A bridge structure anti-deformation monitoring method and system

Abstract

The present invention provides a method and system for monitoring the deformation resistance of a bridge structure, and relates to the technical field of bridge deformation monitoring. The method comprises: dividing a bridge area into multiple segmented areas, setting horizontal test points and vertical test points; determining a first distance and a first yaw angle, as well as a second distance and a second pitch angle; determining a horizontal anti-deformation coefficient; determining a vertical anti-deformation coefficient; obtaining a segmented bridge image; determining measured similarity data; obtaining environmental data; inputting the environmental data and the segmented bridge image at the beginning of the current monitoring cycle into a trained similarity prediction model to obtain predicted similarity data for multiple segmented bridge areas in the current monitoring cycle; determining a surface anti-deformation coefficient; and determining the anti-deformation capability of the bridge structure. According to the present invention, non-contact monitoring can be performed, and the anti-deformation capability of the bridge structure can be comprehensively evaluated from three directions: horizontal, vertical, and environmental, thereby improving the comprehensiveness and accuracy of evaluating the anti-deformation capability of the bridge structure and reducing the monitoring cost.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge deformation monitoring, and in particular to a bridge structure anti-deformation monitoring method and system. Background Art

[0002] Although deformation monitoring can be performed in current related technologies, horizontal and vertical test points are not set up to evaluate the anti-deformation capacity in the horizontal and vertical directions. The impact of the environment on the deformation of the bridge structure is also not considered. In other words, it is impossible to comprehensively evaluate the anti-deformation capacity of the bridge structure from the three directions of horizontal, vertical and environmental aspects.

[0003] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0004] The present invention provides a bridge structure anti-deformation monitoring method and system, which can solve the technical problem that related technologies cannot comprehensively evaluate the anti-deformation ability of bridge structures from three directions: horizontal, vertical and environmental.

[0005] According to a first aspect of the present invention, a method for monitoring the deformation resistance of a bridge structure is provided, comprising: dividing the entire bridge into a plurality of segmented bridge areas, setting a plurality of horizontal test points on the side positions of the bridge deck of the segmented bridge area, and setting a plurality of vertical test points on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; arranging a laser rangefinder at a first preset position in the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated onto the horizontal test point and the vertical test point, and determining a first distance and a first yaw angle of the laser rangefinder when measuring a plurality of horizontal test points, and a second distance and a second pitch angle of the laser rangefinder when measuring a plurality of vertical test points; at the start and end times of the current monitoring period, according to the first distance and the first yaw angle , determine the horizontal anti-deformation coefficient; at the start and end of the current monitoring period, determine the vertical anti-deformation coefficient according to the second distance and the second pitch angle; obtain segmented bridge images of multiple segmented bridge areas at the start and end of the current monitoring period; determine the measured similarity data based on the segmented bridge images; obtain environmental data of the current monitoring period, wherein the environmental data includes temperature data, traffic load data and wind speed data; input the environmental data and the segmented bridge images at the start of the current monitoring period into the trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring period; determine the surface anti-deformation coefficient based on the predicted similarity data and the measured similarity data; determine the anti-deformation capacity of the bridge structure in the current monitoring period based on the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

[0006] Furthermore, at the start and end of the current monitoring cycle, the horizontal anti-deformation coefficient is determined based on the first distance and the first yaw angle, including: obtaining the starting maximum first distance based on the first distance at the start of the current monitoring cycle; obtaining the ending maximum first distance based on the first distance at the end of the current monitoring cycle; determining the horizontal anti-deformation coefficient based on the starting maximum first distance, the ending maximum first distance, the first distance and the first yaw angle.

[0007] Further, determining the horizontal anti-deformation coefficient according to the starting maximum first distance, the ending maximum first distance, the first distance and the first yaw angle includes: according to the formula Determine the horizontal deformation resistance coefficient of the e-th segment bridge area ,in, is the maximum first distance at the beginning of the e-th segment bridge area, is the maximum first distance at the end of the e-th segment bridge area, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring cycle, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the end of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segmented bridge area at the end of the current monitoring period, n is the number of horizontal test points, max is the maximum value function, i≤n, and e, i and n are all positive integers.

[0008] Furthermore, at the start and end of the current monitoring cycle, the vertical anti-deformation coefficient is determined based on the second distance and the second pitch angle, including: obtaining the starting maximum second distance based on the second distance at the start of the current monitoring cycle; obtaining the ending maximum second distance based on the second distance at the end of the current monitoring cycle; obtaining the pier height; and determining the vertical anti-deformation coefficient based on the pier height, the starting maximum second distance, the ending maximum second distance, the second distance and the second pitch angle.

[0009] Further, determining the vertical anti-deformation coefficient according to the pier height, the starting maximum second distance, the ending maximum second distance, the second distance and the second pitch angle includes: according to the formula Determine the vertical deformation resistance coefficient of the e-th segment bridge area ,in, is the second maximum distance at the start of the e-th segment bridge area, is the second maximum distance at the end of the e-th segment bridge area, is the second distance of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring cycle, is the second distance of the jth vertical test point in the eth segment bridge area at the end of the current monitoring cycle, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring period, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the end of the current monitoring period, is the pier height of the e-th segmented bridge area, m is the number of vertical test points, max is the maximum value function, j≤m, and e, j and m are all positive integers.

[0010] Furthermore, based on the segmented bridge image, measured similarity data is determined, including: performing feature extraction processing on the segmented bridge image at the start time of the current monitoring period through the trained image recognition neural network model to obtain starting segmented bridge feature vectors of multiple segmented bridge areas; performing feature extraction processing on the segmented bridge image at the end time of the current monitoring period through the trained image recognition neural network model to obtain ending segmented bridge feature vectors of multiple segmented bridge areas; and determining the similarity between the starting segmented bridge feature vector and the ending segmented bridge feature vector of the e-th segmented bridge area as the measured similarity data of the e-th segmented bridge area.

[0011] Furthermore, the training step of the similarity prediction model includes: obtaining historical segmented bridge images of multiple historical segmented bridge areas at the start and end times of multiple historical monitoring cycles; determining historical measured similarity data based on the historical segmented bridge images; obtaining a third distance between the midpoint position of the pier of each historical segmented bridge area and the midpoint position of the entire bridge; obtaining historical environmental data of multiple historical segmented bridge areas in multiple historical monitoring cycles, wherein the historical environmental data includes historical temperature data, historical traffic load data and historical wind speed data; processing the historical environmental data and the historical segmented bridge images at the start time of the historical monitoring cycle through the similarity prediction model to obtain historical predicted similarity data of multiple historical segmented bridge areas in multiple historical monitoring cycles; determining the loss function of the similarity prediction model based on the third distance, the historical environmental data, the historical measured similarity data and the historical predicted similarity data; and training the similarity prediction model based on the loss function of the similarity prediction model to obtain the trained similarity prediction model.

[0012] Further, according to the third distance, historical environmental data, the historical measured similarity data and the historical predicted similarity data, determining the loss function of the similarity prediction model includes: according to the formula Determine the loss function Loss of the similarity prediction model, where: is the historical measured similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the historical prediction similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the third distance between the midpoint of the bridge pier in the yth historical segment and the midpoint of the entire bridge. The third distance between the midpoint of the bridge pier in the first historical segment and the midpoint of the entire bridge. is the historical temperature data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard temperature data, is the historical traffic load data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the standard traffic load data, is the historical wind speed data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard wind speed data, H is the number of historical monitoring cycles, is the number of historical segmented bridge regions in the xth training batch, N is the number of training batches, h≤H, y≤ , x≤N, and h, x, y, H, and N are both positive integers.

[0013] Further, according to the predicted similarity data and the measured similarity data, determining the surface deformation resistance coefficient includes: according to the formula Determine the surface deformation resistance coefficient ,in, is the measured similarity data of the e-th segment bridge area, is the predicted similarity data of the e-th segmented bridge area, E is the number of segmented bridge areas, e≤E, and both e and E are positive integers.

[0014] According to a second aspect of the present invention, a bridge structure anti-deformation monitoring system is provided, comprising: a segmented bridge area module, for dividing the entire bridge into a plurality of segmented bridge areas, setting a plurality of horizontal test points on the side positions of the bridge deck of the segmented bridge area, and setting a plurality of vertical test points on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; a measurement module, for setting a laser rangefinder at a first preset position of the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated onto the horizontal test point and the vertical test point, and determining a first distance and a first yaw angle of the laser rangefinder when measuring a plurality of horizontal test points, and a second distance and a second pitch angle of the laser rangefinder when measuring a plurality of vertical test points; a horizontal anti-deformation coefficient module, for determining a horizontal anti-deformation coefficient according to the first distance and the first yaw angle at the start and end times of a current monitoring period; a vertical anti-deformation coefficient module, for determining a horizontal anti-deformation coefficient according to the first distance and the first yaw angle at the start and end times of a current monitoring period; and a vertical anti-deformation coefficient module, for determining a horizontal anti-deformation coefficient according to the first distance and the first yaw angle at the start and end times of a current monitoring period. At the start and end of the previous monitoring cycle, the vertical anti-deformation coefficient is determined according to the second distance and the second pitch angle; a segmented bridge image module is used to obtain segmented bridge images of multiple segmented bridge areas at the start and end of the current monitoring cycle; a measured similarity data module is used to determine measured similarity data based on the segmented bridge images; an environmental data module is used to obtain environmental data of the current monitoring cycle, wherein the environmental data includes temperature data, traffic load data and wind speed data; a predicted similarity data module is used to input the environmental data and the segmented bridge image at the start of the current monitoring cycle into a trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring cycle; a surface anti-deformation coefficient module is used to determine the surface anti-deformation coefficient according to the predicted similarity data and the measured similarity data; an anti-deformation capacity evaluation module is used to determine the anti-deformation capacity of the bridge structure in the current monitoring cycle according to the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

[0015] Technical effect: According to the present invention, by setting horizontal test points and vertical test points and using a laser rangefinder for measurement, it is helpful to understand the deformation status of the bridge structure in the horizontal and vertical directions. By combining the influence of environmental data on the deformation of the bridge structure with the similarity prediction model, the deformation trend of the bridge can be predicted more accurately. By conducting non-contact monitoring, the deformation resistance of the bridge structure is comprehensively evaluated from three directions: horizontal, vertical and environmental, which improves the comprehensiveness and accuracy of the evaluation of the deformation resistance of the bridge structure and reduces the monitoring cost. When determining the horizontal anti-deformation coefficient, the weight can be set based on the relative difference between the first yaw angle at the end and the start of the current monitoring cycle, and based on the characteristic that the farther the horizontal test point is from the first preset position, the less obvious the change in the first yaw angle. The relative difference between the first yaw angles of multiple horizontal test points at the end and the start of the current monitoring cycle is weighted averaged to obtain the horizontal anti-deformation coefficient, thereby improving the accuracy of identifying the bridge deck's anti-lateral deformation ability. When determining the vertical anti-deformation coefficient, a weight can be set based on the ratio between the vertical displacement of the vertical test point and the height of the pier, and based on the characteristic that the farther the vertical test point is from the first preset position, the greater the vertical displacement corresponding to the same pitch angle change. In this way, the ratio between the vertical displacement of multiple vertical test points and the height of the pier is weightedly averaged to obtain the vertical anti-deformation coefficient, thereby improving the accuracy of identifying the ability of the pier to resist vertical deformation. When determining the loss function of the similarity prediction model, the influence of historical temperature data, historical traffic load data, and historical wind speed data on the deformation of the bridge can be used to determine the influence of the above data on the error of the historical predicted similarity data. Based on the influence and the relative difference between the historical measured similarity data and the historical predicted similarity data, weights are set based on the characteristic that the closer the historical segmented bridge area is to the midpoint of the entire bridge, the more likely it is to deform and the greater the influence on the error of the historical predicted similarity data. Weights are also set based on the characteristic that the shorter the time interval with the first batch, the lower the accuracy. The errors output by the similarity prediction model of multiple historical segmented bridge areas in each training batch in multiple historical monitoring cycles are weightedly summed to obtain a loss function, so as to improve the design accuracy and objectivity of the loss function, thereby improving training efficiency during the training process and improving the accuracy of the similarity prediction model. When determining the surface anti-deformation coefficient, the surface anti-deformation coefficient can be determined by using predicted similarity data and measured similarity data. Comparing the actual deformation of the bridge with the predicted deformation can reflect the strength of the bridge's surface anti-deformation ability under the influence of environmental factors, thereby improving the reliability and scientific nature of the surface anti-deformation coefficient.

[0016] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and not limiting of the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can derive other embodiments based on these drawings without inventive efforts.

[0018] Figure 1 A schematic flow chart of a bridge structure anti-deformation monitoring method according to an embodiment of the present invention is exemplarily shown;

[0019] Figure 2 A flowchart of calculating a horizontal anti-deformation coefficient according to an embodiment of the present invention is exemplarily shown;

[0020] Figure 3 The flowchart of calculating the vertical anti-deformation coefficient according to an embodiment of the present invention is exemplarily shown;

[0021] Figure 4 The flowchart of calculating the measured similarity data according to an embodiment of the present invention is exemplarily shown;

[0022] Figure 5 A block diagram of a bridge structure anti-deformation monitoring system according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0024] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0025] Figure 1A flow chart of a bridge structure anti-deformation monitoring method according to an embodiment of the present invention is exemplarily shown, the method comprising: step S1, dividing the entire bridge into a plurality of segmented bridge areas, setting a plurality of horizontal test points on the side positions of the bridge deck of the segmented bridge area, and setting a plurality of vertical test points on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; step S2, setting a laser rangefinder at a first preset position in the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated onto the horizontal test point and the vertical test point, and determining the first distance and the first yaw angle of the laser rangefinder when measuring the plurality of horizontal test points, and the second distance and the second pitch angle of the laser rangefinder when measuring the plurality of vertical test points; step S3, determining the horizontal anti-deformation coefficient; step S4, at the start and end of the current monitoring period, determine the vertical anti-deformation coefficient according to the second distance and the second pitch angle; step S5, obtain segmented bridge images of multiple segmented bridge areas at the start and end of the current monitoring period; step S6, determine the measured similarity data based on the segmented bridge images; step S7, obtain environmental data of the current monitoring period, wherein the environmental data includes temperature data, traffic load data and wind speed data; step S8, input the environmental data and the segmented bridge image at the start of the current monitoring period into the trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring period; step S9, determine the surface anti-deformation coefficient according to the predicted similarity data and the measured similarity data; step S10, determine the anti-deformation capacity of the bridge structure in the current monitoring period according to the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

[0026] The bridge structure deformation resistance monitoring method according to an embodiment of the present invention, by setting horizontal and vertical test points and using a laser rangefinder for measurement, helps to understand the horizontal and vertical deformation conditions of the bridge structure. By combining the impact of environmental data on bridge structure deformation with a similarity prediction model, the deformation trend of the bridge can be more accurately predicted. Through non-contact monitoring, the bridge structure's deformation resistance is comprehensively assessed from three perspectives: horizontal, vertical, and environmental. This improves the comprehensiveness and accuracy of the assessment of the bridge structure's deformation resistance and reduces monitoring costs.

[0027] According to one embodiment of the present invention, in step S1, the entire bridge is a land-based bridge, and each segmented bridge area has only one pier. That is, the bridge is divided into multiple segmented bridge areas along the longitudinal direction (the length of the bridge) by the piers. Each segmented bridge area includes a pier, a bridge deck, and infrastructure (e.g., bearings and expansion joints). Multiple horizontal test points are set on the side of the bridge deck (e.g., on the box girder web or flange plate) in each segmented bridge area. All horizontal test points must be located on the same horizontal plane, i.e., at the same height, to monitor horizontal displacement of the bridge deck (e.g., lateral or torsional deformation). Simultaneously, multiple vertical test points are set on the side of the piers in each segmented bridge area (e.g., on the windward side or in the main load direction). The deck and pier sides are located on the same side of the bridge, and all vertical test points must be located on the same vertical line, i.e., along the same axis along the pier height direction, to monitor vertical displacement of the piers (e.g., pier subsidence or arching).

[0028] According to one embodiment of the present invention, in step S2, within the demarcated segmented bridge area, a first preset position is located on the ground of a hard base (which will not sink) next to a pier in the segmented bridge area, for installing a laser rangefinder. Necessary angle adjustments are made so that the emitted laser can illuminate all horizontal test points and all vertical test points, thereby measuring a first distance between the first preset position and the horizontal test point, recording a first yaw angle of the laser rangefinder, and a second distance between the first preset position and the vertical test point, and recording a second pitch angle of the laser rangefinder. The yaw angle is used to describe the angle at which the laser rangefinder rotates to the left or right, and the pitch angle is used to describe the angle at which the laser rangefinder rotates up or down.

[0029] According to one embodiment of the present invention, in step S3, the interval between adjacent moments can be set to 12 hours, 24 hours, etc., and each monitoring period can be set to 3 days, 5 days, etc., which is not limited by the present invention. The first distance and first yaw angle data of each horizontal test point are measured at the beginning of the current monitoring period, and the first distance and first yaw angle data of each horizontal test point are measured at the end of the current monitoring period. The horizontal anti-deformation coefficient reflects the horizontal displacement of the bridge deck. The first preset position of each segmented bridge area remains unchanged during the current monitoring period.

[0030] Figure 2 A flowchart for calculating a horizontal anti-deformation coefficient according to an embodiment of the present invention is exemplarily shown.

[0031] According to one embodiment of the present invention, step S3 includes: step S31, obtaining the starting maximum first distance based on the first distance at the starting moment of the current monitoring cycle; step S32, obtaining the ending maximum first distance based on the first distance at the end moment of the current monitoring cycle; step S33, determining the horizontal anti-deformation coefficient based on the starting maximum first distance, the ending maximum first distance, the first distance and the first yaw angle.

[0032] According to one embodiment of the present invention, at the start of the current monitoring cycle, the first distances measured at each horizontal test point are compared to obtain the maximum starting first distance. Similarly, the maximum ending first distance is obtained. Based on the distance of each horizontal test point from the first preset position (maximum starting first distance, maximum ending first distance, and first distance) and the change in the first yaw angle, the horizontal deformation resistance coefficient calculated objectively and accurately reflects the bridge deck's ability to resist horizontal deformation, providing an important basis for bridge safety assessment and maintenance.

[0033] According to one embodiment of the present invention, the horizontal anti-deformation coefficient is determined according to the starting maximum first distance, the ending maximum first distance, the first distance and the first yaw angle, including: determining the horizontal anti-deformation coefficient of the e-th segment bridge area according to formula (1): , (1), where is the maximum first distance at the beginning of the e-th segment bridge area, is the maximum first distance at the end of the e-th segment bridge area, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring cycle, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the end of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segmented bridge area at the end of the current monitoring period, n is the number of horizontal test points, max is the maximum value function, i≤n, and e, i and n are all positive integers.

[0034] According to one embodiment of the present invention, in formula (1), It is the relative difference between the first yaw angle of the i-th horizontal test point in the e-th segmented bridge area at the end of the current monitoring period and the first yaw angle of the i-th horizontal test point in the e-th segmented bridge area at the beginning of the current monitoring period. The larger the relative difference is, the greater the lateral displacement of the bridge deck is and the weaker the bridge deck's ability to resist deformation is. is the ratio of the first distance of the i-th horizontal test point in the e-th segment bridge area at the start of the current monitoring period to the starting maximum first distance of the e-th segment bridge area, that is, the normalized starting first distance ratio, is the ratio of the first distance of the i-th horizontal test point in the e-th segment bridge area at the end of the current monitoring period to the end maximum first distance of the e-th segment bridge area, that is, the normalized end first distance ratio. To obtain the maximum value of the first distance ratio at the start of normalization and the first distance ratio at the end of normalization, the result of 1 minus the maximum value can be obtained. The smaller the result, the larger the first distance of the i-th horizontal test point in the e-th segmented bridge area, indicating that the farther the horizontal test point is from the first preset position, the less obvious the change in the first yaw angle when the horizontal test point is displaced, and the weaker the ability of the measurement result to characterize the lateral deformation of the bridge deck. That is, the position change of the horizontal test point far away from the first preset position cannot obviously reflect the overall deformation trend of the bridge deck, and therefore, it is given a lower weight. The relative differences between the first yaw angles of multiple horizontal test points in the e-th segmented bridge area at the end and start of the current monitoring period are weighted averaged, and the average value is subtracted from 1 to obtain the horizontal anti-deformation coefficient of the e-th segmented bridge area. The larger the horizontal anti-deformation coefficient, the less lateral deformation of the bridge deck occurs and the greater the anti-deformation ability.

[0035] In this way, weights can be set based on the relative difference between the first yaw angles at the end and start of the current monitoring period, and based on the characteristic that the farther the horizontal test point is from the first preset position, the less obvious the change in the first yaw angle. In this way, the relative differences between the first yaw angles of multiple horizontal test points at the end and start of the current monitoring period are weighted averaged to obtain the horizontal anti-deformation coefficient, thereby improving the accuracy of identifying the bridge deck's ability to resist lateral deformation.

[0036] According to one embodiment of the present invention, in step S4, the second distance and the second pitch angle data of each vertical test point are measured at the beginning of the current monitoring cycle, and the second distance and the second pitch angle data of each vertical test point are measured at the end of the current monitoring cycle. The vertical anti-deformation coefficient reflects the displacement of the bridge pier in the vertical direction.

[0037] Figure 3 A flow chart for calculating a vertical anti-deformation coefficient according to an embodiment of the present invention is exemplarily shown.

[0038] According to one embodiment of the present invention, step S4 includes: step S41, obtaining the starting maximum second distance based on the second distance at the starting time of the current monitoring period; step S42, obtaining the ending maximum second distance based on the second distance at the end time of the current monitoring period; step S43, obtaining the pier height; step S44, determining the vertical anti-deformation coefficient based on the pier height, the starting maximum second distance, the ending maximum second distance, the second distance and the second pitch angle.

[0039] According to one embodiment of the present invention, at the start of the current monitoring cycle, the first distances measured at each vertical test point are compared to obtain the maximum second distance at the start. Similarly, the maximum second distance at the end is obtained. The pier height can be determined using bridge design parameters. Based on the distance of each vertical test point from the first preset position (maximum second distance at the start, maximum second distance at the end, and second distance), as well as the change in vertical displacement, the vertical deformation resistance coefficient is calculated to objectively and accurately reflect the vertical deformation resistance of the pier, providing an important basis for bridge safety assessment and maintenance.

[0040] According to one embodiment of the present invention, the vertical anti-deformation coefficient is determined according to the pier height, the starting maximum second distance, the ending maximum second distance, the second distance and the second pitch angle, including: determining the vertical anti-deformation coefficient of the e-th segment bridge area according to formula (2): , (2), where is the second maximum distance at the start of the e-th segment bridge area, is the second maximum distance at the end of the e-th segment bridge area, is the second distance of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring cycle, is the second distance of the jth vertical test point in the eth segment bridge area at the end of the current monitoring cycle, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring period, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the end of the current monitoring period, is the pier height of the e-th segmented bridge area, m is the number of vertical test points, max is the maximum value function, j≤m, and e, j and m are all positive integers.

[0041] According to one embodiment of the present invention, in formula (2), is the vertical displacement of the jth vertical test point in the eth segment bridge area between the end and start of the current monitoring period, It is the ratio of the vertical displacement of the jth vertical test point in the eth segmented bridge area to the pier height in the eth segmented bridge area. The larger the ratio, the greater the vertical displacement of the pier and the weaker the pier's ability to resist deformation. is the ratio of the second distance of the jth vertical test point in the eth segmented bridge area at the start of the current monitoring period to the starting maximum second distance of the eth segmented bridge area, that is, the normalized starting second distance ratio. is the ratio of the second distance of the jth vertical test point in the eth segmented bridge area at the end of the current monitoring period to the end maximum second distance of the eth segmented bridge area, that is, the normalized end second distance ratio. To obtain the maximum value of the normalized start second distance ratio and the normalized end second distance ratio, the result of 1 minus the maximum value can be obtained. The larger the result, the larger the second distance of the jth vertical test point in the eth segment bridge area, indicating that the vertical test point is farther from the first preset position, and the vertical displacement corresponding to the same pitch angle change is greater, therefore, a higher weight is given. The ratio of the vertical displacement of multiple vertical test points in the e-th segmented bridge area to the pier height of the e-th segmented bridge area is weighted averaged, and the average value is subtracted from 1 to obtain the vertical anti-deformation coefficient of the e-th segmented bridge area. The larger the vertical anti-deformation coefficient, the less vertical deformation of the pier occurs and the greater the anti-deformation capacity.

[0042] In this way, weights can be set based on the ratio between the vertical displacement of the vertical test point and the height of the pier, and based on the characteristic that the farther the vertical test point is from the first preset position, the greater the vertical displacement corresponding to the same pitch angle change. In this way, the ratio between the vertical displacement of multiple vertical test points and the height of the pier can be weighted averaged to obtain the vertical anti-deformation coefficient, thereby improving the accuracy of identifying the ability of the pier to resist vertical deformation.

[0043] According to one embodiment of the present invention, in step S5, segmented bridge images of multiple segmented bridge areas at the start and end times of the current monitoring period may be acquired by a high-definition camera.

[0044] According to one embodiment of the present invention, in step S6, measured similarity data is determined based on the segmented bridge image.

[0045] Figure 4 The following is an exemplary flowchart of calculating measured similarity data according to an embodiment of the present invention.

[0046] According to one embodiment of the present invention, step S6 includes: step S61, using a trained image recognition neural network model, which may be a convolutional neural network model, such as a ResNet model, and the present invention does not limit the specific type of the image recognition neural network model. Feature extraction processing is performed on the segmented bridge image at the start of the current monitoring period to obtain the starting segmented bridge feature vectors of multiple segmented bridge areas; step S62, using the trained image recognition neural network model, feature extraction processing is performed on the segmented bridge image at the end of the current monitoring period to obtain the ending segmented bridge feature vectors of multiple segmented bridge areas; step S63, the similarity between the starting segmented bridge feature vector and the ending segmented bridge feature vector of the e-th segmented bridge area is determined as the measured similarity data of the e-th segmented bridge area.

[0047] According to one embodiment of the present invention, based on the calculation formula of the measured similarity data, ,in, is the measured similarity data of the e-th segment bridge area, is the starting segment bridge feature vector of the e-th segment bridge region, is the ending segment bridge feature vector of the e-th segment bridge region, for The larger the measured similarity data, the more similar the segmented bridge images at the start and end of the current monitoring period are. In other words, the segmented bridge area is less likely to experience surface deformation (e.g., tilting or crack expansion).

[0048] According to one embodiment of the present invention, in step S7, temperature helps understand the environmental thermal conditions and reflects the deformation caused by thermal expansion and contraction of the bridge. Traffic load can reflect the deformation caused by traffic pressure on the bridge structure, and wind speed can reflect the deformation caused by wind. For temperature collection, a high-precision temperature sensor can be used. The maximum and minimum ambient temperatures collected at multiple moments in the current monitoring cycle, with the largest difference from a standard temperature (e.g., 20°C), are used as temperature data. Traffic load collection can use a dynamic weighing system or strain gauges installed at key locations on the bridge to collect the current traffic load on the entire bridge. The maximum value of the traffic load collected at multiple moments in the current monitoring cycle is used as traffic load data. Wind speed collection can use professional instruments such as anemometers, with the maximum wind speed collected at multiple moments in the current monitoring cycle as wind speed data.

[0049] According to one embodiment of the present invention, in step S8, the environmental data and the segmented bridge image at the start of the current monitoring period are input into a trained similarity prediction model. The similarity prediction model can be a convolutional neural network model, which is trained based on a large amount of historical data through machine learning or deep learning technology, and can analyze the predicted similarity data of multiple segmented bridge areas in the current monitoring period.

[0050] According to one embodiment of the present invention, the training step of the similarity prediction model includes: obtaining historical segmented bridge images of multiple historical segmented bridge areas at the start and end times of multiple historical monitoring cycles; determining historical measured similarity data based on the historical segmented bridge images; obtaining a third distance between the midpoint position of the pier of each historical segmented bridge area and the midpoint position of the entire bridge; obtaining historical environmental data of multiple historical segmented bridge areas in multiple historical monitoring cycles, wherein the historical environmental data includes historical temperature data, historical traffic load data and historical wind speed data; processing the historical environmental data and the historical segmented bridge images at the start time of the historical monitoring cycle through the similarity prediction model to obtain historical predicted similarity data of multiple historical segmented bridge areas in multiple historical monitoring cycles; determining the loss function of the similarity prediction model based on the third distance, the historical environmental data, the historical measured similarity data and the historical predicted similarity data; and training the similarity prediction model based on the loss function of the similarity prediction model to obtain the trained similarity prediction model.

[0051] According to one embodiment of the present invention, historical segmented bridge images can be acquired using a high-definition camera. The method for acquiring historical measured similarity data is similar to that for acquiring measured similarity data and will not be further described here. The midpoint of each historical segmented bridge pier is the geometric center of the pier (typically the center coordinates of the bottom or top of the pier). The midpoint of the entire bridge is the global geometric center of the bridge structure (the symmetrical center point of the main beam). The third distance between the midpoint of each historical segmented bridge pier and the midpoint of the entire bridge can be obtained using design drawings or GPS. The method for acquiring historical environmental data is similar to that for acquiring environmental data and will not be further described here. When temperatures are high, the bridge structure expands, and when temperatures are low, the bridge structure contracts. This thermal expansion and contraction phenomenon causes deformation of the bridge. Specifically, the higher or lower the temperature, the greater the deformation of the bridge. Vehicles traveling on the bridge exert pressure on the bridge structure. The greater the traffic load, the greater the deformation. High wind speeds generate wind loads on the bridge, causing wind-induced vibrations. Specifically, the greater the wind speed, the greater the deformation of the bridge. The greater the deformation of the bridge, the less similar the segmented bridge images representing the start and end times are. The similarity prediction model can predict the historical predicted similarity data of multiple historical segmented bridge areas in multiple historical monitoring periods based on the relationship between the above-mentioned temperature, traffic load, wind speed and the deformation of the bridge, based on the third distance and historical environmental data. The loss function is determined according to the relative difference between the historical measured similarity data and the historical predicted similarity data. The multiple historical segmented bridge areas are divided into different training batches of historical segmented bridge areas, and the number of historical segmented bridge areas in each training batch is the same, so as to perform similarity prediction model training for different training batches, for example, , E is the number of historical segmented bridge areas, , ,…, are the number of historical segmented bridge areas in the 1st, 2nd, …, Nth training batches respectively. Then, the trained similarity prediction model is obtained by feedback adjustment of the loss function.

[0052] According to one embodiment of the present invention, determining the loss function of the similarity prediction model based on the third distance, the historical environmental data, the historical measured similarity data, and the historical predicted similarity data includes: determining the loss function Loss of the similarity prediction model according to formula (3),

[0053] (3), where is the historical measured similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the historical prediction similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the third distance between the midpoint of the bridge pier in the yth historical segment and the midpoint of the entire bridge. The third distance between the midpoint of the bridge pier in the first historical segment and the midpoint of the entire bridge. is the historical temperature data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard temperature data, is the historical traffic load data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the standard traffic load data, is the historical wind speed data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard wind speed data, H is the number of historical monitoring cycles, is the number of historical segmented bridge regions in the xth training batch, N is the number of training batches, h≤H, y≤ , x≤N, and h, x, y, H, and N are both positive integers.

[0054] According to one embodiment of the present invention, in formula (3), It is the relative difference between the historical measured similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period and the historical predicted similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period. is the ratio of the historical wind speed data of the bridge area in the yth historical segment in the hth historical monitoring period to the standard wind speed data. The larger the ratio, the larger the historical wind speed data and the greater the deformation of the bridge. Therefore, the change of historical wind speed data has a greater impact on the error of historical prediction similarity data. The standard wind speed data can be 10m / s. is the ratio of the historical traffic load data and the standard traffic load data of the bridge area in the yth historical segment in the hth historical monitoring period. The larger the ratio, the larger the historical traffic load data and the greater the deformation of the bridge. Therefore, the change of historical traffic load data has a greater impact on the error of historical prediction similarity data. The standard traffic load data can be the maximum load-bearing load of the bridge. is the relative difference between the historical temperature data and the standard temperature data of the bridge area in the yth historical segment in the hth historical monitoring period. The larger the relative difference, the greater the difference between the historical temperature data and the standard temperature data (for example, high temperature weather or low temperature weather), and the greater the deformation of the bridge. Therefore, the change in the difference between the historical temperature data and the standard temperature data has a greater impact on the error of the historical prediction similarity data. The standard temperature data can be 20℃. Indicates that historical wind speed data, historical traffic load data, and historical temperature data are positively correlated with the deformation of the bridge. For example, when there is strong wind, the bridge will vibrate continuously and cause deformation. Overloaded vehicles will cause more serious damage to the bridge, which may cause cracks, deformation, and other problems. In hot or cold weather, the bridge structure will expand and contract. Therefore, the historical wind speed data, historical traffic load data, and historical temperature data are placed in the numerator position, indicating that the larger the historical wind speed data, historical traffic load data, and historical temperature data are relative to their respective standard data, that is, 、 and The larger the value of , the greater the impact on the error of historical prediction similarity data. It is the ratio of the third distance between the midpoint of the pier of the yth historical segment bridge area and the midpoint of the entire bridge to the third distance between the midpoint of the pier of the first historical segment bridge area and the midpoint of the entire bridge. The pier of the first historical segment bridge area is the pier closest to the bridge head or tail, and the result is 1 minus the ratio. The larger the result, the smaller the third distance, which means that the pier is closer to the midpoint of the entire bridge. For example, in the middle part of the bridge, the historical segment bridge area at this position is more prone to deformation, and the greater the impact on the error of the historical prediction similarity data, so its weight is higher. is the weight of the x-th training batch, which is used to reasonably weight the relative errors of different training batches in the loss function. For the historical segmented bridge area of ​​the x+1-th training batch, the accuracy of the historical prediction similarity data of the x+1-th training batch output by the similarity prediction model is usually higher than the accuracy of the historical prediction similarity data of the x-th training batch. That is, the shorter the time interval between a training batch and the first training batch, the less accurate its prediction result. In order to improve the training efficiency, the higher its weight is set. Conversely, the more accurate the prediction result, the lower its weight is. Therefore, a higher weight can be given to items with lower accuracy, thereby improving the training intensity and training efficiency.

[0055] According to one embodiment of the present invention, using 、 and The training loss function is obtained by taking a weighted average of the relative differences in historical prediction similarity data for multiple historical segmented bridge areas in the xth training batch over multiple historical monitoring cycles. During the training of the similarity prediction model, the loss function is backpropagated and some internal model parameters are adjusted to reduce the loss value of the similarity prediction model, thereby improving the accuracy of the similarity prediction model and obtaining the trained similarity prediction model.

[0056] In this way, the influence of historical temperature data, historical traffic load data and historical wind speed data on the error of historical predicted similarity data can be determined through the influence of the above data on the deformation of the bridge. Based on the influence and the relative difference between the historical measured similarity data and the historical predicted similarity data, weights are set based on the characteristic that the closer the historical segmented bridge area is to the midpoint of the entire bridge, the more likely it is to deform and the greater the influence on the error of the historical predicted similarity data. Weights are also set based on the characteristic that the shorter the time interval with the first batch, the lower the accuracy. The errors output by the similarity prediction model of multiple historical segmented bridge areas in each training batch in multiple historical monitoring cycles are weightedly summed to obtain a loss function, so as to improve the design accuracy and objectivity of the loss function, thereby improving the training efficiency during the training process and improving the accuracy of the similarity prediction model.

[0057] According to one embodiment of the present invention, in step S9, a surface anti-deformation coefficient is determined based on the predicted similarity data and the measured similarity data.

[0058] According to one embodiment of the present invention, determining the surface anti-deformation coefficient according to the predicted similarity data and the measured similarity data includes: determining the surface anti-deformation coefficient according to formula (4): ,

[0059] (4), where is the measured similarity data of the e-th segment bridge area, is the predicted similarity data of the e-th segmented bridge area, E is the number of segmented bridge areas, e≤E, and both e and E are positive integers.

[0060] According to one embodiment of the present invention, in formula (4), is the relative difference between the measured similarity data for the e-th segmented bridge region and the predicted similarity data for the e-th segmented bridge region. A larger relative difference indicates greater measured similarity and a smaller actual bridge deformation than the predicted deformation (the predicted deformation under the influence of environmental factors). This indicates that the bridge's surface deformation resistance is stronger even under the influence of environmental factors. A smaller relative difference indicates smaller measured similarity and a larger actual bridge deformation than the predicted deformation. This indicates that the bridge's surface deformation resistance is weaker under the influence of environmental factors. The surface deformation resistance coefficient is obtained by averaging the relative differences between the measured and predicted similarity data for multiple segmented bridge regions. A larger surface deformation resistance coefficient indicates a stronger bridge surface deformation resistance.

[0061] In this way, the surface deformation resistance coefficient can be determined by using the predicted similarity data and the measured similarity data. By comparing the actual deformation of the bridge with the predicted deformation, the strength of the bridge's surface deformation resistance under the influence of environmental factors can be reflected, thereby improving the reliability and scientific nature of the surface deformation resistance coefficient.

[0062] According to one embodiment of the present invention, in step S10, the horizontal anti-deformation coefficients of multiple segmented bridge areas are averaged to obtain the horizontal anti-deformation coefficient, the vertical anti-deformation coefficients of multiple segmented bridge areas are averaged to obtain the average vertical anti-deformation coefficient, the horizontal anti-deformation coefficient, the average vertical anti-deformation coefficient and the surface anti-deformation coefficient are averaged to obtain the anti-deformation coefficient. The larger the anti-deformation coefficient is, the stronger the anti-deformation ability of the bridge structure in the current monitoring period is.

[0063] According to an embodiment of the present invention, a bridge structure deformation resistance monitoring method, by setting horizontal and vertical test points and measuring them using a laser rangefinder, helps understand the deformation conditions of a bridge structure in the horizontal and vertical directions. By combining the impact of environmental data on bridge structure deformation with a similarity prediction model, a more accurate prediction of the bridge's deformation trend can be achieved. By conducting non-contact monitoring, a comprehensive assessment of the bridge structure's deformation resistance is conducted from three perspectives: horizontal, vertical, and environmental. This improves the comprehensiveness and accuracy of the assessment and reduces monitoring costs. When determining the horizontal deformation resistance coefficient, a weight can be set based on the relative difference between the first yaw angle at the end and start of the current monitoring cycle. The weighting is based on the characteristic that the farther the horizontal test point is from the first preset position, the less significant the change in the first yaw angle. Thus, the relative differences between the first yaw angles of multiple horizontal test points at the end and start of the current monitoring cycle are weighted averaged to obtain the horizontal deformation resistance coefficient, thereby improving the accuracy of identifying the bridge deck's resistance to lateral deformation. When determining the vertical anti-deformation coefficient, a weight can be set based on the ratio between the vertical displacement of the vertical test point and the height of the pier, and based on the characteristic that the farther the vertical test point is from the first preset position, the greater the vertical displacement corresponding to the same pitch angle change. In this way, the ratio between the vertical displacement of multiple vertical test points and the height of the pier is weightedly averaged to obtain the vertical anti-deformation coefficient, thereby improving the accuracy of identifying the ability of the pier to resist vertical deformation. When determining the loss function of the similarity prediction model, the influence of historical temperature data, historical traffic load data, and historical wind speed data on the deformation of the bridge can be used to determine the influence of the above data on the error of the historical predicted similarity data. Based on the influence and the relative difference between the historical measured similarity data and the historical predicted similarity data, weights are set based on the characteristic that the closer the historical segmented bridge area is to the midpoint of the entire bridge, the more likely it is to deform and the greater the influence on the error of the historical predicted similarity data. Weights are also set based on the characteristic that the shorter the time interval with the first batch, the lower the accuracy. The errors output by the similarity prediction model of multiple historical segmented bridge areas in each training batch in multiple historical monitoring cycles are weightedly summed to obtain a loss function, so as to improve the design accuracy and objectivity of the loss function, thereby improving training efficiency during the training process and improving the accuracy of the similarity prediction model. When determining the surface anti-deformation coefficient, the surface anti-deformation coefficient can be determined by using predicted similarity data and measured similarity data. Comparing the actual deformation of the bridge with the predicted deformation can reflect the strength of the bridge's surface anti-deformation ability under the influence of environmental factors, thereby improving the reliability and scientific nature of the surface anti-deformation coefficient.

[0064] Figure 5A block diagram of a bridge structure anti-deformation monitoring system according to an embodiment of the present invention is exemplarily shown, wherein the system comprises: a segmented bridge area module, for dividing the entire bridge into a plurality of segmented bridge areas, setting a plurality of horizontal test points on the side positions of the bridge deck of the segmented bridge area, and setting a plurality of vertical test points on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; a measurement module, for setting a laser rangefinder at a first preset position of the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated onto the horizontal test point and the vertical test point, and determining a first distance and a first yaw angle of the laser rangefinder when measuring a plurality of horizontal test points, and a second distance and a second pitch angle of the laser rangefinder when measuring a plurality of vertical test points; a horizontal anti-deformation coefficient module, for determining a horizontal anti-deformation coefficient according to the first distance and the first yaw angle at the start and end times of a current monitoring period; a vertical anti-deformation coefficient module, Used to determine the vertical anti-deformation coefficient based on the second distance and the second pitch angle at the start and end of the current monitoring period; a segmented bridge image module, used to obtain segmented bridge images of multiple segmented bridge areas at the start and end of the current monitoring period; a measured similarity data module, used to determine measured similarity data based on the segmented bridge images; an environmental data module, used to obtain environmental data of the current monitoring period, wherein the environmental data includes temperature data, traffic load data and wind speed data; a predicted similarity data module, used to input the environmental data and the segmented bridge image at the start of the current monitoring period into a trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring period; a surface anti-deformation coefficient module, used to determine the surface anti-deformation coefficient based on the predicted similarity data and the measured similarity data; an anti-deformation capacity evaluation module, used to determine the anti-deformation capacity of the bridge structure in the current monitoring period based on the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

[0065] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0066] Those skilled in the art will appreciate that the embodiments of the present invention described above and shown in the accompanying drawings are intended to be illustrative only and are not intended to limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and illustrated in the embodiments. Any variations or modifications may be made to the embodiments of the present invention without departing from the principles described.

Claims

1. A bridge structure anti-deformation monitoring method, characterized in that: include: The entire bridge is divided into a plurality of segmented bridge areas, a plurality of horizontal test points are set on the side positions of the bridge deck of the segmented bridge area, and a plurality of vertical test points are set on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; a laser rangefinder is set at a first preset position in the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated onto the horizontal test point and the vertical test point, and the first distance and the first yaw angle of the laser rangefinder when measuring the plurality of horizontal test points, as well as the second distance and the second pitch angle of the laser rangefinder when measuring the plurality of vertical test points are determined; at the start time and the end time of the current monitoring period, the horizontal anti-deformation coefficient is determined according to the first distance and the first yaw angle; at the start time and the end time of the current monitoring period, the horizontal anti-deformation coefficient is determined according to the first distance and the first yaw angle; at the start time and the end time of the current monitoring period, the horizontal anti-deformation coefficient is determined according to the first distance and the first yaw angle; at the end time of the current monitoring period, the horizontal anti-deformation coefficient is determined according to the first distance and the first yaw angle. At the start and end of the monitoring period, the vertical anti-deformation coefficient is determined according to the second distance and the second pitch angle; segmented bridge images of multiple segmented bridge areas at the start and end of the current monitoring period are obtained; measured similarity data is determined based on the segmented bridge images; environmental data of the current monitoring period is obtained, wherein the environmental data includes temperature data, traffic load data and wind speed data; the environmental data and the segmented bridge images at the start of the current monitoring period are input into the trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring period; the surface anti-deformation coefficient is determined according to the predicted similarity data and the measured similarity data; the anti-deformation capacity of the bridge structure in the current monitoring period is determined according to the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

2. The bridge structure anti-deformation monitoring method according to claim 1, characterized in that: At the start and end of the current monitoring cycle, a horizontal anti-deformation coefficient is determined based on the first distance and the first yaw angle, including: obtaining a starting maximum first distance based on the first distance at the start of the current monitoring cycle; obtaining an ending maximum first distance based on the first distance at the end of the current monitoring cycle; and determining the horizontal anti-deformation coefficient based on the starting maximum first distance, the ending maximum first distance, the first distance, and the first yaw angle.

3. The bridge structure anti-deformation monitoring method according to claim 2, characterized in that: Determining a horizontal anti-deformation coefficient according to the starting maximum first distance, the ending maximum first distance, the first distance, and the first yaw angle includes: determining according to the formula Determine the horizontal deformation resistance coefficient of the e-th segment bridge area ,in, is the maximum first distance at the beginning of the e-th segment bridge area, is the maximum first distance at the end of the e-th segment bridge area, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring cycle, is the first distance of the i-th horizontal test point in the e-th segment bridge area at the end of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segment bridge area at the beginning of the current monitoring period, is the first yaw angle of the i-th horizontal test point in the e-th segmented bridge area at the end of the current monitoring period, n is the number of horizontal test points, max is the maximum value function, i≤n, and e, i and n are all positive integers.

4. The bridge structure anti-deformation monitoring method according to claim 1, characterized in that: At the start and end of the current monitoring cycle, the vertical anti-deformation coefficient is determined according to the second distance and the second pitch angle, including: obtaining the starting maximum second distance according to the second distance at the start of the current monitoring cycle; obtaining the ending maximum second distance according to the second distance at the end of the current monitoring cycle; obtaining the pier height; and determining the vertical anti-deformation coefficient according to the pier height, the starting maximum second distance, the ending maximum second distance, the second distance and the second pitch angle.

5. The bridge structure anti-deformation monitoring method according to claim 4, characterized in that: Determining a vertical anti-deformation coefficient according to the bridge pier height, the starting maximum second distance, the ending maximum second distance, the second distance, and the second pitch angle includes: determining a vertical anti-deformation coefficient according to the formula Determine the vertical deformation resistance coefficient of the e-th segment bridge area ,in, is the second maximum distance at the start of the e-th segment bridge area, is the second maximum distance at the end of the e-th segment bridge area, is the second distance of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring cycle, is the second distance of the jth vertical test point in the eth segment bridge area at the end of the current monitoring cycle, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the beginning of the current monitoring period, is the second pitch angle of the jth vertical test point in the eth segment bridge area at the end of the current monitoring period, is the pier height of the e-th segmented bridge area, m is the number of vertical test points, max is the maximum value function, j≤m, and e, j and m are all positive integers.

6. The bridge structure anti-deformation monitoring method according to claim 1, characterized in that: Based on the segmented bridge image, measured similarity data is determined, including: performing feature extraction processing on the segmented bridge image at the start time of the current monitoring cycle through a trained image recognition neural network model to obtain starting segmented bridge feature vectors of multiple segmented bridge areas; performing feature extraction processing on the segmented bridge image at the end time of the current monitoring cycle through a trained image recognition neural network model to obtain ending segmented bridge feature vectors of multiple segmented bridge areas; and determining the similarity between the starting segmented bridge feature vector and the ending segmented bridge feature vector of the e-th segmented bridge area as the measured similarity data of the e-th segmented bridge area.

7. The bridge structure anti-deformation monitoring method according to claim 1, characterized in that: The training steps of the similarity prediction model include: obtaining historical segmented bridge images of multiple historical segmented bridge areas at the start and end times of multiple historical monitoring cycles; determining historical measured similarity data based on the historical segmented bridge images; obtaining a third distance between the midpoint position of the pier of each historical segmented bridge area and the midpoint position of the entire bridge; obtaining historical environmental data of multiple historical segmented bridge areas in multiple historical monitoring cycles, wherein the historical environmental data includes historical temperature data, historical traffic load data and historical wind speed data; processing the historical environmental data and the historical segmented bridge images at the start time of the historical monitoring cycle through the similarity prediction model to obtain historical predicted similarity data of multiple historical segmented bridge areas in multiple historical monitoring cycles; determining the loss function of the similarity prediction model based on the third distance, the historical environmental data, the historical measured similarity data and the historical predicted similarity data; and training the similarity prediction model based on the loss function of the similarity prediction model to obtain the trained similarity prediction model.

8. The bridge structure anti-deformation monitoring method according to claim 7, characterized in that: Determining the loss function of the similarity prediction model according to the third distance, the historical environmental data, the historical measured similarity data, and the historical predicted similarity data includes: according to the formula Determine the loss function Loss of the similarity prediction model, where: is the historical measured similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the historical prediction similarity data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the third distance between the midpoint of the bridge pier in the yth historical segment and the midpoint of the entire bridge. The third distance between the midpoint of the bridge pier in the first historical segment and the midpoint of the entire bridge. is the historical temperature data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard temperature data, is the historical traffic load data of the bridge area of ​​the yth historical segment in the hth historical monitoring period, is the standard traffic load data, is the historical wind speed data of the bridge area in the yth historical segment in the hth historical monitoring period, is the standard wind speed data, H is the number of historical monitoring cycles, is the number of historical segmented bridge regions in the xth training batch, N is the number of training batches, h≤H, y≤ , x≤N, and h, x, y, H, and N are both positive integers.

9. The bridge structure anti-deformation monitoring method according to claim 1, characterized in that: Determining the surface deformation resistance coefficient according to the predicted similarity data and the measured similarity data includes: according to the formula Determine the surface deformation resistance coefficient ,in, is the measured similarity data of the e-th segment bridge area, is the predicted similarity data of the e-th segmented bridge area, E is the number of segmented bridge areas, e≤E, and both e and E are positive integers.

10. A bridge structure anti-deformation monitoring system, used to execute the bridge structure anti-deformation monitoring method according to any one of claims 1 to 9, characterized in that: include: A segmented bridge area module is used to divide the entire bridge into multiple segmented bridge areas, set multiple horizontal test points on the side positions of the bridge deck of the segmented bridge area, and set multiple vertical test points on the side positions of the piers of the segmented bridge area, wherein the segmented bridge area has only one pier, the horizontal test points are on the same horizontal plane, and the vertical test points are on the same vertical line; a measurement module is used to set a laser rangefinder at a first preset position of the segmented bridge area, so that the laser emitted by the laser rangefinder is irradiated on the horizontal test point and the vertical test point, and determine the first distance and first yaw angle of the laser rangefinder when measuring multiple horizontal test points, and the second distance and second pitch angle of the laser rangefinder when measuring multiple vertical test points; a horizontal anti-deformation coefficient module is used to determine the horizontal anti-deformation coefficient according to the first distance and the first yaw angle at the start and end time of the current monitoring period; a vertical anti-deformation coefficient module is used to, at the start and end time of the current monitoring period, Determine the vertical anti-deformation coefficient based on the second distance and the second pitch angle; a segmented bridge image module is used to obtain segmented bridge images of multiple segmented bridge areas at the start and end times of the current monitoring period; a measured similarity data module is used to determine measured similarity data based on the segmented bridge images; an environmental data module is used to obtain environmental data of the current monitoring period, wherein the environmental data includes temperature data, traffic load data and wind speed data; a predicted similarity data module is used to input the environmental data and the segmented bridge image at the start time of the current monitoring period into a trained similarity prediction model to obtain predicted similarity data of multiple segmented bridge areas in the current monitoring period; a surface anti-deformation coefficient module is used to determine the surface anti-deformation coefficient based on the predicted similarity data and the measured similarity data; an anti-deformation capacity evaluation module is used to determine the anti-deformation capacity of the bridge structure in the current monitoring period based on the horizontal anti-deformation coefficient, the vertical anti-deformation coefficient and the surface anti-deformation coefficient.

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