Structural displacement monitoring method based on three-dimensional structure monitoring robot

Abstract

The invention discloses a structure displacement monitoring method based on a three-dimensional structure monitoring robot, and relates to the technical field of structure health monitoring, and the monitoring method comprises the following steps: S1, carrying out the system deployment in advance, selecting a target area, installing the three-dimensional structure monitoring robot, and completing the calibration; according to the invention, the posture sensing module integrated with the three-dimensional structure monitoring robot can capture the posture change of the robot in real time and accurately compensate the measurement error caused by the drift of the reference point, thereby fundamentally solving the problem of error accumulation caused by the instability of the fixed reference, and remarkably improving the long-term stability and reliability of the monitoring method; meanwhile, the electromagnetic wave phase distance measurement technology and the passive target intensity center detection technology are combined, integrated three-dimensional measurement of vertical settlement and lateral displacement is achieved, the multi-dimensional deformation state of the structure can be comprehensively captured, and the defect that a traditional monitoring method can only conduct measurement in the single direction is overcome.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology, and more specifically, to a method for monitoring structural displacement based on a three-dimensional structural monitoring robot. Background Technology

[0002] Large structures, such as bridges, tunnels, dams, and high-rise buildings, are directly related to the safety of people's lives and property and the stability of society and the economy during long-term operation and service. Due to the combined effects of multiple complex factors, including uneven foundation settlement, aging and fatigue of structural materials, periodic fluctuations in external loads, alternating temperature stresses, and the potential impact of geological disasters such as earthquakes and landslides, structures inevitably experience multidimensional displacement deformations, such as vertical settlement and horizontal lateral shifts. If these displacement deformations accumulate over a long period and exceed the structural design safety threshold, they will lead to a series of safety hazards such as structural cracking, loosening of joints, and decreased load-bearing capacity. In severe cases, they may even cause structural collapse, resulting in major safety accidents and economic losses. Therefore, accurate and continuous monitoring of structural displacements is crucial.

[0003] While existing structural displacement monitoring technologies have made some progress, they still face numerous limitations: traditional manual monitoring methods rely on on-site operation by professional technicians, which is not only labor-intensive and inefficient, but also significantly constrained by natural environmental factors such as weather, lighting, and visibility. This makes it impossible to achieve 24 / 7 continuous monitoring, and it is difficult to capture instantaneous deformation characteristics and long-term cumulative deformation patterns, resulting in high manpower and time costs for long-term monitoring. In automated monitoring technologies, hydrostatic levels require complex connecting pipelines, which are severely limited by installation space and terrain conditions. Pipeline maintenance is difficult, and additional measurement errors can easily occur due to liquid leaks and temperature changes, affecting monitoring accuracy. GPS monitoring technology is also limited in urban building complexes. Signal attenuation is significant in environments with obstructions such as tunnels and canyons, drastically reducing positioning accuracy and making it difficult to meet the measurement requirements of millimeter-level micro-displacements, thus failing to adapt to complex engineering scenarios. Existing fixed measurement robot solutions use fixed points as monitoring benchmarks, but in actual engineering, there are no absolutely stable benchmarks. Fixed benchmarks are susceptible to slight deformations or drifts due to environmental disturbances, leading to the accumulation and amplification of measurement errors, severely affecting the accuracy and reliability of long-term monitoring data. Furthermore, most existing monitoring systems can only measure displacement in a single direction. For lateral displacement perpendicular to the observation direction, they lack high-precision and high-sensitivity measurement methods, failing to comprehensively reflect the three-dimensional deformation state of the structure and making it difficult to meet the all-round safety monitoring needs of complex and large structures. Therefore, developing an automated monitoring method that can automatically calibrate benchmark drift errors, achieve high-precision three-dimensional displacement measurement, and is flexible in deployment and adaptable to complex environments has become an urgent technical challenge to be solved in this field.

[0004] There are currently no effective solutions to the problems in the relevant technologies. Summary of the Invention

[0005] To address the problems in related technologies, this invention proposes a structural displacement monitoring method based on a three-dimensional structural monitoring robot, in order to overcome the aforementioned technical problems existing in the current related technologies.

[0006] The technical solution of this invention is implemented as follows:

[0007] A structural displacement monitoring method based on a 3D structural monitoring robot, comprising a monitoring system built upon a 3D structural monitoring robot, a passive target, and a back-end data processing center, the monitoring method including the following steps:

[0008] Step S1: Deploy the system in advance, select the target area, install the 3D structure monitoring robot and complete the calibration, set up passive targets at the calibration points of the structure under test, start the background data processing center and complete network pairing and parameter initialization, and build a wireless self-organizing network.

[0009] Step S2: Electromagnetic wave signal interaction is performed. The three-dimensional structure monitoring robot emits electromagnetic wave signals of a specific frequency to the passive target at a preset period. After receiving the signal, the passive target reflects or couples to form a signal closed loop.

[0010] Step S3: Perform initial relative displacement and robot pose measurement, including the three-dimensional structure monitoring robot calculating the initial relative displacement based on the phase difference of electromagnetic wave signals, and collecting its own pitch angle, roll angle and yaw angle data through the pose perception module;

[0011] Step S4: Perform data fusion calibration, including fusing pose data using Kalman filtering or complementary filtering algorithms, compensating for reference drift error, and then optimizing high-precision settlement displacement data using compressed sensing technology.

[0012] Step S5: Perform lateral displacement measurement. The passive target detects the center shift of electromagnetic wave intensity through a micro-antenna array and calculates the lateral displacement data.

[0013] Step S6: Settlement displacement data and lateral displacement data are transmitted to the background data processing center via a wireless ad hoc network multi-hop relay.

[0014] Step S7: Data processing and early warning are performed. The background data processing center generates a three-dimensional deformation visualization result. An early warning is activated when the displacement or displacement change rate exceeds the threshold.

[0015] Furthermore, the three-dimensional structure monitoring robot includes: an electromagnetic wave emitting module, a first signal processing module, a pose perception module, a data calibration module, and a first wireless communication module; and the pose perception module includes an electronic gyroscope and a magnetometer.

[0016] Furthermore, the passive target includes: a micro antenna array, a second signal processing module, and a second wireless communication module.

[0017] Furthermore, the initial relative displacement includes: calculating the initial relative displacement data between the three-dimensional structure monitoring robot and each passive target, used to represent the vertical settlement displacement, expressed as:

[0018] ;

[0019] in, Let λ be the initial relative displacement and λ be the wavelength of the electromagnetic wave signal. This represents the phase difference between the transmitted and received signals.

[0020] Furthermore, the robot pose measurement includes: real-time acquisition of robot angular velocity data using a gyroscope, and obtaining the angle change through integration, expressed as:

[0021] ;

[0022] in, The angle value at time t, i.e., the pitch angle. Roll angle or yaw angle ; This is the initial angle value. for angular velocity at time t, For measuring time;

[0023] Among them, the magnetometer measures the components of the Earth's magnetic field along the x, y, and z axes. The robot's absolute azimuth angle is calculated and expressed as:

[0024]

[0025] in, Yaw angle These represent the components of the Earth's magnetic field along the y-axis and x-axis, respectively.

[0026] Furthermore, the data fusion calibration includes the following steps:

[0027] Step S401: The pose data acquired by the gyroscope and magnetometer are fused using a Kalman filter algorithm. The state prediction equation is expressed as:

[0028] ;

[0029] ;

[0030] The measurement update equation is expressed as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] in, Let be the prior state estimate at time k, and A be the state transition matrix. Let B be the posterior state estimate at time k-1, and let B be the control input matrix. This is the control input at time k-1; Let be the prior estimate covariance matrix at time k, and Q be the process noise covariance matrix; H is the Kalman gain, and H is the observation matrix. Let R be the observation value at time k, R be the observation noise covariance matrix, and I be the identity matrix. Let be the posterior estimated covariance matrix at time k.

[0035] Step S402, perform reference drift error compensation, including: adjusting the fused attitude angles Converted to ranging error compensation amount For the initial relative displacement Corrections are made to obtain the compensated displacement data. , is represented as:

[0036] ;

[0037] ;

[0038] in, The initial distance between the robot and the target. The pitch angle, This is the roll angle. Yaw angle The displacement data is after compensation.

[0039] Step S403: Optimize compressed sensing data by using compressed sensing technology. The calibration shift signal is sparse in the Fourier transform domain, and is processed through a random sampling matrix. Obtain discrete samples y, and then reconstruct the original signal using the L1 norm minimization algorithm. , is represented as:

[0040] ;

[0041] ;

[0042] Where y is the sampled data vector, Φ is the M×N random sampling matrix, and Ψ is the Fourier transform matrix. Here, n represents the reconstructed high-precision settlement displacement data vector, ε represents the measurement noise vector, and ε represents the noise tolerance threshold. It is an L1 norm. It is an L2 norm.

[0043] Furthermore, the lateral displacement measurement includes: calibrating the signal strength of the i-th antenna element in the antenna array. Where n is the number of antenna elements, and the coordinates of the antenna elements on the x-axis and y-axis are... Then the coordinates of the center of the signal strength Calculated using a weighted average, it is expressed as:

[0044] ;

[0045] Lateral displacement The intensity center is relative to the array center point The offset is expressed as:

[0046] ;

[0047] in, This represents the lateral displacement along the x-axis. This represents the lateral displacement along the y-axis. These are the coordinates of the center point of the antenna array.

[0048] The beneficial effects of this invention are:

[0049] This invention utilizes a pose perception module integrated into a 3D structural monitoring robot to capture real-time changes in the robot's posture and accurately compensate for measurement errors caused by reference point drift. This fundamentally solves the problem of error accumulation caused by the instability of a fixed reference point, significantly improving the long-term stability and reliability of the monitoring method. Simultaneously, by combining electromagnetic wave phase ranging technology with passive target intensity center detection technology, it achieves integrated 3D measurement of vertical settlement and lateral displacement, comprehensively capturing the multi-dimensional deformation state of the structure and overcoming the limitation of traditional monitoring methods that can only measure in a single direction. Furthermore, the introduction of compressed sensing technology optimizes and reconstructs the displacement data, effectively filtering out environmental noise and electromagnetic interference, and simplifying the data... While ensuring the transmission pressure, it also guarantees the accuracy and robustness of monitoring data; the passive target adopts a passive sensing design, requiring no external power supply or complex wiring, with a compact structure and convenient installation. Combined with the dynamic networking and multi-hop relay capabilities of the wireless self-organizing network, it significantly reduces the difficulty and cost of system deployment and subsequent maintenance, and can flexibly adapt to various complex engineering environments such as bridges, tunnels, and dams; the entire monitoring process is fully automated, completing the entire chain operation of signal transmission, data acquisition, processing, transmission, and early warning without human intervention. It can promptly capture structural deformation dynamics and quickly issue early warnings in case of anomalies, providing comprehensive, timely, and reliable technical support for structural safety operation and maintenance decisions. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating a structural displacement monitoring method based on a three-dimensional structural monitoring robot according to an embodiment of the present invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0053] According to an embodiment of the present invention, a method for monitoring structural displacement based on a three-dimensional structural monitoring robot is provided.

[0054] like Figure 1As shown, the structural displacement monitoring method based on a three-dimensional structural monitoring robot according to an embodiment of the present invention constructs a monitoring system based on a three-dimensional structural monitoring robot, a passive target, and a background data processing center. The monitoring method includes the following steps:

[0055] Step S1: Deploy the system in advance, select the target area, install the 3D structure monitoring robot and complete the calibration, set up passive targets at the calibration points of the structure under test, start the background data processing center and complete network pairing and parameter initialization, and build a wireless self-organizing network.

[0056] This technical solution involves pre-selecting a relatively stable area around the structure, such as a solid foundation or a dedicated reference pier, to install the 3D structure monitoring robot. The robot's installation posture is adjusted to ensure that the electromagnetic wave emission direction covers all passive target deployment areas, completing the robot's horizontal calibration and initial pose setting. Based on the structural monitoring requirements, passive targets are pasted or mechanically fixed at key measuring points of the structure under test, ensuring the targets are firmly installed, have smooth surfaces, and deform synchronously with the structure. The background data processing center is then activated to complete network pairing and communication testing with the 3D structure monitoring robot and passive targets. Basic data such as monitoring point location information, structural design parameters, and displacement safety thresholds are entered, and the measurement parameter matrix is ​​initialized.

[0057] Step S2: Electromagnetic wave signal interaction is performed. The three-dimensional structure monitoring robot emits electromagnetic wave signals of a specific frequency to the passive target at a preset period. After receiving the signal, the passive target reflects or couples to form a signal closed loop.

[0058] In this technical solution, the three-dimensional structure monitoring robot activates the electromagnetic wave transmission module according to a preset cycle to transmit electromagnetic wave signals of a specific frequency to all passive targets; after the micro-antenna array of the passive target receives the electromagnetic wave signals, some of the signals are reflected or coupled back to the three-dimensional structure monitoring robot, forming a signal closed loop.

[0059] Step S3: Perform initial relative displacement and robot pose measurement, including the three-dimensional structure monitoring robot calculating the initial relative displacement based on the phase difference of electromagnetic wave signals, and collecting its own pitch angle, roll angle and yaw angle data through the pose perception module;

[0060] The calculation of the initial relative displacement includes: the first signal processing module of the 3D structure monitoring robot receives the returned electromagnetic wave signal, extracts the signal phase change information, and, in conjunction with the signal wavelength parameters in the measurement parameter matrix, calculates the initial relative displacement data between the 3D structure monitoring robot and each passive target, which is used to represent the vertical settlement displacement, expressed as:

[0061] ;

[0062] in, Let λ be the initial relative displacement and λ be the wavelength of the electromagnetic wave signal. This represents the phase difference between the transmitted and received signals.

[0063] The process of collecting pitch, roll, and yaw angle data through the pose perception module includes the following steps:

[0064] The pose perception module operates continuously, and the gyroscope collects the robot's angular velocity data in real time. The angle change is obtained through integration and is expressed as:

[0065] ;

[0066] in, The angle value at time t, i.e., the pitch angle. Roll angle or yaw angle ; This is the initial angle value. for angular velocity at time t, For measuring time;

[0067] Among them, the magnetometer measures the components of the Earth's magnetic field along the x, y, and z axes. The robot's absolute azimuth angle is calculated and expressed as:

[0068]

[0069] in, Yaw angle These represent the components of the Earth's magnetic field along the y-axis and x-axis, respectively.

[0070] Step S4 involves data fusion calibration, including fusing pose data using Kalman filtering, compensating for reference drift errors, and then optimizing high-precision settlement displacement data using compressed sensing technology. This includes the following steps:

[0071] Step S401: The pose data acquired by the gyroscope and magnetometer are fused using a Kalman filter algorithm. The state prediction equation is expressed as:

[0072] ;

[0073] ;

[0074] The measurement update equation is expressed as follows:

[0075] ;

[0076] ;

[0077] ;

[0078] in, Let be the prior state estimate at time k, and A be the state transition matrix. Let B be the posterior state estimate at time k-1, and let B be the control input matrix. This is the control input at time k-1; Let be the prior estimate covariance matrix at time k, and Q be the process noise covariance matrix; H is the Kalman gain, and H is the observation matrix. Let R be the observation value at time k, R be the observation noise covariance matrix, and I be the identity matrix. Let be the posterior estimated covariance matrix at time k.

[0079] Step S402, perform reference drift error compensation, including: adjusting the fused attitude angles Converted to ranging error compensation amount For the initial relative displacement Corrections are made to obtain the compensated displacement data. , is represented as:

[0080] ;

[0081] ;

[0082] in, The initial distance between the robot and the target. The pitch angle, This is the roll angle. Yaw angle The displacement data is after compensation.

[0083] Step S403: Optimize compressed sensing data by using compressed sensing technology. The calibration shift signal is sparse in the Fourier transform domain, and is processed through a random sampling matrix. Obtain discrete samples y, and then reconstruct the original signal using the L1 norm minimization algorithm. , is represented as:

[0084] ;

[0085] ;

[0086] Where y is the sampled data vector, Φ is the M×N random sampling matrix, and Ψ is the Fourier transform matrix. Here, n represents the reconstructed high-precision settlement displacement data vector, ε represents the measurement noise vector, and ε represents the noise tolerance threshold. It is an L1 norm. It is an L2 norm.

[0087] Step S5: Perform lateral displacement measurement. The passive target detects the center shift of electromagnetic wave intensity through a micro-antenna array and calculates the lateral displacement data.

[0088] In this technical solution, when the structure under test undergoes lateral displacement, the passive target moves synchronously with the structure, causing the center position of the electromagnetic wave intensity received by the micro-antenna array to shift.

[0089] In this technical solution, the signal strength of the i-th antenna element in the antenna array is calibrated as follows: Where n is the number of antenna elements, and the coordinates of the antenna elements on the x-axis and y-axis are... Then the coordinates of the center of the signal strength Calculated using a weighted average, it is expressed as:

[0090] ;

[0091] Lateral displacement The intensity center is relative to the array center point The offset is expressed as:

[0092] ;

[0093] in, This represents the lateral displacement along the x-axis. This represents the lateral displacement along the y-axis. These are the coordinates of the center point of the antenna array.

[0094] Step S6: Settlement displacement data and lateral displacement data are transmitted to the background data processing center via a wireless ad hoc network multi-hop relay.

[0095] In this technical solution, the first wireless communication module of the three-dimensional structure monitoring robot transmits settlement displacement data. The second wireless communication module of the passive target uploads the lateral displacement data to the wireless ad hoc network. The data is uploaded to the network; network nodes transmit all monitoring data to the backend data processing center via multi-hop relay, and AES-128 encryption algorithm is used to ensure data security during transmission.

[0096] Step S7: Data processing and early warning are performed. The background data processing center generates a three-dimensional deformation visualization result. An early warning is activated when the displacement or displacement change rate exceeds the threshold.

[0097] In this technical solution, the background data processing center processes the received settlement displacement data. and lateral displacement data The analysis is performed, and the three-dimensional displacement vector D of the monitoring point is calculated, expressed as:

[0098] ;

[0099] The system generates a three-dimensional visualization of structural deformation by combining the location information of monitoring points, and stores the raw data and analysis results in the database.

[0100] In addition, an early warning is triggered when the displacement or rate of change of displacement exceeds a threshold, including: real-time comparison of monitoring data with a preset safety threshold. When satisfied or or or rate of displacement change Exceeding the rate threshold When the warning is triggered, the early warning module is immediately activated, sending warning information to management personnel through various means.

[0101] In summary, by utilizing the above-described technical solution of the present invention, the following effects can be achieved:

[0102] This invention utilizes a pose perception module integrated into a 3D structural monitoring robot to capture real-time changes in the robot's posture and accurately compensate for measurement errors caused by reference point drift. This fundamentally solves the problem of error accumulation caused by the instability of a fixed reference point, significantly improving the long-term stability and reliability of the monitoring method. Simultaneously, by combining electromagnetic wave phase ranging technology with passive target intensity center detection technology, it achieves integrated 3D measurement of vertical settlement and lateral displacement, comprehensively capturing the multi-dimensional deformation state of the structure and overcoming the limitation of traditional monitoring methods that can only measure in a single direction. Furthermore, the introduction of compressed sensing technology optimizes and reconstructs the displacement data, effectively filtering out environmental noise and electromagnetic interference, and simplifying the data... While ensuring the transmission pressure, it also guarantees the accuracy and robustness of monitoring data; the passive target adopts a passive sensing design, requiring no external power supply or complex wiring, with a compact structure and convenient installation. Combined with the dynamic networking and multi-hop relay capabilities of the wireless self-organizing network, it significantly reduces the difficulty and cost of system deployment and subsequent maintenance, and can flexibly adapt to various complex engineering environments such as bridges, tunnels, and dams; the entire monitoring process is fully automated, completing the entire chain operation of signal transmission, data acquisition, processing, transmission, and early warning without human intervention. It can promptly capture structural deformation dynamics and quickly issue early warnings in case of anomalies, providing comprehensive, timely, and reliable technical support for structural safety operation and maintenance decisions.

[0103] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Those skilled in the art, upon considering the disclosure in the specification and embodiments, will readily conceive of other embodiments of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0104] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for monitoring structural displacement based on a three-dimensional structural monitoring robot, characterized in that, A monitoring system is constructed based on a 3D structure monitoring robot, a passive target, and a back-end data processing center. The monitoring method includes the following steps: Step S1: Deploy the system in advance, select the target area, install the 3D structure monitoring robot and complete the calibration, set up passive targets at the calibration points of the structure under test, start the background data processing center and complete network pairing and parameter initialization, and build a wireless self-organizing network. Step S2: Electromagnetic wave signal interaction is performed. The three-dimensional structure monitoring robot emits electromagnetic wave signals of a specific frequency to the passive target at a preset period. After receiving the signal, the passive target reflects or couples to form a signal closed loop. Step S3: Perform initial relative displacement and robot pose measurement, including the three-dimensional structure monitoring robot calculating the initial relative displacement based on the phase difference of electromagnetic wave signals, and collecting its own pitch angle, roll angle and yaw angle data through the pose perception module; Step S4: Perform data fusion calibration, including fusing pose data using Kalman filtering or complementary filtering algorithms, compensating for reference drift error, and then optimizing high-precision settlement displacement data using compressed sensing technology. Step S5: Perform lateral displacement measurement. The passive target detects the center shift of electromagnetic wave intensity through a micro-antenna array and calculates the lateral displacement data. Step S6: Settlement displacement data and lateral displacement data are transmitted to the background data processing center via a wireless ad hoc network multi-hop relay. Step S7: Data processing and early warning are performed. The background data processing center generates a three-dimensional deformation visualization result. An early warning is activated when the displacement or displacement change rate exceeds the threshold.

2. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 1, characterized in that, The three-dimensional structure monitoring robot includes: an electromagnetic wave emitting module, a first signal processing module, a pose perception module, a data calibration module, and a first wireless communication module; and the pose perception module includes an electronic gyroscope and a magnetometer.

3. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 2, characterized in that, The passive target includes: a micro antenna array, a second signal processing module, and a second wireless communication module.

4. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 3, characterized in that, The initial relative displacement includes: calculating the initial relative displacement data between the three-dimensional structure monitoring robot and each passive target, used to represent the vertical settlement displacement, expressed as: ； in, Let λ be the initial relative displacement and λ be the wavelength of the electromagnetic wave signal. This represents the phase difference between the transmitted and received signals.

5. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 4, characterized in that, The robot pose measurement includes: real-time acquisition of robot angular velocity data by a gyroscope, and obtaining the angle change through integration, expressed as: ； in, The angle value at time t, i.e., the pitch angle. Roll angle or yaw angle ; This is the initial angle value. for angular velocity at time t, For measuring time; Among them, the magnetometer measures the components of the Earth's magnetic field along the x, y, and z axes. The robot's absolute azimuth angle is calculated and expressed as: ； in, Yaw angle These represent the components of the Earth's magnetic field along the y-axis and x-axis, respectively.

6. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 1, characterized in that, The data fusion calibration includes the following steps: Step S401: The pose data acquired by the gyroscope and magnetometer are fused using a Kalman filter algorithm. The state prediction equation is expressed as: ； ； The measurement update equation is expressed as follows: ； ； ； in, Let be the prior state estimate at time k, and A be the state transition matrix. Let B be the posterior state estimate at time k-1, and let B be the control input matrix. This is the control input at time k-1; Let be the prior estimate covariance matrix at time k, and Q be the process noise covariance matrix; H is the Kalman gain, and H is the observation matrix. Let R be the observation value at time k, R be the observation noise covariance matrix, and I be the identity matrix. Let be the posterior estimated covariance matrix at time k; Step S402, perform reference drift error compensation, including: adjusting the fused attitude angles Converted to ranging error compensation amount For the initial relative displacement Corrections are made to obtain the compensated displacement data. , is represented as: ； ； in, The initial distance between the robot and the target. The pitch angle, This is the roll angle. Yaw angle The displacement data after compensation; Step S403: Optimize compressed sensing data by using compressed sensing technology. The calibration shift signal is sparse in the Fourier transform domain, and is processed through a random sampling matrix. Obtain discrete samples y, and then reconstruct the original signal using the L1 norm minimization algorithm. , is represented as: ； ； Where y is the sampled data vector, Φ is the M×N random sampling matrix, and Ψ is the Fourier transform matrix. Here, n represents the reconstructed high-precision settlement displacement data vector, ε represents the measurement noise vector, and ε represents the noise tolerance threshold. It is an L1 norm. It is an L2 norm.

7. The structural displacement monitoring method based on a three-dimensional structural monitoring robot according to claim 6, characterized in that, The lateral displacement measurement includes: calibrating the signal strength of the i-th antenna element in the antenna array. Where n is the number of antenna elements, and the coordinates of the antenna elements on the x-axis and y-axis are... Then the coordinates of the center of the signal strength Calculated using a weighted average, it is expressed as: ； Lateral displacement The intensity center is relative to the array center point The offset is expressed as: ； in, This represents the lateral displacement along the x-axis. This represents the lateral displacement along the y-axis. These are the coordinates of the center point of the antenna array.

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