A multi-point positioning intelligent bridge health monitoring system

Abstract

The present invention relates to the technical field of bridge health monitoring, and in particular to a multi-point positioning intelligent bridge health monitoring system, including a data acquisition module, a water level analysis module, a wind speed analysis module and a correction module. The present invention realizes real-time and comprehensive monitoring of the health status of the bridge by comprehensively using water level, wind speed, impact force sensors and image monitoring technology, effectively improving the accuracy of risk assessment and the timeliness of early warning. The system can automatically identify abnormal conditions and determine the scouring section, and issue flood discharge or wind reinforcement alarms in a timely manner. In addition, the system dynamically adjusts the monitoring distance according to the actual monitored erosion conditions to ensure the reasonable allocation of monitoring resources and the high efficiency of information collection, and effectively solves the problem of poor accuracy in the assessment of the health status of the bridge caused by the lack of comprehensive assessment capabilities for multiple risk factors due to relying only on abnormal data to output alarm information.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge health monitoring, and in particular to a multi-point positioning intelligent bridge health monitoring system. Background Art

[0002] With the acceleration of urbanization and the continuous development of infrastructure, bridges on rivers are facing increasingly severe challenges in the natural environment and operational load. Natural factors such as river erosion, water level fluctuations, wind speed changes, and increased traffic flow may affect the structural safety of bridges. Therefore, it is particularly important to develop an intelligent system that can monitor and evaluate the health of bridges in real time.

[0003] The patent document with the publication number CN111836227A discloses a real-time monitoring system for bridge structure health, including a bridge structure health monitoring subsystem, a data storage subsystem and a central processor; the bridge structure health monitoring subsystem includes a wireless sensor network composed of a convergence node and a plurality of sensor nodes, the sensor nodes monitor and sense each dangerous part of the bridge, collect corresponding bridge dangerous part data, the convergence node aggregates the bridge dangerous part data collected by each sensor node and sends it to the data storage subsystem; the central processor obtains the bridge dangerous part perception data from the data storage subsystem, analyzes and processes the bridge dangerous part perception data, and outputs alarm information when the bridge dangerous part perception data is abnormal. It can be seen that when the deployment of the sensor network is not comprehensive enough or the distribution of the sensor nodes is not uniform enough, the system will lead to monitoring blind spots, affecting the assessment of the overall health status of the bridge. The system only relies on abnormal data to output alarm information, and may lack the ability to comprehensively evaluate multiple risk factors, which limits the accuracy and timeliness of the early warning, and the system is not adaptable to environmental changes, and cannot accurately reflect the true state of the bridge under different environmental conditions. Summary of the invention

[0004] To this end, the present invention provides a multi-point positioning intelligent bridge health monitoring system to overcome the problem in the prior art that the system only relies on abnormal data to output alarm information, resulting in a lack of comprehensive assessment capabilities for multiple risk factors and poor assessment accuracy of bridge health conditions.

[0005] To achieve the above objectives, the present invention provides a multi-point positioning intelligent bridge health monitoring system, including:

[0006] The data acquisition module includes a plurality of water level sensors for monitoring the real-time water level at each monitoring point of the bridge, a plurality of wind speed sensors for monitoring the real-time wind speed at each monitoring point of the bridge, a plurality of pressure sensors for monitoring the real-time impact force at each monitoring point of the bridge, and a plurality of image sensors for monitoring the real-time underwater image at each monitoring point of the bridge, wherein the distance between two adjacent monitoring points is a preset monitoring distance;

[0007] A water level analysis module, connected to the data acquisition module, is used to determine the abnormal water level point according to the real-time water level of each monitoring point and the preset standard water level, and to determine the abnormal water level section according to the number of a number of consecutive abnormal water level points and the preset standard number, and to determine the scouring section according to the real-time impact force of the abnormal water level point in the abnormal water level section and the preset standard impact force;

[0008] A wind speed analysis module, connected to the water level analysis module and the data acquisition module respectively, for calculating the abnormal distribution degree according to the real-time water level at each monitoring point in the scouring section, and making a judgment according to the abnormal distribution degree and the preset standard distribution degree, and issuing an alarm for reinforcement of the scouring section for flood discharge according to the judgment result, or issuing an alarm for reinforcement of the scouring section for wind force according to the real-time wind speed at each monitoring point in the scouring section and the preset standard wind speed;

[0009] The correction module is connected to the data acquisition module and the wind speed analysis module respectively, and is used to correct the monitoring distance according to all real-time underwater images of the scouring section within a preset correction time after the wind speed analysis module issues an alarm and a preset standard erosion area range.

[0010] Further, the water level analysis module determines abnormal water level points based on the real-time water level of each monitoring point and the preset standard water level include:

[0011] The water level analysis module determines that the monitoring point where the real-time water level is greater than the standard water level is an abnormal water level.

[0012] Further, the water level analysis module determines that the abnormal water level section includes:

[0013] When the number of consecutive abnormal water spots is greater than the standard number, the water level analysis module determines that the river section between the consecutive abnormal water spots is an abnormal water level section.

[0014] Further, the water level analysis module determines that the flush section includes: based on the real-time impact force of the abnormal water position in the abnormal water level section and the preset standard impact force:

[0015] The water level analysis module calculates the average value of the real-time impact forces of all abnormal water points in each abnormal water level section. When the average value of all real-time impact forces is greater than the standard impact force, the water level analysis module determines that the abnormal water level section is a scouring section.

[0016] Further, the wind speed analysis module calculates anomaly distribution based on the real-time water level at each monitoring point in the erosion section, including:

[0017] The wind speed analysis module calculates the standard deviation based on the real-time water level at each monitoring point in the erosion section to obtain the abnormal distribution degree.

[0018] Further, the wind speed analysis module determines based on the abnormal distribution degree and the preset standard distribution degree, and issues an alarm for strengthening the flue section for flood discharge based on the determination result, including:

[0019] When the abnormal distribution degree is less than or equal to the standard distribution degree, the wind speed analysis module determines that the water level is generally stable and issues an alarm for reinforcement of the flush section for flood discharge.

[0020] Furthermore, the wind speed analysis module calculates the average value of the real-time wind speed at all monitoring points in the scour section. When the average value of the real-time wind speed is greater than the standard wind speed, it is determined that the wind force has a serious impact and an alarm is issued to reinforce the scour section against wind force.

[0021] Further, when the average value of the real-time wind speed is less than or equal to the standard wind speed, the wind speed analysis module determines that the wind power has a slight impact and issues a traffic evacuation and bridge inspection alarm.

[0022] Furthermore, after the wind speed analysis module issues an alarm, the correction module obtains all real-time underwater images of the scouring section within a preset correction time, extracts the bridge erosion area of ​​the real-time underwater image, and calculates the average value of all bridge erosion areas. When the average value of the bridge erosion area is greater than the maximum value of the standard erosion area range, the correction module increases the correction monitoring distance according to the average value of the bridge erosion area, the maximum value of the standard erosion area range and the preset correction coefficient.

[0023] Furthermore, when the average value of the bridge erosion area is less than the minimum value of the standard erosion area range, the correction module reduces the correction monitoring distance according to the average value of the bridge erosion area, the minimum value of the standard erosion area range and a preset correction coefficient.

[0024] Compared with the prior art, the beneficial effect of the present invention is that, by comprehensively using water level, wind speed, impact force sensors and image monitoring technology, real-time and comprehensive monitoring of the health status of the bridge is achieved, effectively improving the accuracy of risk assessment and the timeliness of early warning. The system can automatically identify abnormal conditions and determine the scouring section, and issue flood discharge or wind reinforcement alarms in a timely manner. In addition, the system dynamically adjusts the monitoring distance according to the actual monitored erosion conditions to ensure the rational allocation of monitoring resources and the high efficiency of information collection, effectively solving the problem of poor accuracy in assessing the health status of the bridge caused by the lack of comprehensive assessment capabilities for multiple risk factors due to relying solely on abnormal data to output alarm information.

[0025] Further, by quickly identifying abnormal water spots, measures can be taken in advance, such as strengthening monitoring, preparing emergency resources or implementing traffic control.

[0026] Further, by identifying abnormal water level sections, the bridge health monitoring system can more accurately locate specific areas in the river affected by water level rise.

[0027] Furthermore, by identifying the scour sections, it is possible to strengthen the monitoring of these areas in a targeted manner and take reinforcement measures in a timely manner, such as improving the direction of water flow or enhancing the scour resistance of the local structure of the bridge, which helps to reduce bridge damage caused by water erosion.

[0028] Further, by calculating the standard deviation to obtain the abnormal distribution, the wind speed analysis module can evaluate the consistency of water level changes in the erosion section.

[0029] Further, by issuing a flood discharge reinforcement alarm, quick action can be prompted, such as adjusting the reservoir discharge volume or strengthening the bridge erosion section to mitigate the potential damage to the bridge by water flow.

[0030] Further, by analyzing real-time wind speed data, the wind speed analysis module can promptly identify the potential risks caused by wind power to the bridge and issue a wind reinforcement alarm if necessary.

[0031] Further, through real-time monitoring and intelligent judgment of the wind speed analysis module, the slight impact of wind power on bridge safety can be identified in a timely manner, which will help reduce the risk of bridge damage caused by wind power.

[0032] Further, through the method of dynamically correcting the monitoring distance, the system can more accurately evaluate the severity of bridge erosion and adjust the monitoring strategy according to actual conditions.

[0033] Furthermore, the method of dynamically adjusting the monitoring distance based on the erosion situation can optimize the allocation of monitoring resources and improve monitoring efficiency. When the erosion situation is mild, reducing the spacing between monitoring points can reduce unnecessary monitoring costs while maintaining sufficient monitoring accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of an intelligent bridge health monitoring system with multi-point positioning in this embodiment;

[0035] Figure 2 The logic diagram for determining abnormal water spots for this embodiment;

[0036] Figure 3 The logic diagram for determining abnormal water level segments for this embodiment;

[0037] Figure 4 The determination logic diagram for determining the flush section for this embodiment. DETAILED DESCRIPTION

[0038] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0040] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] See also Figure 1 It is shown, which is a schematic diagram of the intelligent bridge health monitoring system of multi-point positioning in this embodiment;

[0043] This embodiment provides a multi-point positioning intelligent bridge health monitoring system, including:

[0044] The data acquisition module includes a plurality of water level sensors for monitoring the real-time water level at each monitoring point of the bridge, a plurality of wind speed sensors for monitoring the real-time wind speed at each monitoring point of the bridge, a plurality of pressure sensors for monitoring the real-time impact force at each monitoring point of the bridge, and a plurality of image sensors for monitoring the real-time underwater image at each monitoring point of the bridge, wherein the distance between two adjacent monitoring points is a preset monitoring distance;

[0045] A water level analysis module, connected to the data acquisition module, is used to determine the abnormal water level point according to the real-time water level of each monitoring point and the preset standard water level, and to determine the abnormal water level section according to the number of a number of consecutive abnormal water level points and the preset standard number, and to determine the scouring section according to the real-time impact force of the abnormal water level point in the abnormal water level section and the preset standard impact force;

[0046] A wind speed analysis module, connected to the water level analysis module and the data acquisition module respectively, for calculating the abnormal distribution degree according to the real-time water level at each monitoring point in the scouring section, and making a judgment according to the abnormal distribution degree and the preset standard distribution degree, and issuing an alarm for reinforcement of the scouring section for flood discharge according to the judgment result, or issuing an alarm for reinforcement of the scouring section for wind force according to the real-time wind speed at each monitoring point in the scouring section and the preset standard wind speed;

[0047] The correction module is connected to the data acquisition module and the wind speed analysis module respectively, and is used to correct the monitoring distance according to all real-time underwater images of the scouring section within a preset correction time after the wind speed analysis module issues an alarm and a preset standard erosion area range.

[0048] The preset monitoring distance refers to the spacing of monitoring points along the bridge. It depends on the size of the bridge, the expected monitoring accuracy and the characteristics of the river. It is usually set to one monitoring point every 20 to 50 meters. In this embodiment, it is set to one monitoring point every 30 meters on the bridge deck, which can provide sufficient spatial resolution to detect local erosion phenomena.

[0049] The preset standard water level refers to a normal water level reference value set based on historical data and environmental conditions, which depends on the annual water level and seasonal changes of the river. It is usually set based on the average water level over many years. In this embodiment, it is set to the average water level over many years, which helps to identify abnormal water level events.

[0050] Anomalous water level points refer to monitoring points where the water level exceeds the preset standard water level in real time. Real-time monitoring data depending on water level. In this embodiment, the water level exceeds 10% of the standard water level is considered abnormal.

[0051] The preset standard number refers to the minimum number of consecutive abnormal water point locations for identifying the scour section. It is usually set according to the length of the bridge and risk assessment, and may be 3 or more. In this embodiment, it is set to 3. 3 consecutive abnormal water point locations as the standard for the scour section are helpful in identifying the area widely affected by erosion.

[0052] The scouring section refers to the potential erosion area determined based on abnormal water spots and impact force data.

[0053] The preset standard distribution is used to evaluate the consistency or central tendency of the abnormal distribution of water levels. It is usually set based on the standard deviation or coefficient of variation of the water level data. In this embodiment, the standard distribution is set to 1.5 times the standard deviation of the historical water level data. This value is greater than the average level, which can ensure that an alarm is issued when the water level changes significantly, while avoiding frequent false alarms due to daily fluctuations.

[0054] The preset standard wind speed refers to the wind speed threshold that may affect the safety of the bridge. It depends on the wind speed characteristics of the region and the wind-resistant design of the bridge. It is usually set based on a certain proportion of the historical maximum wind speed. In this embodiment, it is set to 70% of the historical maximum wind speed, which provides a certain safety buffer for the bridge.

[0055] The preset correction time refers to the time used to collect data to correct the monitoring strategy after an alarm is issued. It is usually set according to the urgency of the event and the analysis requirements. In this embodiment, it is set to 0.5 hours in an emergency situation, which can quickly assess the bridge condition and take immediate action to avoid potential structural damage or safety accidents.

[0056] The preset standard erosion area range is a threshold for determining the severity of bridge erosion, which is usually set based on the maintenance history and material properties of the bridge. In this embodiment, it is set to no more than 0.5% of the surface area of ​​the bridge, which helps to promptly detect and repair small-scale erosion.

[0057] The data acquisition module monitors water level, wind speed, impact force and underwater images in real time, and collects data at each monitoring point on the bridge using a preset monitoring distance. The water level analysis module evaluates the water level data to determine the abnormal water level and scour section, while the wind speed analysis module calculates the abnormal distribution based on the water level and wind speed data and issues a flood discharge or wind reinforcement alarm. When the alarm is triggered, the correction module analyzes the underwater image of the scour section within the preset correction time, compares it with the standard erosion area range, and dynamically adjusts the monitoring distance to adapt to the actual erosion situation.

[0058] By comprehensively using water level, wind speed, impact force sensors and image monitoring technology, real-time and comprehensive monitoring of bridge health status is achieved, effectively improving the accuracy of risk assessment and the timeliness of early warning. The system can automatically identify abnormal situations and determine the scouring section, and issue flood discharge or wind reinforcement alarms in a timely manner. In addition, the system dynamically adjusts the monitoring distance according to the actual monitored erosion situation to ensure the rational allocation of monitoring resources and high efficiency of information collection, effectively solving the problem of poor accuracy in bridge health assessment caused by the lack of comprehensive assessment capabilities for multiple risk factors due to relying solely on abnormal data to output alarm information.

[0059] Please continue reading Figure 2 It is shown, which is a logic diagram for determining abnormal water spots in this embodiment;

[0060] Specifically, the water level analysis module determines abnormal water level points based on the real-time water level of each monitoring point and the preset standard water level include:

[0061] The water level analysis module determines that the monitoring point where the real-time water level is greater than the standard water level is an abnormal water level.

[0062] The standard water level refers to a reference value used to compare and evaluate real-time water level data in the bridge health monitoring system. It is based on the average water level or specific water level statistics obtained by analyzing the water level records of rivers or waters over many years. It is also based on the design and structure of the bridge and the historical water level impact. Usually, the standard water level can be set to the multi-year average water level to provide a long-term average reference value. In this embodiment, the multi-year average water level is set to 5.0 meters. The multi-year average water level provides a relatively stable benchmark, which can reduce the impact of daily fluctuations on the monitoring system.

[0063] The main task of the water level analysis module is to continuously collect and analyze the real-time water level data of each monitoring point. The module compares these real-time data with the pre-set standard water level. When the real-time water level of a monitoring point exceeds this preset standard, the monitoring point is determined to be an abnormal water point, thereby realizing the rapid identification of potential floods or water level rise risks.

[0064] By quickly identifying abnormal water spots, measures can be taken in advance, such as strengthening monitoring, preparing emergency resources or implementing traffic control.

[0065] Please continue reading Figure 3 It is shown, which is a logic diagram for determining abnormal water level segments in this embodiment;

[0066] Specifically, the water level analysis module determines that the abnormal water level section includes:

[0067] When the number of consecutive abnormal water spots is greater than the standard number, the water level analysis module determines that the river section between the consecutive abnormal water spots is an abnormal water level section.

[0068] The water level analysis module identifies and analyzes water level data collected from multiple monitoring points along the span of the bridge. The module keeps track of these data points and counts them when a series of consecutive monitoring points show real-time water levels exceeding the preset standard water level. When the number of consecutive abnormal water level points exceeds a preset minimum standard number, these consecutive abnormal points are collectively determined as an abnormal water level segment, which indicates that there may be a large-scale water level rise problem.

[0069] By identifying abnormal water levels, the bridge health monitoring system can more accurately locate specific areas in the river affected by water levels.

[0070] Please continue reading Figure 4 It is shown, which is a determination logic diagram of the determination of the flush section of this embodiment;

[0071] Specifically, the water level analysis module determines that the flush section includes:

[0072] The water level analysis module calculates the average value of the real-time impact forces of all abnormal water points in each abnormal water level section. When the average value of all real-time impact forces is greater than the standard impact force, the water level analysis module determines that the abnormal water level section is a scouring section.

[0073] Standard impact force is a reference value used in bridge health monitoring systems to assess the impact of water flow on bridge structures. It depends on the historical impact force records on bridge structures, the statistical values ​​obtained through analysis, and the water flow conditions that affect the impact force, including flow velocity, water flow, water depth, etc. It is also based on the design, material, size and location of the bridge, which will affect its ability to withstand impact force.

[0074] The water level analysis module first identifies abnormal water level segments, which refers to a series of continuous monitoring points whose water levels exceed the preset standard water level. Next, the module collects real-time impact force data of these abnormal water level points, which comes from the corresponding pressure sensors. The module calculates the average value of these impact force data and compares it with the preset standard impact force. When the average impact force exceeds this standard, the module will determine that the entire abnormal water level segment is a scouring segment, indicating that the area is experiencing water flow impact that may cause erosion to the bridge structure.

[0075] By identifying the scour sections, it is possible to strengthen monitoring of these areas in a targeted manner and take reinforcement measures in a timely manner, such as improving the direction of water flow or enhancing the scour resistance of the local structure of the bridge, which helps to reduce bridge damage caused by water erosion.

[0076] Specifically, the wind speed analysis module calculates anomaly distribution based on the real-time water level at each monitoring point in the erosion section, including:

[0077] The wind speed analysis module calculates the standard deviation based on the real-time water level at each monitoring point in the erosion section to obtain the abnormal distribution degree.

[0078] When the water level analysis module identifies the scour section, the wind speed analysis module collects real-time water level data from all monitoring points in the scour section. Using this data, the module calculates the fluctuation of the water level. Specifically, it quantifies the abnormal distribution of the water level by calculating the standard deviation of these water level data. The smaller the standard deviation, the more consistent the water level changes; conversely, the larger the standard deviation, the more uneven the water level changes.

[0079] By calculating the standard deviation to obtain the abnormal distribution, the wind speed analysis module can evaluate the consistency of water level changes in the erosion section.

[0080] Specifically, the wind speed analysis module determines the abnormality and preset standard distribution, and issues an alarm for reinforcement of the flue section for flood discharge based on the determination result, including:

[0081] When the abnormal distribution degree is less than or equal to the standard distribution degree, the wind speed analysis module determines that the water level is generally stable and issues an alarm for reinforcement of the flush section for flood discharge.

[0082] The wind speed analysis module is responsible for comparing the abnormal distribution of water levels in the scouring section with the preset standard distribution. When the calculated abnormal distribution, i.e. the standard deviation of the water level, is less than or equal to the preset standard distribution, the module will determine that the water level change in the scouring section is within an acceptable stable range, and then issue a flood discharge reinforcement alarm to prompt management personnel to perform corresponding flood discharge operations or reinforcement measures.

[0083] By issuing a flood relief and reinforcement alarm, quick action can be prompted, such as adjusting the reservoir's flood discharge or strengthening the bridge erosion section to mitigate the potential damage to the bridge by water flow.

[0084] Specifically, the wind speed analysis module calculates the average value of the real-time wind speed at all monitoring points in the scour section. When the average value of the real-time wind speed is greater than the standard wind speed, it is determined that the wind force has a serious impact and an alarm is issued to reinforce the scour section against wind force.

[0085] The task of the wind speed analysis module is to evaluate the possible impact of wind force on the bridge in the scour section. The module collects real-time wind speed data from all monitoring points in the scour section and calculates the average value of these data. When the monitored average wind speed exceeds the preset standard wind speed, indicating that the wind force poses a greater threat to the safety of the bridge structure, the module will automatically determine that the current wind force has a serious impact.

[0086] By analyzing real-time wind speed data, the wind speed analysis module can promptly identify the potential risks posed by wind to the bridge and issue a wind reinforcement alarm if necessary.

[0087] Specifically, when the average value of the real-time wind speed is less than or equal to the standard wind speed, the wind speed analysis module determines that the wind power has a slight impact and issues traffic evacuation and bridge inspection alarms.

[0088] The wind speed analysis module is responsible for monitoring the real-time wind speed data of all monitoring points in the bridge scour section and calculating the average value of these data. When the average value is less than or equal to the preset standard wind speed, the module will determine that the impact of wind on the bridge is slight, thereby triggering the alarm mechanism for traffic evacuation and bridge inspection.

[0089] Through real-time monitoring and intelligent judgment of the wind speed analysis module, the slight impact of wind power on bridge safety can be identified in a timely manner, which will help reduce the risk of bridge damage caused by wind power.

[0090] Specifically, after the wind speed analysis module issues an alarm, the correction module obtains all real-time underwater images of the scouring section within a preset correction time, extracts the bridge erosion area of ​​the real-time underwater image, and calculates the average value of all bridge erosion areas. When the average value of the bridge erosion area is greater than the maximum value of the standard erosion area range, the correction module increases the correction monitoring distance according to the average value of the bridge erosion area, the maximum value of the standard erosion area range and the preset correction coefficient.

[0091] The preset correction factor is a parameter used to adjust the monitoring distance, which depends on many factors, including the structural characteristics of the bridge, the erosion characteristics of the river, historical monitoring data, and the expected monitoring accuracy. The correction factor is usually set by bridge engineers or data analysts based on experience and expertise to ensure that the monitoring system can adapt to different environmental conditions and monitoring needs. In this embodiment, it is set to 1.5. This value is greater than 1, which means that when the erosion area is detected to exceed the standard range, the monitoring distance will be increased accordingly. By increasing the density of monitoring points, more detailed erosion data can be obtained, thereby more accurately assessing the health of the bridge.

[0092] After the wind speed analysis module issues an alarm, the correction module starts collecting real-time underwater image data of the scouring section for a preset correction duration. The module extracts the bridge erosion area from these images and calculates the average value of these areas. If this average value exceeds the maximum value of the standard erosion area range, the correction module will increase the monitoring distance accordingly based on the average value of the bridge erosion area, the maximum value of the standard erosion area range, and the preset correction factor.

[0093] Through the method of dynamically correcting monitoring distances, the system can more accurately evaluate the severity of bridge erosion and adjust the monitoring strategy according to actual conditions.

[0094] Specifically, when the average value of the bridge erosion area is less than the minimum value of the standard erosion area range, the correction module reduces the correction monitoring distance according to the average value of the bridge erosion area, the minimum value of the standard erosion area range and a preset correction coefficient.

[0095] When the correction module detects that the average value of the bridge erosion area is less than the minimum value of the preset standard erosion area range, it will start the monitoring distance adjustment procedure. The correction module will calculate the new monitoring distance based on the current average value of the erosion area, the minimum value of the standard erosion area range, and a preset correction coefficient, and reduce the distance between monitoring points accordingly.

[0096] The method of dynamically adjusting the monitoring distance based on the erosion situation can optimize the allocation of monitoring resources and improve monitoring efficiency. When the erosion situation is mild, reducing the spacing between monitoring points can reduce unnecessary monitoring costs while maintaining sufficient monitoring accuracy.

[0097] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-point positioning intelligent bridge health monitoring system, characterized in that: include: The data acquisition module includes a plurality of water level sensors for monitoring the real-time water level at each monitoring point of the bridge, a plurality of wind speed sensors for monitoring the real-time wind speed at each monitoring point of the bridge, a plurality of pressure sensors for monitoring the real-time impact force at each monitoring point of the bridge, and a plurality of image sensors for monitoring the real-time underwater image at each monitoring point of the bridge, wherein the distance between two adjacent monitoring points is a preset monitoring distance; A water level analysis module, connected to the data acquisition module, is used to determine the abnormal water level point according to the real-time water level of each monitoring point and the preset standard water level, and to determine the abnormal water level section according to the number of a number of consecutive abnormal water level points and the preset standard number, and to determine the scouring section according to the real-time impact force of the abnormal water level point in the abnormal water level section and the preset standard impact force; A wind speed analysis module, connected to the water level analysis module and the data acquisition module respectively, for calculating the abnormal distribution degree according to the real-time water level at each monitoring point in the scouring section, and making a judgment according to the abnormal distribution degree and the preset standard distribution degree, and issuing an alarm for reinforcement of the scouring section for flood discharge according to the judgment result, or issuing an alarm for reinforcement of the scouring section for wind force according to the real-time wind speed at each monitoring point in the scouring section and the preset standard wind speed; A correction module, connected to the data acquisition module and the wind speed analysis module respectively, for correcting the monitoring distance according to all real-time underwater images of the scouring section within a preset correction time after the wind speed analysis module issues an alarm and a preset standard erosion area range; After the wind speed analysis module issues an alarm, the correction module obtains all real-time underwater images of the scouring section within a preset correction time, extracts the bridge erosion area of ​​the real-time underwater image, and calculates the average value of all bridge erosion areas. When the average value of the bridge erosion area is greater than the maximum value of the standard erosion area range, the correction module increases the correction monitoring distance according to the average value of the bridge erosion area, the maximum value of the standard erosion area range and the preset correction coefficient.

2. The multi-point positioning intelligent bridge health monitoring system according to claim 1 is characterized in that: The water level analysis module determines the abnormal water level point according to the real-time water level of each monitoring point and the preset standard water level, including: The water level analysis module determines that the monitoring point where the real-time water level is greater than the standard water level is an abnormal water point.

3. The multi-point positioning intelligent bridge health monitoring system according to claim 2 is characterized in that: The water level analysis module determines the abnormal water level segment according to the number of consecutive abnormal water level points and a preset standard number, including: When the number of a plurality of consecutive abnormal water level points is greater than a standard number, the water level analysis module determines that the river section between the plurality of consecutive abnormal water level points is an abnormal water level section.

4. The multi-point positioning intelligent bridge health monitoring system according to claim 3 is characterized in that: The water level analysis module determines the scouring section according to the real-time impact force of the abnormal water point in the abnormal water level section and the preset standard impact force, including: The water level analysis module calculates the average value of the real-time impact forces of all abnormal water points in each abnormal water level section. When the average value of all real-time impact forces is greater than the standard impact force, the water level analysis module determines that the abnormal water level section is a scouring section.

5. The multi-point positioning intelligent bridge health monitoring system according to claim 4 is characterized in that: The wind speed analysis module calculates the abnormal distribution degree according to the real-time water level at each monitoring point in the flushing section, including: The wind speed analysis module calculates the standard deviation according to the real-time water level at each monitoring point in the flushing section to obtain the abnormal distribution degree.

6. The multi-point positioning intelligent bridge health monitoring system according to claim 5 is characterized in that: The wind speed analysis module makes a determination based on the abnormal distribution degree and the preset standard distribution degree, and issues an alarm for reinforcing the scouring section for flood discharge according to the determination result, including: When the abnormal distribution degree is less than or equal to the standard distribution degree, the wind speed analysis module determines that the water level is generally stable and issues an alarm to reinforce the scouring section for flood discharge.

7. The multi-point positioning intelligent bridge health monitoring system according to claim 6 is characterized in that: The wind speed analysis module calculates the average value of the real-time wind speed at all monitoring points in the scour section. When the average value of the real-time wind speed is greater than the standard wind speed, it is determined that the wind force has a serious impact and an alarm is issued to reinforce the scour section against wind force.

8. The multi-point positioning intelligent bridge health monitoring system according to claim 7 is characterized in that: When the average value of the real-time wind speed is less than or equal to the standard wind speed, the wind speed analysis module determines that the wind force has a slight impact and issues a traffic evacuation and bridge inspection alarm.

9. The multi-point positioning intelligent bridge health monitoring system according to claim 8 is characterized in that: When the average value of the bridge erosion area is less than the minimum value of the standard erosion area range, the correction module reduces the correction monitoring distance according to the average value of the bridge erosion area, the minimum value of the standard erosion area range and a preset correction coefficient.

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