresponding maintenance health data, the staff is notified to go for maintenance; when the sensor collects one or more of the bridge health data reaching the corresponding blocking health data, the staff is notified to block the bridge; Combined with big data to obtain data on future environmental changes, the predicted changes of corresponding sensor data are obtained. When the predicted change of the bridge health data reaches the maintenance health data, the staff is notified to perform maintenance before future environmental changes. When the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before future environmental changes. If the maintenance cannot be completed before the future environmental changes, the bridge will be blocked.

[0016] In one embodiment of the present application, it also includes a sensor judgment module. When the sensor collects one or more of the bridge health data that reaches the corresponding maintenance health data or the blockade health data, the bridge health data collected by multiple peripheral sensors of the sensor are simultaneously obtained. If the bridge health data collected by the peripheral sensors does not show data changes, the sensor is used as the sensor to be adopted, and temporary data of the sensor position is obtained through multiple peripheral sensor data.

[0017] In one embodiment of the present application, the sensor judgment module also includes a personnel judgment submodule, and the data processing center sends the location information of the sensor to be adopted to the staff, and the staff goes to check whether the sensor to be adopted is damaged. If damaged, the data collection of the sensor to be adopted is suspended until the sensor to be adopted is repaired or replaced; if not damaged, the surrounding sensors are checked to see if they are damaged.

[0018] In one embodiment of the present application, the sensor judgment module also includes a position impact submodule, which divides the bridge into areas according to the bridge structure, including the bridge deck, piers and railings, and divides the bridge deck into sections according to the bridge deck in the construction drawings, and performs data difference analysis based on the distance between the sensor and the surrounding sensors and whether they are located in different areas.

[0019] In one embodiment of the present application, the sensor judgment module further includes a workflow recognition submodule, the data processing center sets a standard workflow, the standard workflow is obtained by pre-collecting or photographing various predetermined workflows performed by staff, inputting the standard workflow into a model for training, and obtaining the trained recognition model; The image sensor collects working images of the staff, and the recognition model identifies the type of work performed by the staff through the working images, and provides guidance and warnings for subsequent operations of the staff.

[0020] In one embodiment of the present application, a user interface module is also included. The bridge health data displays various types of bridge health data obtained from bridge operation period monitoring through the user interface, and users are classified and graded according to user information. Different static and dynamic data contents are displayed to different users according to different classifications and grades, and different classified and graded users are granted different control permissions over the data processing center.

[0021] This application has the following beneficial effects: 1. By setting sensors on the bridge according to the environment in which the bridge is located, the design load requirements and the actual load conditions, and training the recognition model according to the existing abnormal data, an recognition model that can recognize abnormal data is obtained. When abnormal data appears in the bridge health data, timely warning is issued, which effectively saves labor costs and avoids inaccurate results caused by the inertial thinking and inconsistent subjective judgment standards of the staff; at the same time, by regularly compiling data into reports and comparing reports of different periods, the change trend of the bridge health data is obtained, and slow changes that are difficult to identify are obtained, and early maintenance is carried out to effectively extend the life of the bridge.

[0022] 2. Traffic data is collected through the image sensor, and the recognition model is further trained to identify abnormal events, including vehicle collisions with guardrails. After an abnormal event occurs, staff are notified to go for maintenance. After the guardrail of a bridge is hit, the health of the surrounding structure is likely to decline. The abnormal event is identified and recorded in the report form; further, by comparing with historical report forms, the staff updates the distribution of sensors based on the comparison results, so that more sensors monitor the locations where the structural health has declined, including adding new sensors and moving sensors at locations where the structural health is stable to locations where the structural health has declined, so that the sensor distribution is more reasonable and the locations where the structural health has declined are monitored more.

[0023] 3. Through data drills and historical data, different environmental data lead to corresponding sensor changes, and the corresponding relationships between different environmental data and various bridge health data are obtained one by one; further, according to the health data range required by the current bridge, the corresponding maintenance health data and blockade health data are pre-set. When a certain area of ​​the bridge reaches the maintenance health data, the staff needs to inspect the area of ​​the bridge. Similarly, when a certain area of ​​the bridge reaches the blockade health data, the staff needs to block the area of ​​the bridge. By setting up in this way, when the bridge has a health condition, it is processed in a graded manner to avoid blocking the bridge multiple times to affect traffic. Furthermore, the future environmental change data is screened out from the big data, and the changes of various bridge health data are simulated when the future environment arrives. The predicted changes are compared with the maintenance health data and the blocking health data to achieve pre-maintenance. In particular, when the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before the future environmental change. After the maintenance is completed, the health condition of the bridge improves, and the simulation prediction is performed again, which effectively reduces the number of bridge blocking times, and also avoids serious environmental changes causing the bridge health condition to directly pass the maintenance state and directly reach the severely damaged blocking state. After the bridge reaches the blocking state, even if it is used again through maintenance, it is difficult to recover to the healthy state after maintenance from the maintenance state. Therefore, through pre-maintenance, the bridge health condition reaches the maximum value that can resist environmental changes, reducing the risk and number of bridge conditions reaching the blocking state. If the maintenance is not completed before the future environmental change, the bridge will be blocked to ensure the safety of staff, vehicles and pedestrians.

[0024] 4. Considering that the bridge health data is collected by sensors, if the sensor is abnormal, it will lead to misjudgment of the bridge health status. Sensor abnormalities include sensor damage, sensor abnormality due to detachment, etc., and the bridge data is not collected normally. However, since the bridge is a structural whole, when an abnormality occurs in a certain area of ​​the bridge, the surrounding area will also be accompanied by data changes. Therefore, when it is identified that the data collected by the sensor is abnormal, it is also identified whether the data collected by the sensors around the sensor have corresponding changes. If the bridge health data collected by the surrounding sensors does not show data changes, the sensor is used as a sensor to be adopted, and the sensor position is sent to the staff to check whether the sensor is abnormal. The time when the staff goes to check is obtained through multiple surrounding sensor data, and the temporary data of the sensor position is obtained according to the data continuity; further, considering that the bridge has many materials and plates, the data continuity between different plates and materials is different. The greater the material difference, the worse the data continuity. For example, if the data collected by the sensor of a bridge deck at a certain part of the bridge is abnormal, the data continuity of the surrounding sensors located in the same section of the bridge deck is better, and the data continuity of the bridge deck in the adjacent section is poor. The material difference between the bridge deck and the railing is large, and the data continuity is the worst. Therefore, the bridge is divided into areas, and when temporary data is obtained through data continuity, the data continuity loss is calculated when some of the surrounding sensors and the sensors to be adopted may not be located in the same area; at the same time, when the staff goes to the location of the sensors to be adopted for inspection, if the data of the sensors to be adopted are normal, while maintaining the bridge, check whether the surrounding sensors are damaged.

[0025] 5. Set up a standard workflow, and use the recognition model to identify when the staff is performing various types of work that differ from the standard workflow, and provide reminders and guidance, so that the staff can complete the work more quickly and in a standard manner; further, when the staff receives the task and goes to the designated location to work, the recognition model identifies whether the staff's operation is consistent with the task, so as to avoid the staff from making wrong operations from the beginning; at the same time, the staff's portrait can also be entered, and the recognition model identifies whether the staff performing the work is consistent with the staff receiving the work, so as to reduce the phenomenon of unauthorized substitution and replacement between staff. Furthermore, the recognition model can also be used to identify the staff's expression data and behavior data, and identify whether the staff is working in fatigue or drinking, so as to effectively ensure the quality of the staff's health maintenance of the bridge.

[0026] 6. There is a lot of bridge health data, and users with different user information can manage and view different data. For example, the backend manager needs to classify and maintain the data, and the manager can choose the content to be displayed to the outside, realize the authority of different levels of management, and release bridge health information to the public with a focus on the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0028] Figure 1 A schematic diagram of the electronic device structure of the hardware operating environment involved in an embodiment of the present application.

[0029] Figure 2 It is a flow chart of the steps of a bridge structure health monitoring method provided in an embodiment of the present application.

[0030] Figure 3 It is a functional module diagram of a bridge structure health monitoring system provided in an embodiment of the present application.

[0031] Figure 4 It is a module construction diagram of a bridge structure health monitoring system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0033] The solution of the present application is further described below in conjunction with the accompanying drawings.

[0034] like Figure 1As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM) memory, or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0035] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0036] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a data storage module, a network communication module, a user interface module and an electronic program.

[0037] exist Figure 1 In the electronic device shown, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls a bridge structure health monitoring method stored in the memory 1005 through the processor 1001, and executes a bridge structure health monitoring system provided in an embodiment of the present application.

[0038] Based on the aforementioned hardware operating environment and system architecture, in the first aspect of this application, refer to Figure 2 As shown, a bridge structure health monitoring method is provided, comprising: Sensors are distributed at multiple locations on the bridge to collect bridge health data through the sensors. The bridge health data includes various monitoring signals, and the monitoring signals are converted into electrical data and then transmitted to a data processing center; It should be noted that the bridge health data includes multiple types, including the following: Load monitoring data: including vehicle load, wind load, seismic load, temperature load, etc. Vehicle load is usually monitored by dynamic weighing system, wind load is usually monitored by anemometer, seismic load is usually monitored by strong motion instrument, and temperature load monitoring should take into account the temperature gradient distribution in bridge components.

[0039] Environmental monitoring data: such as temperature, humidity, wind speed, wind direction, rainfall, etc. These data are essential for evaluating the impact of environmental factors on bridges. For example, changes in temperature and humidity may affect the performance of bridge materials and the thermal expansion of bridges.

[0040] Structural response monitoring data: including structural vibration, displacement, deformation, rotation angle, etc. These data can be monitored through GPS systems, displacement meters, inclinometers, accelerometers and other sensors.

[0041] Stress and strain monitoring data: Commonly used sensors include resistance strain gauges, vibrating wire strain sensors, fiber Bragg grating strain gauges, etc. These sensors can monitor the stress and strain changes of bridge structures under load, which is crucial for evaluating the safety of bridge structures.

[0042] Crack monitoring data: Crack detectors are used to monitor the development of cracks on the surface or inside bridge components, which is very important for early detection of structural damage and taking maintenance measures.

[0043] Cable force monitoring data: Cable force meters are used to monitor the cable force of bridge cables or suspenders, which is particularly important for structures such as cable-stayed bridges and suspension bridges.

[0044] Dynamic vibration monitoring data: Accelerometers are used to measure the vibration response of bridges under dynamic loads such as wind loads and vehicle driving, which is of great significance for evaluating the dynamic performance and safety of bridges.

[0045] Steel structure fatigue monitoring data: For steel structure bridges, monitoring their fatigue conditions can prevent structural failure due to fatigue.

[0046] It should be noted that the installation location and number of sensors on the bridge can be set according to the different environments and different design loads of the bridge in each part; The data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a device for screening representative monitoring data of bridge structures and components in the electrical data; It should be noted that after the data is collected by various sensors, it is usually pre-processed, including screening representative monitoring data of bridge structures and components from the numerous data collected by the sensors, and remotely transmitting them to the data processing center for further analysis and processing; The recognition model is trained by using abnormal data corresponding to existing abnormal situations, and the representative monitoring data is input into the trained recognition model for real-time monitoring, and an early warning is issued when abnormal data is identified; It should be noted that the abnormal data corresponding to the existing abnormal situation can be searched and obtained through the Internet, or the abnormal data can be screened in the database. After the recognition model is trained, the data collected by the sensor is recognized to realize the real-time monitoring of the bridge health data; Statistics are collected based on the data from previous inspections and monitoring, and reports are compiled regularly. The reports include the identification of the overall internal force state of the structure, modal analysis, load-bearing capacity assessment, and comparison results with historical reports.

[0047] It should be noted that through these monitoring data, the health status of the bridge can be evaluated in real time, providing a scientific basis for the daily maintenance, inspection, maintenance, reinforcement and repair of the bridge; at the same time, the change trend of the bridge health data can be analyzed according to periodic reports, that is, maintenance is not necessarily carried out only when it exceeds the normal range, and maintenance can be carried out in advance when a change trend appears; In this embodiment, sensors are set on the bridge according to the environment in which the bridge is located, the design load requirements, and the actual load conditions, and the recognition model is trained according to the existing abnormal data to obtain a recognition model that can recognize abnormal data. When abnormal data appears in the bridge health data, timely warnings are issued, which effectively saves labor costs and avoids inaccurate results caused by the inertial thinking and inconsistent subjective judgment standards of the staff. At the same time, by regularly compiling data into reports and comparing reports of different periods, the changing trend of the bridge health data is obtained, and slow changes that are difficult to identify are obtained, so that maintenance can be carried out in advance, effectively extending the life of the bridge.

[0048] In one embodiment of the present application, the sensor includes an image sensor, which is used to collect images of the bridge and vehicles passing through the bridge, form traffic monitoring data and transmit it to the data processing center. The recognition model identifies abnormal events in the traffic monitoring data and records the statistics in the report.

[0049] In one embodiment of the present application, based on the comparison result between the report form and the historical report form, the location where the structural health condition has declined in the comparison result is output, and the sensor distribution is adjusted around the location where the structural health condition has declined.

[0050] In this embodiment, traffic data is collected by the image sensor, and the recognition model is further trained to identify abnormal events, including vehicle collision with guardrails. After the abnormal event occurs, staff are notified to go for maintenance. After the guardrail of the bridge is hit, the health of the surrounding structure is likely to decline. The abnormal event is identified and recorded in the report form; further, by comparing with the historical report form, the staff updates the distribution of sensors based on the comparison results, so that more sensors monitor the locations where the structural health has declined, including adding new sensors and moving sensors at locations where the structural health is stable to locations where the structural health has declined, so that the sensor distribution is more reasonable and the locations where the structural health has declined are monitored more.

[0051] In one embodiment of the present application, the data processing center obtains the corresponding relationship between different environmental data and various bridge health data according to the change of corresponding sensor data under different environmental data in the historical data; Different maintenance health data and blocking health data are set according to the monitoring level required for the bridge. When the sensor collects one or more of the bridge health data reaching the corresponding maintenance health data, the staff is notified to go for maintenance; when the sensor collects one or more of the bridge health data reaching the corresponding blocking health data, the staff is notified to block the bridge; Combined with big data to obtain data on future environmental changes, the predicted changes of corresponding sensor data are obtained. When the predicted change of the bridge health data reaches the maintenance health data, the staff is notified to perform maintenance before future environmental changes. When the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before future environmental changes. If the maintenance cannot be completed before the future environmental changes, the bridge will be blocked.

[0052] In this embodiment, through data drills and historical data, different environmental data lead to corresponding sensor changes, and the corresponding relationships between different environmental data and various bridge health data are obtained one by one; further, according to the health data range required by the current bridge, the corresponding maintenance health data and blockade health data are pre-set. When a certain area of ​​the bridge reaches the maintenance health data, the staff needs to inspect the area of ​​the bridge. Similarly, when a certain area of ​​the bridge reaches the blockade health data, the staff needs to block the area of ​​the bridge. By setting up in this way, when the bridge has a health condition, it is processed in a graded manner to avoid blocking the bridge multiple times to affect traffic. Furthermore, the future environmental change data is screened out from the big data, and the changes of various bridge health data are simulated when the future environment arrives. The predicted changes are compared with the maintenance health data and the blocking health data to achieve pre-maintenance. In particular, when the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to carry out maintenance before the future environmental change. After the maintenance is completed, the health condition of the bridge improves, and the simulation prediction is carried out again, which effectively reduces the number of bridge blocking times, and also avoids serious environmental changes causing the bridge health condition to directly pass the maintenance state and directly reach the severely damaged blocking state. After the bridge reaches the blocking state, even if it is used again through maintenance, it is difficult to recover to the healthy state after maintenance from the maintenance state. Therefore, through pre-maintenance, the bridge health condition reaches the maximum value that can resist environmental changes, reducing the risk and number of bridge conditions reaching the blocking state. If the maintenance is not completed before the future environmental change, the bridge will be blocked to ensure the safety of staff, vehicles and pedestrians.

[0053] In one embodiment of the present application, when the sensor collects one or more of the bridge health data that reaches the corresponding maintenance health data or the blockade health data, the bridge health data collected by multiple sensors surrounding the sensor are simultaneously obtained. If the bridge health data collected by the surrounding sensors does not show any data change, the sensor is used as the sensor to be adopted, and temporary data of the sensor's position is obtained through data from multiple surrounding sensors.

[0054] In one embodiment of the present application, the data processing center sends the location information of the sensor to be adopted to the staff, and the staff goes to check whether the sensor to be adopted is damaged. If damaged, the data collection of the sensor to be adopted is suspended until the sensor to be adopted is repaired or replaced; if not damaged, the staff checks whether the surrounding sensors are damaged.

[0055] In one embodiment of the present application, the bridge is divided into areas according to the bridge structure, including the bridge deck, piers and railings. The bridge deck is also divided according to the bridge deck segments in the construction drawings. Data difference analysis is performed based on the distance between the sensor and the surrounding sensors and whether they are located in different areas.

[0056] In this embodiment, considering that the bridge health data is collected by sensors, if the sensor is abnormal, it will lead to misjudgment of the bridge health status. Sensor abnormalities include sensor damage, sensor abnormality due to detachment and other reasons that the bridge data is not collected normally. However, since the bridge is a structural whole, when an abnormality occurs in a certain area of ​​the bridge, the surrounding area will also be accompanied by data changes. Therefore, when it is identified that the data collected by the sensor is abnormal, it is also identified whether the data collected by the sensors around the sensor have corresponding changes. If the bridge health data collected by the surrounding sensors does not show data changes, the sensor is used as a sensor to be adopted, and the sensor position is sent to the staff to check whether the sensor is abnormal. The time when the staff goes to check is obtained through multiple surrounding sensor data, and the temporary data of the sensor position is obtained according to the data continuity; further, considering that the bridge has many materials and plates, the data continuity between different plates and materials is different. The greater the material difference, the worse the data continuity. For example, if the data collected by the sensor of a bridge deck at a certain place on the bridge is abnormal, the data continuity of the surrounding sensors located in the same section of the bridge deck is better, and the data continuity of the bridge deck in the adjacent section is poorer. The material difference between the bridge deck and the railing is large, and the data continuity is the worst. Therefore, the bridge is divided into areas, and when temporary data is obtained through data continuity, the data continuity loss is calculated when some of the surrounding sensors and the sensors to be adopted may not be located in the same area; at the same time, when the staff goes to the location of the sensors to be adopted for inspection, if the data of the sensors to be adopted are normal, while maintaining the bridge, check whether the surrounding sensors are damaged.

[0057] In one embodiment of the present application, the data processing center sets a standard work process, and the standard work process is obtained by pre-collecting or photographing various predetermined work processes performed by staff, inputting the standard work process into the model for training, and obtaining the trained recognition model; The image sensor collects working images of the staff, and the recognition model identifies the type of work performed by the staff through the working images, and provides guidance and warnings for subsequent operations of the staff.

[0058] In this embodiment, a standard workflow is set, and the recognition model is used to identify the differences between the various types of work performed by the staff and the standard workflow, and to provide reminders and guidance, so that the staff can complete the work more quickly and in a standard manner; further, when the staff receives the task and goes to the designated location to work, the recognition model identifies whether the operation performed by the staff is consistent with the task, so as to avoid the staff from making wrong operations from the beginning; at the same time, the staff can also be entered with a portrait, and the recognition model identifies whether the staff performing the work is consistent with the staff receiving the work, so as to reduce the phenomenon of unauthorized substitution and replacement between staff. Furthermore, the recognition model can also be used to identify the expression data and behavior data of the staff, and identify whether the staff is working in fatigue or drinking, so as to effectively ensure the quality of the staff's health maintenance of the bridge.

[0059] In one embodiment of the present application, the bridge health data displays various types of bridge health data obtained from bridge operation period monitoring through a user interface, and at the same time, users are classified and graded according to user information, different static and dynamic data contents are displayed to different users according to different classifications and grades, and different classified and graded users are granted different control permissions over the data processing center.

[0060] In this embodiment, there is a large amount of bridge health data, and users with different user information can manage and view different data. For example, the backend manager needs to classify and maintain the data, and the manager can choose the content to be displayed to the outside, realize the authority of different levels of management, and release bridge health information to the public with a focus on the data.

[0061] In the second aspect of the present application, reference is made to Figure 3 and Figure 4 As shown, a bridge structure health monitoring system is provided, comprising: A data acquisition module, which is equipped with sensors at multiple locations on the bridge to collect bridge health data, including various monitoring signals, and convert the monitoring signals into electrical data before transmitting them to a data processing center; Data processing module, the data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a module for screening representative monitoring data of bridge structures and components in the electrical data; The model recognition module trains the recognition model through the abnormal data corresponding to the existing abnormal situation, inputs the representative monitoring data into the trained recognition model, performs real-time monitoring, and issues an early warning when abnormal data is recognized; The report output module collects statistics based on the data of previous inspections and monitoring, and regularly compiles reports, which include the overall internal force state identification, modal analysis, load-bearing capacity assessment of the structure, and comparison results with historical reports. In one embodiment of the present application, the data acquisition module also includes an image acquisition submodule, and the sensor in the image acquisition submodule includes an image sensor, which is used to collect images of the bridge and vehicles passing through the bridge to form traffic monitoring data for transmission to the data processing center. The recognition model identifies abnormal events in the traffic monitoring data and records the statistics in the report form.

[0062] In one embodiment of the present application, it also includes a distribution adjustment module, which outputs the location where the structural health status has declined in the comparison result based on the comparison result between the report form and the historical report form, and adjusts the sensor distribution around the location where the structural health status has declined.

[0063] In one embodiment of the present application, a big data prediction module is also included, and the data processing center obtains the corresponding relationship between different environmental data and various bridge health data according to the changes in corresponding sensor data under different environmental data in the historical data; Different maintenance health data and blocking health data are set according to the monitoring level required for the bridge. When the sensor collects one or more of the bridge health data reaching the corresponding maintenance health data, the staff is notified to go for maintenance; when the sensor collects one or more of the bridge health data reaching the corresponding blocking health data, the staff is notified to block the bridge; Combined with big data to obtain data on future environmental changes, the predicted changes of corresponding sensor data are obtained. When the predicted change of the bridge health data reaches the maintenance health data, the staff is notified to perform maintenance before future environmental changes. When the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before future environmental changes. If the maintenance cannot be completed before the future environmental changes, the bridge will be blocked.

[0064] In one embodiment of the present application, it also includes a sensor judgment module. When the sensor collects one or more of the bridge health data that reaches the corresponding maintenance health data or the blockade health data, the bridge health data collected by multiple peripheral sensors of the sensor are simultaneously obtained. If the bridge health data collected by the peripheral sensors does not show data changes, the sensor is used as the sensor to be adopted, and temporary data of the sensor position is obtained through multiple peripheral sensor data.

[0065] In one embodiment of the present application, the sensor judgment module also includes a personnel judgment submodule, and the data processing center sends the location information of the sensor to be adopted to the staff, and the staff goes to check whether the sensor to be adopted is damaged. If damaged, the data collection of the sensor to be adopted is suspended until the sensor to be adopted is repaired or replaced; if not damaged, the surrounding sensors are checked to see if they are damaged.

[0066] In one embodiment of the present application, the sensor judgment module also includes a position impact submodule, which divides the bridge into areas according to the bridge structure, including the bridge deck, piers and railings, and divides the bridge deck into sections according to the bridge deck in the construction drawings, and performs data difference analysis based on the distance between the sensor and the surrounding sensors and whether they are located in different areas.

[0067] In one embodiment of the present application, the sensor judgment module further includes a workflow recognition submodule, the data processing center sets a standard workflow, the standard workflow is obtained by pre-collecting or photographing various predetermined workflows performed by staff, inputting the standard workflow into a model for training, and obtaining the trained recognition model; The image sensor collects working images of the staff, and the recognition model identifies the type of work performed by the staff through the working images, and provides guidance and warnings for subsequent operations of the staff.

[0068] In one embodiment of the present application, a user interface module is also included. The bridge health data displays various types of bridge health data obtained from bridge operation period monitoring through the user interface, and users are classified and graded according to user information. Different static and dynamic data contents are displayed to different users according to different classifications and grades, and different classified and graded users are granted different control permissions over the data processing center.

[0069] It should be noted that the specific implementation of a bridge structure health monitoring system in an embodiment of the present application refers to the specific implementation of a bridge structure health monitoring method proposed in the first aspect of the aforementioned embodiment of the present application, and will not be repeated here.

[0070] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that an article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of more restrictions, the elements defined by the sentence "includes..." do not exclude the existence of other identical elements in the article or device including the elements.

[0071] The above is a detailed introduction to a bridge structure health monitoring method provided. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the bridge structure health monitoring method and its core idea of ​​the present application; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A bridge structure health monitoring method, characterized in that: include: Sensors are distributed at multiple locations on the bridge to collect bridge health data through the sensors. The bridge health data includes various monitoring signals, and the monitoring signals are converted into electrical data and then transmitted to a data processing center; The data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a device for screening representative monitoring data of bridge structures and components in the electrical data; The recognition model is trained by using abnormal data corresponding to existing abnormal situations, and the representative monitoring data is input into the trained recognition model for real-time monitoring, and an early warning is issued when abnormal data is identified; Statistics are collected based on the data from previous inspections and monitoring, and reports are compiled regularly. The reports include the identification of the overall internal force state of the structure, modal analysis, load-bearing capacity assessment, and comparison results with historical reports.

2. A bridge structure health monitoring method according to claim 1, characterized in that: The sensor includes an image sensor, which is used to collect images of the bridge and vehicles passing through the bridge to form traffic monitoring data and transmit it to the data processing center. The recognition model identifies abnormal events in the traffic monitoring data and records the statistics in the report form.

3. A bridge structure health monitoring method according to claim 2, characterized in that: According to the comparison result between the report form and the historical report form, the position where the structural health condition is reduced in the comparison result is output, and the sensor distribution is adjusted with the position where the structural health condition is reduced as the center.

4. A bridge structure health monitoring method according to claim 1, characterized in that: The data processing center obtains the corresponding relationship between different environmental data and various bridge health data according to the changes of corresponding sensor data under different environmental data in the historical data; Different maintenance health data and blocking health data are set according to the monitoring level required for the bridge. When the sensor collects one or more of the bridge health data reaching the corresponding maintenance health data, the staff is notified to go for maintenance; when the sensor collects one or more of the bridge health data reaching the corresponding blocking health data, the staff is notified to block the bridge; Combined with big data to obtain data on future environmental changes, the predicted changes of corresponding sensor data are obtained. When the predicted change of the bridge health data reaches the maintenance health data, the staff is notified to perform maintenance before future environmental changes. When the predicted change of the bridge health data reaches the corresponding blocking health data, the staff is notified to perform maintenance before future environmental changes. If the maintenance cannot be completed before the future environmental changes, the bridge will be blocked.

5. A bridge structure health monitoring method according to claim 4, characterized in that: When the sensor collects one or more of the bridge health data that reaches the corresponding maintenance health data or the blocked health data, the sensor simultaneously obtains the bridge health data collected by multiple sensors surrounding the sensor. If the bridge health data collected by the surrounding sensors does not show any data change, the sensor is used as the sensor to be adopted, and temporary data of the sensor position is obtained through the data of multiple surrounding sensors.

6. A bridge structure health monitoring method according to claim 5, characterized in that: The data processing center sends the location information of the sensor to be adopted to the staff, and the staff goes to check whether the sensor to be adopted is damaged. If damaged, the data collection of the sensor to be adopted is suspended until the sensor to be adopted is repaired or replaced; if not damaged, the staff checks whether the surrounding sensors are damaged.

7. A bridge structure health monitoring method according to claim 6, characterized in that: The bridge is divided into areas according to the bridge structure, including the bridge deck, piers and railings. The bridge deck is also divided according to the bridge deck sections in the construction drawings. Data difference analysis is performed based on the distance between the sensor and the surrounding sensors and whether they are located in different areas.

8. A bridge structure health monitoring method according to claim 6, characterized in that: The data processing center sets a standard work process, and the standard work process is obtained by pre-collecting or photographing various predetermined work processes performed by staff, inputting the standard work process into the model for training, and obtaining the trained recognition model; The image sensor collects working images of the staff, and the recognition model identifies the type of work performed by the staff through the working images, and provides guidance and warnings for subsequent operations of the staff.

9. A bridge structure health monitoring method according to any one of claims 1 to 8, characterized in that: The bridge health data displays various types of bridge health data obtained from bridge operation period monitoring through a user interface, and at the same time classifies and grades users according to user information, displays different static and dynamic data contents to different users according to different classifications and grades, and grants different classification and graded users different control permissions over the data processing center.

10. A bridge structure health monitoring system, characterized in that: include: A data acquisition module, which is equipped with sensors at multiple locations on the bridge to collect bridge health data, including various monitoring signals, and convert the monitoring signals into electrical data before transmitting them to a data processing center; Data processing module, the data processing center is used for archiving, querying, storing, managing and calling all monitoring static and dynamic data; the data processing center also includes a module for screening representative monitoring data of bridge structures and components in the electrical data; The model recognition module trains the recognition model through the abnormal data corresponding to the existing abnormal situation, inputs the representative monitoring data into the trained recognition model, performs real-time monitoring, and issues an early warning when abnormal data is recognized; The report output module collects statistics based on the data from previous inspections and monitoring, and regularly prepares reports, which include the identification of the overall internal force state of the structure, modal analysis, and load-bearing capacity assessment, as well as comparison results with historical reports.

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