Real-time Monitoring Method and System for Bridge Displacement Based on UAV Phase Interference

Abstract

The present invention discloses a method and system for real-time monitoring of bridge displacement based on UAV phase interference, which relates to the technical field of bridge structure monitoring. The method includes: collecting wrapped phase data, performing spatial difference and periodic correction to obtain discrete phase differences; using discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; compensating for phase jumps between the wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase, calculating fractional gradient and local fractal index, and constructing an objective function to obtain an optimized continuous phase; 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. Discontinuity and distortion problems are eliminated through spatial difference and periodic correction, the signal feature expression ability is enhanced through multi-scale waveform factors, external interference is suppressed by using fractional gradient and local fractal index, and iterative optimization is performed by constructing an objective function to improve the monitoring accuracy.

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 drone 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 drone platforms have gradually received attention, and among them, using phase interference technology to achieve bridge structure displacement detection has become a means with promising applications.

[0003] The drone carries an interference sensor to collect the 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 discontinuity and distortion risks 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 prior art, the purpose of the present invention is to provide a method and system for real-time monitoring of bridge displacement based on drone 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 drone phase interference, including:

[0006] Collect discrete wrapped phase data on the bridge surface through an interference sensor set on the drone, perform spatial difference and period correction on the wrapped phase data to obtain discrete phase differences in space;

[0007] Perform discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors;

[0008] Compensate for the phase jumps between the discrete wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase;

[0009] 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;

[0010] Construct an objective function with anisotropic adaptive regulation based on discrete phase difference, fractional gradient, and local fractal index, and obtain the optimized continuous phase through iterative optimization;

[0011] 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.

[0012] The present invention is further configured such that the discrete wrapped phase data is denoted as , perform spatial difference and periodic correction on the wrapped phase data, and the calculation logic is: ; , and is the discrete phase difference in the direction and is the modulo operation.

[0013] The present invention is further configured to obtain multi-scale waveform factors by performing discrete wavelet transform on the wrapped phase data, including:

[0014] Set the wrapped 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 coefficients;

[0015] Calculate the local detail metric based on the detail coefficients;

[0016] Calculate the multi-scale waveform factor based on the approximation coefficient and the local detail metric.

[0017] The present invention is further configured such that the recursive calculation logic of the approximation coefficient is:

[0018] , 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;

[0019] The recursive calculation logic of the detail coefficients is: , is the -level detail coefficient, is the high-pass filter coefficient;

[0020] 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;

[0021] The calculation logic of the multi-scale waveform factor is: , is the multi-scale waveform factor, is the global trend scale factor.

[0022] 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 .

[0023] 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 order weight;

[0024] The calculation logic of the local fractal index is: , is the local fractal index, is the multi-scale waveform factor.

[0025] The present invention is further configured such that the construction logic of the objective function is: , is the objective function of the integer compensation field, and is the anisotropy weight in the direction and is the penalty function, , is the control coefficient, is the fractal regulation coefficient, is the independent variable.

[0026] 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 the direction and .

[0027] The present invention is further configured to calculate the optimized continuous phase according to the optimal integer field updated by the gradient descent method;

[0028] The mapping logic of the physical displacement value is as follows: , is the scale factor, is the phase mapping regulation coefficient, is the waveform factor regulation coefficient.

[0029] 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:

[0030] Acquisition module: Collect discrete wrapped phase data on the bridge surface through an interference sensor arranged on the UAV, perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space;

[0031] Processing module: Perform discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors;

[0032] Compensation module: Compensate the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase;

[0033] 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 multi-scale waveform factors;

[0034] Optimization module: Construct an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient, and local fractal index, and obtain an optimized continuous phase through iterative optimization;

[0035] 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.

[0036] The present invention provides a real-time bridge displacement monitoring method and system based on UAV phase interference. The method collects discrete wrapped phase data on the bridge surface through an interference sensor arranged on the UAV, performs spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space; performs discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; compensates the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase; calculates the fractional gradient in space according to the reconstructed continuous phase, and calculates the local fractal index according to the discrete phase difference and multi-scale waveform factors; constructs an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient, and local fractal index, and obtains an 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. The beneficial effects generated include:

[0037] High-precision continuous phase recovery: It can effectively correct the periodic ambiguity in the wrapped phase data, ensure the accurate recovery of continuous phase data, eliminate the discontinuity and distortion problems caused by the wrapping effect, and provide a reliable basis for subsequent displacement inversion;

[0038] Multi-scale feature extraction and fusion: Use discrete wavelet transform to extract multi-scale waveform factors, effectively taking into account the information of local details and global trends, thereby enhancing the signal feature expression ability, improving the robustness of data processing and the accuracy of continuous phase reconstruction;

[0039] Fractional-order description and local detail characterization: Use fractional-order 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 results;

[0040] Adaptive anisotropic objective function construction: Construct an objective function with anisotropic adaptive regulation, and through iterative optimization, further refine the continuous phase data, ensure that local details and overall consistency are taken into account in global optimization, and significantly improve the optimization accuracy and stability.

[0041] 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 following specifically gives the specific implementation manners of this application. Brief Description of the Drawings

[0042] 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 following drawings 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:

[0043] 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;

[0044] 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 Description of the Invention

[0045] The embodiments of the present invention will be described below with reference to the accompanying 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 illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0046] It should be noted that the diagrams 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 diagrams, 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 actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0047] 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

[0048] A real-time monitoring method for bridge displacement based on UAV phase interference, as Figure 1 shown, includes:

[0049] Collect discrete wrapped phase data on the bridge surface through an interference sensor set on the UAV, perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space;

[0050] Perform discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors;

[0051] Compensate for the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain a reconstructed continuous phase;

[0052] 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;

[0053] Construct an objective function with anisotropic adaptive regulation according to the discrete phase difference, fractional gradient, and local fractal index, and obtain an optimized continuous phase through iterative optimization;

[0054] 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.

[0055] The present invention is further configured such that discrete wrapped phase data is denoted as , and spatial difference and period correction are performed on the wrapped phase data, and the calculation logic is: ; , and is direction and direction of the discrete phase difference, is the modulo operation. Specifically, the surface of the bridge is imaged and measured by an interference sensor provided on the unmanned aerial vehicle to obtain discrete wrapped phase data. The wrapped phase data refers to the phase information obtained by the interference sensor. Since the measuring instrument can only capture range of phase values, the data is represented in the form of "wrapping", that is, the measured phase value falls between 0 and or to . 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 period, its data is represented in the form of modulo . Spatial difference refers to performing difference calculation on the collected wrapped phase data spatially, including direction and 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 of the bridge surface; due to the periodic characteristic of the wrapped phase data, the directly calculated phase difference may cross the period boundary and show 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 [− , ), to ensure 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 of the bridge surface.

[0056] The present invention is further configured such that discrete wavelet transform is performed on the wrapped phase data to obtain multi-scale waveform factors, including:

[0057] The wrapped phase data is set as the initial approximation coefficient, and the initial approximation coefficient is decomposed through a recursive formula to obtain the Approximation coefficients and detail coefficients; The present invention is further configured such that the recursive calculation logic of the approximation coefficients 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 coefficients is: , is the -level detail coefficient, is the high-pass filter coefficient; Specifically, the wrapped phase data is regarded as the initial approximation coefficient , is the sampling point in the two-dimensional discrete space. The initial approximation coefficient is decomposed by a recursive formula for multi-scale decomposition, so as to extract the low-frequency trend information and high-frequency local detail information of the signal at different scales; The current-scale approximation coefficient is convolved and downsampled by a low-pass filter to obtain the -level approximation coefficient; The current-scale approximation coefficient is convolved and downsampled by a high-pass filter to obtain the -level detail coefficient; In the recursive formula, downsampling (scale transformation) is implemented in the form of and , so that the decomposition result of each level gradually decreases 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 the local details, thereby providing rich and multi-level input information for subsequent continuous phase recovery and displacement inversion;

[0058] Calculate the local detail metric according to the detail coefficients; 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; 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 realize the smooth aggregation of the detail information at each scale, obtaining a unified local detail metric ; The detail response index is a parameter for regulating the "response sensitivity" of the exponential function in the logarithmic sum exponential structure, and its value range is [1, 10]. The multi-scale sensitivity index is for the The power regulation parameter of the level detail coefficient is used to adjust the contribution weight of the detail information at each scale during 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 and provide a richer and more comprehensive feature input for continuous phase reconstruction.

[0059] Calculate the multi-scale waveform factor according to the approximation coefficient 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 coefficient representing the global low-frequency trend are obtained respectively. By fusing the two, the multi-scale waveform factor is constructed; the global trend scale factor is used to adjust the proportion of the global low-frequency information in the final waveform factor, and its value range is [0.1, 10]. By fusing the local detail metric and the global approximation information, the multi-scale waveform factor can simultaneously capture fine local changes and the overall trend, make up for the limitations of single-scale information, and ensure that the information at different levels of the data is effectively utilized.

[0060] The present invention is further set such that the integer compensation field is denoted as , and its initial value is 0, and the reconstructed continuous phase is .

[0061] The present invention is further set such that the calculation logic of the fractional gradient in space is: ; ; and are the fractional gradients in the direction and the direction, is the fractional-order weight. Specifically, through fractional-order differentiation, the change of the continuous phase data in the discrete space is described by a non-integer-order (fractional-order) derivative, and the weighted sum of the differences between adjacent points is calculated 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 the direction respectively, that is, the difference between the current point and the previous point; by introducing the fractional-order weight , the difference terms are weighted and accumulated with different step sizes to form a type of gradient estimation that can reflect long-range dependencies and non-local information; this fractional-step gradient can not only describe local fine changes but also capture the gradual change characteristics in the overall trend, which helps with subsequent continuous phase optimization and feature extraction; the fractional-order weight is a coefficient determined according to the fractional calculus theory for weighting the contributions of each level of difference, and the calculation logic is: ; where , , is the order of the fractional derivative, and its value range is between (0, 1], representing the degree of non-integer-order differentiation; is the Gamma function, which is used for normalization in this formula to make the fractional-order weight meet the theoretical requirements and ensure that when is an integer, the classical differential form is reproduced; the fractional-step gradient uses the non-integer-order weighted difference method, 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 description of local changes in complex dynamic environments; by assigning lower weights to the difference terms far from the current sampling point through the fractional-order weight, 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;

[0062] The calculation logic of the local fractal index is: ,

[0063] is the local fractal index, is the multi-scale waveform factor; specifically, using the discrete difference values in the direction and and in the direction of the local wrapped phase data to reflect the magnitude of local phase jumps, and combining the global and local comprehensive information expressed by the multi-scale waveform factor to construct the local fractal index ; the numerator part takes the logarithm after adding a constant 1 to the local phase difference (representing local changes or discontinuities), which plays a role in smoothing the changes and magnifying 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 The calculation can achieve the quantitative analysis of the local surface roughness of signals or structures, which helps to identify possible local anomalies or damaged areas.

[0064] 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 weights in the direction and is the penalty function, , is the control coefficient, is the fractal regulation coefficient, is the independent variable. Specifically, the objective function performs a weighted sum of the errors at each sampling point in the discrete space direction and direction. 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 , playing the role of smoothing the change and suppressing extreme errors. The logarithmic function provides a continuous and differentiable response, making the growth rate slower at larger errors and having the characteristic of anti-outlier interference; in the error term of each direction, the part is subtracted, and the fractal exponent that incorporates local details is fused with the multi-scale waveform factor . This term is used to compensate for the deviation caused by insufficient local information or noise interference in continuous phase reconstruction, thereby making the overall error description closer to the true physical state. and respectively assign different weights to the residual terms in the direction and 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 its value range is [0.1, 10]. The fractal regulation coefficient is used to adjust the proportion of the local fractal exponent and the multi-scale waveform factor in the influence of the error on the objective function, and its value range is [0.1, 5].

[0065] The present invention is further configured such that the integer field is updated by 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 updates, the continuous phase finally achieves global consistency and high-precision recovery. Among them, the objective function combines the local errors in each direction and suppresses outliers through a logarithmic penalty function; while in the gradient (partial derivative) term , the error combines the fractional gradient of the continuous phase, the direct wrapped phase difference, and the local regulation term fused through the local fractal index and the multi-scale waveform factor . The overall objective function reflects the reconstruction error of the continuous phase, and the gradient descent update adjusts constantly 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 using the gradient descent method, and utilizing the comprehensive description of multi-directional errors, non-linear penalties, and local regulation terms in the objective function, a fine correction of the ambiguity problem in the wrapped phase data is achieved. 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.

[0066] The present invention is further configured to calculate the optimized continuous phase according to the optimal integer field updated by the gradient descent method; ;

[0067] 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 Compress and smooth the continuous phase signal and its supplementary multi-scale information to limit large fluctuations; subsequently, use to further invert the signal after non-linear processing and adjust the amplitude of the output through the scale factor so as to achieve 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 meaning, and its value is determined based on the working wavelength of the sensor, the system calibration data, and the actual structure size. The phase mapping regulation coefficient is a parameter that controls the sensitivity of the continuous phase data in the non-linear transformation. Its role is to amplify or compress the influence of the continuous phase change on the output mapping, and its value range is [0.1, 10]. The waveform factor regulation coefficient is a parameter used to adjust the contribution ratio of the multi-scale waveform factor to the mapped result, and its value range is [0.1, 5]; the mapping logic non-linearly combines the continuous phase with multi-scale and local fractal information, and uses and two-stage transformations, and through parameterized control by the scale factor and regulation coefficients, an accurate, robust, and adaptive mapping from the continuous phase to the physical displacement is achieved. This method has beneficial effects such as smooth noise resistance, accurate dimension conversion, and balanced utilization of local and global information, thus providing an efficient and reliable technical solution for real-time monitoring of bridge displacement.

[0068] Please refer to Figure 2 for an exemplary real-time bridge displacement monitoring system based on UAV phase interference, which is used to implement the above-mentioned real-time bridge displacement monitoring method based on UAV phase interference, and includes:

[0069] Acquisition module: Collect discrete wrapped phase data on the bridge surface through an interference sensor set on the UAV, perform spatial difference and periodic correction on the wrapped phase data to obtain discrete phase differences in space;

[0070] Processing module: Obtain the multi-scale waveform factor by performing discrete wavelet transform on the wrapped phase data;

[0071] Compensation module: Compensate the phase jumps between discrete wrapped phase data through a constructed integer compensation field to obtain the reconstructed continuous phase;

[0072] 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 the multi-scale waveform factor;

[0073] 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;

[0074] 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.

[0075] It should be noted that the real-time bridge displacement monitoring system based on UAV phase interference provided in the above embodiments belongs to the same concept as the real-time bridge displacement monitoring method based on UAV phase interference provided in the above embodiments. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be elaborated here. In practical applications, the real-time bridge displacement monitoring system based on UAV phase interference provided in the above embodiments 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.

[0076] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. 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, all or part of the processes or functions described in the embodiments of the present application are generated. 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 computer-readable storage medium. 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 wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access 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.

[0077] It should be understood that the term "and / or" in this text 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 three situations: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this text generally represents an "or" relationship between the preceding and following associated objects, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context before and after.

[0078] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following items (pieces)" or its similar expressions refer to any combination of these items, including any combination of single items (pieces) or plural items (pieces). 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.

[0079] It should be understood that in various embodiments of this application, the magnitude of the sequence numbers of the above processes does not imply the order of execution. The execution order 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 this application.

[0080] 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 in this text can be implemented by electronic hardware, or 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. Professional technicians 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.

[0081] Those skilled in the art can clearly understand that for the convenience and conciseness 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 here.

[0082] In several embodiments provided in this application, it should be understood that the disclosed system 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, and there can be other division methods in actual implementation. 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 coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other forms.

[0083] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may 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.

[0084] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0085] If the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or 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 to enable 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 the present application. The foregoing 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.

[0086] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A real-time monitoring method for bridge displacement based on drone phase interference, characterized in that Including: 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; 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 based on the reconstructed continuous phase, and calculate the local fractal index based on the discrete phase difference and multi-scale waveform factors; 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; 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; Denote the integer compensation field as , and its initial value is 0. The reconstructed continuous phase is ; The calculation logic of the spatial fractional gradient is as follows: ; ; and is the fractional gradient in the direction and is the fractional order weight; The calculation logic of the local fractal index is as follows: , is the local fractal index, is the multi-scale waveform factor; 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 direction and direction, ; Optimal integer field updated according to the gradient descent method Calculated optimized continuous phase ; The mapping logic of the physical displacement value is as follows: , is the scale factor, is the phase mapping regulation coefficient, is the waveform factor regulation coefficient.

2. The real-time bridge displacement monitoring method based on drone phase interference according to claim 1, characterized in that Denote the discrete wrapped phase data as , perform spatial difference and period correction on the wrapped phase data, and the calculation logic is as follows: ; , and are the discrete phase differences in the direction and the is the modulo operation.

3. The real-time bridge displacement monitoring method based on drone phase interference according to claim 1, wherein Performing discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors, including: Set the wrapped 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; Calculating the local detail metric according to the detail coefficients; Calculating the multi-scale waveform factor according to the approximation coefficients and the local detail metric.

4. The real-time bridge displacement monitoring method based on UAV phase interference according to claim 3, characterized in that The recursive calculation logic of the approximation coefficient is as follows: , is the approximation coefficient of the -th 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 as follows: , is the detail coefficient of the th level, and is the high-pass filter coefficient; The calculation logic of the local detail metric is as follows: , 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 as follows: , is the multi-scale waveform factor, is the global trend scale factor.

5. The real-time bridge displacement monitoring method based on UAV phase interference according to claim 4, characterized in that The construction logic of the objective function is as follows: , is the objective function of the integer compensation field, and are the anisotropic weights in the direction and the direction, , is the penalty function, is the control coefficient, is the independent variable.

6. A real-time bridge displacement monitoring system based on UAV phase interference, which is used to implement the real-time bridge displacement monitoring method based on UAV phase interference according to any one of claims 1-5, characterized in that, Including: Acquisition module: Collect discrete wrapped phase data on the bridge surface through an 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: Perform discrete wavelet transform on the wrapped phase data to obtain multi-scale waveform factors; Compensation module: Compensate for the 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.

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