Bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision

Abstract

The application discloses a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, and the method comprises the following steps: establishing a common coordinate system, sampling the measurement data in the ground-based interferometric radar coordinate system and the inertial vision technology coordinate system to the common coordinate system respectively to obtain registration data of the ground-based interferometric radar and the inertial vision technology in the common coordinate system, interpolating the registration data in the inertial vision technology to obtain interpolation registration data matched with the spatial density of the ground-based interferometric radar data, respectively calculating the spatial model matrix of the ground-based interferometric radar line-of-sight deformation, the inertial vision technology vertical deformation and the inertial vision technology bridge lateral deformation according to the interpolation registration data and the registration data of the ground-based interferometric radar, calculating the fusion weight matrix of both by using an iterative almost unbiased estimation method, and calculating the multi-dimensional dynamic deformation time sequence of the full span of the bridge by using a least square method according to the fusion weight matrix, so that the bridge deformation monitoring is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge measurement, in particular to a bridge dynamic deformation measurement method and system fusing ground-based interferometric radar and inertial vision, a terminal and a computer readable storage medium. BACKGROUND

[0002] During the service period, bridges are affected by various factors such as structural aging, environmental changes, dynamic loads, etc., and may have various bridge safety problems and accidents, causing loss of life and property. Bridge dynamic deformation monitoring can reflect the bridge structure disease condition by obtaining the bridge deformation and vibration characteristics, and can provide effective protection for bridge safety operation.

[0003] In the prior art, ground-based radar interferometric measurement technology has the advantages of flexible monitoring, high spatial and temporal resolution and high precision, and is one of the effective means for bridge dynamic deformation monitoring. Ground-based radar interferometric technology can be used to obtain the full-span line-of-sight dynamic deformation of the bridge, and the monitoring frequency can reach more than 200Hz. However, bridge deformation is caused by various factors, and these factors will exert deformation on the bridge from different directions, for example, wind load causes lateral deformation of the bridge; the self-weight of the bridge causes vertical deformation of the bridge; the deformation caused by traffic load is complex and usually causes deformation in the vertical, lateral and forward directions. Therefore, the bridge deformation is three-dimensional and complex, and the deformation monitoring of a single ground-based interferometric radar in a single direction cannot reflect the deformation characteristics of the bridge under various loads, which may lead to missed diagnosis of bridge diseases. SUMMARY

[0004] Therefore, the present application provides a bridge dynamic deformation measurement method and system fusing ground-based interferometric radar and inertial vision, a terminal and a computer readable storage medium to solve the problem that the deformation monitoring of a single ground-based interferometric radar in a single direction in the prior art cannot reflect the deformation characteristics of the bridge under various loads, which may lead to missed diagnosis of bridge diseases.

[0005] The present application provides a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, which comprises:

[0006] obtaining a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system, and obtaining a first visual vertical array and a first visual bridge side array of the inertial vision technology on the vertical direction and the bridge side direction of each monitoring point in an inertial vision technology coordinate system;

[0007] establishing a common coordinate system, sampling the first radar array, the first vision vertical array and the first vision bridge side array into the common coordinate system respectively to obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second vision vertical array and a second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system in the vertical direction and the bridge side direction respectively;

[0008] interpolating the second vision vertical array and the second vision bridge side array to obtain a third vision vertical array and a third vision bridge side array, so that the third vision vertical array and the third vision bridge side array match the spatial density of the second radar array;

[0009] calculating a spatial model matrix of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology according to the third vision vertical array, the third vision bridge side array and the second radar array respectively to obtain a vision vertical model matrix, a vision bridge side model matrix and a radar model matrix;

[0010] calculating a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the vision vertical model matrix, the vision bridge side model matrix and the radar model matrix using an iterative almost unbiased estimation method;

[0011] calculating a multi-dimensional dynamic deformation time sequence of the whole span of the bridge according to the fusion weight matrix using a least square method.

[0012] Optionally, the establishing a common coordinate system, sampling the first radar array, the first vision vertical array and the first vision bridge side array into the common coordinate system respectively to obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second vision vertical array and a second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system in the vertical direction and the bridge side direction respectively, specifically comprises:

[0013] establishing a common coordinate system, setting a coordinate origin of the common coordinate system as a main span near end of the bridge, setting a direction of a first coordinate axis of the common coordinate system as parallel to a traffic direction of the bridge, setting a direction of a second coordinate axis of the common coordinate system as a bridge transverse direction away from a position of a sensor of the ground-based interferometric radar, and setting a direction of a third coordinate axis of the common coordinate system as vertically downward;

[0014] The inertial cameras and targets of the inertial vision technology are arranged along the axis of the bridge, and the relative positions of adjacent inertial monitoring points are arranged, and the absolute positions of the inertial monitoring points and the origin of the common coordinate system are calculated according to the relative positions, the first vision vertical array and the first vision bridge side array, to obtain the second vision vertical array and the second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system respectively.

[0015] The distance of each radar detection point to the sensor of the ground-based interferometric radar is obtained, the actual distance of the sensor of the ground-based interferometric radar to the coordinate origin is obtained, and the first radar array is registered into the common coordinate system according to the distance and the actual distance, to obtain the second radar array of each radar detection point in the common coordinate system.

[0016] Optionally, the coordinate calculation formula of the first coordinate axis is:

[0017] Inte(s)=(tanγ s -tanγ s-1 )(y G -y0);

[0018]

[0019] s=1,2,…n;

[0020] wherein, Inte(s) represents the coordinate of the first coordinate axis, s represents the serial number of the monitoring point, γ s represents the azimuth angle of the s-th monitoring point, γ s-1 represents the azimuth angle of the s-1-th monitoring point, y G represents the coordinate of the position of the ground-based interferometric radar, y0 represents the origin of the second coordinate axis, x s represents the coordinate of the monitoring point in the first coordinate axis, y s represents the coordinate of the monitoring point in the second coordinate axis, x0 represents the origin of the first coordinate axis, y0 represents the origin of the second coordinate axis, and n represents the number of monitoring points.

[0021] Optionally, the second vision vertical array and the second vision bridge side array are interpolated to obtain a third vision vertical array and a third vision bridge side array, and the interpolation specifically includes:

[0022] The spatial characteristics of the second vision vertical array and the second vision bridge side array are described by using a robust semi-variogram function;

[0023] The spatial correlation of each monitoring point is described by using a spatial covariance matrix based on a spherical model;

[0024] According to the spatial feature and the spatial correlation, a position of a known point is obtained, a distance between the known point and an unknown point is calculated according to the known point, and the spatial interpolation weight vector of the unknown point is obtained by substituting the spatial covariance function;

[0025] Deformation data of the known point is obtained, the interpolation result of the unknown point is calculated according to the spatial interpolation weight vector and the deformation data, and a third visual vertical array and a third visual bridge side array are obtained.

[0026] Optionally, the spatial model matrix of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial visual technology and the bridge side direction deformation of the inertial visual technology is calculated according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, and a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix are obtained, and specifically, the calculation includes:

[0027] The generalized bending energy matrix of the spatial covariance function of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial visual technology and the bridge side direction deformation of the inertial visual technology is calculated according to the third visual vertical array, the third visual bridge side array and the second radar array respectively.

[0028] The generalized bending energy matrix is decomposed and reduced in dimension, and a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix are obtained.

[0029] Optionally, the fusion weight matrix of the ground-based interferometric radar and the inertial visual technology is calculated according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix by using the iterative almost unbiased estimation method, and specifically, the calculation includes:

[0030] The initial variance of the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix is obtained, and the variance factor is estimated according to the spatial covariance matrix multiple times;

[0031] The almost unbiased estimation variance component is iteratively calculated according to the initial variance and the variance factor;

[0032] The iteration is stopped when the posterior variance of the iterative almost unbiased estimation method converges to 1, and the fusion weight matrix of the ground-based interferometric radar and the inertial visual technology is obtained.

[0033] Optionally, the calculation formula of the least square method is:

[0034]

[0035]

[0036] wherein, represents the result vector containing the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology in the fusion weight matrix; Pr represents the projection relationship of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, P represents the weight obtained by the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, T represents transposition, Z t represents the observation matrix at time t, θ represents the included angle between the ground-based interferometric radar line-of-sight direction and the vertical direction, γ represents the included angle between the ground-based interferometric radar line-of-sight direction and the observation plane of inertial vision technology, P1, P2 and P3 respectively represent the weight matrix corresponding to the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, f1, f2 and f3 respectively represent the variance factor corresponding to P1, P2 and P3, and respectively represent the diagonal matrix corresponding to the ground-based interferometric radar line-of-sight direction deformation data, the vertical direction deformation of inertial vision technology and the bridge side direction deformation data of inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology obtained by solving the fusion weight matrix.

[0037] The application also provides a bridge dynamic deformation measurement system fusing ground-based interferometric radar and inertial vision, which comprises:

[0038] a measurement data acquisition module, configured to acquire a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system, and acquire a first vision vertical array and a first vision bridge side array of the inertial vision technology in a vertical direction and a bridge side direction of each monitoring point in an inertial vision technology coordinate system;

[0039] a data registration module, configured to establish a common coordinate system, sample the first radar array, the first vision vertical array and the first vision bridge side array to the common coordinate system respectively, so as to obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system, and a second vision vertical array and a second vision bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction of the inertial vision technology in the common coordinate system respectively;

[0040] a data interpolation module configured to interpolate the second visual vertical array and the second visual bridge side array to obtain a third visual vertical array and a third visual bridge side array, so that the third visual vertical array and the third visual bridge side array match the spatial density of the second radar array;

[0041] a spatial model establishing module configured to calculate spatial model matrices of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology respectively according to the third visual vertical array, the third visual bridge side array and the second radar array to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix;

[0042] a fusion weight calculating module configured to calculate a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix using an iterative almost unbiased estimation method;

[0043] a data fusion solving module configured to calculate a multi-dimensional dynamic deformation time series of a full span of a bridge using a least square method according to the fusion weight matrix.

[0044] The application further provides a terminal, which comprises a memory, a processor and a bridge dynamic deformation measurement program of fusion ground-based interferometric radar and inertial vision stored in the memory and capable of running on the processor, and the bridge dynamic deformation measurement program of fusion ground-based interferometric radar and inertial vision, when executed by the processor, implements the steps of the bridge dynamic deformation measurement method of fusion ground-based interferometric radar and inertial vision.

[0045] The application further provides a computer readable storage medium, which stores a bridge dynamic deformation measurement program of fusion ground-based interferometric radar and inertial vision, and the bridge dynamic deformation measurement program of fusion ground-based interferometric radar and inertial vision, when executed by a processor, implements the steps of the bridge dynamic deformation measurement method of fusion ground-based interferometric radar and inertial vision.

[0046] The beneficial effects of the present application are: different from the prior art, the present application obtains a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system, obtains a first visual vertical array and a first visual bridge side array of the inertial vision technology on the vertical direction and the bridge side direction of each monitoring point in an inertial vision technology coordinate system, establishes a common coordinate system, samples the first radar array, the first visual vertical array and the first visual bridge side array to the common coordinate system respectively, obtains a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction of the inertial vision technology in the common coordinate system respectively, so that the measurement results of the ground-based interferometric radar and the inertial vision technology are unified in the coordinate system, facilitating the calculation of multi-dimensional deformation monitoring; secondly, the present application obtains a third visual vertical array and a third visual bridge side array by interpolating the second visual vertical array and the second visual bridge side array, so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array, reduces the accuracy difference of the two measurement methods, calculates the spatial model matrix of the line-of-sight direction deformation of the ground-based interferometric radar, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, and obtains a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix; thirdly, the present application calculates the fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix by using the iterative almost unbiased estimation method, and obtains the weight of each deformation; in addition, the present application calculates the multi-dimensional dynamic deformation time series of the whole span of the bridge according to the fusion weight matrix by using the least square method, realizes the multi-dimensional deformation monitoring of the bridge, makes the monitoring result more accurate, and avoids the missed judgment of the bridge disease.

[0047] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, rather than limiting the present application. BRIEF DESCRIPTION OF DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0049] Figure 1 is a flow chart of a preferred embodiment of the bridge dynamic deformation measurement method of the present application which fuses the ground-based interferometric radar and the inertial vision;

[0050] Figure 2is a method flow chart in a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision of the present application;

[0051] Figure 3 is a two-dimensional deformation time sequence graph of real data in a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision of the present application;

[0052] Figure 4 is a time and space profile graph of a two-dimensional deformation time sequence in a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision of the present application;

[0053] Figure 5 is a principle schematic diagram of a preferred embodiment of a bridge dynamic deformation measurement system fusing ground-based interferometric radar and inertial vision of the present application;

[0054] Figure 6 is a running environment schematic diagram of a preferred embodiment of a terminal of the present application. DETAILED DESCRIPTION

[0055] In order for those skilled in the art to better understand the technical solutions of the present application, the bridge dynamic deformation measurement method, system, terminal and computer readable storage medium provided by the present application are further described in detail below in combination with the drawings and specific embodiments. It can be understood that the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] The terms "first", "second", and the like in the present application are used to distinguish different objects, not to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to the process, method, product or device.

[0057] The present application provides a bridge dynamic deformation measurement method, system, terminal and computer readable storage medium fusing ground-based interferometric radar and inertial vision, to solve the problem that the deformation monitoring of a single direction by a single ground-based interferometric radar in the prior art is difficult to reflect the deformation characteristics of a bridge under various loads, which may lead to missed diagnosis of bridge diseases.

[0058] Please refer to Figures 1 to 4 , Figure 1is a flow chart of a preferred embodiment of a bridge dynamic deformation measurement method of the application fusing ground-based interferometric radar and inertial vision; Figure 2 is a method flow chart in a bridge dynamic deformation measurement method of the application fusing ground-based interferometric radar and inertial vision; Figure 3 is a two-dimensional deformation time sequence graph of real data in a bridge dynamic deformation measurement method of the application fusing ground-based interferometric radar and inertial vision; Figure 4 is a time and space profile graph of a two-dimensional deformation time sequence in a bridge dynamic deformation measurement method of the application fusing ground-based interferometric radar and inertial vision.

[0059] The application provides a bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, as shown in Figure 1 and Figure 2 The bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision comprises the steps of:

[0060] Step S100: acquiring a first radar array of a ground-based interferometric radar in a ground-based interferometric radar coordinate system, and acquiring a first vision vertical array and a first vision bridge side array of an inertial vision technology on a vertical direction and a bridge side direction of each monitoring point in an inertial vision technology coordinate system.

[0061] Specifically, the measurement results of a ground-based interferometric radar (GBIR for short) on each radar detection point in a ground-based interferometric radar coordinate system are acquired and set as a first radar array, and the measurement results of an inertial vision technology (IVM for short) on a vertical direction and a bridge side direction of each monitoring point in an inertial vision technology coordinate system are acquired and set as a first vision vertical array and a first vision bridge side array, so as to quantitatively express the deformation under each monitoring mode.

[0062] Wherein, the GBIR and the IVM can specifically monitor the line-of-sight deformation and the inertial vision technology can observe the horizontal and vertical deformations in a plane (as shown in Figure 3 , wherein Figure 3 (a) is the vertical direction deformation, Figure 3 (b) is the horizontal direction deformation), when three different deformation components of the same monitoring point are different in a plane, the three-dimensional dynamic deformation of the monitoring point can be obtained.

[0063] Step S200: Establish a common coordinate system, sample the first radar array, the first vision vertical array and the first vision bridge side array to the common coordinate system respectively, to obtain the second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system, and the second vision vertical array and the second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system respectively.

[0064] Specifically, a common coordinate system is established, the first radar array, the first vision vertical array and the first vision bridge side array are sampled to the common coordinate system respectively, to obtain the second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system, and the second vision vertical array and the second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system respectively, so that the measurement results of the ground-based interferometric radar and the inertial vision technology are unified in the coordinate system, facilitating the calculation of multi-dimensional deformation monitoring.

[0065] The step S200: establishing a common coordinate system, sampling the first radar array, the first vision vertical array and the first vision bridge side array to the common coordinate system respectively, to obtain the second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system, and the second vision vertical array and the second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system respectively, specifically includes:

[0066] The common coordinate system is established, the coordinate origin of the common coordinate system is set as the main span near end of the bridge, the direction of the first coordinate axis of the common coordinate system is set as parallel to the traffic direction of the bridge, the direction of the second coordinate axis of the common coordinate system is set as the lateral direction of the bridge away from the position of the sensor of the ground-based interferometric radar, and the direction of the third coordinate axis of the common coordinate system is set as vertically downward;

[0067] The inertial cameras and targets of the inertial vision technology are set to be distributed along the axis of the bridge, the relative positions of adjacent inertial monitoring points are set, the absolute positions of each inertial monitoring point and the origin of the common coordinate system are calculated according to the relative positions, the first vision vertical array and the first vision bridge side array, to obtain the second vision vertical array and the second vision bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system respectively;

[0068] The distance from each radar detection point to the sensor of the ground-based interferometric radar is obtained, the actual distance from the sensor of the ground-based interferometric radar to the coordinate origin is obtained, the first radar array is registered into the common coordinate system according to the distance and the actual distance, and the second radar array of the ground-based interferometric radar of each radar detection point in the common coordinate system is obtained.

[0069] Specifically, the GBIR and the IVM obtain the bridge deformation based on respective different observation coordinate systems, and therefore, a common coordinate system needs to be established for the space-time registration of the GBIR and the IVM deformation data. The coordinate origin (x0, y0, z0) of the common coordinate system is set as the proximal end of the main span of the bridge, the direction of the first coordinate axis of the common coordinate system is set as parallel to the traffic direction of the bridge (i.e. the x-axis direction), the direction of the second coordinate axis of the common coordinate system is set as the lateral direction of the bridge away from the position of the sensor of the ground-based interferometric radar (i.e. the y-axis direction), and the direction of the third coordinate axis of the common coordinate system is set as vertically downward (i.e. the z-axis direction).

[0070] The coordinate calculation formula of the first coordinate axis is:

[0071] Inte(s) = (tan γ s -tan γ s-1 )(y G -y0);

[0072]

[0073] s = 1, 2, … n.

[0074] wherein Inte(s) represents the coordinate of the first coordinate axis, s represents the serial number of the monitoring point, γ s represents the azimuth angle of the s-th monitoring point, γ s-1 represents the azimuth angle of the s-1-th monitoring point, y G represents the coordinate of the position of the ground-based interferometric radar, y0 represents the origin of the second coordinate axis, x s represents the coordinate of the monitoring point on the first coordinate axis, y s represents the coordinate of the monitoring point on the second coordinate axis, x0 represents the origin of the first coordinate axis, y0 represents the origin of the second coordinate axis, and n represents the number of monitoring points, γ0 = 0.

[0075] The monitoring results in the respective coordinate systems of the GBIR and the IVM are resampled to the common coordinate system, wherein the GBIR and the IVM respectively adopt coordinated universal time (UTC).

[0076] For the inertial vision technology: the inertial cameras and targets of the inertial vision technology are preset to be distributed along the axis of the bridge, and the relative positions of adjacent inertial monitoring points are known, so that the absolute positions of the inertial monitoring points relative to the origin of the coordinate system can be calculated according to the relative positions, and thus the coordinates of the corresponding monitoring points are obtained, that is, the absolute positions of the inertial monitoring points relative to the origin of the common coordinate system are calculated according to the relative positions and the first vision vertical array and the first vision bridge side array, and the second vision vertical array and the second vision bridge side array of the inertial monitoring points in the common coordinate system are obtained.

[0077] For the ground-based interferometric radar: the distances of the radar detection points on the bridge to the GBIR sensor can be calculated from the image obtained by the rotating scanning mode, that is, the distances of the radar detection points to the sensor of the ground-based interferometric radar are obtained, the actual distance of the sensor of the ground-based interferometric radar to the origin of the coordinate system is obtained, and the first radar array is registered into the common coordinate system according to the distances and the actual distance, and the second radar array of the radar detection points in the common coordinate system is obtained.

[0078] That is, the pixel where each radar detection point is located is obtained from the image obtained by the rotating scanning mode, and the distance of each radar detection point to the GBIR sensor is represented as the product of the distance resolution and the column number of the pixel:

[0079] d GBIR =Δd sr ×N;

[0080] wherein d GBIR represents the distance of each radar detection point to the GBIR sensor, Δd sr represents the distance resolution, and N represents the column number of the pixel.

[0081] By matching the actual distance of the GBIR to the origin (x0, y0, z0) of the coordinate system, the rotating scanning mode data of the GBIR can be registered into the common coordinate system, and finally the deformation monitoring is performed using the fixed scanning mode at the same monitoring site, that is, the monitoring results in this mode can be registered into the common coordinate system.

[0082] wherein Beijing time is taken as the time standard, and the two standards differ by 8 hours. The monitoring frequencies of the GBIR and the IVM are both more than 10 Hz, and at this time, the data matched in the sampling time can be obtained by time interpolation in step S300, and due to the high sampling frequency, such interpolation will not affect the quality of the deformation monitoring data.

[0083] Step S300: interpolating the second vision vertical array and the second vision bridge side array to obtain a third vision vertical array and a third vision bridge side array, so that the spatial density of the third vision vertical array and the third vision bridge side array matches that of the second radar array.

[0084] Specifically, the deformation monitoring data of the IVM is sparsely distributed in space and cannot achieve the same spatial density as the GBIR, so it is necessary to interpolate the second visual vertical array and the second visual bridge side array of the IVM to obtain the third visual vertical array and the third visual bridge side array, so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array.

[0085] The step S300 of interpolating the second visual vertical array and the second visual bridge side array to obtain the third visual vertical array and the third visual bridge side array so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array specifically comprises:

[0086] The spatial characteristics of the second visual vertical array and the second visual bridge side array are described using a robust semi-variogram function;

[0087] The spatial correlation of each monitoring point is described using a spatial covariance matrix based on a spherical model;

[0088] The positions of known points are obtained according to the spatial characteristics and the spatial correlation, the distances between the known points and unknown points are calculated according to the known points, and the spatial interpolation weight vectors of the unknown points are obtained by substituting the spatial covariance function into the spatial interpolation weight vectors and the deformation data of the known points;

[0089] The deformation data of the known points are obtained, the interpolation results of the unknown points are calculated according to the spatial interpolation weight vectors and the deformation data, and the third visual vertical array and the third visual bridge side array are obtained so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array.

[0090] Specifically, since Kriging interpolation is widely used in spatial interpolation, its basic assumption is that the closer the sampling points in space, the greater the similarity. Due to the accuracy difference between GBIR and IVM, a robust semi-variogram function is selected to describe the spatial characteristics of the second visual vertical array and the second visual bridge side array:

[0091]

[0092] wherein r(d) represents the robust semi-variogram function, m(t,d) represents the number of monitoring points with a lag distance of d at time t, ML is the total number of monitoring times after time registration, D t (x s ) is the deformation monitored at time t, x s is the x-axis direction distance of the monitoring point (x s , y s , z s ) to the coordinate origin, and s represents the s-th detection point.

[0093] The spatial correlation of each monitoring point is described using a spatial covariance matrix based on a spherical model, and the fitting of the covariance matrix can reduce the influence of outliers in the data to a certain extent.

[0094] D(i,j) = σ(x i ,x j );

[0095] σ(x i ,x j ) = C0 + C1- θ(||x i -x j ||);

[0096]

[0097] where D represents the spatial covariance matrix, D(i,j) represents the matrix element of the ith row and the jth column, σ(x i ,x j ) represents the spatial covariance function, θ(d) is the selected spherical model, C0 represents the nugget value of the spherical model, C1 represents the bias base value of the spherical model, a represents the range of the spherical model, and ||x i -x j || represents the distance between point i and point j, and d represents the variable of the spherical model range.

[0098] The positions of the monitoring points are known, and the positions of the known points are obtained according to the spatial characteristics and spatial correlation. The distance between the known points and the unknown points is calculated according to the known points, and substituted into the spatial covariance function to obtain the spatial interpolation weight vector V of the unknown point:

[0099]

[0100] where V represents the spatial interpolation weight vector of the unknown point, v n is the weight corresponding to the nth monitoring point, σ ij is the spatial covariance function of the ith and jth monitoring points (i = 1, …, n; j = 0, …, n;), n represents the number of monitoring points, and λ is a constant.

[0101] After obtaining the spatial interpolation weight vector V, the unknown point interpolation result can be obtained with the known point deformation data:

[0102]

[0103] wherein, represents the unknown point interpolation result, respectively represent the x s , y s , and z sThe vertical and horizontal spatial interpolation results of the IVM data of the positions, V represents a spatial interpolation weight vector of the unknown point, and d1 and d2 respectively represent the original data of the IVM in the corresponding directions.

[0104] Obtain deformation data of the known points, calculate the interpolation results of the unknown points according to the spatial interpolation weight vector and the deformation data, obtain a third visual vertical array and a third visual bridge side array, so that the third visual vertical array and the third visual bridge side array match the spatial density of the second radar array.

[0105] Step S400: Calculate the spatial model matrixes of the LOS direction deformation of the GBIR, the vertical direction deformation of the IVM and the bridge side direction deformation of the IVM according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix.

[0106] Specifically, after obtaining the LOS direction deformation of the GBIR monitoring, the vertical direction deformation of the IVM monitoring and the bridge side direction deformation of the IVM monitoring with the same spatial density, the spatial model matrixes of the deformation data can be calculated to describe the spatial distribution of the deformation data, and the spatial model matrixes of the LOS direction deformation of the GBIR, the vertical direction deformation of the IVM and the bridge side direction deformation of the IVM are calculated according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix.

[0107] The step S400: calculating the spatial model matrixes of the LOS direction deformation of the GBIR, the vertical direction deformation of the IVM and the bridge side direction deformation of the IVM according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix, specifically includes:

[0108] calculating the generalized bending energy matrix of the spatial covariance function of the LOS direction deformation of the GBIR, the vertical direction deformation of the IVM and the bridge side direction deformation of the IVM according to the third visual vertical array, the third visual bridge side array and the second radar array respectively;

[0109] Decompose and reduce the dimension of the generalized bending energy matrix to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix.

[0110] Specifically, the generalized bending energy matrix of the spatial covariance function of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology is calculated according to the third visual vertical array, the third visual bridge side array and the second radar array respectively:

[0111]

[0112] Wherein, A represents a trend matrix, D represents a spatial covariance matrix, B represents a generalized bending energy matrix, the generalized bending energy matrix can be used to represent the degree of spatial variation, F represents a spatial trend field matrix, T The matrix transposition symbol is represented.

[0113] Since the deformation monitoring data of the GBIR and the IVM are large, in order to reduce the calculation amount, the generalized bending energy matrix is decomposed and reduced in dimension to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix.

[0114] Wherein, the decomposition and dimension reduction formula is:

[0115]

[0116] U=[u1,u2,…,u n ];

[0117] E=[e1,e2,…,e n ];

[0118] Wherein, B represents a generalized bending energy matrix, u n and e n are the eigenvectors and eigenvalues of the B matrix respectively, U represents an eigenvector matrix, and E represents an eigenvalue matrix.

[0119] The spatial model matrix H is composed of the GBIR line-of-sight direction deformation spatial model H GBIR , the IVM vertical direction deformation spatial model and the IVM horizontal direction deformation spatial model:

[0120]

[0121] H i =[h i (S1),h i (S2),…,h i (S n )] T

[0122] h(s)=[F(s) T ,e3σ(s) T u3,…,e n σ(s)T u n ]

[0123] where H represents the spatial model matrix, H GBIR represents the spatial model matrix of the GBIR line-of-sight direction, represents the spatial model matrix of the IVM vertical direction, respectively represent the spatial model matrix of the IVM side direction, H i represents the composition of the spatial model matrix, h i (S n ) represents, h(s) represents the first intermediate calculation model matrix, F(s) T represents the first spatial trend field matrix, e n represents the eigenvalue of the B matrix, σ(s) represents the s th monitoring point multi-space covariance function, u n represents the eigenvector of the B matrix.

[0124] Step S500: using an iterative almost unbiased estimation method to calculate the fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix.

[0125] Specifically, the fusion weight matrix of the ground-based interferometric radar and the inertial vision technology is calculated using an iterative almost unbiased estimation method according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix, and the weight of each deformation is obtained.

[0126] The step S500: using an iterative almost unbiased estimation method to calculate the fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix, specifically includes:

[0127] Obtaining the initial variance of the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix, and estimating the variance factor multiple times according to the spatial covariance matrix;

[0128] Iteratively calculating the almost unbiased estimation variance component according to the initial variance and the variance factor;

[0129] When the posterior variance of the iterative almost unbiased estimation method converges to 1, the iteration stops, and the fusion weight matrix of the ground-based interferometric radar and the inertial vision technology is obtained.

[0130] Specifically, the weight strategy of the GBIR line-of-sight direction, the IVM vertical direction and the IVM side deformation data is obtained by iterative almost unbiased estimation (IAUE). This method does not directly estimate the variance component but estimates the variance factor f iThe indirect estimation is performed to obtain initial variances of the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix, the variance factor is estimated by multiple iterations according to the spatial covariance matrix, and the almost unbiased estimation variance component is calculated by iterations according to the initial variance and the variance factor, that is, the almost unbiased estimation variance component is obtained by multiplying the initial variance and the variance factor:

[0131]

[0132] wherein, denote true variances of the GBIR observation, the IVM vertical observation and the lateral observation, f i denotes the i-th variance factor, and k denotes the number of spatial model matrices.

[0133] The covariance matrix of the observation vector Z t is denoted as:

[0134]

[0135] wherein, R denotes the covariance matrix of the observation vector Z t , f i denotes the i-th variance factor, B i denotes an intermediate matrix, b i denotes a diagonal matrix related to the i-th group of data, I i denotes a unit matrix, f1, f2 and f3 respectively denote variance factors of three dimensions, and respectively denote diagonal matrices corresponding to the line-of-sight direction deformation data of the ground-based interferometric radar, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation data of the inertial vision technology.

[0136] The calculation formula of the variance factor is as follows:

[0137]

[0138] W=P-PH(H T PH) -1 H T P;

[0139] wherein, f i denotes the i-th variance factor, Z t denotes the observation matrix at the t time, W denotes a matrix generated in an intermediate process, B i denotes an intermediate matrix, tr(*) denotes the sum of diagonal elements of a matrix, P denotes the inverse matrix of the covariance matrix R, and H denotes a spatial model matrix.

[0140] In order to simplify the calculation amount, the variance factor calculation adopts an approximate expression:

[0141]

[0142] DE=IH(H T PH) -1 H T P;

[0143] Among them, f i Denotes the i-th variance factor, [de] i ] represents the sum of the diagonal elements in matrix DE that are related to the i-th group, Z t Let B represent the observation matrix at time t, W represent the matrix generated during the intermediate process, and B represent the matrix generated during the intermediate process. i Let X represent the intermediate matrix, DE represent the matrix generated during the operation, P represent the inverse of the covariance matrix R, and H represent the spatial model matrix.

[0144] The iteration stops when the posterior variance of the almost unbiased estimation method converges to 1, yielding the fusion weight matrix of ground-based interferometric radar and inertial vision technology.

[0145] Among them, posterior variance The calculation formula is:

[0146]

[0147] in, Let N represent the posterior variance, N represent the number of GBIR observation points, and p represent the number of observed deformation components. In the three-dimensional deformation model of this application, p = 3. i ] represents the sum of the diagonal elements in matrix DE related to the i-th group, f i Let i represent the i-th variance factor.

[0148] The Iterative Nearly Unbiased Estimation (IAUE) method avoids negative variance components and exhibits good performance in variance component estimation for GBIR and IVM.

[0149] Step S600: Calculate the multidimensional dynamic deformation time series of the bridge's full span using the least squares method based on the fusion weight matrix.

[0150] Specifically, after the spatiotemporal registration of deformation monitoring data is completed, the weighted least squares method can be used to solve the problem. That is, based on the fused weight matrix, the least squares method is used to calculate the multidimensional dynamic deformation time series of the entire span of the bridge (e.g., ...). Figure 4 As shown, Figure 4 In the middle (a), the spatial section is in the vertical direction. Figure 4 (b) shows the time profile in the vertical direction. Figure 4 (c) is a horizontal spatial section. Figure 4 (d) represents the horizontal time profile, enabling multi-dimensional deformation monitoring of bridges, making the monitoring results more accurate, and avoiding missed diagnoses of bridge defects.

[0151] wherein the calculation formula of the least square method is:

[0152]

[0153] wherein, represents a result vector containing the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology in the fusion weight matrix; Pr represents the projection relationship of the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology, P represents the weight obtained by the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology, T represents transposition, Z t represents the observation matrix at t time, θ represents the included angle between the ground-based interferometric radar line-of-sight direction and the vertical direction, γ represents the included angle between the ground-based interferometric radar line-of-sight direction and the observation plane of the inertial vision technology, P1, P2 and P3 respectively represent the weight matrix corresponding to the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology, f1, f2 and f3 respectively represent the variance factors corresponding to P1, P2 and P3, and respectively represent the diagonal matrix corresponding to the ground-based interferometric radar line-of-sight deformation data, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation data of the inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology obtained by solving the fusion weight matrix.

[0154] The bridge multi-dimensional deformation monitoring method fusing ground-based radar interferometry and inertial vision measurement of the application realizes the full-span multi-dimensional dynamic deformation monitoring of the ground-based interferometric radar (GBIR) by combining the dynamic deformation data of the inertial vision-based measurement (IVM). The proposed method is experimentally verified using real data, and the experimental results show that the method can better monitor the full-span multi-dimensional dynamic deformation of the bridge, and has a smaller monitoring error than the single ground-based interferometric radar.

[0155] Please refer to Figures 5 to 6 , Figure 5is a principle diagram of a preferred embodiment of a bridge dynamic deformation measurement system of the application fusing ground-based interferometric radar and inertial vision; Figure 6 is a running environment diagram of a preferred embodiment of a terminal of the application.

[0156] In some embodiments, as shown in Figure 5 Based on the above-mentioned bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, the application further proposes a bridge dynamic deformation measurement system fusing ground-based interferometric radar and inertial vision, which comprises:

[0157] A measurement data acquisition module 51 is configured to acquire a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system, and acquire a first visual vertical array and a first visual bridge side array of the inertial vision technology on each monitoring point in a vertical direction and a bridge side direction in an inertial vision technology coordinate system;

[0158] A data registration module 52 is configured to establish a common coordinate system, sample the first radar array, the first visual vertical array and the first visual bridge side array to the common coordinate system respectively, and obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system, and a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the inertial vision technology in the common coordinate system in the vertical direction and the bridge side direction respectively;

[0159] A data interpolation module 53 is configured to interpolate the second visual vertical array and the second visual bridge side array to obtain a third visual vertical array and a third visual bridge side array, so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array;

[0160] A spatial model establishment module 54 is configured to calculate spatial model matrices of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, and obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix;

[0161] A fusion weight calculation module 55 is configured to calculate a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix using an iterative almost unbiased estimation method;

[0162] A data fusion solution module 56 is configured to calculate a multi-dimensional dynamic deformation time series of the whole span of the bridge according to the fusion weight matrix using a least square method.

[0163] In some embodiments, as shown in Figure 6 Based on the above-mentioned bridge dynamic deformation measurement method and system fusing ground-based interferometric radar and inertial vision, the application also correspondingly proposes a terminal, which comprises a memory 20, a processor 10, a display 30, Figure 6 Only some components of the terminal are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead.

[0164] The memory 20 can be an internal storage unit of the terminal in some embodiments, such as a hard disk or a memory of the terminal. The memory 20 can also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Further, the memory 20 can include both the internal storage unit and the external storage device of the terminal. The memory 20 is used to store application software and various data installed on the terminal, such as program codes of the terminal, etc. The memory 20 can also be used to temporarily store data that has been output or will be output.

[0165] In an embodiment, the memory 20 stores a bridge dynamic deformation measurement program 40 fusing ground-based interferometric radar and inertial vision, which can be executed by the processor 10 to implement the bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision in the application.

[0166] The processor 10 can be a central processing unit (CPU), a microprocessor or other data processing chip in some embodiments, which is used to run program codes or process data stored in the memory 20, such as to execute the bridge dynamic deformation measurement method fusing ground-based interferometric radar and inertial vision, etc.

[0167] The display 30 can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. in some embodiments. The display 30 is used to display information of the terminal and to display visualized user interfaces. The components 10-30 of the terminal communicate with each other through a system bus.

[0168] The application further provides a computer readable storage medium storing a bridge dynamic deformation measurement program integrating ground-based interferometric radar and inertial vision, which, when executed by a processor, implements the steps of the bridge dynamic deformation measurement method integrating ground-based interferometric radar and inertial vision.

[0169] To sum up, the application obtains a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system, obtains a first visual vertical array and a first visual bridge side array of the inertial vision technology on each monitoring point in a vertical direction and a bridge side direction in an inertial vision technology coordinate system, establishes a common coordinate system, samples the first radar array, the first visual vertical array and the first visual bridge side array to the common coordinate system respectively, obtains a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction in the inertial vision technology in the common coordinate system respectively, so that the measurement results of the ground-based interferometric radar and the inertial vision technology are unified in the coordinate system, facilitating the calculation of multi-dimensional deformation monitoring; secondly, the application interpolates the second visual vertical array and the second visual bridge side array to obtain a third visual vertical array and a third visual bridge side array, so that the spatial density of the third visual vertical array and the third visual bridge side array matches that of the second radar array, reduces the accuracy difference between the two measurement methods, calculates a spatial model matrix of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology according to the third visual vertical array, the third visual bridge side array and the second radar array respectively, and obtains a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix; thirdly, the application calculates a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix by using an iterative almost unbiased estimation method, and obtains the weight of each deformation; in addition, the application calculates a multi-dimensional dynamic deformation time sequence of the bridge full span according to the fusion weight matrix by using a least square method, realizes multi-dimensional deformation monitoring of the bridge, makes the monitoring result more accurate, and avoids the missed judgment of bridge diseases.

[0170] It should be noted that the various optional embodiments introduced in the embodiments of the application can be combined with each other to be realized, or can be realized independently, and the embodiments of the application do not limit this.

[0171] In the description of the application, it needs to be understood that the terms "upper", "lower", "left", "right", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, and a particular orientation configuration and operation. Therefore, it cannot be understood as a limitation on the application. In addition, "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more features. In the description of the application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0172] In the description of the application, it needs to be understood that unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" and the like should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the application can be understood according to the specific circumstances.

[0173] The above embodiments are described with reference to the accompanying drawings, and other different forms and embodiments are also possible without departing from the principles of the application, so the application should not be construed as being limited to the embodiments presented herein. Rather, these embodiments are provided to make the application perfect and complete, and to convey the scope of the application to those skilled in the art. In the drawings, the sizes and relative sizes of components may be exaggerated for clarity. The terms used herein are only based on the purpose of describing specific embodiments, and are not intended to be limiting. The terms "comprise" and / or "include" are used in the specification to indicate the presence of the described features, integers, components and / or components, but do not exclude the presence or addition of one or more other features, integers, components, components and / or groups thereof. Unless otherwise indicated, when stated, the numerical range includes the upper and lower limits of the range and any sub-range therebetween.

[0174] The above only describes some embodiments of the application, and does not limit the protection scope of the application, and any equivalent device or equivalent process transformation using the contents of the application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the application.

Claims

1. A method for measuring dynamic deformation of a bridge by fusing ground-based interferometric radar and inertial vision, characterized in that, The method comprises the following steps: obtaining a first radar array of a ground-based interferometric radar in a ground-based interferometric radar coordinate system, and obtaining a first visual vertical array and a first visual bridge side array of an inertial vision technology on each monitoring point in a vertical direction and a bridge side direction in an inertial vision technology coordinate system; establishing a common coordinate system, sampling the first radar array, the first visual vertical array and the first visual bridge side array to the common coordinate system respectively to obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction in the inertial vision technology in the common coordinate system; interpolating the second visual vertical array and the second visual bridge side array to obtain a third visual vertical array and a third visual bridge side array, so that the third visual vertical array and the third visual bridge side array match the spatial density of the second radar array; calculating a spatial model matrix of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge side direction deformation of the inertial vision technology according to the third visual vertical array, the third visual bridge side array and the second radar array respectively to obtain a visual vertical model matrix, a visual bridge side model matrix and a radar model matrix; calculating a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix by using an iterative almost unbiased estimation method; calculating a multi-dimensional dynamic deformation time sequence of the whole span of the bridge according to the fusion weight matrix by using a least square method.

2. The method of claim 1, wherein the method further comprises: The step of establishing a common coordinate system, sampling the first radar array, the first visual vertical array and the first visual bridge side array to the common coordinate system respectively to obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction in the inertial vision technology in the common coordinate system comprises the following steps: establishing a common coordinate system, setting the coordinate origin of the common coordinate system as the proximal end of the main span of the bridge, setting the direction of the first coordinate axis of the common coordinate system as parallel to the traffic direction of the bridge, setting the direction of the second coordinate axis of the common coordinate system as the lateral direction of the bridge away from the position of the sensor of the ground-based interferometric radar, and setting the direction of the third coordinate axis of the common coordinate system as vertically downward; setting each inertial camera and target of the inertial vision technology to be distributed along the axis of the bridge, setting the relative positions of adjacent inertial monitoring points, calculating the absolute positions of each inertial monitoring point and the origin of the common coordinate system according to the first visual vertical array and the first visual bridge side array to obtain a second visual vertical array and a second visual bridge side array of each inertial monitoring point in the vertical direction and the bridge side direction in the common coordinate system respectively. Obtaining the distance from each radar detection point to the sensor of the ground-based interferometric radar, obtaining the actual distance from the sensor of the ground-based interferometric radar to the coordinate origin, registering the first radar array into the common coordinate system according to the distance and the actual distance, and obtaining the second radar array of the ground-based interferometric radar in the common coordinate system.

3. The method of claim 2, wherein the method further comprises: The coordinate calculation formula of the first coordinate axis is: Inte(s) = (tan γ s -tan γ s-1 )(y G -y0); s = 1, 2, … n; wherein Inte(s) represents the coordinate of the first coordinate axis, s represents the serial number of the monitoring point, γ s represents the azimuth angle of the s-th monitoring point, γ s-1 represents the azimuth angle of the s-1-th monitoring point, γ G represents the coordinate of the first coordinate axis, y s represents the coordinate of the first coordinate axis, y s represents the coordinate of the second coordinate axis, x0 represents the origin of the first coordinate axis, y0 represents the origin of the second coordinate axis, and n represents the number of monitoring points.

4. The method of claim 1, wherein the method further comprises: The interpolation of the second visual vertical array and the second visual bridge side array to obtain the third visual vertical array and the third visual bridge side array specifically includes: Using a robust semi-variogram function to describe the spatial characteristics of the second visual vertical array and the second visual bridge side array; Using a spatial covariance matrix based on a spherical model to describe the spatial correlation of each monitoring point; According to the spatial characteristics and the spatial correlation, the position of the known point is obtained, the distance between the known point and the unknown point is calculated according to the known point, and the spatial interpolation weight vector of the unknown point is obtained by substituting the spatial covariance function; Obtaining the deformation data of the known point, calculating the interpolation result of the unknown point according to the spatial interpolation weight vector and the deformation data, and obtaining the third visual vertical array and the third visual bridge side array.

5. The method of claim 4, wherein the method further comprises: The calculation of the spatial model matrix of the line-of-sight deformation of the ground-based interferometric radar, the vertical direction deformation of the inertial visual technology and the bridge side direction deformation of the inertial visual technology according to the third visual vertical array, the third visual bridge side array and the second radar array respectively to obtain the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix specifically includes: According to the third visual vertical array, the third visual bridge side array and the second radar array, the generalized bending energy matrix of the spatial covariance function of the line-of-sight deformation of the ground-based interferometric radar, the vertical direction deformation of the inertial visual technology and the bridge side direction deformation of the inertial visual technology is calculated; The generalized bending energy matrix is decomposed and reduced in dimension to obtain the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix.

6. The method of claim 4, wherein the method further comprises: The calculation of the fusion weight matrix of the ground-based interferometric radar and the inertial visual technology according to the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix using the iterative almost unbiased estimation method specifically includes: Obtaining the initial variance of the visual vertical model matrix, the visual bridge side model matrix and the radar model matrix, and estimating the variance factor according to the spatial covariance matrix multiple times; Iteratively calculating the almost unbiased estimation variance component according to the initial variance and the variance factor; When the posterior variance of the iterative almost unbiased estimation method converges to 1, the iteration stops, and the fusion weight matrix of the ground-based interferometric radar and the inertial visual technology is obtained.

7. The method of claim 1, wherein the method further comprises: The calculation formula of the least square method is: wherein, represents the result vector of the fusion weight matrix containing the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology; Pr represents the projection relationship of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, P represents the weight obtained by the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, T represents transposition, Z t represents the observation matrix at time t, θ represents the included angle between the ground-based interferometric radar line-of-sight direction and the vertical direction, γ represents the included angle between the ground-based interferometric radar line-of-sight direction and the observation plane of inertial vision technology, P1, P2 and P3 respectively represent the weight matrix corresponding to the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, f1, f2 and f3 respectively represent the variance factor corresponding to P1, P2 and P3, and respectively represent the diagonal matrix corresponding to the ground-based interferometric radar line-of-sight direction deformation data, the vertical direction deformation of inertial vision technology and the bridge side direction deformation data of inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology, and respectively represent the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of inertial vision technology and the bridge side direction deformation of inertial vision technology obtained by solving the fusion weight matrix.

8. A bridge dynamic deformation measurement system that fuses ground-based interferometric radar and inertial vision, characterized by, The bridge dynamic deformation measurement system for fusing the ground-based interferometric radar and the inertial visual technology specifically includes: The measurement data acquisition module is configured to acquire a first radar array of the ground-based interferometric radar in a ground-based interferometric radar coordinate system and acquire a first visual vertical array and a first visual bridge-side array of the inertial vision technology in a vertical direction and a bridge-side direction of each monitoring point in an inertial vision technology coordinate system; The data registration module is configured to establish a common coordinate system, sample the first radar array, the first visual vertical array and the first visual bridge-side array to the common coordinate system respectively, and obtain a second radar array of each radar detection point in the ground-based interferometric radar in the common coordinate system and a second visual vertical array and a second visual bridge-side array of each inertial monitoring point in the vertical direction and the bridge-side direction of the inertial vision technology in the common coordinate system respectively; The data interpolation module is configured to interpolate the second visual vertical array and the second visual bridge-side array to obtain a third visual vertical array and a third visual bridge-side array, so that the third visual vertical array and the third visual bridge-side array match the spatial density of the second radar array; The spatial model establishment module is configured to calculate spatial model matrices of the ground-based interferometric radar line-of-sight direction deformation, the vertical direction deformation of the inertial vision technology and the bridge-side direction deformation of the inertial vision technology according to the third visual vertical array, the third visual bridge-side array and the second radar array respectively, and obtain a visual vertical model matrix, a visual bridge-side model matrix and a radar model matrix; The fusion weight calculation module is configured to calculate a fusion weight matrix of the ground-based interferometric radar and the inertial vision technology according to the visual vertical model matrix, the visual bridge-side model matrix and the radar model matrix using an iterative almost unbiased estimation method; The data fusion solution module is configured to calculate a multi-dimensional dynamic deformation time series of the whole span of the bridge according to the fusion weight matrix using a least squares method.

9. A terminal, characterized by comprising: The terminal comprises a memory, a processor and a fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement program stored on the memory and executable on the processor, and the fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement program implements the steps of the fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement method according to any one of claims 1-7 when executed by the processor.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement program, and the fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement program implements the steps of the fusion ground-based interferometric radar and inertial vision bridge dynamic deformation measurement method according to any one of claims 1-7 when executed by the processor.

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