Cofferdam positioning method for bridge construction based on Beidou positioning

Abstract

The invention relates to the technical field of high-precision positioning in water surface construction, and discloses a Beidou positioning-based cofferdam positioning method for bridge construction, which comprises the following steps: S1, initializing Beidou RTK, setting a base station, acquiring an initial position of a cofferdam, and realizing centimeter-level positioning by using differential correction; s2, fluid-solid coupling modeling is carried out, a dynamic model of the cofferdam under the action of water flow, tide and wind power is established based on a computational fluid mechanics method, and stress and deformation of the cofferdam are calculated in combination with finite element analysis; and S3, estimating an unscented Kalman filtering state, and calculating the current position, speed and error range of the cofferdam based on the historical position information of the cofferdam and fluid-solid coupling modeling prediction data. According to the method, the timeliness of error correction is ensured through an error loop feedback mechanism and a real-time alarm function, the robustness and stability of the system are improved, and the positioning precision and reliability of the cofferdam in the construction process are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-precision positioning in water surface construction, and particularly to a cofferdam positioning method for bridge construction based on Beidou positioning. Background Art

[0002] During the cofferdam construction process, an accurate positioning system is crucial for the safety and accuracy of the construction. To ensure the stable positioning of the cofferdam, traditional technologies rely on the RTK (Real-Time Kinematic) system to obtain the real-time position of the cofferdam and combine fluid-structure interaction modeling to correct the environmental impact. However, although these technologies have improved the positioning accuracy to a certain extent, they still face many problems, especially in the application in complex water areas and dynamic environments, such as unstable positioning accuracy and error accumulation.

[0003] The existing technology consists of an RTK system, Kalman filtering (especially the Extended Kalman Filter EKF), and H∞ filtering, etc. The RTK system can provide relatively high positioning accuracy, but it is vulnerable to factors such as electromagnetic interference and multipath effects, resulting in fluctuations or even jumps in the positioning data. Although Kalman filtering performs well in dealing with linear systems, for the non-linear environment of water surface construction, its filtering effect is limited, and in the face of sudden environmental changes, traditional Kalman filtering cannot effectively eliminate errors. Although H∞ filtering can provide a certain degree of robustness, its gain setting is fixed and cannot be adjusted in real time, and its ability to cope with environmental changes is poor. More importantly, most systems in the existing technology lack a real-time monitoring and error feedback mechanism. When the system fails or errors accumulate, it cannot be timely feedback and corrected, thus affecting the positioning accuracy. Therefore, the existing technology is difficult to ensure the high-precision and long-term stable operation of the cofferdam in complex environments. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a cofferdam positioning method for bridge construction based on Beidou positioning, which solves the problem that the traditional positioning system in the existing technology cannot effectively cope with dynamic changes and error accumulation in complex water surface environments.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A cofferdam positioning method for bridge construction based on Beidou positioning, comprising the following steps:

[0006] S1. Initialize Beidou RTK, set up a reference station, obtain the initial position of the cofferdam, and use differential correction to achieve centimeter-level positioning;

[0007] S2. Fluid-structure interaction modeling, establish a dynamic model of the cofferdam under the action of water flow, tides, and wind forces based on computational fluid dynamics methods, and combine finite element analysis to calculate the stress and deformation of the cofferdam;

[0008] S3. Unscented Kalman filter state estimation, calculating the current position, velocity and error range of the cofferdam based on the historical position information of the cofferdam and the prediction data of fluid-structure interaction modeling;

[0009] S4. H∞ optimization filtering error correction, optimizing the filtering gain matrix through the linear matrix inequality method to improve the anti-interference ability of the system;

[0010] S5. Error compensation and monitoring, when the error of the cofferdam exceeds the dynamically set threshold, triggering the compensation mechanism, adjusting the positioning data of the cofferdam, and sending data to the remote monitoring system.

[0011] Preferably, the initialization of the Beidou RTK system S1 includes:

[0012] Setting up a reference station and correcting the position through the differential data between the mobile receiver and the reference station;

[0013] Obtaining the longitude, latitude coordinates and elevation information of the cofferdam in real time through the RTK-GNSS system, and compensating for the error in combination with the differential data.

[0014] Preferably, the fluid-structure interaction modeling in S2 includes:

[0015] Adopting the computational fluid dynamics method to simulate the pressure distribution and velocity field of the water flow around the cofferdam;

[0016] Combining finite element analysis to calculate the deformation and stability of the cofferdam under the action of water flow, and determining the real-time displacement prediction value of the cofferdam;

[0017] During the fluid-structure interaction process, considering the elastic modulus, density of the cofferdam material and hydrodynamic influence.

[0018] Preferably, the unscented Kalman filter state estimation in S3 includes:

[0019] Setting the position and velocity state variables of the cofferdam, and predicting the position of the cofferdam at the next moment through the discrete state transition equation;

[0020] Calculating the error between the state prediction value and the observation value, and correcting the positioning data of the cofferdam through the filtering gain;

[0021] Adopting the unscented transformation method to optimize the non-linear state estimation.

[0022] Preferably, the H∞ optimization filtering error correction in S4 includes:

[0023] Adopting the linear matrix inequality to optimize the H∞ filtering gain, reducing the positioning error of the system in the high-dynamic environment, and improving the robustness of the system;

[0024] During the filtering error estimation process, environmental interference factors are considered, including water flow turbulence, wind force changes, and the micro-vibrations of the cofferdam itself;

[0025] Combined with the historical error data of the cofferdam, an error statistical model is established to achieve adaptive compensation for the long-term error accumulation effect.

[0026] Preferably, the error compensation and monitoring in S5 include:

[0027] Set the cofferdam error compensation trigger threshold, which is dynamically adjusted based on historical data statistics and real-time error estimation;

[0028] When the error exceeds the set value, the compensation mechanism is automatically triggered, and feedback is provided through the remote monitoring system;

[0029] Adopt a wireless transmission system to send the real-time displacement data of the cofferdam to the construction control center, and present the displacement state of the cofferdam through a visualization interface.

[0030] Preferably, the method is applicable to various cofferdam structures, including rigid cofferdams, flexible cofferdams, and composite material cofferdams, and can adjust the fluid-structure interaction model parameters according to the physical characteristics of different cofferdam types.

[0031] Preferably, the method can be applied to the precise positioning of other water structures, including floating platforms on water, temporary construction piers, and cofferdams for bridge pier foundation construction, and can be extended to the positioning management of offshore wind power platforms and ocean engineering equipment.

[0032] Preferably, the error compensation and monitoring in S5 further include environmental factor monitoring steps, including:

[0033] Obtain the water flow velocity, water depth, and tidal change conditions in the construction area through hydrological monitoring equipment, and establish a hydrodynamic database;

[0034] Obtain environmental data such as wind speed, air pressure, and temperature through meteorological monitoring equipment to provide environmental parameter support for subsequent cofferdam positioning correction;

[0035] Adopt an intelligent monitoring algorithm to analyze the impact of environmental data on the stability of the cofferdam and give early warnings of abnormal situations.

[0036] Preferably, the error compensation and monitoring in S5 also include data fusion analysis steps, including:

[0037] Adopt multi-sensor data fusion technology to fuse Beidou RTK positioning data, fluid-structure interaction prediction data, and UKF filtering data;

[0038] Use artificial intelligence algorithms to learn from historical error data and optimize the error compensation model.

[0039] The present invention provides a cofferdam positioning method for bridge construction based on Beidou positioning, with the following beneficial effects:

[0040] 1. The present invention adopts a real-time monitoring and error feedback mechanism, achieving a dynamic optimization effect on the cofferdam positioning accuracy. Compared with the existing technology that simply relies on static filtering and positioning systems, it solves the problem that they cannot adjust in real time and cope with instantaneous errors and sudden interferences.

[0041] 2. The present invention can adjust the filtering gain in real time through an adaptive PID controller, capable of coping with complex environmental changes. Compared with the existing technology with inflexible traditional gain settings, it solves the problem that it is difficult to maintain stable positioning accuracy in different construction environments.

[0042] 3. The present invention combines an error loop feedback mechanism, which can quickly respond and correct when the system error is too large. Compared with the existing technology lacking effective real-time error compensation means, it solves the problem that it cannot quickly adapt to and correct positioning errors, improving the robustness of the system.

[0043] 4. The present invention introduces a real-time feedback monitoring and alarm mechanism, ensuring the high-precision operation of the system. Compared with the existing technology without real-time error detection and alarm, it solves the risk of reduced positioning accuracy caused by error accumulation, ensuring the long-term stability of cofferdam positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to the attached Figure 1 , the embodiment of the present invention provides a cofferdam positioning method for bridge construction based on Beidou positioning, including the following steps:

[0047] S1. Initialize Beidou RTK, set up a reference station, obtain the initial position of the cofferdam, and achieve centimeter-level positioning by using differential correction;

[0048] In this embodiment, first, a Beidou RTK reference station is set up in the construction area. The site selection of the reference station needs to ensure a stable foundation to avoid systematic errors caused by the position drift of the reference station. The antenna of the reference station should be installed in an unobstructed area and maintain a good signal reception state with Beidou satellites to reduce measurement errors caused by multipath effects. In some embodiments, to further improve the stability of the reference station data, a multi-station layout method can be adopted to enhance the regional coverage ability through network RTK technology and improve the reliability of differential data.

[0049] Specifically, mobile receivers on the cofferdam are installed at multiple key points of the cofferdam, such as the four corners or the center position of the cofferdam, in order to obtain the global attitude information of the cofferdam. As an option, the mobile receiver should use a dual-frequency or multi-frequency GNSS receiver to improve the anti-interference ability and cooperate with an IMU (Inertial Measurement Unit) for attitude correction.

[0050] In a possible implementation manner, the Beidou RTK system adopts carrier-phase differential (RTK-GNSS) technology, and calculates the three-dimensional coordinates of the cofferdam through the relative measurement between the reference station and the mobile receiver. Generally, the basic equation for RTK solution is as follows:

[0051] λ(φ i -φ r )=ρ i -ρ r +Nλ+ε;

[0052] Where: λ is the carrier wavelength; φ i and φ r are the carrier phases received by the mobile receiver and the reference station respectively; ρ i and ρ r are the pseudorange measurement values from the mobile receiver and the reference station to the satellite respectively; N is the integer ambiguity, which needs to be solved by the ambiguity resolution method; ε is the measurement error, including error factors such as ionospheric delay and tropospheric delay.

[0053] In some embodiments, for the measurement errors that may occur during the RTK solution process, such as ionospheric delay and tropospheric delay, the present invention adopts a dual-frequency observation method to eliminate the errors. Specifically, linear combination observation values are used, that is, the ionosphere-free combined pseudorange is calculated using signals of different frequencies to reduce the influence of the ionospheric effect. The calculation formula is as follows:

[0054]

[0055] Where: f1 and f2 are the two carrier frequencies respectively; ρ1 and ρ2 are the pseudorange measurement values corresponding to the frequencies respectively.

[0056] In a possible approach, if there is strong electromagnetic interference in the construction area, such as high-voltage power lines, large-scale mechanical equipment, etc., the present invention can adopt multi-path suppression techniques, such as the Adaptive Filtering method, to attenuate the non-direct path propagation components in the received signal and improve the measurement accuracy.

[0057] In terms of data processing, the present invention adopts real-time differential data processing. The observation data of the reference station is sent to the remote server through the NTRIP protocol, and the mobile receiver obtains the correction data through the 4G / 5G network to achieve remote high-precision differential resolution. As an option, UHF radio can also be used for data transmission between the reference station and the mobile receiver to adapt to the construction environment without network coverage.

[0058] Generally, the initialization of the Beidou RTK system requires baseline solution, and the calculation formula for the baseline length is as follows:

[0059]

[0060] Where: X i , Y i , Z i are the coordinates of the mobile receiver; X r , Y r , Z r are the coordinates of the reference station; B is the baseline length from the reference station to the mobile receiver.

[0061] In some embodiments, to improve the stability of the baseline solution, the Kalman Filtering method can be adopted to smooth the baseline change rate and reduce data fluctuations.

[0062] After completing the RTK initialization, the present invention further conducts system error detection to determine whether the initial positioning error of the cofferdam meets the construction accuracy requirements. Generally, the error should be controlled within 2 cm. If the error exceeds the threshold, the present invention adopts a recursive correction method to improve the positioning accuracy by accumulating data through multiple observations.

[0063] In some embodiments, if the water depth in the area where the cofferdam is located is relatively deep and there is a fast-flowing water current, it may cause a large swing of the cofferdam. In the RTK initialization stage, the present invention combines the accelerometer and gyroscope data and optimizes the position information of the cofferdam through the Loose Coupling technology to improve the positioning accuracy.

[0064] As a possible improvement, in some application scenarios, the present invention can add an Underwater Acoustic Positioning System (USBL) to assist in calibrating the positioning of the cofferdam bottom. The USBL system calculates the spatial position of the cofferdam bottom through the propagation delay of underwater acoustic waves and fuses it with the Beidou RTK data to improve the overall positioning accuracy.

[0065] Through the above technical solutions, in the initialization stage of Beidou RTK, the present invention fully considers the dynamic environmental factors of the cofferdam, and combines methods such as carrier phase differential, high-precision baseline solution, multipath error suppression, dual-frequency error elimination, and loose coupling filtering to ensure that the initial positioning of the cofferdam reaches centimeter-level accuracy, and provides a high-precision reference benchmark for subsequent fluid-structure interaction modeling, filtering correction, and error compensation.

[0066] S2. Fluid-structure interaction modeling: Based on the computational fluid dynamics method, establish a dynamic model of the cofferdam under the action of water flow, tides, and wind, and combine finite element analysis to calculate the stress and deformation of the cofferdam.

[0067] In this embodiment, the fluid environment of the water area where the cofferdam is located is simulated by the computational fluid dynamics (CFD) method. The hydrodynamic calculation uses the Reynolds-averaged Navier-Stokes (RANS) equation to describe the pressure field and velocity field of the water flow on the cofferdam. Generally, the water flow motion satisfies the continuity equation:

[0068]

[0069] where u is the water flow velocity vector, and the velocity components in each direction are u, v, and w.

[0070] Specifically, the fluid force on the cofferdam is calculated by the RANS equation:

[0071]

[0072] where: ρ is the fluid density; p is the pressure; μ is the fluid viscosity; F is the external force, including influencing factors such as wind force and tides.

[0073] As an option, in the case of high water flow velocity or large force on the cofferdam, the large eddy simulation (LES) method can be used to improve the accuracy of hydrodynamic calculation. In some embodiments, the VOF (Volume of Fluid) method can be used to calculate the water surface fluctuation at the top of the cofferdam to enhance the authenticity of the fluid simulation.

[0074] In this embodiment, the structural modeling of the cofferdam uses finite element analysis (FEM). Whether the cofferdam is a rigid structure or an elastic structure, its stress response is described by the structural dynamics equation:

[0075]

[0076] Where: M is the mass matrix; C is the damping matrix; K is the stiffness matrix; u is the displacement vector; F is the external force, including water flow pressure and wind force.

[0077] In a possible implementation, the stiffness matrix K of the cofferdam is determined by material parameters and structural form and can be adjusted according to construction needs. In some embodiments, shell elements (ShellElements) can be used for modeling to improve the calculation efficiency.

[0078] Specifically, the fluid-structure interaction adopts an implicit two-way coupling method. The hydrodynamic calculation and the structural dynamics calculation are carried out alternately, and the pressure field and the deformation field are iterated with each other, and finally the dynamic displacement response of the cofferdam is obtained. Generally, the fluid-structure interaction solution follows the weak coupling (WeakCoupling) or strong coupling (StrongCoupling) method. In the strong coupling mode, the hydrodynamic force and the structural response need to be iteratively calculated at each time step to improve the solution accuracy. In some embodiments, if the stiffness of the cofferdam is large and the influence of the water flow on the deformation of the cofferdam is small, the weak coupling method can be used to improve the calculation efficiency.

[0079] In addition, in this embodiment, the result of the fluid-structure interaction calculation will be used for subsequent filtering processing to compensate for the error of the RTK data and improve the dynamic positioning accuracy of the cofferdam.

[0080] The present invention adopts fluid-structure interaction modeling combined with CFD and FEM, achieving the technical effect of accurately calculating the stress state of the cofferdam. Compared with the prior art solution that uses RTK alone for position monitoring, it solves the deficiency that it cannot consider the influence of hydrodynamic disturbance on the position of the cofferdam.

[0081] The present invention uses the two-way implicit fluid-structure interaction method to realize the real-time simulation of the influence of external factors such as water flow and tide on the position change of the cofferdam. Compared with the prior art solution based only on static mechanics modeling, it solves the problems that it cannot adapt to complex water environments and cannot dynamically correct the position of the cofferdam.

[0082] The present invention adopts the Reynolds-averaged Navier-Stokes equation combined with the VOF method, which can accurately describe the flow field change around the cofferdam. Compared with the prior art solution that uses only empirical formulas to calculate the water flow force, it solves the problems that it cannot accurately reflect the transient flow field and has a large prediction error.

[0083] S3. Unscented Kalman filter state estimation, calculating the current position, speed and error range of the cofferdam based on the historical position information of the cofferdam and the prediction data of the fluid-structure interaction modeling;

[0084] In this embodiment, the state variables of the cofferdam are position, speed and acceleration, which respectively constitute the state vector x k. Generally, the UKF uses the Unscented Transformation (UT) to capture the high-order statistical characteristics of the nonlinear system, avoiding the linearization error in the traditional Extended Kalman Filter (EKF).

[0085] Specifically, the state vector x of the cofferdam k is set as:

[0086]

[0087] where: u k represents the three-dimensional position coordinates [x, y, z] of the cofferdam; v k represents the velocity components [v x , v y , v z ; a k represents the acceleration components [a x , a y , a z .

[0088] As an option, the state transition equation adopts a discrete-time state model for predicting the position and velocity of the cofferdam at the next moment, expressed as follows:

[0089] x k+1 = f(x k , w k );

[0090] where: f(·) is the state transition function, determined by the dynamic equation; w k is the process noise, usually assumed to be zero-mean Gaussian noise.

[0091] Generally, the key steps of the Unscented Kalman Filter include generating Sigma Points, state propagation, measurement update, etc. Specifically, the selection of the sampling points is based on the mean and covariance matrix, and the calculation method is as follows:

[0092]

[0093] where: are the sampling points of the unscented transformation; is the current state estimate value; P k is the state covariance matrix; L is the dimension of the state variable; λ is the scaling parameter, usually related to the noise level.

[0094] In some embodiments, the state vector predicted by the UKF is fused with the RTK measurement data. The measurement equation can be expressed as:

[0095] y k = h(xk , v k );

[0096] Where: y k is the measurement value provided by RTK; h(·) is the measurement function, describing the non - linear relationship of sensor measurement; v k is the measurement noise.

[0097] As a possible implementation, in order to improve the stability of filtering, this embodiment adopts an adaptive noise covariance update strategy, that is, adjusting the covariance matrices Q k and R k of the process noise and the measurement noise in each filtering period, and its update rules are as follows:

[0098]

[0099] Where: α, β are smoothing factors, used to balance the update speed of noise estimation; and are the state value and the measurement value predicted by UKF respectively.

[0100] In some embodiments, the data of IMU (Inertial Measurement Unit) can be combined, and UKF is used to correct the acceleration and angular velocity to improve the attitude estimation accuracy of the cofferdam in a dynamic environment.

[0101] In addition, the results of UKF will be further adjusted in the subsequent H∞ optimization filtering step to enhance the anti - interference ability of the system.

[0102] The present invention adopts the Unscented Kalman Filter (UKF) combined with state estimation, achieving the technical effect of smoothing the position and velocity information of the cofferdam. Compared with the existing solutions that simply rely on RTK or fluid - structure interaction models, it solves the problems of large fluctuations in real - time positioning data and ineffective elimination of short - term errors.

[0103] The present invention uses the Unscented Transformation (UT) to optimize state propagation, realizing the accurate prediction of the movement trajectory of the cofferdam. Compared with the existing solutions where the traditional Kalman Filter (EKF) is greatly affected by linearization errors, it solves the problems of decreased estimation accuracy in a non - linear environment and inability to effectively adapt to complex hydrodynamic disturbances.

[0104] The present invention adopts an adaptive noise covariance adjustment strategy, which can optimize the process noise and measurement noise parameters in real time. Compared with the existing solutions with fixed noise covariance settings, it solves the problems that the filtering accuracy is greatly affected by environmental changes and lacks the ability of adaptive adjustment.

[0105] The present invention combines IMU data fusion to further optimize the dynamic attitude estimation of the cofferdam in a complex water environment. Compared with the prior art that only based on GNSS measurement and ignores the attitude change of the cofferdam, it solves the defect that it cannot accurately judge the attitude drift of the cofferdam and affects the overall positioning accuracy.

[0106] S4. H∞ optimization filter error correction, optimizing the filter gain matrix through the linear matrix inequality method to improve the anti-interference ability of the system;

[0107] In this embodiment, H∞ filtering is used to optimize the positioning error of the cofferdam in the worst case. Generally, the goal of H∞ filtering is to minimize the state estimation error under the most adverse disturbance conditions, making the filtering system more robust to measurement noise and environmental interference. Specifically, the present invention solves the H∞ optimal filter gain based on the linear matrix inequality (LMI).

[0108] The state equation is described as follows:

[0109] x k+1 = Ax k + Bu k + w k ;

[0110] The measurement equation is described as follows:

[0111] y k = Hx k + v k ;

[0112] Where: x k is the state vector of the cofferdam, including position, velocity, and acceleration; A is the state transition matrix, defining the system dynamics characteristics; B is the control matrix, describing the influence of the external environment on the cofferdam; w k is the process noise, describing external uncertainty factors; y k is the measurement vector, including the positioning results of RTK and UKF; H is the measurement matrix, defining the measurement process; v k is the measurement noise, including RTK positioning error, fluid-structure coupling calculation error, etc.

[0113] In some embodiments, the optimization objective of H∞ filtering is:

[0114]

[0115] That is, in the worst case, minimizing the upper bound of the estimation error.

[0116] As an option, this embodiment uses the linear matrix inequality (LMI) method to solve the optimal H∞ gain matrix K k , and its constraint conditions are as follows:

[0117] P > 0, (A - K k H)P(A - K k H) T + Q - P < 0;

[0118] Where: P is the filtering error covariance matrix; K k is the H∞ filtering gain matrix; Q is the state noise covariance matrix.

[0119] Generally, H∞ filtering needs to adjust the gain matrix in real time to adapt to different environmental interference intensities. In some embodiments, the present invention adopts an adaptive gain adjustment mechanism, and the calculation is as follows:

[0120]

[0121] Where: γ is an adjustment factor, which is dynamically adjusted according to the error growth rate; is the current filtering estimated value.

[0122] In a possible implementation manner, in order to avoid filtering gain oscillation, upper and lower limit thresholds are set for the gain adjustment range of H∞ filtering to ensure system stability.

[0123] In some embodiments, the estimated covariance matrix output by UKF can be combined with the state estimation of H∞ filtering for weighted fusion to further enhance the anti-interference ability.

[0124] In addition, the error correction result of H∞ filtering will be used in the next error compensation and real-time monitoring module to ensure high-precision cofferdam positioning during the construction process.

[0125] The present invention adopts H∞ optimal filtering combined with LMI solution, achieving the technical effect of improving the robustness of cofferdam positioning. Compared with the existing solution that only uses UKF for state estimation, it solves the problem of its poor adaptability to high-dynamic environments and inability to cope with extreme hydrodynamic changes.

[0126] The present invention uses an adaptive gain adjustment strategy to achieve error compensation under different environmental disturbance conditions. Compared with the existing solution with a fixed filtering gain setting, it solves the problem of decreased estimation accuracy when the noise changes violently, improving the self-adaptability of the filtering system.

[0127] The present invention adopts optimal H∞ gain solution combined with error covariance constraint, which can ensure stable positioning accuracy even in the most adverse situations. Compared with the existing Kalman filtering (KF) solution with degraded performance in non-Gaussian noise environments, it solves the defect of its inability to effectively suppress sudden interference and model errors.

[0128] The present invention combines the weighted fusion of UKF and H∞ filtering, making the final positioning data of the cofferdam smoother and the error compensation more accurate. Compared with the solutions of single filtering strategies in the prior art, it solves the problems of insufficient anti-interference ability and inability to maintain millimeter-level accuracy for a long time.

[0129] S5. Error compensation and monitoring: When the error of the cofferdam exceeds the dynamically set threshold, trigger the compensation mechanism, adjust the positioning data of the cofferdam, and send data to the remote monitoring system.

[0130] In this embodiment, the system monitoring module collects the positioning data from the RTK system, UKF output, H∞ filtering results, and other sensors in real time through data acquisition and anomaly monitoring. The data will be transmitted to the error feedback control module for analysis. Specifically, the error feedback mechanism monitors the difference between the output of the system and the ideal trajectory, and uses an adaptive control algorithm to adjust the filtering gain in real time.

[0131] As an option, the error feedback model is constructed based on the error loop, and the error loop feedback mechanism is as follows:

[0132]

[0133] Where: e k is the error vector, representing the difference between the current estimated value and the true value; is the currently estimated state vector (such as position, velocity, etc.); is the ideal or true state value, usually obtained through simulation or high-precision sensor data.

[0134] The core of the error feedback control mechanism is to optimize the final positioning result of the system by calculating the error and performing gain correction. Specifically, this embodiment uses a proportional-integral-differential (PID) control strategy to perform error correction. The error adjustment formula of the PID controller is as follows:

[0135]

[0136] Where: u k is the control input, representing the gain correction to the system; K p , K i , K d are the proportional, integral, and differential gains respectively, which are specifically adjusted according to system requirements.

[0137] Generally, the gains of the PID controller will be dynamically adjusted according to the motion state of the cofferdam. In some embodiments, the PID gains will be adaptively adjusted according to the dynamic changes of the environment (such as water flow velocity, wind speed, etc.) to ensure that the system can always be maintained within a stable control range.

[0138] In addition, this embodiment further includes a real-time feedback monitoring and alarm mechanism, which can issue an alarm when the positioning error is detected to exceed a predetermined threshold and initiate corresponding compensation or adjustment strategies. For example, when the error exceeds the set value, the system will adjust the filtering gain according to the feedback mechanism or switch to a high-precision measurement mode when necessary.

[0139] In a possible implementation, the monitoring system displays the position information of the cofferdam, the error estimation value, and the status of each sensor in real time through a graphical interface, providing reference for the operators to ensure the stable operation of the system.

[0140] The present invention adopts a real-time monitoring and error feedback mechanism, achieving a dynamic optimization effect on the positioning accuracy of the cofferdam. Compared with the prior art solutions that simply rely on static filtering and positioning systems, it solves the deficiency that they cannot adjust in real time and cope with instantaneous errors and sudden interferences.

[0141] The present invention can adjust the filtering gain in real time through an adaptive PID controller to cope with complex environmental changes. Compared with the prior art solutions with inflexible traditional gain settings, it solves the problem that it is difficult to maintain stable positioning accuracy in different construction environments.

[0142] The present invention combines an error loop feedback mechanism, which can respond quickly and correct when the system error is too large. Compared with the prior art solutions lacking effective real-time error compensation means, it solves the problem that they cannot quickly adapt to and correct positioning errors, improving the robustness of the system.

[0143] The present invention introduces a real-time feedback monitoring and alarm mechanism, ensuring the high-precision operation of the system. Compared with the prior art solutions without real-time error detection and alarm, it solves the risk of reduced positioning accuracy caused by error accumulation and ensures the long-term stability of the cofferdam positioning.

[0144] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for locating cofferdams for bridge construction based on Beidou positioning, characterized in that: The steps include: S1. Initialize BeiDou RTK, set up the base station, obtain the initial position of the cofferdam, and use differential correction to achieve centimeter-level positioning; S2. Fluid-solid coupling modeling: Based on computational fluid dynamics, a dynamic model of the cofferdam under the action of water flow, tide and wind force is established, and the force and deformation of the cofferdam are calculated in combination with finite element analysis; S3, unscented Kalman filter state estimation, based on the historical position information of the cofferdam and the prediction data of fluid-solid coupling modeling, calculates the current position, velocity and error range of the cofferdam; S4, H∞ optimization filter error correction, optimize the filter gain matrix through the linear matrix inequality method to improve the system's anti-interference ability; S5, Error compensation and monitoring, when the error of the cofferdam exceeds the dynamically set threshold, the compensation mechanism is triggered, the positioning data of the cofferdam is adjusted, and the data is sent to the remote monitoring system.

2. A method for locating cofferdams for bridge construction based on Beidou positioning according to claim 1, characterized in that: The BeiDou RTK system initialization S1 includes: Establish a base station and perform position correction using differential data between the mobile receiver and the base station; The RTK-GNSS system is used to obtain the latitude and longitude coordinates and elevation information of the cofferdam in real time, and error compensation is performed in combination with differential data.

3. The method for locating a cofferdam for bridge construction based on Beidou positioning according to claim 1, characterized in that: The fluid-structure interaction modeling in S2 includes: Computational fluid dynamics methods are used to simulate the pressure distribution and velocity field of water flow around the cofferdam; Combined with finite element analysis, the deformation and stability of the cofferdam under the action of water flow are calculated to determine the real-time displacement prediction value of the cofferdam; In the process of fluid-solid coupling, the elastic modulus, density and hydrodynamic effects of the cofferdam material are considered.

4. The method for locating a cofferdam for bridge construction based on Beidou positioning according to claim 1, characterized in that: The unscented Kalman filter state estimation in S3 includes: Set the position and velocity state variables of the cofferdam, and predict the position of the cofferdam at the next moment through the discrete state transfer equation; Calculate the error between the state prediction value and the observed value, and correct the cofferdam positioning data through the filter gain; The unscented transformation method is used to optimize the nonlinear state estimation.

5. The method for locating a cofferdam for bridge construction based on Beidou positioning according to claim 1, characterized in that: The H∞ optimization filtering error correction in S4 includes: The linear matrix inequality is used to optimize the H∞ filter gain to reduce the positioning error of the system in a high dynamic environment and improve the robustness of the system. In the process of filtering error estimation, environmental interference factors are considered, including water turbulence, wind changes, and micro-vibration of the cofferdam itself; Combined with the historical error data of the cofferdam, an error statistical model is established to achieve adaptive compensation for the long-term error accumulation effect.

6. The method for locating cofferdams for bridge construction based on Beidou positioning according to claim 1, characterized in that: The error compensation and monitoring in S5 include: Set the cofferdam error compensation trigger threshold, which is dynamically adjusted based on historical data statistics and real-time error estimation; When the error exceeds the set value, the compensation mechanism is automatically triggered and feedback is provided through the remote monitoring system; A wireless transmission system is used to send the real-time displacement data of the cofferdam to the construction control center, and the displacement status of the cofferdam is presented through a visual interface.

7. The method for locating cofferdams for bridge construction based on Beidou positioning according to claim 1, characterized in that: The method is applicable to a variety of cofferdam structures, including rigid cofferdams, flexible cofferdams and composite cofferdams, and can adjust the fluid-solid coupling model parameters according to the physical characteristics of different cofferdam types.

8. The method for locating cofferdams for bridge construction based on Beidou positioning according to claim 1, characterized in that: The method can be applied to the precise positioning of other water structures, including floating platforms, temporary construction docks, and pier foundation construction enclosure structures, and can be extended to the positioning management of offshore wind power platforms and marine engineering equipment.

9. The method for locating a cofferdam for bridge construction based on Beidou positioning according to claim 1, characterized in that: The error compensation and monitoring in S5 further includes an environmental factor monitoring step, including: Obtain water velocity, water depth, and tidal changes in the construction area through hydrological monitoring equipment, and establish a hydrodynamic database; Obtain environmental data on wind speed, air pressure, and temperature through meteorological monitoring equipment to provide environmental parameter support for subsequent cofferdam positioning corrections; Intelligent monitoring algorithms are used to analyze the impact of environmental data on cofferdam stability and provide early warning of abnormal situations.

10. The method for locating cofferdams for bridge construction based on Beidou positioning according to claim 1, characterized in that: The error compensation and monitoring in S5 also includes a data fusion analysis step, including: Adopt multi-sensor data fusion technology to fuse Beidou RTK positioning data, fluid-solid coupling prediction data, and UKF filtering data; Historical error data is learned through artificial intelligence algorithms, and the error compensation model is optimized.

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