Method and system for monitoring deflection of bridge under action of heavy-load vehicle

Abstract

The invention relates to the technical field of bridge deflection monitoring, in particular to a method and system for monitoring bridge deflection under the action of a heavy-load vehicle. And acquiring bridge vehicle passing data in multiple historical days. When a heavy-load vehicle appears, environment time sequence and real-time traffic data are collected in real time. And analyzing historical data to determine a heavy-load vehicle driving rule to obtain reference intensity. The vehicle speed difference, the displacement change and the vehicle weight in the real-time traffic data are considered, the load comprehensive influence degree is obtained, and the dynamic pressure is evaluated. And analyzing environmental time sequence data fluctuation, and determining the environmental influence degree. Based on the heavy-load vehicle characteristics, the load comprehensive influence degree, the environment influence degree and the reference intensity, the acquisition frequency is dynamically adjusted, the monitoring resource configuration is optimized, and the monitoring precision and efficiency are improved. The adjusted acquisition frequency ensures that deflection data are accurately obtained when heavy-load vehicles pass, the detection accuracy is improved, and effective utilization of resources and refinement of bridge deflection monitoring are realized.

Description

Technical Field

[0001] This invention relates to the field of bridge deflection monitoring technology, specifically to a method and system for monitoring bridge deflection under heavy vehicle loads. Background Technology

[0002] Bridge deflection refers to the linear displacement perpendicular to the bridge axis that occurs in a bridge structure under load. It is an important indicator for measuring the load-bearing capacity and structural performance of a bridge. With economic development and increasing logistics demands, heavy-duty vehicles (such as large trucks and container trucks) are increasingly frequently passing over bridges. The dynamic load effect of these heavy-duty vehicles on the bridge structure is significant, which may lead to increased bridge deflection, thereby affecting the overall performance and service life of the bridge. Therefore, monitoring bridge deflection under heavy-duty vehicle loads is an important research direction in the field of bridge health monitoring.

[0003] When monitoring bridge deflection, specialized monitoring equipment, such as deflection meters, is typically used to collect bridge deflection data. Current technologies usually employ a fixed data acquisition frequency. However, in everyday traffic environments, the weight borne by the bridge changes dynamically with traffic flow, and environmental factors (such as temperature, humidity, and wind speed) also have a coupled effect on bridge deflection monitoring. Therefore, a fixed acquisition frequency cannot adequately adapt to dynamically changing traffic conditions and environmental factors, ultimately leading to inaccurate monitoring results. Summary of the Invention

[0004] To address the problem that existing technologies typically use a fixed sampling frequency for deflection data collection, which is inadequate due to the dynamic changes in bridge load caused by traffic flow in daily traffic environments, and the coupled influence of environmental factors (such as temperature, humidity, and wind speed) on bridge deflection monitoring, a fixed sampling frequency cannot effectively adapt to dynamically changing traffic conditions and environmental factors, ultimately leading to inaccurate monitoring results. The present invention aims to provide a method and system for monitoring bridge deflection under heavy vehicle loads. The specific technical solution adopted is as follows: A method for monitoring bridge deflection under heavy vehicle loads includes: Historical traffic data of vehicles on the bridge over multiple days is acquired; when heavy-load vehicles are present, environmental time-series data of various environmental factors and real-time traffic data of vehicles on the bridge are acquired within a preset time period; the traffic data includes vehicle weight, number of vehicles, vehicle speed time-series data and displacement time-series data. The historical data is divided into daily periods, and the reference density of heavy-load vehicles in each period is determined based on the vehicle weight and number of vehicles in the historical traffic data. Based on the difference in vehicle speed and the change in displacement between vehicles and heavy-load vehicles in the real-time traffic data, and by comparing the vehicle weight of vehicles and heavy-load vehicles, the comprehensive load impact of heavy-load vehicles is determined. In various environmental time series data, the fluctuation changes of environmental values ​​are analyzed to determine the environmental impact. Based on the vehicle weight, load-bearing combined influence of heavy-duty vehicles, the environmental influence, and the reference density of the corresponding period when heavy-duty vehicles appear, the preset acquisition frequency is adjusted to obtain the adjusted acquisition frequency; based on the adjusted acquisition frequency of all heavy-duty vehicles on the current bridge, bridge deflection data is collected for bridge deflection monitoring.

[0005] Furthermore, the method for obtaining the reference density includes: The history is divided into several periods each day to obtain multiple cycles, and cycles that fall in the same period across multiple days are considered to be of the same type. In the historical traffic data for each period of each day, the proportion of heavy-load vehicles in the total number of vehicles is used as the density factor of heavy-load vehicles in each period. For any given period, the number of heavy-load vehicles is weighted and averaged based on the density factor of heavy-load vehicles in that period over several historical days, thus obtaining the reference density of heavy-load vehicles in that period.

[0006] Furthermore, the method for obtaining the comprehensive load influence degree includes: All vehicles appearing within a preset time period corresponding to a heavily loaded vehicle will be tracked. Within a preset time period, analyze the speed differences between the heavy-loaded vehicle and each tracked vehicle to determine the speed proximity trend value between the heavy-loaded vehicle and each tracked vehicle. Within a preset time period, analyze the displacement changes between the heavy-load vehicle and each tracking vehicle to determine the proximity trend value of the distance between the heavy-load vehicle and each tracking vehicle. Within a preset time period, the product of the speed proximity trend value and the distance proximity trend value between the heavy-load vehicle and each tracking vehicle is used as the load influence factor between the heavy-load vehicle and each tracking vehicle. The ratio of the weight of each tracked vehicle to the weight of a heavily loaded vehicle is used as the influence weight of each tracked vehicle. The load influence factors between the tracking vehicles and the heavy-load vehicles are weighted and fused based on the influence weight of the tracking vehicles to obtain the comprehensive load influence of the heavy-load vehicles.

[0007] Furthermore, the method for obtaining the velocity proximity trend value includes: Within a preset time period, the intersection of the time periods corresponding to the speed time series data of the heavy-load vehicle and each tracked vehicle is taken to obtain the first intersection time period. Under the first intersection time period, the absolute value of the difference between the speed of the heavy-load vehicle and each tracked vehicle at the same time is used as the speed difference factor. The mean value of the speed difference factor between the heavy-load vehicle and each tracked vehicle during the first intersection period is negatively correlated and normalized, and then used as the speed proximity trend value.

[0008] Furthermore, the method for obtaining the vehicle proximity trend value includes: Within a preset time period, the time periods corresponding to the displacement time series data of the heavy-load vehicle and each tracking vehicle are intersected to obtain the second intersection time period. Under the second intersection time period, the absolute value of the difference between the displacement of the heavy-load vehicle and each tracking vehicle at the same time is taken as the distance between the heavy-load vehicle and each tracking vehicle. At adjacent moments of the second intersection period, the normalized value of the difference between the distance between the heavy-load vehicle and each tracking vehicle at the previous moment and the distance between the heavy-load vehicle and each tracking vehicle at the next moment is used as the proximity factor between the heavy-load vehicle and each tracking vehicle. The sum of all vehicle proximity factors between the heavy-load vehicle and each tracked vehicle within the second intersection period is normalized and used as the vehicle proximity trend value.

[0009] Furthermore, the method for obtaining the degree of environmental impact includes: In each type of environmental time series data, the mean of all environmental values ​​is used as the mean characteristic value, the absolute value of the difference between each environmental value and the mean characteristic value is used as the environmental deviation factor, and the maximum environmental deviation factor is used as the environmental impact factor of each environmental factor. The sum of environmental impact factors corresponding to all environmental factors is normalized and used as the environmental impact degree.

[0010] Furthermore, the method for adjusting the acquisition frequency includes: The normalized weight of the heavy-duty vehicle is used as the adjustment weight. Based on the adjusted weights, comprehensive load impact, environmental impact, and reference density of the corresponding cycle for heavy-duty vehicles, the multi-dimensional monitoring intensity of heavy-duty vehicles is obtained. The product of the difference between the preset maximum acquisition frequency and the preset minimum acquisition frequency and the multidimensional monitoring intensity is used as the adjustment range; The sum of the adjustment range and the preset minimum acquisition frequency is taken as the adjusted acquisition frequency.

[0011] Furthermore, the formula model for the multidimensional monitoring intensity includes: in, This indicates the intensity of multi-dimensional monitoring; This indicates the reference density of the cycle corresponding to heavy-duty vehicles; This indicates an adjustment of the weights; Indicates the overall impact of the load; Indicates the degree of environmental impact; This represents the normalization function.

[0012] Furthermore, the step of collecting bridge deflection data based on the adjusted collection frequency of all heavy-load vehicles on the current bridge for bridge deflection monitoring includes: The average of the adjusted collection frequencies of all heavy-load vehicles on the bridge is used as the final collection frequency. Bridge deflection data is collected based on the final collection frequency. When the deflection value is greater than or equal to a preset safety threshold, a deflection warning is issued; when the deflection value is less than the preset safety threshold, no deflection warning is required.

[0013] A bridge deflection monitoring system under heavy vehicle load includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps of the bridge deflection monitoring method under heavy vehicle load.

[0014] The present invention has the following beneficial effects: Due to transportation constraints and traffic restrictions imposed by logistics demands or commercial activities, the daily passage of heavy-duty vehicles across bridges often follows certain patterns. Therefore, acquiring historical traffic data on bridges over multiple days provides a more comprehensive understanding of the bridge's daily usage. When heavy-duty vehicles are present, real-time environmental and traffic data are collected immediately, ensuring the monitoring system can respond promptly to special circumstances and quickly capture the impact of heavy-duty vehicles. Firstly, analysis of historical traffic data determines the reference density of heavy-duty vehicles within each period, reflecting their driving patterns. When heavy-duty vehicles travel on bridges, especially when they maintain similar speeds to adjacent vehicles, dynamic interactions may occur, leading to greater dynamic deflection of the bridge. Therefore, based on speed differences, displacement changes, and comparison of vehicle weight, the comprehensive load impact of heavy-duty vehicles is determined. This method refines the specific impact of loads on the bridge and assesses the dynamic pressure exerted by heavy-duty vehicles on the bridge structure. Bridge deflection is affected by daily environmental factors. Therefore, analyzing the fluctuations in environmental values ​​in time-series environmental data determines the degree of environmental impact, reflecting the influence of external environmental factors on bridge deflection. Finally, the acquisition frequency is dynamically adjusted based on heavy-load vehicle characteristics, load influence, environmental influence, and reference density, achieving optimized allocation of monitoring resources and improving monitoring efficiency while ensuring accuracy. Acquiring bridge deflection data based on the adjusted acquisition frequency ensures more accurate and appropriate data acquisition during heavy-load vehicle traffic, thereby improving the accuracy of deflection detection and promoting efficient resource utilization. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for monitoring bridge deflection under heavy vehicle load, as provided in one embodiment of the present invention. Figure 2 A flowchart illustrating a method for obtaining the comprehensive influence of loads according to an embodiment of the present invention; Figure 3 This is a system block diagram of a bridge deflection monitoring system under heavy vehicle load, provided as an embodiment of the present invention. Figure 4This is a schematic diagram of the system structure of a bridge deflection monitoring system under heavy vehicle load, provided as an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for monitoring bridge deflection under heavy-load vehicle loads proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of a method and system for monitoring bridge deflection under heavy-load vehicle loads provided by this invention.

[0020] Please see Figure 1 The diagram illustrates a method flowchart for monitoring bridge deflection under heavy vehicle load, according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain historical traffic data of vehicles on the bridge over multiple days; when heavy-load vehicles are present, obtain environmental time-series data of various environmental factors within a preset time period and real-time traffic data of vehicles on the bridge; the traffic data includes vehicle weight, number of vehicles, vehicle speed time-series data and displacement time-series data.

[0021] Bridge deflection refers to the vertical displacement of structural elements (such as beams, arches, and cables) at mid-span or specific cross-sections of a bridge under load, along a direction perpendicular to the bridge axis. It directly reflects the bridge's condition and safety performance. When heavy vehicles travel on a bridge, significant deflection may occur in localized areas, particularly near the mid-span or supports. Although these deformations are usually temporary, exceeding certain limits and subject to repeated heavy vehicle traffic over a long period can lead to fatigue damage, causing long-term effects on the bridge structure and even resulting in cracks or other structural problems. Therefore, the safety and stability of bridges under heavy vehicle loads become a critical issue.

[0022] In this embodiment of the invention, a heavy-duty vehicle is defined as a vehicle whose weight is greater than or equal to a preset weight. It should be noted that the preset weight is set to 15 tons, but the specific value can be adjusted according to the implementation scenario and is not limited here.

[0023] Existing technologies for monitoring bridge deflection typically employ fixed data acquisition frequencies, which are ill-suited to the combined effects of dynamic load changes from heavy vehicles, the spatiotemporal distribution of traffic flow, and environmental interference. This leads to inappropriate allocation of monitoring resources—data redundancy during low-risk periods (such as high-frequency sampling during low-traffic nighttime hours), while key signals are missed or lack accuracy in high-risk scenarios (sudden heavy loads, multi-vehicle resonance). Consequently, it becomes difficult to accurately monitor bridge deflection, impacting the safety and stability analysis of bridges.

[0024] In the daily monitoring of bridge deflection, especially under the action of heavy-load vehicles, it is necessary to fully consider the influence of multiple factors. In this embodiment of the invention, by analyzing the dynamic load resonance effect of heavy-load vehicles running in real time with other vehicles, and combining the density of heavy-load vehicles in the current cycle and the influence of environmental factors, the monitoring intensity of heavy-load vehicles is comprehensively determined, and the collection frequency of bridge deflection data is determined, so as to achieve dynamic adjustment of monitoring intensity and optimize the allocation of monitoring resources.

[0025] Therefore, the historical traffic data of all vehicles on the bridge is first obtained over several days; when heavy-load vehicles are present, environmental time-series data of various environmental factors and real-time traffic data of all vehicles on the bridge are obtained in real time within a preset time period.

[0026] In this embodiment of the invention, the traffic data should include vehicle weight, number of vehicles, vehicle speed, and displacement. Specifically, a smart sensor network can be deployed at key locations on the bridge, such as the bridge entrance, to accurately identify and record the weight of each passing vehicle and the number of passing vehicles; and a lidar can be installed at the entrance to obtain the driving speed (vehicle speed) and driving distance (displacement, specifically the distance between the vehicle and the lidar) of each vehicle at each moment, thus obtaining time-series data of vehicle speed and displacement. Environmental factors should at least include temperature, wind speed, and humidity, which can be obtained by installing multi-parameter smart sensors at key locations on the bridge, such as the bridge entrance, to obtain environmental time-series data corresponding to each environmental factor.

[0027] It should be noted that in this embodiment of the present invention, the historical data can be 7 days, the preset time period can be set to 30 seconds before and after the occurrence of a heavy-load vehicle, totaling one minute, and the collection time interval of environmental time series data, vehicle speed time series data and displacement time series data is one second. All of the aforementioned data acquisition-related indicators can be adjusted according to the implementation scenario, and are not limited here.

[0028] In this embodiment of the invention, the collection and acquisition of various data are authorized by the relevant users, and the process does not violate relevant laws and regulations, nor does it violate public order and good morals.

[0029] Step S2: Divide the historical days into periods and determine the reference density of heavy-load vehicles in each period based on the vehicle weight and number of vehicles in the historical traffic data; determine the comprehensive load impact of heavy-load vehicles by comparing the differences in vehicle speed and displacement between vehicles and heavy-load vehicles in the real-time traffic data and comparing the vehicle weights of vehicles and heavy-load vehicles; analyze the fluctuations in environmental values ​​in various environmental time series data to determine the environmental impact.

[0030] Traffic flow on bridges varies significantly across different time periods, with denser traffic during morning and evening rush hours and relatively sparser traffic at night. Therefore, historical daily data can be divided into periods to analyze the reference density of heavy-load vehicles within each period, reflecting the regularity of heavy-load vehicle traffic flow and providing a reference for adjusting subsequent data collection frequencies. The impact of heavy-load vehicles on bridges depends not only on their weight but also on the driving status of surrounding vehicles (such as speed and displacement). Therefore, based on the differences and changes in real-time traffic data between regular vehicles and heavy-load vehicles within a preset time period, the comprehensive load impact of heavy-load vehicles is determined to reflect the dynamic impact of heavy-load vehicles on bridge deflection. Environmental factors such as temperature, wind speed, and humidity also significantly affect bridge structures. For example, high temperatures may cause bridge materials to expand, increasing deflection; while changes in wind speed may affect the vibration characteristics of the bridge. Therefore, in this embodiment of the invention, the fluctuations in environmental values ​​in the environmental time series data are analyzed to determine the environmental impact degree, which reflects the degree of interference of environmental factors on bridge deflection. After identifying three influencing indicators of bridge deflection under heavy vehicle loads, the sampling frequency can be adaptively adjusted based on these three influencing indicators, which helps to improve monitoring accuracy and optimize the allocation of monitoring resources.

[0031] During daily driving, traffic flow on bridges usually exhibits certain patterns. This is because the transportation of heavy-duty vehicles is often influenced by logistics demands or commercial activities, as well as traffic restrictions during certain periods. This results in a high frequency of heavy-duty vehicle transport during specific times, which helps to monitor vehicles operating during key periods more closely in the future. Therefore, historical days can be divided into periods, and the reference density of heavy-duty vehicles in each period can be determined based on the vehicle weight and number of vehicles in historical traffic data.

[0032] Preferably, in one embodiment of the present invention, the method for obtaining the reference density includes: Traffic flow on bridges varies significantly across different time periods, directly impacting the stress state of the bridge structure. To accurately assess the impact of heavy vehicles on bridges, it's necessary to analyze the characteristics of traffic flow changes across different time periods, particularly the distribution of heavy vehicles. This can be achieved by dividing each historical day into multiple periods, classifying periods occurring within the same time frame across multiple historical days as the same type of period. For example, if the historical data spans three days, dividing each 24 hours into 24 periods, the period from 0:00 to 1:00 each day would belong to the same type of period. It's important to note that the historical traffic data should also be divided into periods to obtain vehicle data, vehicle weight, and other information for each period.

[0033] Then, in the historical traffic data for each period within each day, the proportion of heavy-load vehicles in the total number of vehicles is used as the density factor of heavy-load vehicles in each period. The larger the density factor, the more concentrated the heavy-load vehicles are in that period, and the more severe and obvious the impact on the bridge deflection data will be in that period.

[0034] Traffic flow variations can exhibit randomness and volatility, and daily traffic conditions may not accurately reflect long-term distributions. Therefore, for any given period, a weighted average of the number of heavily loaded vehicles is calculated based on the density factor of heavy-load vehicles over several historical days for that period. This yields a reference density for heavy-load vehicles within that period: the product of the density factor corresponding to that period each day and the number of heavily loaded vehicles is used as the weighted density. A higher weighted density indicates denser heavy-load vehicle traffic. The normalized average of the weighted densities over several historical days is then used as the reference density for each period. A higher reference density indicates more concentrated heavy-load vehicle traffic within that period, thus posing a greater potential threat to the bridge. Therefore, the frequency of deflection monitoring data collection should be appropriately increased to facilitate more timely and accurate detection of potential hazards. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear or standard normalization, etc. The specific normalization method is not limited here.

[0035] It should be noted that in this embodiment of the present invention, when the historical days are divided into multiple periods according to time periods, the interval is 1 hour. In other embodiments of the present invention, the specific division method can be adjusted according to the actual situation, and is not limited here.

[0036] When heavy-duty vehicles travel on a bridge, the impact and vibration generated trigger additional dynamic responses, primarily caused by the transmission and superposition of vibrations between the vehicle and the bridge. If two vehicles are close in speed or position, especially when the heavy-duty vehicle is significantly heavier, the interaction between the vehicle and the bridge becomes more pronounced. Furthermore, the impact force of the heavy-duty vehicle may resonate with the dynamic load of nearby vehicles, leading to greater dynamic deflection of the bridge. Therefore, based on real-time traffic data, the differences in vehicle speeds and displacements between vehicles and heavy-duty vehicles, and by comparing the weights of vehicles and heavy-duty vehicles, the overall load impact of heavy-duty vehicles can be determined.

[0037] Preferably, in one embodiment of the present invention, the method for obtaining the comprehensive load influence includes: Please see Figure 2 The diagram illustrates a method flowchart for obtaining the comprehensive load influence degree in one embodiment of the present invention. The method includes the following steps: Step S201: During the preset time period corresponding to the heavily loaded vehicle, all vehicles that appear will be tracked.

[0038] For heavy-duty vehicles, dynamic load resonance may occur with adjacent vehicles during their operation. These adjacent vehicles may appear before or after the heavy-duty vehicle. Therefore, in order to comprehensively analyze the impact of other vehicles on the heavy-duty vehicle during its operation, in this embodiment of the present invention, all vehicles appearing within a preset time period are considered as tracking vehicles of the heavy-duty vehicle.

[0039] Step S202: Within a preset time period, analyze the speed differences between the heavy-load vehicle and each tracked vehicle, and determine the speed proximity trend value between the heavy-load vehicle and each tracked vehicle.

[0040] The difference in vehicle speed can reflect the relative motion state between two vehicles. Based on the above analysis, it can be seen that vehicles with similar speeds are more likely to influence each other. Therefore, the analysis focused on the situation where the speeds of the heavy-duty vehicle and each tracking vehicle are similar.

[0041] In this embodiment of the invention, the preset time period is selected as 30 seconds before and after the appearance of the heavy-load vehicle. Therefore, for the tracking vehicle before the appearance of the heavy-load vehicle, the length of the vehicle speed time series data will be longer than that of the heavy-load vehicle. For the heavy-load vehicle, the length of the vehicle speed time series data of the tracking vehicle after it will be shorter than that of the heavy-load vehicle. Therefore, in order to align the time periods, the first intersection time period is obtained by taking the intersection of the time periods corresponding to the vehicle speed time series data of the heavy-load vehicle and each tracking vehicle within the preset time period. For example, if the duration of the vehicle speed time series data of the heavy-load vehicle is within 30s to 60s within one minute, and the duration of the vehicle speed time series data of a certain tracking vehicle is within 40s to 60s within one minute, then the first intersection time period is the 40s to 60s period.

[0042] Then, during the first intersection period, the absolute value of the difference between the speed of the heavy-load vehicle and each tracking vehicle at the same moment is used as the speed difference factor. The smaller the speed difference factor, the closer the speed of the heavy-load vehicle and the tracking vehicle are. In this case, the impact force of the heavy-load vehicle may resonate with the dynamic load of the tracking vehicle.

[0043] Finally, the mean value of the speed difference factor between the heavy-load vehicle and each tracking vehicle within the first intersection time period is calculated. The smaller this mean value, the greater the potential dynamic impact on bridge deflection. Therefore, a negative correlation mapping and normalization process is applied to this mean value to correct the logical relationship and obtain the speed proximity trend value. The larger the speed proximity trend value between the heavy-load vehicle and a tracking vehicle, the closer their speeds are. This indicates that the dynamic effect caused by the vibration transmission and superposition between the vehicle and the bridge will be more pronounced, thus having a more significant impact on bridge deflection monitoring. The negative correlation mapping and normalization process here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0044] Step S203: Within a preset time period, analyze the displacement changes between the heavy-load vehicle and each tracking vehicle to determine the proximity trend value of the distance between the heavy-load vehicle and each tracking vehicle.

[0045] Similarly, within a preset time period, the intersection of the time periods corresponding to the displacement time series data of the heavy-load vehicle and each tracked vehicle is obtained to obtain the second intersection time period. In the second intersection time period, the absolute value of the difference between the displacement of the heavy-load vehicle and each tracked vehicle at the same moment is taken as the distance between the two vehicles at the same moment. The smaller the distance, the closer the positions of the two vehicles are.

[0046] Then, at adjacent moments in the second intersection period, the difference between the distance between the heavily loaded vehicle and each tracking vehicle at the previous moment and the distance between the heavily loaded vehicle and each tracking vehicle at the next moment is calculated. This difference is then normalized and used as the proximity factor between the heavily loaded vehicle and each tracking vehicle. A larger positive difference indicates a larger proximity factor, meaning the two vehicles will be closer in the next moment than in the previous moment, increasing the likelihood of dynamic load situations due to proximity. The difference can be positive or negative, so the normalization method uses... function.

[0047] Finally, the sum of all proximity factors between the heavy-load vehicles and each tracked vehicle within the second intersection time period is normalized and used as the proximity trend value. The larger the proximity trend value, the greater the spatial proximity between the two vehicles, and the more significant the vibration transmission and superposition effect between the vehicles and the bridge will be, thus having a greater impact on the monitoring of bridge deflection data. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0048] Step S204: Within a preset time period, integrate the speed proximity trend value and the distance proximity trend value between the heavy-load vehicle and all tracking vehicles, and analyze the weight difference between the heavy-load vehicle and the tracking vehicles to obtain the comprehensive load influence of the heavy-load vehicle.

[0049] When analyzing the dynamic load resonance effect between a heavy-load vehicle and its corresponding tracking vehicle, it is also necessary to consider the case where the tracking vehicle is also a heavy-load vehicle. This is because when two heavy-load vehicles jointly cause dynamic load resonance, the impact on bridge deflection will be greater than that under the case of a single heavy-load vehicle. Therefore, a comparative analysis of the vehicle weights of the heavy-load vehicle and its corresponding tracking vehicle is conducted here to further comprehensively determine the estimated comprehensive impact of the dynamic load.

[0050] Within a preset time period, the product of the proximity trend values ​​of speed and distance between the heavy-load vehicle and each tracking vehicle is first used as the load influence factor between the heavy-load vehicle and each tracking vehicle. Based on the analysis in steps S202 and S203 above, it can be seen that the larger the load influence factor is at this time, the more obvious the vibration transmission and superposition effect between the vehicle and the bridge will be, and thus the greater the impact on the monitoring of bridge deflection data.

[0051] Then, the ratio of the weight of each tracked vehicle to the weight of a heavy-duty vehicle is used as the influence weight of each tracked vehicle. The larger the influence weight, the greater the weight of the tracked vehicle, and the stronger the dynamic load resonance effect will be. Similarly, the influence on bridge deflection data monitoring will also be greater.

[0052] Therefore, the load influence factors between tracking vehicles and heavy-load vehicles are weighted and fused based on the influence weights of the tracking vehicles to obtain the comprehensive load influence degree of heavy-load vehicles. The product of the influence weight of each tracking vehicle and the load influence factor is used as the weighted influence factor. The sum of the weighted influence factors of all tracking vehicles is normalized and used as the comprehensive load influence degree of heavy-load vehicles. This comprehensive load influence degree comprehensively assesses the degree of dynamic load resonance that occurs when heavy-load vehicles are present. The larger the value, the greater the degree, and the greater the impact on bridge deflection data monitoring. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0053] Under the influence of daily environmental factors (temperature, humidity, wind speed, etc.), bridge deflection will be affected to a certain extent. However, if the environmental factors change normally, such as the bridge slowly expanding and contracting with temperature changes, the bridge usually has enough time to adapt to this change, so it will not cause excessive instantaneous deflection changes. However, when extreme weather changes occur, that is, sudden or instantaneous changes in environmental factors, they will have a significant impact on bridge deflection.

[0054] Therefore, in this embodiment of the invention, the fluctuations of environmental values ​​can be analyzed in various environmental time series data to quantify the sudden changes of environmental factors and obtain the degree of environmental impact, which can be used to evaluate the impact of environmental factors on bridge deflection.

[0055] Preferably, in one embodiment of the present invention, the method for obtaining the environmental impact degree includes: In each type of environmental time series data, the mean of all environmental values ​​is used as the mean characteristic value. The mean characteristic value represents the average level of environmental values ​​in each type of environmental time series data. It is a benchmark point that can be used to measure the degree to which subsequent environmental values ​​deviate from this benchmark, thereby quantifying the sudden changes in environmental factors.

[0056] The absolute value of the difference between each environmental value and the mean characteristic value is used as the environmental deviation factor. The environmental deviation factor reflects the difference between each environmental value and the mean characteristic value. This difference can reveal the fluctuations and changes in the data. The larger the value, the more obvious the fluctuations and the more drastic the changes. Since the impact of environmental factors is often determined by the maximum fluctuations and changes, because they represent the worst situation or extreme events, the maximum environmental deviation factor is used as the environmental impact factor for each environmental factor. The larger the environmental impact factor, the more obvious the sudden changes in environmental factors, and the more significant the impact on bridge deflection.

[0057] Finally, the environmental impact factors corresponding to all environmental factors are integrated: the sum of the environmental impact factors corresponding to all environmental factors is normalized and used as the environmental impact degree. The larger the environmental impact degree, the more significant the fluctuations and changes in the environmental factors, and thus the greater the impact on bridge deflection. Therefore, in subsequent deflection data collection, the collection frequency should be appropriately increased to facilitate more timely response to potential risks. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0058] Step S3: Based on the vehicle weight, load comprehensive influence, environmental influence, and reference density of the corresponding period when heavy-load vehicles appear, adjust the preset acquisition frequency to obtain the adjusted acquisition frequency; collect bridge deflection data based on the adjusted acquisition frequency of all heavy-load vehicles on the current bridge for bridge deflection monitoring.

[0059] Based on the aforementioned steps, we analyzed various factors that affect bridge deflection and obtained multiple indicators. Since the greater the weight of a heavy-duty vehicle, more attention should be paid to the potential impact of the heavy-duty vehicle itself; conversely, the smaller the weight of a heavy-duty vehicle, the more attention should be paid to changes in environmental factors. Therefore, in this step, based on the aforementioned multiple indicators, we can combine the weight of the heavy-duty vehicle as a balancing parameter to determine the multi-dimensional monitoring intensity of the heavy-duty vehicle. This allows us to adjust the preset acquisition frequency, adaptively adjusting the acquisition frequency to make more rational use of monitoring resources and better adapt to dynamically changing traffic conditions and environmental conditions, thereby improving the accuracy of deflection monitoring.

[0060] Preferably, in one embodiment of the present invention, the method for adjusting the acquisition frequency includes: Vehicle weight is a crucial factor influencing bridge deflection. Therefore, the normalized weight of heavy-duty vehicles is used as an adjustment weight. A larger adjustment weight indicates a heavier vehicle, suggesting that subsequent adjustments should focus more on the impact indicators related to the heavy-duty vehicle itself, i.e., the overall load impact. Conversely, a smaller adjustment weight indicates a lighter vehicle, suggesting a relatively smaller impact on the bridge, thus requiring more attention to the influence of environmental factors on bridge deflection, i.e., the environmental impact. Normalization is a well-known technique in this field. The normalization function can be linear or standard, and the specific normalization method is not limited here.

[0061] Based on the foregoing analysis, in this embodiment of the invention, the multi-dimensional monitoring intensity of heavy-duty vehicles is obtained by adjusting the weights, the comprehensive influence of the load, the environmental impact, and the reference density of the corresponding cycle of the heavy-duty vehicles. The formula model for the monitoring intensity includes: in, This indicates the intensity of multi-dimensional monitoring; This indicates the reference density of the cycle corresponding to heavy-duty vehicles; This indicates an adjustment of the weights; Indicates the overall impact of the load; Indicates the degree of environmental impact; This represents the normalization function.

[0062] In the formula model for multidimensional monitoring intensity, a larger adjustment weight indicates a greater need to focus on factors related to the heavy-load vehicle itself, meaning an increase in the proportion of the overall load influence. Conversely, a smaller adjustment weight indicates a smaller vehicle weight and limited impact on bridge deflection. In this case, environmental factors are more likely to become the primary factor influencing bridge deflection, so the proportion of environmental influence should be increased. Based on this logic, the formula is constructed. By adjusting the weights, the proportions of the comprehensive load impact and the environmental impact can be better balanced, and the calculated value is recorded as the comprehensive impact factor. Since the traffic density of heavy-load vehicles varies in different periods, to better measure the impact of traffic density when heavy-load vehicles are present on bridge deflection, the reference density in the corresponding period when heavy-load vehicles are present is multiplied by the comprehensive impact factor, and the product is normalized to obtain the final multidimensional monitoring intensity. A higher multidimensional monitoring intensity indicates a greater impact of the presence of heavy-load vehicles on bridge deflection monitoring. Normalization is a technique well-known to those skilled in the art; the normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0063] Then, the product of the difference between the preset maximum acquisition frequency and the preset minimum acquisition frequency and the multidimensional monitoring intensity is used as the adjustment range. The difference here can be regarded as the adjustment range of the acquisition frequency, and the adjustment range reflects the degree of adjustment of the acquisition frequency according to the multidimensional monitoring intensity. The larger the value, the greater the degree of adjustment.

[0064] Finally, the sum of the adjustment range and the preset minimum acquisition frequency is used as the adjusted acquisition frequency. In other words, the acquisition frequency is increased based on the preset minimum acquisition frequency according to the adjustment range. This ensures that the acquisition frequency will not fall below the preset minimum value, while also allowing for flexible adjustment according to the actual situation.

[0065] It should be noted that the minimum sampling frequency is set to 10Hz and the maximum sampling frequency is set to 100Hz. The specific values ​​can be adjusted according to the implementation scenario and are not limited here.

[0066] Based on the aforementioned process, the adjustment collection frequency corresponding to the heavy-load vehicle can be obtained when the heavy-load vehicle appears. Since the judgment criterion for the appearance of a heavy-load vehicle in this embodiment of the invention is the bridge entrance, when the adjustment collection frequency of the newly appearing heavy-load vehicle is calculated, there may already be multiple heavy-load vehicles on the bridge. Therefore, the bridge deflection data can be collected based on the adjustment collection frequency of all heavy-load vehicles on the current bridge and used for bridge deflection monitoring.

[0067] Preferably, in one embodiment of the present invention, collecting bridge deflection data based on the adjusted collection frequency of all heavy-load vehicles on the current bridge and using it for bridge deflection monitoring includes: The average of the adjusted collection frequencies of all heavy-load vehicles on the bridge is used as the final collection frequency.

[0068] Bridge deflection data is collected based on the final acquisition frequency. When the deflection value in the bridge deflection data is greater than or equal to the preset safety threshold, a deflection warning needs to be issued; when the deflection value is less than the preset safety threshold, no deflection warning is required.

[0069] It should be noted that the preset safety threshold can be 1 / 500 of the span of the bridge segments. The specific value can be adjusted according to the actual scenario and is not limited here.

[0070] In this embodiment of the invention, the main analysis focuses on how to adaptively adjust the collection frequency of bridge deflection data when heavy-load vehicles are present. When no heavy-load vehicles are present, the collection frequency of bridge deflection data can be set to the preset minimum collection frequency. The judgment process for bridge deflection monitoring is as follows: when the deflection value in the bridge deflection data is greater than or equal to the preset safety threshold, a deflection warning needs to be issued; when the deflection value is less than the preset safety threshold, no deflection warning is required.

[0071] In summary, due to the influence of logistics demands or commercial activities and traffic restrictions, the daily passage of heavy-duty vehicles across bridges typically follows certain patterns. Therefore, acquiring historical traffic data on bridges over multiple days provides a more comprehensive understanding of the bridge's daily usage. When heavy-duty vehicles are present, real-time environmental and traffic data are collected immediately, ensuring the monitoring system can respond promptly to special circumstances and quickly capture the impact of heavy-duty vehicles. Firstly, analyzing historical traffic data allows for the determination of reference density for heavy-duty vehicles within each period, reflecting their driving patterns. When heavy-duty vehicles travel on bridges, especially when they maintain similar speeds to adjacent vehicles, dynamic effects may occur due to interactions. This effect can lead to greater dynamic deflection of the bridge. Therefore, based on speed differences, displacement changes, and comparison of vehicle weight, the comprehensive load impact of heavy-duty vehicles is determined. This method refines the specific impact of loads on the bridge and assesses the dynamic pressure exerted by heavy-duty vehicles on the bridge structure. Bridge deflection is affected by daily environmental factors. Therefore, analyzing the fluctuations in environmental values ​​in time-series environmental data determines the degree of environmental impact, reflecting the influence of external environmental factors on bridge deflection. Finally, the acquisition frequency is dynamically adjusted based on heavy-load vehicle characteristics, load influence, environmental influence, and reference density, achieving optimized allocation of monitoring resources and improving monitoring efficiency while ensuring accuracy. Acquiring bridge deflection data based on the adjusted acquisition frequency ensures more accurate and appropriate data acquisition during heavy-load vehicle traffic, thereby improving the accuracy of deflection detection and promoting efficient resource utilization.

[0072] This invention also proposes a system for monitoring bridge deflection under heavy vehicle loads; please refer to [link to relevant documentation]. Figure 3 The diagram shows a system block diagram, including a data acquisition module 301 for implementing step S1 in the above method embodiment; a deflection monitoring influencing factor analysis module 302 for implementing step S2 in the above method embodiment; and a deflection monitoring module 303 for implementing step S3 in the above method embodiment.

[0073] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the bridge deflection monitoring system under heavy vehicle load and the bridge deflection monitoring method under heavy vehicle load provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiment, which will not be repeated here.

[0074] Please see Figure 4 This diagram illustrates a system structure of a bridge deflection monitoring system under heavy vehicle loads according to an embodiment of the present invention. The system includes a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, communication interface 403, and memory 401 are connected via the bus 402. The memory 401 may contain a high-speed random access memory, and the bus 402 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 400 may be an integrated circuit chip with signal processing capabilities. The memory 401 stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps in a bridge deflection monitoring method under heavy vehicle loads.

[0075] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for monitoring bridge deflection under heavy vehicle loads, characterized in that, The method includes: Historical traffic data of vehicles on the bridge over multiple days is acquired; when heavy-load vehicles are present, environmental time-series data of various environmental factors and real-time traffic data of vehicles on the bridge are acquired within a preset time period; the traffic data includes vehicle weight, number of vehicles, vehicle speed time-series data and displacement time-series data. The historical data is divided into daily periods, and the reference density of heavy-load vehicles in each period is determined based on the vehicle weight and number of vehicles in the historical traffic data. Based on the difference in vehicle speed and the change in displacement between vehicles and heavy-load vehicles in the real-time traffic data, and by comparing the vehicle weight of vehicles and heavy-load vehicles, the comprehensive load impact of heavy-load vehicles is determined. In various environmental time series data, the fluctuation changes of environmental values ​​are analyzed to determine the environmental impact. Based on the vehicle weight, load-bearing combined influence of heavy-duty vehicles, the environmental influence, and the reference density of the corresponding period when heavy-duty vehicles appear, the preset acquisition frequency is adjusted to obtain the adjusted acquisition frequency; based on the adjusted acquisition frequency of all heavy-duty vehicles on the current bridge, bridge deflection data is collected for bridge deflection monitoring.

2. The method for monitoring bridge deflection under heavy vehicle load as described in claim 1, characterized in that, The method for obtaining the reference density includes: The history is divided into several periods each day to obtain multiple cycles, and cycles that fall in the same period across multiple days are considered to be of the same type. In the historical traffic data for each period of each day, the proportion of heavy-load vehicles in the total number of vehicles is used as the density factor of heavy-load vehicles in each period. For any given period, the number of heavy-load vehicles is weighted and averaged based on the density factor of heavy-load vehicles in that period over several historical days, thus obtaining the reference density of heavy-load vehicles in that period.

3. The method for monitoring bridge deflection under heavy vehicle load as described in claim 1, characterized in that, The method for obtaining the comprehensive load influence includes: All vehicles appearing within a preset time period corresponding to a heavily loaded vehicle will be tracked. Within a preset time period, analyze the speed differences between the heavy-loaded vehicle and each tracked vehicle to determine the speed proximity trend value between the heavy-loaded vehicle and each tracked vehicle. Within a preset time period, analyze the displacement changes between the heavy-load vehicle and each tracking vehicle to determine the proximity trend value of the distance between the heavy-load vehicle and each tracking vehicle. Within a preset time period, the product of the speed proximity trend value and the distance proximity trend value between the heavy-load vehicle and each tracking vehicle is used as the load influence factor between the heavy-load vehicle and each tracking vehicle. The ratio of the weight of each tracked vehicle to the weight of a heavily loaded vehicle is used as the influence weight of each tracked vehicle. The load influence factors between the tracking vehicles and the heavy-load vehicles are weighted and fused based on the influence weight of the tracking vehicles to obtain the comprehensive load influence of the heavy-load vehicles.

4. The method for monitoring bridge deflection under heavy vehicle load according to claim 3, characterized in that, The method for obtaining the velocity proximity trend value includes: Within a preset time period, the intersection of the time periods corresponding to the speed time series data of the heavy-load vehicle and each tracked vehicle is taken to obtain the first intersection time period. Under the first intersection time period, the absolute value of the difference between the speed of the heavy-load vehicle and each tracked vehicle at the same time is used as the speed difference factor. The mean value of the speed difference factor between the heavy-load vehicle and each tracked vehicle during the first intersection period is negatively correlated and normalized, and then used as the speed proximity trend value.

5. A method for monitoring bridge deflection under heavy vehicle load according to claim 3, characterized in that, The method for obtaining the vehicle proximity trend value includes: Within a preset time period, the time periods corresponding to the displacement time series data of the heavy-load vehicle and each tracking vehicle are intersected to obtain the second intersection time period. Under the second intersection time period, the absolute value of the difference between the displacement of the heavy-load vehicle and each tracking vehicle at the same time is taken as the distance between the heavy-load vehicle and each tracking vehicle. At adjacent moments of the second intersection period, the normalized value of the difference between the distance between the heavy-load vehicle and each tracking vehicle at the previous moment and the distance between the heavy-load vehicle and each tracking vehicle at the next moment is used as the proximity factor between the heavy-load vehicle and each tracking vehicle. The sum of all vehicle proximity factors between the heavy-load vehicle and each tracked vehicle within the second intersection period is normalized and used as the vehicle proximity trend value.

6. The method for monitoring bridge deflection under heavy vehicle load according to claim 1, characterized in that, The methods for obtaining the environmental impact include: In each type of environmental time series data, the mean of all environmental values ​​is used as the mean characteristic value, the absolute value of the difference between each environmental value and the mean characteristic value is used as the environmental deviation factor, and the maximum environmental deviation factor is used as the environmental impact factor of each environmental factor. The sum of environmental impact factors corresponding to all environmental factors is normalized and used as the environmental impact degree.

7. The method for monitoring bridge deflection under heavy vehicle load according to claim 1, characterized in that, The method for adjusting the acquisition frequency includes: The normalized weight of the heavy-duty vehicle is used as the adjustment weight. Based on the adjusted weights, comprehensive load impact, environmental impact, and reference density of the corresponding cycle for heavy-duty vehicles, the multi-dimensional monitoring intensity of heavy-duty vehicles is obtained. The product of the difference between the preset maximum acquisition frequency and the preset minimum acquisition frequency and the multidimensional monitoring intensity is used as the adjustment range; The sum of the adjustment range and the preset minimum acquisition frequency is taken as the adjusted acquisition frequency.

8. A method for monitoring bridge deflection under heavy vehicle load according to claim 7, characterized in that, The formula model for the multidimensional monitoring intensity includes: in, This indicates the intensity of multi-dimensional monitoring; This indicates the reference density of the cycle corresponding to heavy-duty vehicles; This indicates an adjustment of the weights; Indicates the overall impact of the load; Indicates the degree of environmental impact; This represents the normalization function.

9. A method for monitoring bridge deflection under heavy vehicle load according to claim 1, characterized in that, The process of collecting bridge deflection data based on the adjusted collection frequency of all heavy-load vehicles on the bridge for bridge deflection monitoring includes: The average of the adjusted collection frequencies of all heavy-load vehicles on the bridge is used as the final collection frequency. Bridge deflection data is collected based on the final collection frequency. When the deflection value is greater than or equal to a preset safety threshold, a deflection warning is issued; when the deflection value is less than the preset safety threshold, no deflection warning is required.

10. A system for monitoring bridge deflection under heavy vehicle loads, characterized in that, The method includes a processor and a memory, the memory storing at least one instruction, at least one program, code set, or instruction set, wherein when the at least one instruction, at least one program, code set, or instruction set is loaded and executed by the processor, it implements the steps of the method for monitoring bridge deflection under heavy-load vehicle load as described in any one of claims 1-9.

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