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

Abstract

The invention discloses a bridge deformation monitoring method based on RTK differential positioning and IMU fusion and a medium, which fully considers the requirements for displacement, angle and other data in the bridge deformation monitoring process, realizes multi-angle and multi-azimuth monitoring by using GNSS and IMU, and improves the accuracy of bridge monitoring. The RTK positioning data and the IMU measurement data are fused through the extended Kalman filtering algorithm, the IMU error can be compensated, the advantages of the two kinds of data can be fully mined, and data complementation and optimization are achieved. Under the condition that satellite signals are shielded or interfered, IMU data can assist in maintaining the stability of a monitoring system, and interruption of positioning information is avoided; when the IMU data fluctuates for a short time, the high-precision positioning result of the RTK can correct the fusion result to ensure that the output bridge deformation monitoring data is always accurate and continuous, so that the robustness and reliability of the whole monitoring system in a complex environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bridge deformation monitoring, and in particular to a bridge deformation monitoring method based on RTK differential positioning and IMU fusion and a medium. BACKGROUND

[0002] Currently, GNSS (Global Navigation Satellite System) can achieve centimeter-level real-time positioning accuracy in precise point positioning. RTK (Real-time kinematic) is a high-precision positioning technology based on carrier phase differential technology. It supports millimeter-level positioning accuracy through the cooperative work of the reference station and the rover station.

[0003] However, GNSS positioning is more sensitive to horizontal deformation, and the monitoring accuracy of elevation and angle change is low. At the same time, the load deformation of the bridge generally occurs in the elevation direction, which results in the change of the angle of the bridge deck and the vibration of the bridge deck, such as bridge vortex vibration. Due to the influence of the natural environment, the left and right shaking of the cable-supported bridge cannot be avoided, and the monitoring of the horizontal displacement change of the bridge is also very important.

[0004] 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. Since INS (Inertial Navigation System) calculates position and attitude by integrating acceleration and angular velocity, but the integration process will accumulate errors, especially zero bias errors. Zero bias error refers to the error that the output is not zero when the sensor is stationary. The zero bias of the gyroscope will cause the angle calculation to drift over time, and the zero bias of the accelerometer will cause the velocity and position to drift. Under long-time work, it is easy to cause large deviation in angle and acceleration measurement. SUMMARY

[0005] The bridge deformation monitoring method based on RTK differential positioning and IMU fusion provided by the present application realizes accurate monitoring of various data such as deformation deviation and angle change in the three-dimensional coordinate scene of the bridge through the RTK differential positioning and IMU fusion algorithm; the present application can solve the problem of large positioning error in multiple directions of the bridge deformation monitoring method in the prior art.

[0006] To achieve the above purpose, the present application adopts the following technical solutions: A bridge deformation monitoring method based on RTK differential positioning and IMU fusion, comprising the following steps, Step A, a mobile station is arranged on the bridge deck to be measured, the mobile station is composed of a GNSS receiver, an IMU inertial navigation module and a signal receiving module, and is placed according to the bridge connection, the bridge deck 1 / 4 and 3 / 4 and the bridge middle position arrangement principle. A base station is arranged on a known accurate coordinate point on a flat ground near the bridge, the base station is composed of a GNSS receiver and a signal transmitting module.

[0007] Step B, the GNSS receiver of the base station receives satellite signals in real time, the base station knows its own accurate coordinates, compares the theoretical value of the satellite signals with the actual observed value, calculates the error (such as ionospheric delay, clock difference, etc.), and encodes the error information into an RTCM format and sends it to the mobile station arranged on the bridge deck.

[0008] Step C, the mobile station synchronously receives the same satellite signals as the base station, analyzes the correction information sent by the base station, and corrects the observed value of the mobile station. The double difference observation values of the base station and the mobile station are used, and then the accurate positioning of the current position of the mobile station is output, and the cloud server is uploaded for storage.

[0009] Step D, the mobile station processes the original data of the IMU inertial navigation module, and calculates the average values of the gyro and accelerometer axis data respectively. Since in the static state, the ideal output of the gyro should be 0, and the ideal output of the accelerometer should be the component of the gravitational acceleration in each axis, therefore, the difference between these average values and the ideal values is the zero offset value.

[0010] Step E, considering that the zero offset error of the MPU6050 will change with time and environmental factors, an extended Kalman filter is used to establish an error model to describe the evolution process of the zero offset error.

[0011] Step F, the three-dimensional coordinates and speed information are extracted from the calibrated accurate positioning information output by the mobile station GNSS receiver in step C, the zero offset error of the MPU6050 is taken as a state variable, the measurement value output by the MPU6050 and the motion state parameter calculated by the GNSS are taken as observation values, and state equation and observation equation are established. The extended Kalman filter algorithm is used to fuse and process these data. Through iterative calculation, the estimated value of the zero offset error is constantly updated, and the output data of the MPU6050 is compensated in real time, so that the accurate state quantity is output.

[0012] Step G, the three-dimensional coordinates and the bridge deck angle information of the mobile station on the bridge are compared in the cloud, and the multi-direction deformation data of the bridge is calculated in real time.

[0013] From the above technical solution, it can be known that the bridge deformation monitoring method based on RTK differential positioning and IMU fusion has the following advantages: The present technology develops a unique data fusion algorithm, which corrects the IMU data using GNSS data, quickly and accurately obtains accurate tilt information, and reduces the need for frequent calibration due to excessive errors compared to traditional algorithms. In addition, compared to the way of relying solely on IMU for inertial navigation, the present application is particularly advantageous in long-term monitoring.

[0014] The present application fully considers the demand for various data such as displacement and angle in the process of bridge monitoring, and realizes multi-angle and multi-direction monitoring by using GNSS and IMU, thereby improving the accuracy of bridge monitoring. Compared with the traditional single GNSS positioning technology, the monitoring accuracy in the height direction is significantly improved, and the small deformation of the bridge in the vertical direction can be accurately captured, such as the up and down vibration of the bridge deck caused by vehicle load or wind load, vortex vibration and other phenomena, thereby providing more accurate data basis for bridge safety evaluation. At the same time, the GNSS data is used to correct the zero offset of the IMU data, which can improve the accuracy and utilization rate of the IMU data, thereby saving data resources.

[0015] By extending the Kalman filter algorithm to fuse RTK positioning data and IMU measurement data, the present application not only compensates for the errors of IMU, but also fully exploits the advantages of the two kinds of data to realize data complementation and optimization. In the case of satellite signal obstruction or interference, IMU data can assist in maintaining the stability of the monitoring system and avoid interruption of positioning information. When the IMU data fluctuates in the short term, the high-precision positioning results of RTK can correct the fusion results, ensuring that the output bridge deformation monitoring data is always accurate and continuous, and improving the robustness and reliability of the entire monitoring system in complex environments.

[0016] RTK differential positioning technology can effectively weaken the influence of ionospheric delay, tropospheric delay and satellite clock error on positioning accuracy through error correction of satellite signals by the reference station, thereby improving the adaptability of the system in different environments. At the same time, IMU is a self-contained sensor that does not rely on external satellite signals. In special environments where satellite signals are limited (such as urban canyons, near the entrance and exit of bridge tunnels, etc.), it can still provide dynamic monitoring data of the bridge, complementing the RTK data and enhancing the survival ability and monitoring continuity of the entire monitoring system in complex environments.

[0017] By uploading the monitoring data to the cloud server for storage and analysis, the system can timely issue an alarm when the monitoring data exceeds the preset threshold range, reminding the bridge management personnel to pay attention to the safety condition of the bridge and take corresponding measures, thereby effectively improving the intelligent level and management efficiency of bridge safety monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The base station is composed of the present application. Figure 2 It is composed of a mobile station of the present invention; Figure 3 This is the hardware layout diagram of the present invention; Figure 4 is a flow chart of the method of the present invention; Figure 5 This is the original data of the IMU in this embodiment when the zero bias error is not removed during long-term operation; Figure 6 This is a schematic diagram showing the difference between the real-time position received by GNSS and the position estimated by IMU (uncalibrated) in this embodiment; Figure 7 This is the IMU data after GNSS data zero bias calibration in this embodiment; Figure 8 FIG. 1 is a schematic diagram showing the difference between the real-time position received by GNSS and the position estimated by IMU (calibrated) in this embodiment. DETAILED DESCRIPTION

[0019] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments.

[0020] like Figure 1 As shown, the bridge deformation monitoring method based on RTK differential positioning and IMU fusion described in this embodiment includes the following steps: In Step A, a mobile station is deployed on the bridge deck to be tested. The mobile station consists of a GNSS receiver, an IMU module, and a signal receiving module. The mobile station is placed at bridge joints, one-quarter and three-quarters of the bridge deck, and in the middle of the bridge. A base station is deployed 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.

[0021] In step B, the GNSS receiver at the base station receives satellite signals in real time. The base station knows its own precise coordinates, compares the theoretical value of the satellite signal with the actual observed value, calculates the error (such as ionospheric delay, clock error, etc.), and encodes the error information in RTCM format and sends it to the mobile station deployed on the bridge deck.

[0022] In step C, the rover receives the same satellite signals as the base station, interprets the correction information sent by the base station, and corrects its own observations. Using the double-difference observations from the base and rover, it outputs a precise position of the rover's current location and uploads it to the cloud server for storage.

[0023] Step D, the mobile station processes the raw data collected by the IMU inertial navigation module, and calculates the average value of each axis data of the gyroscope and accelerometer respectively. Since in the static state, the ideal output of the gyroscope should be 0, and the ideal output of the accelerometer should be the component of the gravitational acceleration in each axis, the difference between these average values and the ideal values is the zero offset value.

[0024] Step E, considering that the zero offset 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 process of the zero offset error.

[0025] Step F, from the calibrated precise positioning information output by the GNSS receiver of the mobile station in step C, the three-dimensional coordinates and speed information are extracted, the zero offset error of MPU6050 is taken as the state variable, and the measurement value output by MPU6050 and the motion state parameter calculated by GNSS are taken as the observation value, and the state equation and observation equation are established. The extended Kalman filter algorithm is used to fuse and process these data. Through iterative calculation, the estimated value of the zero offset error is constantly updated, and the output data of MPU6050 is compensated in real time, so as to output accurate state quantity.

[0026] Step G, compare the three-dimensional coordinates and bridge deck angle information of the mobile station on the bridge in the cloud, and calculate the multi-direction deformation data of the bridge in real time.

[0027] The following is a specific description: Step S100, arrange mobile stations at preset points on the bridge deck, and arrange base stations on the ground plane beside the bridge to be monitored, and the accurate coordinate information of the position of the base station needs to be measured.

[0028] Specifically, mobile stations are arranged at the bridge connection, 1 / 4 and 3 / 4 of the bridge deck, and the middle section of the bridge, and the mobile stations form a group as shown in Figure 2 Base stations are arranged on the ground plane beside the bridge to be monitored, and the accurate coordinate information of the position of the base station is measured, and the base stations form a group as shown in Figure 1 , and the overall layout is as shown in Figure 3 .

[0029] Step S200, the GNSS receiver at the base station receives the broadcast text in real time, compares the theoretical value and the actual observation value of the satellite signal, calculates the error, and encodes the error information into RTCM format and sends it to the mobile stations arranged on the bridge deck.

[0030] The step S200 specifically includes: Step S201, the GNSS receiver at the base station receives the broadcast text in real time, extracts relevant parameters from the broadcast text, including the Kepler orbit parameters of the satellite, etc.

[0031] Such as orbit root number: semi-major axis , eccentricity , right ascension of the ascending node , orbital inclination , perigee angular distance , mean anomaly And some parameters for correction, such as 、 、 、 、 、 、 wait.

[0032] Step S202: Calculate planning time :

[0033] in, is the satellite ephemeris reference time.

[0034] Step S203: Calculate the mean anomaly angle :

[0035] in, , is the Earth's gravitational constant, is the correction value of the mean angular velocity, is the mean anomaly at the reference time.

[0036] Step S204: Use equation (2) to solve the anomaly angle :

[0037] in, is the orbital eccentricity, take As the initial value, it is modified by iteration until it satisfies .

[0038] Step S205: Calculate the true anomaly angle using formula (3): :

[0039] Step S206: Use equation (4) to calculate the true anomaly angle , calculate the angular distance of the ascending node at the time of signal transmission :

[0040] in, is the angular distance of perigee.

[0041] Step S207: Calculate The ascending node argument perturbation correction term, The satellite radial perturbation correction term, The satellite orbit inclination perturbation correction term:

[0042]

[0043]

[0044] wherein, is the ascending node argument cosine harmonic correction term amplitude, is the ascending node argument sine harmonic correction term amplitude, is the satellite to geocenter distance cosine harmonic correction term amplitude, is the satellite to geocenter distance sine harmonic correction term amplitude, is the orbit inclination cosine harmonic correction term amplitude, is the orbit inclination sine harmonic correction term amplitude.

[0045] Step S208, using formula (1), (3), (6), (7), (8), calculate the corrected ascending node argument distance , the orbit radial and the orbit inclination :

[0046]

[0047]

[0048] wherein, is the satellite orbit semi-major axis, is the orbit inclination at the reference time, is the orbit inclination rate of change.

[0049] Step S209, using formula (1) to calculate the longitude of the ascending node at the launch time :

[0050] wherein, is the ascending node right ascension at the reference time, is the rate of change of the ascending node longitude with respect to time, is the Earth rotation speed.

[0051] Step S210, using formula (9), (10), calculate the coordinates of the satellite in the ascending node orbit rectangular coordinate system:

[0052]

[0053] Step S211, using formula (11), (12), (13), (14), calculate the spatial position coordinates of the satellite in the earth-fixed coordinate system 、 、 :

[0054]

[0055]

[0056] Step S212, after the satellite signal is received by the ground receiver, the phase difference between the two is obtained by phase comparison with the locally generated carrier signal, the distance from the satellite to the receiver is calculated, and the carrier phase observation value is calculated , a virtual carrier phase is first set and the actual carrier phase actually received :

[0057] wherein, is the time when the signal is received by the receiver, is the time when the satellite signal is transmitted.

[0058] Step S213, the receiving frequency of the receiver when receiving the satellite signal is consistent with the satellite transmitting frequency, both are , and is expressed as:

[0059] Step S214, substituting formula (19) into formula (18), the following can be obtained:

[0060] Step S215, calculate the real geometric distance between the satellite and the receiver :

[0061] wherein, is the speed of light.

[0062] Step S216, calculate the carrier phase observation value finally considering the ionospheric delay, the tropospheric delay, and the satellite clock error :

[0063] wherein, is the receiver clock error, is the ionospheric delay, for tropospheric delay, for carrier phase integer ambiguity, for measurement noise.

[0064] Step S217, using Klobuchar model to eliminate satellite ionospheric delay, calculate geocentric angle :

[0065] wherein, is satellite elevation angle.

[0066] Step S218, using formula (23), calculate sub-ionospheric latitude of piercing point and do amplitude limiting:

[0067]

[0068] wherein, , is geographic latitude of the position where the receiver is located, is satellite azimuth angle.

[0069] Step S219, using formula (23), (24), calculate sub-ionospheric longitude of piercing point :

[0070] wherein, , is geographic longitude of the position where the receiver is located.

[0071] Step S220, using formula (24), (25), calculate geomagnetic latitude of piercing point :

[0072] Step S221, using formula (25), calculate local time of piercing point :

[0073]

[0074] wherein, is satellite time of piercing point.

[0075] Step S222, calculate satellite inclination factor :

[0076] Step S223, ionospheric delay is calculated by using formula (26), (28) :

[0077] wherein, , is Klobuchar model parameter.

[0078] Step S224, tropospheric error is eliminated by Saastamoinen model, first, standard atmosphere model is constructed, atmospheric pressure is solved atmospheric temperature atmospheric water vapor pressure :

[0079]

[0080]

[0081] wherein, is altitude, is relative humidity.

[0082] Step S225, tropospheric delay is calculated by using formula (30), (31), (32) according to Saastamoinen model can be expressed as statics delay and wet delay :

[0083]

[0084]

[0085] wherein, is zenith angle, is satellite elevation angle.

[0086] Step S300, base station sends ionospheric error, tropospheric error and other correction data to mobile station through RTGM3.x protocol by transmitting module.

[0087] Step S301, mobile station receives RTCM 1004 message and RTCM 1005 / 1006 message to obtain base station coordinates and ionospheric error and other correction data, for eliminating receiver clock difference and satellite clock difference , double difference is carried out on two satellites , double difference carrier phase observation value is calculated :

[0088] wherein, is the ionospheric delay for the receiver to satellite , is the ionospheric delay for the receiver to satellite , is the instantaneous geometric distance from the satellite to the mobile station, is the instantaneous geometric distance from the satellite to the mobile station.

[0089] Step S302, the double-difference carrier phase observations are least squares solved to obtain the correction numbers of three-dimensional coordinates, and high-precision positioning coordinates of the current position are output.

[0090] Step S400, the mobile station acquires IMU data, including position, speed, attitude (quaternion or Euler angle), gyroscope zero offset , accelerometer zero offset , and performs inertial navigation solution.

[0091] Step S401, the corrected angular velocity is calculated:

[0092] wherein, is the zero offset error of the angular velocity, is the original angular velocity.

[0093] Step S402, the quaternion differential equation and formula (37) are used to update the attitude quaternion by the angular velocity:

[0094] Step S403, formula (37), (38) are used to discretize the quaternion differential equation by the Runge-Kutta method, and the updated attitude quaternion is calculated:

[0095] wherein, is the change time.

[0096] Step S404, the corrected acceleration is calculated:

[0097] wherein, is the zero offset error of the acceleration, is the original acceleration.

[0098] Step S405, the acceleration is converted from the carrier coordinate system to the navigation ENU coordinate system by using formula (40) to calculate the acceleration in the navigation coordinate system :

[0099] wherein, is the direction cosine matrix corresponding to the attitude quaternion, is the gravity vector in the navigation coordinate system.

[0100] Step S406, the acceleration is integrated to obtain the velocity by using formula (41) Relationship equation:

[0101] Step S407, the meridian curvature radius is calculated , the prime vertical curvature radius is calculated :

[0102]

[0103] wherein, is the Earth equatorial radius, is the Earth eccentricity, is the local longitude.

[0104] Step S408, the longitude , the latitude , the altitude rate of change are calculated by using formula (43), (44) , :

[0105] wherein, is the longitude direction velocity rate of change, is the latitude direction velocity rate of change, is the altitude direction velocity rate of change. Step S409, the updated longitude , the latitude , the altitude are calculated by using formula (45)

[0106]

[0107]

[0108] Step S500, using formula (37), establish extended Kalman filter real-time estimate zero bias state quantity, establish state vector :

[0109] wherein, is high-precision position information provided by GNSS, is speed information provided by GNSS.

[0110] Step S501, using formula (49), establish state vector State differential equation of:

[0111] wherein, is inertial navigation solution process in step S400, is process noise.

[0112] Step S502, using GNSS provided position and speed accurate data to establish observation equation :

[0113] wherein, is observation noise.

[0114] Step S600, using formula (37), (40), proceed zero bias state prediction and covariance prediction:

[0115]

[0116] wherein, is Jacobian matrix, is process noise covariance.

[0117] Step S601, using formula (53), calculate Kalman gain :

[0118] wherein, is observation matrix, is noise.

[0119] Step S602, using formula (51), (52), (53), (54), proceed state data correction and covariance update:

[0120]

[0121] Among them, the Kalman gain A portion of the observed residuals will be allocated to the zero bias , thereby dynamically correcting the zero bias. This allows accurate IMU data to be calculated, including position, velocity, and attitude (quaternion or Euler angle), thereby obtaining accurate bridge deck inclination data.

[0122] Step S700: Upload the processed accurate data to the cloud platform for storage. Compare it with the original data to obtain the change amount, thereby determining whether the three-dimensional displacement and angle change of the bridge exceed the threshold and making an accurate judgment on the bridge's safety. [B1][Juan Miao 2] Figure 5 It is the raw data of IMU without removing the bias error during long-term operation; Figure 6 This is a diagram showing the difference between the real-time position received by GNSS and the position estimated by IMU (uncalibrated); Figure 7 It is the IMU data after GNSS data zero bias calibration; Figure 8 This diagram illustrates the difference between the real-time position received by GNSS and the position estimated by the calibrated IMU. This embodiment of the present invention uploads monitoring data to a cloud server for storage and analysis. When the monitoring data exceeds a preset threshold, the system promptly issues an alarm, alerting bridge managers to the bridge's safety status and enabling them to take appropriate measures. This effectively improves the intelligence and management efficiency of bridge safety monitoring.

[0123] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the above method.

[0124] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0125] In another embodiment provided in the present application, a computer program product comprising instructions is also provided, which, when executed on a computer, enables the computer to execute any of the bridge deformation monitoring methods based on RTK differential positioning and IMU fusion in the above embodiments.

[0126] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above methods.

[0127] In the embodiments described above, all or some of the steps can be implemented by hardware, software, firmware or any combination thereof. When implemented by software, all or some of the steps can be implemented in the form of one or more computer programs or program elements. The computer programs reside (at least temporarily) in a memory of a computer during execution. The memory can be a RAM memory, a flash memory, a ROM memory, an EPROM memory, or any other suitable memory. The memory can be integral to or separate from the computer. The computer programs can be written in any suitable programming language, such as C, C++, Java, Visual Basic, etc. The computer programs can be written in assembly or machine language, if desired. The computer programs can be distributed over network coupled file servers, or can be distributed by any other suitable means.

[0128] It is to be understood that the terminology "first", "second", etc. used herein merely for the purpose of distinguishing one entity or action from another, and does not necessarily imply these entities or actions are mutually exclusive or are in a certain sequence. Furthermore, the terms "comprise", "comprising", "include", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise, include, or are including entities listed thereafter are not limited to those entities but can also include other entities not expressly listed or inherent to such processes, methods, articles, or apparatuses.

[0129] Each of the embodiments described in the specification adopt a relevant manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. In particular, for the system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiments.

[0130] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements 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 application.

Claims

1. A bridge deformation monitoring method based on RTK differential positioning and IMU fusion, characterized in that: The method comprises the following steps, Step S100, arranging a mobile station at a preset point on the bridge deck, the mobile station being composed of a GNSS receiver, an IMU inertial navigation module and a signal receiving module, arranging a base station on the ground beside the bridge to measure the accurate coordinate information of the position of the base station; Step S200, the GNSS receiver at the base station receives broadcast messages in real time, compares the theoretical value of the satellite signal with the actual observed value, calculates the error, encodes the error information into RTCM format and sends it to the mobile station arranged on the bridge deck; Step S300, the base station uses a transmitting module to send ionospheric error and tropospheric error correction data to the mobile station through RTCM 3.x protocol; Step S400, the mobile station acquires IMU data, including position, speed, attitude, gyroscope zero offset , accelerometer zero offset , and performs inertial navigation solution; Step S500, establishing an extended Kalman filter to estimate the zero bias state quantity in real time, and establishing a state vector; Step S600, performing zero bias state prediction and covariance prediction to obtain accurate bridge deck inclination data; Step S700, uploading the obtained accurate bridge deck inclination data to a cloud platform for storage, comparing with the original data to obtain the change, and identifying whether the three-dimensional displacement and angle change of the bridge exceed the threshold value to make an accurate judgment on the safety of the bridge. 2.The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 1, characterized in that: Step S100, arranging mobile stations at the bridge connection, 1 / 4 of the bridge deck, 3 / 4 of the bridge deck and the middle position of the bridge, arranging a base station on the ground beside the bridge to measure the accurate coordinate information of the position of the base station. 3.The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 2, characterized in that: Step S200 specifically comprises, Step S201, the GNSS receiver at the base station receives broadcast messages in real time, extracts relevant parameters from the broadcast messages, including satellite Kepler orbit parameters, orbit roots and some parameters for correction; Step S202, calculating the planning time : wherein, Tsp is the satellite ephemeris reference time; Step S203, calculating the mean anomaly : wherein , is the Earth gravitational constant, is the correction value of the mean angular velocity, is the reference time mean anomaly; Step S204, solving the mean anomaly by using formula (2) : wherein is the orbital eccentricity, taken as as an initial value, corrected by iteration until ; Step S205, calculating the true anomaly angle using formula (3) : Step S206, the true anomaly angle is obtained by using formula (4) , the ascending node angle distance of the signal transmission moment is calculated : wherein is the perigee argument; Step S207, calculating Satellite argument perturbation correction term, Satellite argument perturbation correction term, Satellite orbit inclination perturbation correction term: wherein, is an amplitude of the cosine harmonic correction term for the ascending node, is an amplitude of the sine harmonic correction term for the ascending node, is an amplitude of the cosine harmonic correction term for the satellite to geocenter range, is an amplitude of the sine harmonic correction term for the satellite to geocenter range, is an amplitude of the cosine harmonic correction term for the orbit inclination, is an amplitude of the sine harmonic correction term for the orbit inclination. Step S208, the corrected ascending node distance is calculated by using formula (1), (3), (6), (7), (8) , orbit eccentricity , and orbit inclination : wherein, is the semi-major axis of the satellite orbit, is the inclination of the orbit at the reference time, is the rate of change of the inclination of the orbit; Step S209, calculating the longitude of the ascending node of the time of emission using formula (1) : wherein, is the right ascension of the ascending node at the reference time, is the rate of change of the right ascension of the ascending node with respect to time, is the earth rotation speed; Step S210, calculating the coordinates of the satellite in the ascending node orbital rectangular coordinate system by using formulas (9) and (10): Step S211, calculate the spatial position coordinates of the satellite in the earth-fixed coordinate system by using formula (11), (12), (13), (14) 、 、 : Step S212, after the satellite signal is received by the ground receiver, the phase difference between the two is obtained by phase comparison with the locally generated carrier signal, the distance from the satellite to the receiver is calculated, and the carrier phase observation value is calculated At this time, a virtual carrier phase is first set And the actual carrier phase actually received : wherein, is the time at which the signal was received by the receiver, is the time at which the satellite signal was transmitted; Step S213, the receiving frequency of the receiver when receiving the satellite signal is consistent with the satellite transmitting frequency, both are , the satellite signal is received by the receiver at the receiving frequency of the satellite transmitting frequency, both are is represented as: Step S214, substituting formula (19) into formula (18) to obtain: Step S215, calculating the real geometric distance between the satellite and the receiver : wherein c is the speed of light; Step S216, calculate the carrier phase observation value finally considering ionospheric delay, tropospheric delay, satellite clock error : wherein, is a receiver clock bias, is an ionospheric delay, is a tropospheric delay, is a carrier phase integer ambiguity, is a measurement noise; Step S217, eliminate satellite ionospheric delay using Klobuchar model, calculate geocentric angle : wherein, is the satellite elevation angle; Step S218, the sub-ionospheric latitude of the puncture point is calculated by using formula (23) and is limited: wherein, , is the geographic latitude of the location at which the receiver is located, is the satellite azimuth angle; Step S219, the sub-ionospheric longitude of the piercing point is calculated by using formula (23), (24) : wherein, , the geographic longitude of the location at which the receiver is situated; Step S220, calculate the piercing point geomagnetic latitude using formula (24), (25) : Step S221, using formula (25), calculates the local time at the puncture point : wherein for the puncture point satellite time; Step S222, calculating satellite tilt factor : Step S223, calculate ionospheric delay using formula (26), (28) : wherein , Klobuchar model parameters; Step S224, tropospheric error is eliminated by Saastamoinen model, first, standard atmosphere model is constructed, atmospheric pressure is solved atmospheric temperature atmospheric water vapor pressure : wherein, is altitude, is relative humidity; Step S225, tropospheric delay is computed according to the Saastamoinen model, using equations (30), (31), (32) denoted as static delay and wet delay : wherein is the zenith angle, is the satellite elevation angle.

4. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 3, characterized in that: Step S300 specifically comprises, Step S301, the mobile station receives RTCM 1004 message and RTCM 1005 / 1006 message to obtain base station coordinates and ionospheric error correction data for eliminating receiver clock difference and satellite clock difference , double difference is performed on two satellites and to calculate double difference carrier phase observation value : wherein is the ionospheric delay for the receiver to satellite link, is the ionospheric delay for the receiver to satellite link, is the instantaneous geometric distance from the satellite to the mobile station, is the instantaneous geometric distance from the satellite to the mobile station. Step S302, performing least squares solution on the double-difference carrier phase observation value to obtain the correction number of each coordinate in three dimensions, and outputting the high-precision positioning coordinates of the current position.

5. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 4, characterized in that: Step S400 specifically comprises, Step S401, calculate correction angular velocity : wherein is the zero offset error of the angular velocity, is the raw angular velocity; Step S402, update the attitude quaternion by the angular velocity using the quaternion differential equation and equation (37) : Step S403, using formula (37), (38), the Runge-Kutta method is used to discretize the quaternion differential equation, and the updated attitude quaternion is calculated : wherein is the time of change; Step S404, calculating the correction acceleration : wherein, is the zero offset error of the acceleration, is the raw acceleration; Step S405, using formula (40), the acceleration is converted from the carrier coordinate system to the navigation ENU coordinate system, to calculate the acceleration in the navigation coordinate system : wherein, is a direction cosine matrix corresponding to the attitude quaternion, is a gravity vector in the navigation coordinate frame; Step S406, using formula (41), the acceleration is integrated to obtain the velocity Relationship equation: Step S407, calculating the meridian curvature radius , the prime vertical curvature radius : wherein, is the radius of the Earth's equator, is the eccentricity of the Earth, is the local longitude; Step S408, longitude is calculated by using formula (43), (44) , latitude , altitude , rate of change , , : wherein, is a rate of change of the longitudinal direction velocity, is a rate of change of the latitudinal direction velocity, is a rate of change of the altitude direction velocity; Step S409, using formula (45), calculate the updated longitude , latitude , altitude : 。 6. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 5, characterized in that: Step S500 specifically comprises, Using formula (37), the extended Kalman filter is established to estimate the zero bias state quantity in real time, and the state vector is established : wherein high precision position information provided for the GNSS, speed information provided for the GNSS; Step S501, using formula (49), the state vector is established state differential equation: wherein, is the inertial navigation solution process in step S400, is the process noise; Step S502, establish observation equation by using position and speed accurate data provided by GNSS : wherein is the observation noise.

7. The bridge deformation monitoring method based on RTK differential positioning and IMU fusion according to claim 6, characterized in that: Step S600 specifically comprises, Utilizing equations (37), (40), the zero bias state is performed Prediction and covariance Prediction: wherein, is the Jacobian matrix, is the process noise covariance; Step S601, calculate the Kalman gain using formula (53) : wherein is an observation matrix, is noise; Step S602, using formula (51), (52), (53), (54), state data correction and covariance update: Wherein, Kalman gain A portion of the observation residual will be allocated to the zero bias , so as to dynamically correct the zero bias; solve the accurate IMU data, including position, speed, attitude, so as to obtain the accurate bridge deck inclination data.

8. A computer readable storage medium storing a computer program, characterized in that: The computer program is executed by the processor to make the processor execute the steps of the method according to any one of claims 1 to 7.

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