Bridge displacement real-time monitoring method and system based on unmanned aerial vehicle phase interference

Abstract

The invention discloses a bridge displacement real-time monitoring method and system based on unmanned aerial vehicle phase interference, and relates to the technical field of bridge structure monitoring, and the method comprises the steps: collecting packaging phase data, and carrying out the spatial difference and periodic correction to obtain a discrete phase difference; performing discrete wavelet transform on the packaging phase data to obtain a multi-scale waveform factor; compensating the phase jump between the packaging phase data through the constructed integer compensation field to obtain a reconstructed continuous phase, calculating a fractional gradient and a local shape index, and constructing a target function to obtain an optimized continuous phase; based on the mapping relation between the optimized continuous phase and the physical displacement, the optimized continuous phase is converted into a physical displacement value, and real-time monitoring is carried out. The problems of discontinuity and distortion are eliminated through spatial difference and periodic correction, the signal feature expression ability is enhanced through a multi-scale waveform factor, external interference is suppressed by adopting a fractional gradient and a local shape index, and the monitoring precision is improved by constructing an objective function and performing iterative optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of bridge structure monitoring, and specifically to a method and system for real-time monitoring of bridge displacement based on unmanned aerial vehicle (UAV) phase interference. Background Art

[0002] In the existing bridge structure health monitoring technology, higher requirements are put forward for the real-time monitoring of bridge displacement. Especially in harsh environments and occasions with high real-time requirements, traditional sensor layout schemes have problems such as complex installation, limited data acquisition, and insufficient coverage. In recent years, non-contact monitoring methods based on UAV platforms have gradually attracted attention, and among them, using phase interference technology to realize bridge structure displacement detection has become a means with promising applications.

[0003] The UAV carries an interference sensor to collect the wrapped phase data on the bridge surface. By capturing the wrapped phase information, the structural displacement situation can be indirectly reflected. However, due to the wrapping effect existing in the phase data itself, its data has a 2π periodic truncation problem. Directly applying the wrapped phase for physical quantity mapping will lead to the risk of discontinuity and distortion in the displacement inversion result. Therefore, how to eliminate the periodic ambiguity in the wrapped phase data, restore the continuous phase, and on this basis, establish the mapping relationship between the continuous phase and the structural physical displacement has become a key technical problem to be solved urgently. Summary of the Invention

[0004] Based on the above-mentioned disadvantages of the existing technology, the purpose of the present invention is to provide a method and system for real-time monitoring of bridge displacement based on UAV phase interference to solve the above technical problems.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for real-time monitoring of bridge displacement based on UAV phase interference, including: Collecting discrete wrapped phase data on the bridge surface through an interference sensor set on the UAV, performing spatial difference and period correction on the wrapped phase data to obtain discrete phase differences in space; Performing discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; Compensating the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase; Calculating the fractional gradient in space according to the reconstructed continuous phase, and calculating the local fractal index according to the discrete phase difference and multi-scale waveform factors; Constructing an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient, and local fractal index, and obtaining an optimized continuous phase through iterative optimization; Based on the mapping relationship between the optimized continuous phase and the physical displacement, converting the optimized continuous phase into a physical displacement value for real-time monitoring.

[0006] The present invention is further configured to record discrete packaging phase data as , perform spatial difference and periodic correction on the packaging phase data, and the calculation logic is: ; , and is the discrete phase difference in the direction and is the modulo operation.

[0007] The present invention is further configured to obtain a multi-scale waveform factor by performing discrete wavelet transform on the packaging phase data, including: Set the packaging phase data as the initial approximation coefficient, and decompose the initial approximation coefficient through a recursive formula to obtain the -level approximation coefficient and detail coefficient; Calculate the local detail metric according to the detail coefficient; Calculate the multi-scale waveform factor according to the approximation coefficient and the local detail metric.

[0008] The present invention is further configured that the recursive calculation logic of the approximation coefficient is: , is the -level approximation coefficient, , is the total number of scales, , is the low-pass filter coefficient, is the spatial index of the filter coefficient; The recursive calculation logic of the detail coefficient is: , is the -level detail coefficient, is the high-pass filter coefficient; The calculation logic of the local detail metric is: , is the local detail metric, is the detail response index, is the multi-scale sensitivity index; The calculation logic of the multi-scale waveform factor is: , is the multi-scale waveform factor, is the global trend scale factor.

[0009] The present invention is further configured to record the integer compensation field as , and the initial value is 0, and the reconstructed continuous phase is .

[0010] The present invention is further configured such that the calculation logic of the fractional gradient in space is as follows: ; ; and is the fractional gradient in the direction and the direction, and is the fractional-order weight; , is the local fractal index, and is the multi-scale waveform factor.

[0011] The present invention is further configured such that the construction logic of the objective function is as follows: , is the objective function of the integer compensation field, and is the anisotropic weight in the direction and the direction, , is the penalty function, is the control coefficient, is the independent variable.

[0012] The present invention is further configured to update the integer field using the gradient descent method: , is the integer field at the th iteration, is the step size, , is the direction variable, including the direction and the direction, .

[0013] The present invention is further configured to calculate the optimized continuous phase based on the optimized integer field updated according to the gradient descent method; The mapping logic of the physical displacement value is as follows: , is the scale factor, is the phase mapping control coefficient, is the waveform factor control coefficient.

[0014] The present invention also provides a real-time bridge displacement monitoring system based on UAV phase interference for implementing the above-mentioned real-time bridge displacement monitoring method based on UAV phase interference. The system includes: Acquisition module: Collect discrete wrapped phase data on the bridge surface through an interference sensor set on the unmanned aerial vehicle (UAV), perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space; Processing module: Apply discrete wavelet transform to the wrapped phase data to obtain multi-scale waveform factors; Compensation module: Compensate for phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain reconstructed continuous phase; Calculation module: Calculate the fractional gradient in space based on the reconstructed continuous phase, and calculate the local fractal index based on the discrete phase difference and multi-scale waveform factors; Optimization module: Construct an objective function with anisotropic adaptive regulation based on the discrete phase difference, fractional gradient, and local fractal index, and obtain the optimized continuous phase through iterative optimization; Monitoring module: Based on the mapping relationship between the optimized continuous phase and physical displacement, convert the optimized continuous phase into a physical displacement value for real-time monitoring.

[0015] The present invention provides a method and system for real-time monitoring of bridge displacement based on UAV phase interference. The method collects discrete wrapped phase data on the bridge surface through an interference sensor set on the UAV, performs spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space; applies discrete wavelet transform to the wrapped phase data to obtain multi-scale waveform factors; compensates for phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain reconstructed continuous phase; calculates the fractional gradient in space based on the reconstructed continuous phase, and calculates the local fractal index based on the discrete phase difference and multi-scale waveform factors; constructs an objective function with anisotropic adaptive regulation based on the discrete phase difference, fractional gradient, and local fractal index, and obtains the optimized continuous phase through iterative optimization; based on the mapping relationship between the optimized continuous phase and physical displacement, convert the optimized continuous phase into a physical displacement value for real-time monitoring. The beneficial effects generated include: High-precision continuous phase recovery: Effectively correct periodic ambiguities in the wrapped phase data, ensure the accurate recovery of continuous phase data, eliminate discontinuity and distortion problems caused by the wrapping effect, and provide a reliable basis for subsequent displacement inversion; Multi-scale feature extraction and fusion: Use discrete wavelet transform to extract multi-scale waveform factors, effectively taking into account information on local details and global trends, thereby enhancing the signal feature expression ability and improving the robustness of data processing and the accuracy of continuous phase reconstruction; Fractional-order description and local detail characterization: Use fractional gradient and local fractal index to describe the continuous phase in detail, realize the characterization of the long-range dependence characteristics of local data changes, and effectively suppress the influence of external environmental interference and platform vibration on the recovery result; Adaptive anisotropic objective function construction: Construct an objective function with anisotropic adaptive regulation, and through iterative optimization, further refine the continuous phase data to ensure that local details and overall consistency are taken into account in global optimization, significantly improving the optimization accuracy and stability.

[0016] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the specific embodiments of this application are specifically given below. Brief Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. In the drawings: Figure 1 It is a flowchart of a method for real-time monitoring of bridge displacement based on UAV phase interference shown in an exemplary embodiment of the present invention; Figure 2 It is a schematic structural diagram of a system for real-time monitoring of bridge displacement based on UAV phase interference shown in an exemplary embodiment of the present invention. Detailed Embodiments

[0018] The following will describe the embodiments of the present invention with reference to the drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0019] It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape and size of the components in actual implementation. The type, quantity and ratio of each component in its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0020] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand. Embodiment 1

[0021] A real-time monitoring method for bridge displacement based on drone phase interference, as Figure 1 shown, includes: Collect discrete wrapped phase data of the bridge surface through an interference sensor set on the drone, perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space; Perform discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; Compensate for the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain reconstructed continuous phase; Calculate the fractional gradient in space according to the reconstructed continuous phase, and calculate the local fractal index according to the discrete phase difference and multi-scale waveform factors; Construct an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient and local fractal index, and obtain the optimized continuous phase through iterative optimization; Based on the mapping relationship between the optimized continuous phase and the physical displacement, convert the optimized continuous phase into a physical displacement value for real-time monitoring.

[0022] The present invention is further set such that the discrete wrapped phase data is denoted as , and the calculation logic for performing spatial difference and periodic correction on the wrapped phase data is: ; , and is the discrete phase difference in the direction and is the modulo operation. Specifically, the bridge surface is imaged and measured through an interference sensor set on the drone to obtain discrete wrapped phase data. The wrapped phase data refers to the phase information obtained through the interference sensor. Since the measuring instrument can only capture within the range of phase values, the data is represented in the form of "wrapping", that is, the measured phase value falls within 0 to or to Between them, although the wrapped phase directly reflects the preliminary information of the signal, due to its periodicity limitation, it must be unwrapped in subsequent processing to restore the continuous phase. The wrapped phase data reflects the phase information of the sensor return signal, but since the phase is only measured within cycles, its data is represented in modulo form. Spatial difference refers to performing difference calculations on the collected wrapped phase data spatially, including in the direction and the direction, that is, calculating the phase difference between adjacent sampling points. The purpose of spatial difference is to quantify the local phase change and reveal the information of local deformation or dynamic vibration on the bridge surface; due to the periodic characteristic of the wrapped phase data, the directly calculated phase difference may cross the period boundary and exhibit large jumps. To eliminate such non-physical large jumps, a period correction method is adopted. By adding , performing modulo operation, and then subtracting , the calculated phase difference is mapped to a predetermined interval, including [− , ), ensuring that the true local phase change between adjacent points is correctly characterized; through the above spatial difference and period correction, the obtained spatially discrete phase difference reflects the true local phase change on the bridge surface.

[0023] The present invention is further configured to obtain multi-scale waveform factors by performing discrete wavelet transform on the wrapped phase data, including: Setting the wrapped phase data as the initial approximation coefficient, and decomposing the initial approximation coefficient through a recursive formula to obtain the -level approximation coefficient and detail coefficient; the present invention is further configured that the recursive calculation logic of the approximation coefficient is: , is the -level approximation coefficient, , is the total number of scales, , is the low-pass filter coefficient, is the spatial index of the filter coefficient; the recursive calculation logic of the detail coefficient is: , is the -level detail coefficient, is the high-pass filter coefficient; specifically, regarding the wrapped phase data as the initial approximation coefficient , is a sampling point in a two-dimensional discrete space. A recursive formula is used to perform multi-scale decomposition on the initial approximation coefficients, thereby extracting the low-frequency trend information and high-frequency local detail information of the signal at different scales; the current scale approximation coefficients are convolved and downsampled through a low-pass filter to obtain the -level approximation coefficients; the current scale approximation coefficients are convolved and downsampled through a high-pass filter to obtain the -level detail coefficients; in the recursive formula, downsampling (scale transformation) is implemented in the form of and , so that the decomposition results at each level gradually decrease in spatial resolution, thus corresponding to different scales; the original wrapped phase data is decomposed into approximation and detail coefficients at multiple scales through recursive decomposition, realizing the representation of the signal at different resolutions, which can capture both the global trend and reveal local details, thereby providing rich and multi-level input information for subsequent continuous phase recovery and displacement inversion; Calculate the local detail metric based on the detail coefficients; the calculation logic of the local detail metric is: , is the local detail metric, is the detail response exponent, is the multi-scale sensitivity exponent; specifically, the detail coefficients at each scale obtained in the multi-scale wavelet decomposition are fused, and the logarithmic sum exponential structure is used to achieve smooth aggregation of the detail information at each scale, obtaining a unified local detail metric ; the detail response exponent is a parameter used to regulate the "response sensitivity" of the exponential function in the logarithmic sum exponential structure, and its value range is [1, 10]. The multi-scale sensitivity exponent is the power regulation parameter for the -level detail coefficients, which is used to adjust the contribution weight of the detail information at each scale in the fusion process, and its value range is [1, 2]; by weighted integration of the detail coefficients at each scale, it can simultaneously reflect the local detail information from different resolutions, providing a richer and more comprehensive feature input for continuous phase reconstruction; Calculate the multi-scale waveform factor based on the approximation coefficients and the local detail metric; the calculation logic of the multi-scale waveform factor is: , is the multi-scale waveform factor, is the global trend scale factor; specifically, based on the results after multi-scale decomposition, the local detail metric representing local details and the -level approximation coefficients representing the global low-frequency trend are respectively obtained. By fusing the two, the multi-scale waveform factor is constructed; the global trend scale factor It is used to adjust the proportion of global low-frequency information in the final waveform factor, and the value range is [0.1, 10]; by fusing local detail metrics and global approximation information, the multi-scale waveform factor can simultaneously capture subtle local changes and overall trends, make up for the limitations of single-scale information, and ensure that information at different levels of data is effectively utilized.

[0024] The present invention is further configured such that the integer compensation field is denoted as and the initial value is 0, and the reconstructed continuous phase is .

[0025] The present invention is further configured such that the calculation logic of the fractional gradient in space is: ; ; and is the fractional gradient in the direction and is the fractional weight; specifically, through fractional differentiation, the change of the continuous phase data in the discrete space is described by a non-integer order (fractional order) derivative, and the differences between adjacent points are weighted and summed in an accumulative form. The calculation logic is defined in the horizontal ( direction) and vertical ( direction) respectively. For the given continuous phase data (the continuous phase restored by the previous processing), the local differences are calculated in the direction and direction respectively, that is, the difference between the current point and the previous point; by introducing the fractional weight , the difference terms are weighted and accumulated according to different step sizes to form a gradient estimate that can reflect long-range dependence and non-local information; this fractional gradient can not only describe local fine changes but also capture the gradual change characteristics in the overall trend, which is helpful for subsequent continuous phase optimization and feature extraction; the fractional weight is the coefficient used to weight the contributions of each level of difference determined according to the fractional calculus theory, and the calculation logic is: ; where , , is the order of the fractional derivative, and the value range is between (0, 1], indicating the degree of non-integer order differentiation; is the Gamma function, and the Gamma function is used for normalization in this formula to make the fractional weight meet the theoretical requirements and ensure that when When it is an integer, the classical differential form is reproduced; the fractional gradient uses a weighted difference method of non-integer order, which can capture the long-range dependence of signals that is difficult to describe by traditional first-order or integer-order differentials, and improve the characterization of local changes in complex dynamic environments; by assigning lower weights to the difference terms far from the current sampling point through fractional weights, the local gradient estimation is more robust in suppressing noise and abnormal data, while maintaining sensitivity to the true change trend and improving the accuracy of continuous phase optimization and physical displacement inversion; The calculation logic of the local fractal index is as follows: , is the local fractal index, is the multi-scale waveform factor; specifically, using the local wrapped phase data in direction and direction, the discrete difference values and reflect the magnitude of the local phase jump. Combining the global and local comprehensive information expressed by the multi-scale waveform factor , the local fractal index is constructed; the numerator part takes the logarithm after adding a constant 1 to the local phase difference (representing local changes or discontinuities), which plays the role of smoothing changes and amplifying the effect of small changes; the denominator part also takes the logarithm after processing the multi-scale waveform factor , reflecting the comprehensive information after combining the overall low-frequency trend and local details in the region; by dividing the two, a dimensionless local fractal index can be obtained, which describes the fineness or coarseness of the phase change in the local area relative to the global information. Through the calculation of the local fractal index , the quantitative analysis of the local surface roughness of the signal or structure can be realized, which helps to identify possible local anomalies or damaged areas.

[0026] The present invention is further set as follows. The construction logic of the objective function is: , is the objective function of the integer compensation field, and are the anisotropic weights in the direction and direction, is the penalty function, , is the control coefficient, is the fractal regulation coefficient, is the independent variable. Specifically, the objective function for each sampling point in the discrete space at direction and The errors in the directions are weighted and summed. The residuals in the two directions are respectively composed of the difference between the fractional gradient and the directly calculated wrapped phase difference , and the local fractal and multi-scale information; the penalty function is used to perform a non-linear mapping on the error term , which plays a role in smoothing the change and suppressing extreme errors. The logarithmic function provides a continuous and differentiable response, making the growth rate slower when the error is large, and having the characteristic of anti-outlier interference; in the error term in each direction, the part is subtracted, and the fractal exponent that integrates local details and the multi-scale waveform factor are fused. This term is used to compensate for the deviation caused by insufficient local information or noise interference in the continuous phase reconstruction, so that the overall error description is closer to the true physical state. and respectively assign different weights to the residual terms in the direction and the direction, reflecting the differences in signal characteristics and noise interference in each direction, so as to achieve adaptive regulation in the direction; the control coefficient is used to control the sensitivity of the error to the response of the objective function in the penalty function, and the value range is [0.1, 10], and the fractal regulation coefficient is used to adjust the proportion of the local fractal exponent and the multi-scale waveform factor in the objective function to the error, and the value range is [0.1, 5]. and the directly calculated wrapped phase difference constitute; the penalty function is used to perform a non-linear mapping on the error term , playing a role in smoothing the change and suppressing extreme errors. The logarithmic function provides a continuous and differentiable response, making the growth rate slower when the error is large, and having the characteristic of anti-outlier interference; in the error term in each direction, is subtracted, and the fractal exponent that integrates local details and the multi-scale waveform factor . This term is used to compensate for the deviation caused by insufficient local information or noise interference in the continuous phase reconstruction, so that the overall error description is closer to the true physical state, and respectively direction and direction residual terms are given different weights, reflecting the differences in signal characteristics and noise interference in each direction, so as to achieve adaptive regulation in the direction; the control coefficient is used to control the sensitivity of the error to the response of the objective function in the penalty function, and the value range is [0.1, 10], and the fractal regulation coefficient is used to adjust the local fractal exponent and the multi-scale waveform factor in the objective function to the proportion of the error, and the value range is [0.1, 5].

[0027] The present invention is further configured to update the integer field by using the gradient descent method: , is the integer field at the th iteration, is the step size, , is the direction variable, including direction and direction, . Specifically, the integer field is used to eliminate the periodic ambiguity in the wrapped phase data, and through iterative update, the continuous phase finally achieves global consistency and high-precision recovery, where the objective function integrates the local errors in each direction and suppresses outliers through the logarithmic penalty function; and in the gradient (partial derivative) term , the error integrates the fractional gradient of the continuous phase, the direct wrapped phase difference, and the local fractal exponent and the multi-scale waveform factor The fused local regulation term. The overall objective function reflects the reconstruction error of the continuous phase, and the gradient descent update continuously adjusts to minimize this error; the step size is used to control the amplitude of each gradient descent update, and its value range is (0, 0.1]; through the iterative process of updating the integer field by the gradient descent method, using the comprehensive description of multi-directional error, non-linear penalty and local regulation term in the objective function, the fine correction of the wrapped phase data is realized for the ambiguity problem. This update process not only improves the accuracy and stability of continuous phase recovery, but also effectively enhances the overall robustness and real-time monitoring effect of the system through adaptive parameter regulation and multi-scale fusion. This invention is further set to calculate the optimized continuous phase

[0028] based on the optimal integer field updated by the gradient descent method ; ; The mapping logic of the physical displacement value is: ; is the scale factor, is the phase mapping regulation coefficient, is the waveform factor regulation coefficient. Specifically, the signal obtained through continuous phase recovery and multi-scale processing is used to invert the actual physical displacement. The mapping logic adopts a non-linear function combination. After fusing the continuous phase with local and global features, through double non-linear transformations, double tanh and arctan transformations, the output of the physical displacement value is realized. In the above calculation logic, first, the highly sensitive non-linear function is used to compress and smooth the continuous phase signal and its supplementary multi-scale information, so that large-scale changes are restricted; subsequently, is used to further invert the signal after non-linear processing, and the amplitude of the output is adjusted through the scale factor , so as to realize the mapping from the continuous phase and its local features to the actual physical displacement value; the scale factor is a parameter used to globally scale the mapped result, converting the dimensionless function output into a displacement value with actual physical significance, and its value is determined according to the working wavelength of the sensor, system calibration data and actual structural dimensions. The phase mapping regulation coefficient is a parameter that controls the sensitivity of the continuous phase data in the non-linear transformation. Its function is to amplify or compress the influence of continuous phase changes on the output mapping, and its value range is [0.1, 10]. The waveform factor regulation coefficient is used to adjust the multi-scale waveform factor The parameter for the contribution ratio of the mapping result has a value range of [0.1, 5]; the mapping logic non-linearly combines the continuous phase with multi-scale and local fractal information, and adopts and two-level transformation, and performs parametric control through the scale factor and the regulation coefficient, realizing an accurate, robust and adaptive mapping from the continuous phase to the physical displacement. This method has beneficial effects such as smooth anti-noise, accurate dimension conversion, and balanced utilization of local and global information, thus providing an efficient and reliable technical solution for the real-time monitoring of bridge displacement.

[0029] Please refer to Figure 2 , the exemplary real-time bridge displacement monitoring system based on UAV phase interference is used to implement the above-mentioned real-time bridge displacement monitoring method based on UAV phase interference, and includes: Acquisition module: Collect discrete wrapped phase data on the bridge surface through the interference sensor set on the UAV, perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space; Processing module: Use discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; Compensation module: Compensate the phase jumps between discrete wrapped phase data through the constructed integer compensation field to obtain the reconstructed continuous phase; Calculation module: Calculate the fractional gradient in space according to the reconstructed continuous phase, and calculate the local fractal index according to the discrete phase difference and the multi-scale waveform factor; Optimization module: Construct an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient and local fractal index, and obtain the optimized continuous phase through iterative optimization; Monitoring module: Based on the mapping relationship between the optimized continuous phase and the physical displacement, convert the optimized continuous phase into a physical displacement value for real-time monitoring.

[0030] It should be noted that the above-mentioned real-time bridge displacement monitoring system based on UAV phase interference and the above-mentioned real-time bridge displacement monitoring method based on UAV phase interference belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be repeated here. In practical applications, the above-mentioned real-time bridge displacement monitoring system based on UAV phase interference can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This is not limited here either.

[0031] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0032] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context.

[0033] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0034] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0035] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0036] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0037] In several embodiments provided in this application, it should be understood that the disclosed systems can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be electrical, mechanical, or other forms.

[0038] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] In addition, the functional units in each embodiment of this application can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0040] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0041] As described above, the above are only specific implementation manners of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed by this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A real-time monitoring method for bridge displacement based on UAV phase interferometry, characterized in that: include: The interferometric sensor installed on the UAV collects the discrete package phase data on the bridge surface, performs spatial differentiation and period correction on the package phase data, and obtains the spatial discrete phase difference. The multi-scale waveform factors are obtained by using discrete wavelet transform on the package phase data; The phase jump between discrete package phase data is compensated by constructing an integer compensation field to obtain a reconstructed continuous phase; The fractional gradient in space is calculated based on the reconstructed continuous phase, and the local fractal index is calculated based on the discrete phase difference and the multi-scale waveform factor; According to the discrete phase difference, fractional gradient and local fractal index, an objective function with anisotropic adaptive regulation is constructed, and the optimized continuous phase is obtained through iterative optimization. Based on the mapping relationship between the optimized continuous phase and the physical displacement, the optimized continuous phase is converted into a physical displacement value for real-time monitoring.

2. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 1 is characterized in that: The discrete package phase data is recorded as , perform spatial differentiation and period correction on the package phase data, and the calculation logic is: ; , and for Direction and The discrete phase difference in the direction, It is the remainder operation.

3. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 1 is characterized in that: The package phase data is transformed using discrete wavelet transform to obtain multi-scale waveform factors, including: Set the package phase data as the initial approximation coefficient, and decompose the initial approximation coefficient through the recursive formula to obtain the first Level approximation coefficient and detail coefficient; Calculate a local detail measure based on the detail coefficient; The multi-scale shape factors are calculated based on the approximation coefficients and the local detail measure.

4. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 3 is characterized in that: The recursive calculation logic of the approximate coefficient is: , For the The approximation coefficient of level, , is the total number of scales, , is the low-pass filter coefficient, is the spatial index of the filter coefficient; The recursive calculation logic of the detail coefficient is: , For the Level detail factor, is the high-pass filter coefficient; The calculation logic of local detail measurement is: , is the local detail measure, is the detail response index, is a multi-scale sensitivity index; The calculation logic of the multi-scale waveform factor is: , is the multi-scale waveform factor, is the global trend scale factor.

5. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 2 is characterized in that: The integer compensation field is denoted as , and the initial value is 0, the reconstructed continuous phase is .

6. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 5 is characterized in that: The calculation logic of the fractional gradient in space is: ; ; and for Direction and The fractional gradient of the direction, is the fractional weight; The calculation logic of the local fractal index is: , is the local fractal index, is the multi-scale waveform factor.

7. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 6 is characterized in that: The construction logic of the objective function is: , is the objective function of the integer compensation field, and for Direction and The weight of opposite sex in direction, is the penalty function, , is the control coefficient, is the fractal control coefficient, is the independent variable.

8. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 7 is characterized in that: Update the integer field using gradient descent: , For the The integer field at the iteration, is the step length, , is the direction variable, including Direction and direction, .

9. The real-time monitoring method of bridge displacement based on UAV phase interferometry according to claim 1 is characterized in that: The optimal integer field updated by gradient descent Calculate the optimized continuous phase ; The mapping logic of physical displacement value is: , is the scale factor, is the phase mapping control coefficient, is the waveform factor control coefficient.

10. A real-time monitoring system for bridge displacement based on UAV phase interferometry, used to implement the real-time monitoring method for bridge displacement based on UAV phase interferometry according to any one of claims 1 to 9, characterized in that: include: Acquisition module: The interferometric sensor installed on the drone collects the discrete package phase data on the bridge surface, performs spatial differentiation and period correction on the package phase data, and obtains the spatial discrete phase difference; Processing module: using discrete wavelet transform to obtain multi-scale waveform factors for packaging phase data; Compensation module: Compensate the phase jump between discrete package phase data through the constructed integer compensation field to obtain the reconstructed continuous phase; Calculation module: The fractional gradient in space is calculated based on the reconstructed continuous phase, and the local fractal index is calculated based on the discrete phase difference and multi-scale waveform factor; Optimization module: constructs an objective function with anisotropic adaptive regulation based on discrete phase difference, fractional gradient and local fractal index, and obtains the optimized continuous phase through iterative optimization; Monitoring module: Based on the mapping relationship between the optimized continuous phase and the physical displacement, the optimized continuous phase is converted into a physical displacement value for real-time monitoring.

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