Bridge deformation monitoring method based on RTK differential positioning and IMU fusion and medium

Abstract

The application discloses a bridge deformation monitoring method based on RTK differential positioning and IMU fusion and a medium, fully considers the requirements for various data such as displacement and angle in the bridge deformation monitoring process, realizes multi-angle and multi-direction monitoring by using GNSS and IMU, and improves the accuracy of bridge monitoring. The application fuses and processes RTK positioning data and IMU measurement data by using an extended Kalman filtering algorithm, can compensate for the error of the IMU, can fully exploit the advantages of the two kinds of data, and realizes the complementation and optimization of the data. In the case that satellite signals are blocked or interfered, the IMU data can assist in maintaining the stability of the monitoring system, and the interruption of positioning information is avoided. When the IMU data appears short-term fluctuation, the high-precision positioning result of the RTK can correct the fusion result, ensures that the output bridge deformation monitoring data is always accurate and continuous, and thus the robustness and reliability of the whole monitoring system in a complex environment are improved.

Description

Technical Field

[0001] This invention relates to the field of bridge deformation monitoring technology, specifically to a bridge deformation monitoring method and medium based on RTK differential positioning and IMU fusion. Background Technology

[0002] Currently, GNSS (Global Navigation Satellite System) can achieve centimeter-level real-time positioning accuracy for precise single-point positioning. RTK (Real-time kinematic) is a high-precision positioning technology based on carrier phase differential technology. Through the coordinated operation of a base station and a rover, it can support positioning accuracy up to millimeter-level.

[0003] However, GNSS positioning is more sensitive to horizontal deformation and has lower accuracy in monitoring elevation and angle changes. Meanwhile, the load deformation of bridges generally occurs primarily in the elevation direction, resulting in changes in the bridge deck's angle and vertical vibrations, such as vortex-induced vibration. Due to the influence of the natural environment, lateral swaying of cable-stayed bridges is unavoidable, making the monitoring of horizontal displacement changes crucial.

[0004] An IMU (Inertial Measurement Unit) is a sensor device used to measure the angular velocity and linear acceleration of an object, and is widely used in navigation, attitude control, motion tracking, and other fields. Because an INS (Inertial Navigation System) calculates position and attitude by integrating acceleration and angular velocity, the integration process accumulates errors, especially zero-bias error. Zero-bias error refers to the error where the output is not zero when the sensor is stationary. Zero bias in a gyroscope causes angle calculations to drift over time, while zero bias in an accelerometer causes drift in velocity and position. Over long periods of operation, this can easily lead to significant deviations in angle and acceleration measurements. Summary of the Invention

[0005] This invention proposes a bridge deformation monitoring method based on RTK differential positioning and IMU fusion. The method achieves accurate monitoring of various data such as deformation offset and angle change in a three-dimensional coordinate scene of the bridge through the RTK differential positioning and IMU fusion algorithm. This invention can solve the problem of large positioning errors in multiple directions in existing bridge deformation monitoring methods.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A bridge deformation monitoring method based on RTK differential positioning and IMU fusion includes the following steps:

[0008] Step A: Deploy a mobile station on the bridge deck to be tested. The mobile station consists of a GNSS receiver, an IMU inertial navigation module, and a signal receiving module, and is placed according to the following principles: at bridge joints, at 1 / 4 and 3 / 4 of the bridge deck, and in the middle section of the bridge. Deploy a base station at a known precise coordinate point on flat ground near the bridge. The base station consists of a GNSS receiver and a signal transmitting module.

[0009] Step B: The GNSS receiver at the base station receives satellite signals in real time. The reference station knows its own precise coordinates, compares the theoretical value of the satellite signal with the actual observation value, calculates the error (such as ionospheric delay, clock error, etc.), and encodes the error information into RTCM format and sends it to the mobile station deployed on the bridge deck.

[0010] Step C: The rover station synchronously receives the same satellite signals as the base station, analyzes the correction information sent by the base station, and corrects its own observations. Using the double-difference observations from the base station and the rover station, the precise location of the rover station's current position is then output and uploaded to a cloud server for storage.

[0011] Step D: The rover station processes the raw data collected from the IMU inertial navigation module, calculating the average values ​​of the gyroscope and accelerometer data for each axis. Since the ideal output of the gyroscope should be 0 and the ideal output of the accelerometer should be the components of gravitational acceleration along each axis when stationary, the difference between these average values ​​and the ideal values ​​is the zero bias value.

[0012] Step E: Considering that the zero bias error of MPU6050 will change with time and environmental factors, an extended Kalman filter is used to establish an error model to describe the evolution of the zero bias error.

[0013] Step F involves extracting three-dimensional coordinates and velocity information from the calibrated precise positioning information output by the rover's GNSS receiver in Step C. The zero-bias error of the MPU6050 is used as a state variable, and the measured values ​​output by the MPU6050 and the motion state parameters calculated by GNSS are used as observation values ​​to establish state equations and observation equations. The extended Kalman filter algorithm is then used to fuse these data. Through iterative calculations, the estimated value of the zero-bias error is continuously updated, and the output data of the MPU6050 is compensated in real time, thereby outputting accurate state variables.

[0014] Step G involves comparing the three-dimensional coordinates of the mobile station on the bridge with the bridge deck angle information in the cloud, and calculating the multi-directional deformation data of the bridge in real time.

[0015] As can be seen from the above technical solution, the bridge deformation monitoring method based on RTK differential positioning and IMU fusion of the present invention has the following advantages:

[0016] This technology features a unique data fusion algorithm that uses GNSS data to correct IMU data, quickly and accurately obtaining precise tilt information. Compared to traditional algorithms, this reduces the need for frequent calibrations due to excessive errors. Furthermore, compared to methods that solely rely on IMUs for inertial navigation, this invention offers significant advantages in long-term monitoring.

[0017] This invention fully considers the data requirements for displacement, angle, and other parameters during bridge monitoring. It utilizes GNSS and IMU to achieve multi-angle and multi-directional monitoring, improving the accuracy of bridge monitoring. Compared to traditional single GNSS positioning technology, its monitoring accuracy in the elevation direction is significantly improved, enabling precise capture of minute vertical deformations of the bridge, such as vertical vibrations and vortex-induced vibrations caused by vehicle or wind loads, providing more accurate data for bridge safety assessment. Simultaneously, using GNSS data to perform zero-bias correction on IMU data improves IMU data accuracy and data utilization, thereby conserving data resources.

[0018] By fusing RTK positioning data and IMU measurement data using the extended Kalman filter algorithm, this invention not only compensates for IMU errors but also fully leverages the advantages of both types of data, achieving data complementarity and optimization. When satellite signals are blocked or interfered with, IMU data can help maintain the stability of the monitoring system and prevent interruptions in positioning information. Conversely, when short-term fluctuations occur in IMU data, the high-precision positioning results of RTK can correct the fusion results, ensuring that the output bridge deformation monitoring data remains accurate and continuous, thus improving the robustness and reliability of the entire monitoring system in complex environments.

[0019] RTK differential positioning technology, through error correction of satellite signals by a reference station, can effectively reduce the impact of error factors such as ionospheric delay, tropospheric delay, and satellite clock bias on positioning accuracy, thus improving the system's adaptability in different environments. Meanwhile, as an autonomous sensor, the IMU does not rely on external satellite signals. Even in special environments with limited satellite signals (such as urban canyons, near bridge and tunnel entrances / exits), it can still provide dynamic monitoring data of bridges, complementing RTK data and enhancing the overall monitoring system's survivability and monitoring continuity in complex environments.

[0020] By uploading monitoring data to a cloud server for storage and analysis, the system can issue timely alarms when the monitoring data exceeds the preset threshold range, reminding bridge managers to pay attention to the safety status of the bridge and take corresponding measures, thereby effectively improving the intelligence level and management efficiency of bridge safety monitoring. Attached Figure Description

[0021] Figure 1 This is the base station composition of the present invention;

[0022] Figure 2 This is a component of the mobile station of the present invention;

[0023] Figure 3 This is a hardware layout diagram of the present invention;

[0024] Figure 4 This is a flowchart of the method of the present invention;

[0025] Figure 5 The original data of the IMU in this embodiment was not removed during long-term operation due to zero bias error.

[0026] Figure 6 This is a schematic diagram illustrating the difference between the real-time position received by GNSS and the position estimate of the IMU (uncalibrated) in this embodiment.

[0027] Figure 7 This is the IMU data after GNSS data zero-bias calibration in this embodiment;

[0028] Figure 8 This is a schematic diagram illustrating the difference between the real-time location received by GNSS and the location estimate of the IMU (calibrated) in this embodiment. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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.

[0030] like Figure 1 As shown in the figure, the bridge deformation monitoring method based on RTK differential positioning and IMU fusion described in this embodiment includes the following steps:

[0031] Step A: Deploy a mobile station on the bridge deck to be tested. The mobile station consists of a GNSS receiver, an IMU inertial navigation module, and a signal receiving module, and is placed according to the following principles: at bridge joints, at 1 / 4 and 3 / 4 of the bridge deck, and in the middle section of the bridge. Deploy a base station at a known precise coordinate point on flat ground near the bridge. The base station consists of a GNSS receiver and a signal transmitting module.

[0032] Step B: The GNSS receiver at the base station receives satellite signals in real time. The reference station knows its own precise coordinates, compares the theoretical value of the satellite signal with the actual observation value, calculates the error (such as ionospheric delay, clock error, etc.), and encodes the error information into RTCM format and sends it to the mobile station deployed on the bridge deck.

[0033] Step C: The rover station synchronously receives the same satellite signals as the base station, analyzes the correction information sent by the base station, and corrects its own observations. Using the double-difference observations from the base station and the rover station, the precise location of the rover station's current position is then output and uploaded to a cloud server for storage.

[0034] Step D: The rover station processes the raw data collected from the IMU inertial navigation module, calculating the average values ​​of the gyroscope and accelerometer data for each axis. Since the ideal output of the gyroscope should be 0 and the ideal output of the accelerometer should be the components of gravitational acceleration along each axis when stationary, the difference between these average values ​​and the ideal values ​​is the zero bias value.

[0035] Step E: Considering that the zero bias error of MPU6050 will change with time and environmental factors, an extended Kalman filter is used to establish an error model to describe the evolution of the zero bias error.

[0036] Step F involves extracting three-dimensional coordinates and velocity information from the calibrated precise positioning information output by the rover's GNSS receiver in Step C. The zero-bias error of the MPU6050 is used as a state variable, and the measured values ​​output by the MPU6050 and the motion state parameters calculated by GNSS are used as observation values ​​to establish state equations and observation equations. The extended Kalman filter algorithm is then used to fuse these data. Through iterative calculations, the estimated value of the zero-bias error is continuously updated, and the output data of the MPU6050 is compensated in real time, thereby outputting accurate state variables.

[0037] Step G involves comparing the three-dimensional coordinates of the mobile station on the bridge with the bridge deck angle information in the cloud, and calculating the multi-directional deformation data of the bridge in real time.

[0038] The following is a detailed explanation:

[0039] Step S100: Deploy mobile stations at preset locations on the bridge deck and base stations on the open ground plane next to the monitored bridge. At the same time, it is necessary to obtain the precise coordinate information of the location of the base stations.

[0040] Specifically, mobile stations are deployed at bridge connections, at one-quarter and three-quarters of the bridge deck, and in the middle section of the bridge. The mobile stations consist of the following components: Figure 2 Base stations were deployed on an open ground plane next to the monitoring bridge to obtain precise coordinates of their locations. The base station configuration is as follows: Figure 1 The overall layout is as follows Figure 3 .

[0041] Step S200: The GNSS receiver at the base station receives the broadcast message in real time, compares the theoretical value of the satellite signal with the actual observation value, calculates the error, and encodes the error information into RTCM format and sends it to the mobile station deployed on the bridge.

[0042] Step S200 specifically includes:

[0043] Step S201: The GNSS receiver at the base station receives broadcast messages in real time and extracts relevant parameters from the broadcast messages, including the Kepler orbit parameters of the satellites.

[0044] For example, the number of orbital elements: major semi-axis eccentricity Right ascension of ascending node Track inclination Angular distance from perigee , and the near point angle And some parameters used for correction, such as , , , , , , wait.

[0045] Step S202: Calculate the planning time :

[0046]

[0047] in, This is the reference time for the satellite ephemeris.

[0048] Step S203: Calculate the plan anterior angle. :

[0049]

[0050] in, , It is the Earth's gravitational constant. This is the correction value for the average angular velocity. The reference angle is the mean aperitoneal angle.

[0051] Step S204: Use equation (2) to solve for the near-point angle. :

[0052]

[0053] in, Let be the orbital eccentricity, and take . As the initial value, iterative methods are used to correct it until the condition is met. .

[0054] Step S205: Calculate the true anterior angle using equation (3). :

[0055]

[0056] Step S206: Obtain the true anterior angle using equation (4) Calculate the angular distance of the ascending node at the moment of signal transmission. :

[0057]

[0058] in, This is the angular distance from the perigee.

[0059] Step S207, Calculation The correction term for the angle perturbation of the intersection point. Satellite radius perturbation correction item Satellite orbital inclination perturbation correction items:

[0060]

[0061]

[0062]

[0063] in, To correct the amplitude of the cosine harmonic term of the rising intersection angle, To adjust the amplitude of the sinusoidal harmonic correction term for the upward displacement angle, The amplitude of the cosine harmonic correction term for the distance from the satellite to the Earth's center. The amplitude of the sinusoidal harmonic correction term for the distance from the satellite to the Earth's center. The amplitude of the cosine harmonic correction term for the orbital inclination angle. The amplitude of the sinusoidal harmonic correction term for the orbital inclination angle.

[0064] Step S208: Using equations (1), (3), (6), (7), and (8), calculate the corrected ascending node angular distance. orbital radius and track inclination :

[0065]

[0066]

[0067]

[0068] in, For the long radius of the satellite orbit, For reference time, the orbital inclination angle This represents the rate of change of the orbital inclination angle.

[0069] Step S209: Calculate the longitude of the ascending node at the launch time using equation (1). :

[0070]

[0071] in, For reference time, the right ascension of the ascending node, The rate of change of the longitude of the ascending node with respect to time. This is the Earth's rotation speed.

[0072] Step S210: Using equations (9) and (10), calculate the coordinates of the satellite in the rectangular coordinate system of the ascending node orbit:

[0073]

[0074]

[0075] Step S211: Using equations (11), (12), (13), and (14), calculate the spatial position coordinates of the satellite in the Earth-fixed coordinate system. , , :

[0076]

[0077]

[0078]

[0079] Step S212: After receiving the satellite signal using a ground receiver, the phase difference between the signal and the locally generated carrier signal is compared to calculate the distance from the satellite to the receiver, and the carrier phase observation value is calculated. First, a virtual carrier phase is set. Phase with the actual received carrier :

[0080]

[0081] in, The time when the receiver receives the signal. This refers to the time when the satellite signal was transmitted.

[0082] Step S213: The receiver's receiving frequency when receiving satellite signals is consistent with the satellite's transmission frequency, which is... ,Will Represented as:

[0083]

[0084] Step S214: Substituting equation (19) into equation (18), we get:

[0085]

[0086] Step S215: Calculate the true geometric distance between the satellite and the receiver. :

[0087]

[0088] in, It is the speed of light.

[0089] Step S216: Calculate the final carrier phase observations considering ionospheric delay, tropospheric delay, and satellite clock bias. :

[0090]

[0091] in, For receiver clock bias, For ionospheric delay, For tropospheric delay, For carrier phase integer ambiguity, For measuring noise.

[0092] Step S217: Use the Klobuchar model to eliminate satellite ionospheric delay and calculate the geocentric angle. :

[0093]

[0094] in, This refers to the satellite's elevation angle.

[0095] Step S218: Using equation (23), calculate the sub-ionospheric latitude of the puncture point. And limit the amplitude:

[0096]

[0097]

[0098] in, , This refers to the geographical latitude of the receiver's location. This refers to the satellite azimuth angle.

[0099] Step S219: Using equations (23) and (24), calculate the sub-ionospheric longitude of the puncture point. :

[0100]

[0101] in, , This refers to the geographical longitude of the receiver's location.

[0102] Step S220: Calculate the geomagnetic latitude of the puncture point using equations (24) and (25). :

[0103]

[0104] Step S221: Calculate the local time of the puncture point using equation (25). :

[0105]

[0106]

[0107] in, When the puncture point is a satellite.

[0108] Step S222: Calculate the satellite tilt factor :

[0109]

[0110] Step S223: Calculate the ionospheric delay using equations (26) and (28). :

[0111]

[0112] in, , These are parameters for the Klobuchar model.

[0113] Step S224: Tropospheric errors are eliminated using the Saastamoinen model. First, a standard atmospheric model is constructed, and atmospheric pressure is solved. Atmospheric temperature Atmospheric water vapor pressure :

[0114]

[0115]

[0116]

[0117] in, Altitude This refers to relative humidity.

[0118] Step S225: Based on the Saastamoinen model, using equations (30), (31), and (32), the tropospheric delay is calculated. This can be expressed as static delay. With wet delay :

[0119]

[0120]

[0121]

[0122] in, Zenith angle, This refers to the satellite's elevation angle.

[0123] Step S300: The base station transmits correction data such as ionospheric error and tropospheric error to the mobile station via the RTCM3.x protocol through the transmission module.

[0124] Step S301: The mobile station receives RTCM 1004 and RTCM 1005 / 1006 messages to obtain base station coordinates and ionospheric error correction data, etc., in order to eliminate receiver clock bias. and satellite clock bias For two satellites Perform double difference calculation to obtain the double-difference carrier phase observations. :

[0125]

[0126] in, For receiver to satellite Ionospheric delay, For receiver to satellite Ionospheric delay, For satellite Instantaneous geometric distance from the mobile station For satellite Instantaneous geometric distance from the mobile station.

[0127] Step S302: Perform least squares calculation on the double-difference carrier phase observation values ​​to obtain the correction values ​​of each three-dimensional coordinate, and output the high-precision positioning coordinates of the current position.

[0128] Step S400: The rover station acquires IMU data, including position, velocity, attitude (quaternion or Euler angles), and gyroscope zero bias. accelerometer zero bias Inertial navigation calculations are performed.

[0129] Step S401: Calculate the corrected angular velocity :

[0130]

[0131] in, For the zero bias error of angular velocity, This represents the original angular velocity.

[0132] Step S402: Update the attitude quaternion using quaternion differential equations and formula (37) through angular velocity. :

[0133]

[0134] Step S403: Using equations (37) and (38), the Runge-Kutta method is used to discretize the quaternion differential equations and calculate the updated attitude quaternions. :

[0135]

[0136] in, For the time of change.

[0137] Step S404: Calculate the corrected acceleration :

[0138]

[0139] in, For the zero bias error of acceleration, This is the original acceleration.

[0140] Step S405: Using equation (40), transform the acceleration from the carrier coordinate system to the navigation north-south coordinate system, and calculate the acceleration in the navigation coordinate system. :

[0141]

[0142] in, Let be the direction cosine matrix corresponding to the attitude quaternion. This is the gravity vector in the navigation coordinate system.

[0143] Step S406: Using equation (41), integrate the acceleration to obtain the velocity. Relationship equation:

[0144]

[0145] Step S407: Calculate the radius of curvature of the meridian. radius of curvature of the y-axis :

[0146]

[0147]

[0148] in, The radius of the Earth's equator. For the Earth's eccentricity, The longitude is the local longitude.

[0149] Step S408: Calculate the longitude using equations (43) and (44). ,latitude ,high rate of change , , :

[0150]

[0151] in, The rate of change of velocity along the longitude direction. The rate of change of velocity in the latitudinal direction. Let be the rate of change of velocity in the altitude direction. Step S409: Calculate the updated longitude using equation (45). ,latitude ,high :

[0152]

[0153]

[0154]

[0155] Step S500: Using equation (37), establish the extended Kalman filter to estimate the zero bias state variables in real time, and establish the state vector. :

[0156]

[0157] in, High-precision location information provided for GNSS Speed ​​information provided for GNSS.

[0158] Step S501: Using equation (49), establish the state vector. State differential equations:

[0159]

[0160] in, This refers to the inertial navigation solution process in step S400. This is process noise.

[0161] Step S502: Establish observation equations using accurate position and velocity data provided by GNSS. :

[0162]

[0163] in, To observe noise.

[0164] Step S600: Using equations (37) and (40), perform zero bias state. Prediction and covariance predict:

[0165]

[0166]

[0167] in, For Jacobian matrices, Let be the process noise covariance.

[0168] Step S601: Calculate the Kalman gain using equation (53). :

[0169]

[0170] in, For the observation matrix, It is noise.

[0171] Step S602: Use equations (51), (52), (53), and (54) to process the state data. Correction and covariance renew:

[0172]

[0173]

[0174] Among them, Kalman gain A portion of the observation residuals will be allocated to the zero bias. This allows for dynamic correction of the zero bias. Accurate IMU data, including position, velocity, and attitude (quaternions or Euler angles), is then calculated, leading to accurate bridge deck tilt data.

[0175] Step S700: Upload the processed, precise data to the cloud platform for storage. Simultaneously, compare the data with the original data to obtain the changes, thereby determining whether the bridge's three-dimensional displacement and angle changes exceed thresholds, and making an accurate judgment on bridge safety. [B1][Juanmiao2]

[0176] Figure 5 This is the raw data from an IMU that was not removed after long-term operation; Figure 6 This is a schematic diagram illustrating the difference between the real-time position received by GNSS and the position estimate from an IMU (uncalibrated). Figure 7It is IMU data after GNSS data zero-bias calibration; Figure 8 This is a schematic diagram illustrating the difference between the real-time location received by GNSS and the estimated location by an IMU (calibrated). As can be seen, this embodiment of the invention uploads monitoring data to a cloud server for storage and analysis. When the monitoring data exceeds a preset threshold range, the system can promptly issue an alarm, reminding bridge management personnel to pay attention to the bridge's safety status and take appropriate measures, thereby effectively improving the intelligence level and management efficiency of bridge safety monitoring.

[0177] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0178] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0179] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the bridge deformation monitoring methods based on RTK differential positioning and IMU fusion in the above embodiments.

[0180] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0181] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. 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 via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0182] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0183] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0184] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bridge deformation monitoring method based on RTK differential positioning and IMU fusion, characterized in that: Includes the following steps, Step S100: Deploy mobile stations at preset locations on the bridge deck. The mobile station consists of a GNSS receiver, an IMU inertial navigation module, and a signal receiving module. Deploy base stations on the open ground next to the monitored bridge and simultaneously measure the precise coordinates of the base station's location. Step S200: The GNSS receiver at the base station receives the broadcast message in real time, compares the theoretical value of the satellite signal with the actual observation value, calculates the error, and encodes the error information into RTCM format and sends it to the mobile station deployed on the bridge. Step S300: The base station uses the transmission module to send ionospheric error and tropospheric error correction data to the mobile station via the RTCM 3.x protocol; Step S400: The rover station acquires IMU data, including position, velocity, attitude, and gyroscope zero bias. accelerometer zero bias Inertial navigation calculations are performed. Step S500: Establish extended Kalman filter for real-time estimation of zero-bias state variables and establish state vectors; Step S600: Perform zero-bias state prediction and covariance prediction to obtain accurate bridge deck tilt data; Step S700: Upload the obtained accurate bridge deck tilt data to the cloud platform for storage, and compare it with the original data to obtain the amount of change, thereby identifying whether the bridge's three-dimensional displacement and angle changes exceed the threshold and making an accurate judgment on bridge safety. Step S300 specifically includes, Step S301: The mobile station receives RTCM 1004 and RTCM 1005 / 1006 messages to obtain base station coordinates and ionospheric error correction data, in order to eliminate receiver clock bias. and satellite clock bias For two satellites and Perform double difference calculation to obtain the double-difference carrier phase observations. : in, For receiver to satellite Ionospheric delay, For receiver to satellite Ionospheric delay, For satellite Instantaneous geometric distance from the mobile station For satellite Instantaneous geometric distance from the mobile station; Step S302: Perform least squares calculation on the double-difference carrier phase observation values ​​to obtain the correction values ​​of each three-dimensional coordinate, and output the high-precision positioning coordinates of the current position; Step S400 specifically includes, Step S401: Calculate the corrected angular velocity : in, For the zero bias error of angular velocity, The original angular velocity; Step S402: Update the attitude quaternion using quaternion differential equations and formula (37) through angular velocity. : Step S403: Using equations (37) and (38), the Runge-Kutta method is used to discretize the quaternion differential equations and calculate the updated attitude quaternions. : in, For the time of change; Step S404: Calculate the corrected acceleration : in, For the zero bias error of acceleration, This is the original acceleration; Step S405: Using equation (40), transform the acceleration from the carrier coordinate system to the navigation north-south coordinate system, and calculate the acceleration in the navigation coordinate system. : in, Let be the direction cosine matrix corresponding to the attitude quaternion. This is the gravity vector in the navigation coordinate system; Step S406: Using equation (41), integrate the acceleration to obtain the velocity. Relationship equation: Step S407: Calculate the radius of curvature of the meridian. radius of curvature of the y-axis : in, The radius of the Earth's equator. For the Earth's eccentricity, Local longitude; Step S408: Calculate the longitude using equations (43) and (44). ,latitude ,high rate of change , , : in, The rate of change of velocity along the longitude direction. The rate of change of velocity in the latitudinal direction. This represents the rate of change of velocity in the vertical direction. Step S409: Calculate the updated longitude using equation (45). ,latitude ,high : Step S500 specifically includes, Using equation (37), an extended Kalman filter is established to estimate the zero-bias state variables in real time, and a state vector is established. : in, High-precision location information provided for GNSS Speed ​​information provided for GNSS; Step S501: Using equation (49), establish the state vector. State differential equations: in, This refers to the inertial navigation solution process in step S400. This is process noise; Step S502: Establish observation equations using accurate position and velocity data provided by GNSS. : in, To observe noise.

2. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion as described in claim 1, characterized in that: In step S100, mobile stations are deployed sequentially at the bridge connection, 1 / 4 of the bridge deck, 3 / 4 of the bridge deck, and the middle section of the bridge. Base stations are deployed on the open ground next to the monitored bridge to obtain the precise coordinate information of the plane position of the base stations.

3. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 2, characterized in that: Step S200 specifically includes, Step S201: The GNSS receiver at the base station receives the broadcast message in real time and extracts relevant parameters from the broadcast message, including the satellite's Kepler orbit parameters and orbital elements, as well as some parameters used for correction. Step S202: Calculate the planning time : in, For satellite ephemeris reference time; Step S203: Calculate the plan anterior angle. : in, , It is the Earth's gravitational constant. This is the correction value for the average angular velocity. The angle of approach at the reference time; Step S204: Solve for the near-point angle using equation (2). : in, Let be the orbital eccentricity, and take . As the initial value, iterative methods are used to correct it until the condition is met. ; Step S205: Calculate the true anterior angle using equation (3). : Step S206: Obtain the true anterior angle using equation (4) Calculate the angular distance of the ascending node at the moment of signal transmission. : in, Angular distance from perigee; Step S207, Calculation The correction term for the angle perturbation of the ascending intersection point. Satellite radius perturbation correction item Satellite orbital inclination perturbation correction items: in, To correct the amplitude of the cosine harmonic term of the rising intersection angle, To adjust the amplitude of the sinusoidal harmonic correction term for the upward displacement angle, The amplitude of the cosine harmonic correction term for the distance from the satellite to the Earth's center. The amplitude of the sinusoidal harmonic correction term for the distance from the satellite to the Earth's center. The amplitude of the cosine harmonic correction term for the orbital inclination angle. The amplitude of the sinusoidal harmonic correction term for the orbital inclination angle; Step S208: Using equations (1), (3), (6), (7), and (8), calculate the corrected ascending node angular distance. orbital radius and track inclination : in, For the long radius of the satellite orbit, For reference time, the orbital inclination angle, This represents the rate of change of the orbital inclination angle; Step S209: Calculate the longitude of the ascending node at the launch time using equation (1). : in, For reference time, the right ascension of the ascending node, The rate of change of the longitude of the ascending node with respect to time. This refers to the Earth's rotation speed; Step S210: Using equations (9) and (10), calculate the coordinates of the satellite in the rectangular coordinate system of the ascending node orbit: Step S211: Using equations (11), (12), (13), and (14), calculate the spatial position coordinates of the satellite in the Earth-fixed coordinate system. , , : Step S212: After receiving the satellite signal using a ground receiver, the phase difference between the signal and the locally generated carrier signal is compared to calculate the distance from the satellite to the receiver, and the carrier phase observation value is calculated. First, a virtual carrier phase is set. Phase with the actual received carrier : in, The time when the receiver receives the signal. The time of satellite signal transmission; Step S213: The receiver's receiving frequency when receiving satellite signals is consistent with the satellite's transmission frequency, which is... ,Will Represented as: Step S214: Substituting equation (19) into equation (18), we get: Step S215: Calculate the true geometric distance between the satellite and the receiver. : in, The speed of light; Step S216: Calculate the final carrier phase observations considering ionospheric delay, tropospheric delay, and satellite clock bias. : in, For receiver clock bias, For ionospheric delay, For tropospheric delay, For carrier phase integer ambiguity, For measuring noise; Step S217: Use the Klobuchar model to eliminate satellite ionospheric delay and calculate the geocentric angle. : in, This refers to the satellite's elevation angle; Step S218: Using equation (23), calculate the sub-ionospheric latitude of the puncture point. And limit the amplitude: in, , This refers to the geographical latitude of the receiver's location. This refers to the satellite azimuth angle. Step S219: Using equations (23) and (24), calculate the sub-ionospheric longitude of the puncture point. : in, , The geographical longitude of the receiver's location; Step S220: Calculate the geomagnetic latitude of the puncture point using equations (24) and (25). : Step S221: Calculate the local time of the puncture point using equation (25). : in, When the puncture point is a satellite; Step S222: Calculate the satellite tilt factor : Step S223: Calculate the ionospheric delay using equations (26) and (28). : in, , For Klobuchar model parameters; Step S224: Tropospheric errors are eliminated using the Saastamoinen model. First, a standard atmospheric model is constructed, and atmospheric pressure is solved. Atmospheric temperature Atmospheric water vapor pressure : in, Altitude Relative humidity; Step S225: Based on the Saastamoinen model, using equations (30), (31), and (32), the tropospheric delay is calculated. Represented as static delay With wet delay : in, Zenith angle, This refers to the satellite's elevation angle.

4. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 1, characterized in that: Step S600 specifically includes, Using equations (37) and (40), the zero bias state is determined. Prediction and covariance predict: in, For Jacobian matrices, For process noise covariance; Step S601: Calculate the Kalman gain using equation (53). : in, For the observation matrix, For noise; Step S602: Use equations (51), (52), (53), and (54) to process the state data. Correction and covariance renew: Among them, Kalman gain A portion of the observation residuals will be allocated to the zero bias. This allows for dynamic correction of zero bias; accurate IMU data, including position, velocity, and attitude, is calculated to obtain accurate bridge deck tilt data.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 4.

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