Prefabricated part mounting and positioning method under complex hydrological condition

Abstract

The invention relates to the field of water engineering, and discloses a method for installing and positioning a prefabricated part under a complex hydrological condition, which comprises the following steps of: arranging a sensor network on the surface of the part, and establishing an initial reconstruction model of strain-hydrodynamic load mapping based on finite element analysis; acquiring sensor strain data in real time, and synchronously acquiring component pose information of an external positioning system; real-time hydrodynamic disturbance is calculated through the initial model, and sensor health diagnosis is carried out in combination with a dynamic threshold value method; the extended Kalman filter fuses the pose and disturbance data, and model parameters are updated online to generate a calibration model; and on the basis of calibration model output and state estimation, a feedforward-feedback composite control instruction is generated to drive an execution mechanism to adjust the pose. Through an online calibration model and a composite control architecture, and in combination with optimal distribution of a redundant execution mechanism, real-time offset of hydrodynamic disturbance and millimeter-level pose regulation are realized, and the technical bottlenecks of low precision and poor adaptability of a traditional method are broken through.

Description

Technical Field

[0001] The present invention relates to the field of water engineering, and in particular to a method for installing and positioning prefabricated components under complex hydrological conditions. Background Art

[0002] As marine resource development expands into the deep sea, underwater engineering equipment faces severe challenges in complex hydrodynamic environments. Disturbances such as strong currents and vortices can cause component posture instability, directly impacting operational accuracy and equipment safety. Traditional posture control methods, which rely heavily on feedback regulation and empirical models, struggle to meet the high-precision control requirements in dynamic flow fields. There is an urgent need to develop new intelligent control technologies to enhance system anti-interference capabilities.

[0003] In existing technologies, typical approaches employ feedforward compensation based on a fixed-parameter model combined with PID feedback control. Pressure sensor arrays placed on the component surface acquire flow field information, establishing a static mapping between hydrodynamic disturbances and actuator thrust. Some improved approaches incorporate adaptive filtering algorithms to reduce noise in sensor data, or update model parameters through regular offline calibration.

[0004] While existing technologies have improved attitude control stability to a certain extent, they still have some shortcomings: First, fixed-parameter models cannot adapt to the time-varying characteristics caused by material degradation and sensor drift, resulting in a gradual decline in control accuracy during long-term operations. Second, the single feedback control architecture is unable to cope with sudden flow field changes due to disturbance compensation lag. Third, the sensor network lacks an online health monitoring mechanism, and abnormal data directly leads to model inaccuracy and control command oscillation. Finally, the actuator thrust distribution strategy does not consider energy consumption optimization under redundant configurations, which restricts the system's ability to continue operating. These shortcomings stem from the lack of core links in existing technologies, such as dynamic model calibration, multi-source data fusion, and anomaly diagnosis, resulting in insufficient robustness and adaptability in complex marine environments. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method for installing and positioning prefabricated components under complex hydrological conditions, which solves the problems in the existing technology of low underwater component posture control accuracy and poor environmental adaptability caused by model parameter solidification, disturbance compensation lag and insufficient sensor reliability.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for installing and positioning prefabricated components under complex hydrological conditions, comprising the following steps: S1. Deploy a sensor network on the surface of the prefabricated component and establish an initial reconstruction model through finite element analysis. The initial reconstruction model maps the sensor strain response to an equivalent hydrodynamic load; S2. collecting strain data of the sensor network in real time and synchronously acquiring component posture data of the external positioning system; S3. Based on the strain data, calculate the real-time hydrodynamic disturbance using the initial reconstruction model, and simultaneously perform health diagnosis on the sensor network to eliminate abnormal data; S4, inputting the posture data and the real-time hydrodynamic disturbance into an extended Kalman filter, updating the parameters of the initial reconstruction model online, and generating a calibrated reconstruction model; S5. Generate control instructions based on the disturbance estimation output by the calibrated reconstruction model and the state estimation result of the extended Kalman filter to offset the hydrodynamic disturbance and drive the actuator to adjust the component posture.

[0007] Preferably, the step S1 includes the following steps: A distributed fiber Bragg grating sensor array is embedded beneath the outer surface of the underwater portion of the prefabricated component, with the sensor spacing no greater than 5% of the component's characteristic length, forming a fully covered strain sensing network. Use finite element analysis software to establish a three-dimensional mechanical model of the component and define the elastic modulus of the material , Poisson's ratio and boundary conditions; Apply six-degree-of-freedom unit loads to the model in sequence: , calculate the strain response of all sensors under each load ; Constructing the initial reconstruction matrix , establish strain-load mapping relationship .

[0008] Preferably, the step S2 includes: Through high-speed fiber optic interrogator Sampling frequency real-time reading of the wavelength offset of the FBG sensor , and through the calibration formula Converted to strain value, where For the The strain sensitivity coefficient of each sensor; For the A fiber Bragg grating sensor at time The wavelength shift of For the Sensors at the time Strain measurement value; Through the GNSS / INS combined system Frequency acquisition of global pose data of the water surface part of the component , where is the east coordinate of the centroid of the water surface part of the component in the global coordinate system; is the north coordinate of the centroid of the water surface part of the component in the global coordinate system; is the vertical coordinate of the centroid of the water surface part of the component in the global coordinate system; is the member roll angle; is the pitch angle of the component; is the heading angle of the component; Underwater acoustic positioning system Relative position of the underwater part of the frequency acquisition component , and use the timestamp alignment algorithm to and Fusion into complete pose data , where is the eastward offset of the reference point of the underwater part of the component relative to the acoustic beacon; is the north offset of the reference point of the underwater part of the component relative to the acoustic beacon; It is the vertical offset of the reference point of the underwater part of the component relative to the acoustic beacon.

[0009] Preferably, the timestamp alignment algorithm is: For each strain data point and pose data points ,when hour , generate synchronization data by linear interpolation , where is the strain measurement value; is the fused pose data; is the maximum allowable time deviation; is the unified timestamp after alignment.

[0010] Preferably, step S3 includes: For the sensor network sensors, defining a set of spatially adjacent sensors , calculate the local strain residual of the sensor , the formula is: ; Where, For the Sensors at the time Strain measurement value; is the number of valid sensors in the neighborhood set; For the The spatiotemporal residuals of a fiber Bragg grating sensor; For the The spatial neighborhood of each sensor; For the Neighbor sensors at time Strain measurement value; Setting dynamic fault thresholds , where For historical strain data in a sliding time window If the standard deviation within and continues beyond the time window , then determine the A sensor failure; From the strain data vector Remove all elements corresponding to the faulty sensors to generate a valid strain vector , where is the raw strain measurement vector of all sensors; For the Sensors at the time The strain value; is the total number of sensors; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors; Reconstruct the matrix from the initial Delete the The corresponding rows are used to obtain the dimension reduction and reconstruction matrix and calculate the real-time hydrodynamic disturbance by the least squares method : ; Where, is the estimated value of the six-degree-of-freedom hydrodynamic disturbance; The dimension reduction and reconstruction matrix after removing the faulty sensor; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors; is the total number of sensors.

[0011] Preferably, the step S4 includes: The motion state of the component and the reconstructed model parameters are combined into an augmented state vector, and the formula is: ; Where, is the reconstructed model parameter vector; is the motion state of the component, including: ; is the position of the component's centroid in the global coordinate system; is the component attitude quaternion; is the linear velocity of the component; is the component angular velocity; Based on the component dynamic equation and hydrodynamic disturbance input, a nonlinear state transfer function is constructed, and the formula is: ; Where, is the state vector at the current moment, including motion state and model parameters; is the nonlinear state transfer function, describing the dynamic evolution; is the state vector at the previous moment; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is process noise, which obeys Gaussian distribution; The pose data measured by the external positioning system is mapped to the observation function of the state vector. The formula is: ; Where, For the moment The observation vector contains the pose data measured by the external positioning system; As a nonlinear observation function, the state vector Mapping to the pose observation space; is the augmented state vector; is the observation noise; In the prediction stage, the prior state estimate and covariance are calculated using the formula: ; ; Where, For the moment A priori state estimate of ; is the nonlinear state transfer function, describing the system dynamics; For the moment The posterior state estimate of ; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is the prior estimated covariance matrix; is the Jacobian matrix of the state transfer function; For the moment The posterior covariance matrix of ; is the process noise covariance matrix.

[0012] Preferably, in the update phase of the extended Kalman filter, the pose observation data is integrated and the state estimate is updated. The formula is: ; Where, is the Kalman gain matrix; is the Jacobian matrix of the observation function; is the identity matrix; For the moment The posterior state estimate of ; is the updated posterior covariance matrix; is the observation noise covariance matrix; is the prior state estimate; is the actual observed value; is the predicted observation value; Online calibration of model parameters: Estimation from updated state Extract parameter vector from , update the initial reconstruction matrix according to the preset mapping relationship , generate the calibrated reconstruction model ,satisfy: ; Where, For the moment The effective strain vector of is the reconstruction matrix after calibration; is the equivalent six-degree-of-freedom hydrodynamic load vector.

[0013] Preferably, step S5 includes: Based on the disturbance estimate output by the calibrated reconstructed model, the feedforward compensation term is calculated as follows: ; Where, is the feedforward control vector used to directly offset the hydrodynamic disturbance; Assign a matrix to the control; for The pseudo-inverse matrix of is the reconstructed equivalent six-degree-of-freedom hydrodynamic load; Based on the state estimation results of the extended Kalman filter, the closed-loop feedback term is calculated as follows: ; Where, is the feedback control vector used to eliminate the deviation between the target and the estimated state; is the proportional gain matrix, the preset weight parameters; is the differential gain matrix and the preset damping parameters; is the target state vector; is the estimated motion state vector; is the target state change rate; To estimate the state change rate; The feedforward and feedback control quantities are superimposed to generate the overall control instruction and drive the actuator. The formula is: ; Where, is the total control instruction vector, which is sent to the actuator; is the feedforward control term; is the feedback control item; Real-time communication protocol Sent to the following actuators: Dynamic positioning thruster array: according to thrust command Adjust the speed of each thruster to generate six-degree-of-freedom control force; Anchor winch system: Assists in suppressing low-frequency drift through cable tension control; Hydraulic leveling mechanism: fine-tuning the pitch angle of components and roll angle , ensure the installation surface is level.

[0014] Preferably, the control allocation matrix The construction method is: For each thruster , define the control allocation matrix No. Listed as The thrust contribution of each thruster is: ; in: For the The contribution vector of each actuator to the six-degree-of-freedom control force; For the The unit vector of the thrust direction of each propeller; For the The position vector of each thruster installation position relative to the center of mass of the component; is the vector cross product operator.

[0015] Preferably, the actuator includes: Dynamic positioning thruster array, whose thrust direction covers six degrees of freedom; Anchor cable winch system, which controls the position and posture of auxiliary components through tension control; Hydraulic leveling mechanism, used to fine-tune the pitch and roll attitude of the component.

[0016] The present invention provides a method for installing and positioning prefabricated components under complex hydrological conditions. It has the following beneficial effects: 1. This invention utilizes feedforward control in conjunction with an online calibration and reconstruction model to generate compensation commands directly based on real-time observed disturbance values, effectively overcoming the hysteresis inherent in traditional feedback control. Combined with the extended Kalman filter's dynamic parameter calibration capabilities, this improves the accuracy of disturbance estimation in complex flow environments and ensures rapid response in component posture control.

[0017] 2. This invention incorporates a sensor health diagnosis mechanism and a redundant actuator optimization allocation strategy. In the event of sensor anomalies or partial actuator failure, this design maintains stable system operation through dynamic model reconstruction and thrust redistribution. This design significantly reduces the risk of loss of control due to single-point failures and enhances the ability of marine equipment to continue operating under harsh conditions.

[0018] 3. This invention adaptively compensates for time-varying factors such as material degradation and sensor drift through the fusion of multi-source pose data and an online update mechanism for model parameters. The combination of a spatiotemporal synchronization algorithm and closed-loop calibration technology enables the system to maintain high-precision control performance over the long term, avoiding the accuracy degradation caused by environmental changes that plagues traditional offline calibration methods.

[0019] 4. This invention decouples feedforward disturbance cancellation from feedback error correction through a composite control architecture, reducing the oscillation amplitude of control commands. Combined with a weighted optimization allocation strategy for actuators, this prioritizes high-efficiency actuators while meeting control requirements, extending the lifespan of critical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the specification of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 The embodiment of the present invention provides a method for installing and positioning prefabricated components under complex hydrological conditions, comprising the following steps: S1. Deploy a sensor network on the surface of the prefabricated component and establish an initial reconstruction model through finite element analysis. The initial reconstruction model maps the sensor strain response to the equivalent hydrodynamic load. In this embodiment, the layout of the prefabricated component surface sensor network and the establishment of the initial reconstruction model are achieved through the following technical solutions: A distributed fiber Bragg Grating (FBG) sensor array is embedded in a gridded pattern beneath the outer surface of the prefabricated component's underwater portion. The sensor array covers the component's incoming flow surface, corners, and key stress-bearing locations identified through finite element analysis. The optimal sensor spacing is no more than 5% of the component's characteristic length (e.g., maximum side length or diameter) to ensure spatial resolution of complex hydrodynamic loads.

[0023] The fiber Bragg grating sensor is constructed entirely of quartz glass, housed in a titanium alloy sheath and filled with silicone gel cushioning material, achieving an IP68 protection rating and withstanding water pressure at depths of 50 meters and long-term seawater corrosion. The sensor leads are connected to the surface signal processing unit via a waterproof conduit embedded within the sensor, preventing damage from external water flow.

[0024] Based on the 3D geometric model of the component, a mechanical simulation model is established using finite element analysis software (such as ANSYS Mechanical). The material properties of the component are clearly defined in the model, including the elastic modulus. , Poisson's ratio and density , and set fixed constraint boundary conditions that match the mounting base.

[0025] Apply six-degree-of-freedom unit loads to the mechanical model in sequence, including unit forces in three orthogonal directions and unit moment , forming a unit load set , among which The load is expressed as: ; Where, For the A six-degree-of-freedom unit load vector; is the Kronecker function, when = The value is 1 when , otherwise it is 0.

[0026] Calculate each unit load Strain responses of all sensors under action , where is the total number of sensors; Indicates the The sensor in The strain value under the load.

[0027] Combine all unit load responses by column to construct the initial reconstruction matrix , the formula is: ; Where, is the initial reconstruction matrix; are the strain response vectors under the action of the 1st to 6th six-degree-of-freedom unit loads respectively; The total number of sensors deployed on the surface of prefabricated components; A mathematical space representing matrices.

[0028] This matrix establishes a linear mapping relationship from strain response to equivalent hydrodynamic load ,in is the equivalent hydrodynamic load vector to be determined.

[0029] Through the above technology, the sensor network can sensitively capture the changes in the surface strain distribution of components caused by water flow impact. The application of unit load in the finite element model simulates the actual hydrodynamic load, and the initial reconstruction matrix The construction of is essentially to map discrete strain measurements to equivalent load distribution in continuous space through the principle of linear superposition.

[0030] S2, real-time collection of strain data from the sensor network and simultaneous acquisition of component pose data from the external positioning system; In this embodiment, the real-time collection and synchronization of sensor network strain data and component posture data are achieved through the following technical solutions: The wavelength offset signal of each sensor on the surface of the prefabricated component is read in real time by the fiber grating demodulator. and strain value The conversion relationship is: ; Where, For the The strain sensitivity coefficient of each sensor; For the A fiber Bragg grating sensor at time The wavelength shift of For the Sensors at the time The strain measurement value.

[0031] Preferably, the demodulator adopts wavelength scanning demodulation technology, supports multi-channel parallel acquisition, and ensures that the strain data of all sensors are output with a unified time base.

[0032] Synchronously collect pose data from the following heterogeneous positioning systems: GNSS / INS integrated navigation system: outputs the global position of the surface part of the component , contains three-dimensional position and Euler angle information, where, is the east coordinate of the centroid of the water surface part of the component in the global coordinate system; is the north coordinate of the centroid of the water surface part of the component in the global coordinate system; is the vertical coordinate of the centroid of the water surface part of the component in the global coordinate system; is the member roll angle; is the pitch angle of the component; is the heading angle of the component; Underwater acoustic positioning system: Calculates the local position of the underwater component relative to the mounting base by transmitting acoustic signals through the array , the preferred acoustic signal frequency is between 30kHz-50kHz to balance accuracy and anti-interference ability, where, is the eastward offset of the reference point of the underwater part of the component relative to the acoustic beacon; is the north offset of the reference point of the underwater part of the component relative to the acoustic beacon; It is the vertical offset of the reference point of the underwater part of the component relative to the acoustic beacon.

[0033] To address the time base differences of multi-source heterogeneous data, a sliding window interpolation algorithm is used to achieve timestamp alignment: Add high-precision hardware clock tags to each data source, and the time synchronization error does not exceed ; In the time window In the process, cubic spline interpolation is performed on the asynchronously arriving pose data to generate a unified time series , where is the position vector after fusion; is the normalized attitude quaternion.

[0034] Strain data acquisition leverages the wavelength modulation properties of fiber Bragg Bragg gratings (FBGs), monitoring the Bragg wavelength offset to invert the dynamic strain distribution on the component surface. Multi-source fusion of position data addresses issues such as signal attenuation and multipath effects that can occur with single-source positioning systems in underwater environments. A spatiotemporal synchronization algorithm ensures causal consistency between strain and position data.

[0035] S3, based on the strain data, calculates the real-time hydrodynamic disturbance through the initial reconstruction model, and performs health diagnosis on the sensor network to eliminate abnormal data; In this embodiment, real-time hydrodynamic disturbance calculation and sensor health diagnosis are achieved through the following technical solutions: Dynamic evaluation of sensor health status: Based on the real-time strain data obtained in step S2, perform local consistency check on each sensor node. A collection of sensors that are spatially adjacent , calculate the local strain residual of the sensor , the formula is: ; Where, For the Sensors at the time Strain measurement value; is the number of valid sensors in the neighborhood set; For the The spatiotemporal residuals of a fiber Bragg grating sensor; For the The spatial neighborhood of each sensor; For the Neighbor sensors at time Preferably, the proximity relationship is predefined according to the sensor layout topology to ensure that sensors adjacent to each other in physical space are included in the same inspection group.

[0036] Dynamic threshold setting and fault judgment: Calculate historical data in a sliding time window Standard deviation within , and set the dynamic fault threshold to: ; Where, For historical strain data in a sliding time window If the standard deviation within and continues beyond the time window , then determine the A sensor failure.

[0037] From the strain data vector Remove all elements corresponding to the faulty sensors to generate a valid strain vector , where is the raw strain measurement vector of all sensors; For the Sensors at the time The strain value; is the total number of sensors; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors.

[0038] Reconstruction model dynamic dimensionality reduction: from the initial reconstruction matrix Remove the row vector corresponding to the faulty sensor and obtain the dimension-reduced reconstruction matrix .

[0039] Real-time hydrodynamic disturbance reconstruction: using the reconstruction matrix after dimensionality reduction With effective strain data , solve the real-time hydrodynamic disturbance estimate by the least squares method: ; Where, is the estimated value of the six-degree-of-freedom hydrodynamic disturbance; The dimension reduction and reconstruction matrix after removing the faulty sensor; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors; is the total number of sensors.

[0040] Health diagnosis is based on the role of strain responses of spatially adjacent sensors: Under normal working conditions, adjacent sensors have consistent change trends due to similar loads, and residual differences often indicate sensor failure or local structural damage.

[0041] The dynamic threshold mechanism can adapt to changes in environmental noise levels, avoiding misjudgments or missed detections caused by fixed thresholds.

[0042] The dimension reduction and reconstruction matrix maintains the effective rank of the model by eliminating the row vectors corresponding to the fault data, ensuring the numerical stability of the disturbance estimation.

[0043] S4, inputting the posture data and the real-time hydrodynamic disturbance into the extended Kalman filter, updating the parameters of the initial reconstruction model online, and generating a calibrated reconstruction model; In this embodiment, the online calibration of the parameters of the extended Kalman filter is achieved by the following technical solutions: The dynamic motion characteristics of the component and the time-varying parameters of the reconstruction model are jointly modeled to construct an augmented vector containing multi-dimensional state information. Specifically, the augmented state vector is defined as: ; Where, is the reconstructed model parameter vector; is the motion state of the component, including: ; is the position of the component's centroid in the global coordinate system; is the component attitude quaternion; is the linear velocity of the component; is the angular velocity of the component. By embedding the parameters into the state vector, the model error and the motion state are estimated synchronously.

[0044] Based on the component dynamic equation and hydrodynamic disturbance input, a nonlinear state transfer function is constructed, and the formula is: ; Where, is the state vector at the current moment, including motion state and model parameters; is the nonlinear state transfer function, describing the dynamic evolution; is the state vector at the previous moment; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is the process noise and obeys Gaussian distribution.

[0045] The pose data measured by the external positioning system is mapped to the observation function of the state vector. The formula is: ; Where, For the moment The observation vector contains the pose data measured by the external positioning system; As a nonlinear observation function, the state vector Mapping to the pose observation space; is the augmented state vector; is the observation noise.

[0046] In the prediction stage, the prior state estimate and covariance are calculated using the formula: ; ; Where, For the moment A priori state estimate of ; is the nonlinear state transfer function, describing the system dynamics; For the moment The posterior state estimate of ; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is the prior estimated covariance matrix; is the Jacobian matrix of the state transfer function; For the moment The posterior covariance matrix of ; is the process noise covariance matrix.

[0047] Fuse the pose observation data and update the state estimate. The formula is: ; Where, is the Kalman gain matrix; is the Jacobian matrix of the observation function; is the identity matrix; For the moment The posterior state estimate of ; is the updated posterior covariance matrix; is the observation noise covariance matrix; is the prior state estimate; is the actual observed value; is the predicted observation value.

[0048] Estimation from the updated state Extract parameter vector from , update the initial reconstruction matrix according to the preset mapping relationship , generate the calibrated reconstruction model ,satisfy: ; Where, For the moment The effective strain vector of is the reconstruction matrix after calibration; is the equivalent six-degree-of-freedom hydrodynamic load vector.

[0049] Preferably, to address the numerical stability issue of quaternion updates, the attitude quaternion is normalized after each filtering iteration, using the formula: ; At the same time, for the parameter vector Physical constraints are imposed to ensure the physical rationality of the reconstruction matrix, where The attitude quaternion estimate after the extended Kalman filter is updated; Represents the Euclidean norm of a quaternion; Represents the assignment operation, which reassigns the normalized quaternion to To ensure that it meets the unit constraint.

[0050] S5. Generate control instructions based on the disturbance estimation output by the calibrated reconstruction model and the state estimation result of the extended Kalman filter to offset the hydrodynamic disturbance and drive the actuator to adjust the component posture; In this embodiment, the hydrodynamic disturbance offset and component posture adjustment are achieved through the following technical solutions: Real-time hydrodynamic disturbance estimates based on the output of the calibrated reconstructed model , design the feedforward control term to directly offset the influence of the disturbance on the component posture. The calculation of the feedforward control quantity is based on the pseudo-inverse operation of the control allocation matrix, and the formula is: ; Where, is the feedforward control vector used to directly offset the hydrodynamic disturbance; Assign a matrix to the control; for The pseudo-inverse matrix of is the reconstructed equivalent six-degree-of-freedom hydrodynamic load. The pseudo-inverse of the matrix Calculated by Singular Value Decomposition (SVD), the specific form is: ; Where, Assign a matrix to the control; is the left singular vector matrix; is the singular value matrix; is the right singular vector matrix; is a pseudo-inverse matrix; this method can effectively deal with the thrust distribution problem under the redundant configuration of actuators, ensuring that the system controllability can be maintained when some actuators fail.

[0051] Component motion state estimation combined with the output of the extended Kalman filter , the proportional-derivative (PD) control strategy is used to generate feedback control to eliminate the accumulated deviation of the target posture. The calculation formula of the feedback control is: ; Where, is the feedback control vector used to eliminate the deviation between the target and the estimated state; is the proportional gain matrix, the preset weight parameters; is the differential gain matrix and the preset damping parameters; is the target state vector; is the estimated motion state vector; is the target state change rate; To estimate the state change rate. By adjusting the weight coefficient of the gain matrix, the response speed and stability of the system can be balanced.

[0052] The feedforward and feedback control quantities are superimposed to generate the overall control command and realize the posture adjustment through the actuator drive system: ; Where, is the total control instruction vector, which is sent to the actuator; is the feedforward control term; is the feedback control item.

[0053] Control instructions are transmitted to the following actuators via real-time communication protocols: Azimuth thruster array: Each thruster's speed and vector direction are adjusted according to thrust commands, generating six-degree-of-freedom control forces and torques. The thruster's mounting position and thrust direction are pre-calibrated through 3D modeling to ensure the accuracy of the control distribution matrix.

[0054] Anchor winch system: Dynamically adjust cable tension to suppress low-frequency drift in components. Preferably, a tension-position dual closed-loop control strategy is employed to track the target position while maintaining the preset tension.

[0055] Hydraulic leveling mechanism: Drives multiple telescopic cylinders to adjust the pitch and roll angles of the components, ensuring the installation surface is level to millimeter-level precision. The hydraulic system's closed-loop pressure-flow control effectively suppresses vibration caused by water impact.

[0056] Control Allocation Matrix The construction is based on the geometric layout and mechanical characteristics of the actuator. The contribution vector of the thrust of a thruster to the six-degree-of-freedom control force is: ; in: For the The contribution vector of each actuator to the six-degree-of-freedom control force; For the The unit vector of the thrust direction of each propeller; For the The position vector of each thruster installation position relative to the center of mass of the component; is the vector cross product operator.

[0057] Preferably, for redundant actuator configuration, the weighted least square method is used to optimize thrust distribution, and the formula is: ; Where, is the optimal control instruction vector to be solved; is the weight matrix; is the ideal thrust set value vector; Assign a matrix to the control; is the desired six-degree-of-freedom control force vector.

[0058] Step S5 achieves efficient hydrodynamic disturbance cancellation and precise position control through a feedforward-feedback hybrid control architecture. Feedforward control directly generates compensating thrust based on real-time disturbance observations, proactively eliminating the effects of major disturbances. Feedback control eliminates residual errors and unmodeled disturbances through closed-loop regulation. A control allocation algorithm maps abstract control forces into coordinated actions of multiple actuators, combining optimization strategies to balance system energy consumption and actuator life.

[0059] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for installing and positioning prefabricated components under complex hydrological conditions, characterized in that: The following steps are involved: S1. Deploy a sensor network on the surface of the prefabricated component and establish an initial reconstruction model through finite element analysis. The initial reconstruction model maps the sensor strain response to an equivalent hydrodynamic load; S2. collecting strain data of the sensor network in real time and synchronously acquiring component posture data of the external positioning system; S3. Based on the strain data, calculate the real-time hydrodynamic disturbance using the initial reconstruction model, and simultaneously perform health diagnosis on the sensor network to eliminate abnormal data; S4, inputting the posture data and the real-time hydrodynamic disturbance into an extended Kalman filter, updating the parameters of the initial reconstruction model online, and generating a calibrated reconstruction model; S5. Generate control instructions based on the disturbance estimation output by the calibrated reconstruction model and the state estimation result of the extended Kalman filter to offset the hydrodynamic disturbance and drive the actuator to adjust the component posture.

2. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 1, characterized in that: The step S1 comprises the following steps: A distributed fiber Bragg grating sensor array is embedded beneath the outer surface of the underwater portion of the prefabricated component, with the sensor spacing no greater than 5% of the component's characteristic length, forming a fully covered strain sensing network. Use finite element analysis software to establish a three-dimensional mechanical model of the component and define the elastic modulus of the material , Poisson's ratio and density , and set fixed constraint boundary conditions that match the mounting base; Apply six-degree-of-freedom unit loads to the model in sequence: , calculate the strain response of all sensors under each load , where For the A six-degree-of-freedom unit load vector; is the Kronecker function; Constructing the initial reconstruction matrix , establish strain-load mapping relationship , where is the initial reconstruction matrix; are the strain response vectors under the action of the 1st to 6th six-degree-of-freedom unit loads respectively; The total number of sensors deployed on the surface of prefabricated components; A mathematical space representing matrices.

3. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 2, characterized in that: The step S2 comprises: Through high-speed fiber optic interrogator Sampling frequency real-time reading of the wavelength offset of the FBG sensor , and through the calibration formula Converted to strain value, where For the The strain sensitivity coefficient of each sensor; For the A fiber Bragg grating sensor at time The wavelength shift of For the Sensors at the time Strain measurement value; Through the GNSS / INS combined system Frequency acquisition of global pose data of the water surface part of the component , where is the east coordinate of the centroid of the water surface part of the component in the global coordinate system; is the north coordinate of the centroid of the water surface part of the component in the global coordinate system; is the vertical coordinate of the centroid of the water surface part of the component in the global coordinate system; is the member roll angle; is the pitch angle of the component; is the heading angle of the component; Underwater acoustic positioning system Relative position of the underwater part of the frequency acquisition component , and use the timestamp alignment algorithm to and Fusion into complete pose data , where is the eastward offset of the reference point of the underwater part of the component relative to the acoustic beacon; is the north offset of the reference point of the underwater part of the component relative to the acoustic beacon; It is the vertical offset of the reference point of the underwater part of the component relative to the acoustic beacon.

4. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 3, characterized in that: The timestamp alignment algorithm is: For each strain data point and pose data points ,when hour , generate synchronization data by linear interpolation , where is the strain measurement value; is the fused pose data; is the maximum allowable time deviation; is the unified timestamp after alignment.

5. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 4, characterized in that: The step S3 comprises: For the sensor network sensors, defining a set of spatially adjacent sensors , calculate the local strain residual of the sensor , the formula is: ； Where, For the Sensors at the time Strain measurement value; is the number of valid sensors in the neighborhood set; For the The spatiotemporal residuals of a fiber Bragg grating sensor; For the The spatial neighborhood of each sensor; For the Neighbor sensors at time Strain measurement value; Setting dynamic fault thresholds , where For historical strain data in a sliding time window If the standard deviation within and continues beyond the time window , then determine the A sensor failure; From the strain data vector Remove all elements corresponding to the faulty sensors to generate a valid strain vector , where is the raw strain measurement vector of all sensors; For the Sensors at the time The strain value; is the total number of sensors; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors; Reconstruct the matrix from the initial Delete the The corresponding rows are used to obtain the dimension reduction and reconstruction matrix and calculate the real-time hydrodynamic disturbance by the least squares method : ； Where, is the estimated value of the six-degree-of-freedom hydrodynamic disturbance; The dimension reduction and reconstruction matrix after removing the faulty sensor; Index collection for faulty sensors; is the effective strain vector after removing the faulty sensor; is the number of faulty sensors; is the total number of sensors.

6. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 5, characterized in that: The step S4 comprises: The motion state of the component and the reconstructed model parameters are combined into an augmented state vector, and the formula is: ； Where, is the reconstructed model parameter vector; is the motion state of the component, including: ； is the position of the component's centroid in the global coordinate system; is the component attitude quaternion; is the linear velocity of the component; is the component angular velocity; Based on the component dynamic equation and hydrodynamic disturbance input, a nonlinear state transfer function is constructed, and the formula is: ； Where, is the state vector at the current moment, including motion state and model parameters; is the nonlinear state transfer function, describing the dynamic evolution; is the state vector at the previous moment; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is process noise, which obeys Gaussian distribution; The pose data measured by the external positioning system is mapped to the observation function of the state vector. The formula is: ； Where, For the moment The observation vector contains the pose data measured by the external positioning system; As a nonlinear observation function, the state vector Mapping to the pose observation space; is the augmented state vector; is the observation noise; In the prediction stage, the prior state estimate and covariance are calculated using the formula: ； ； Where, For the moment A priori state estimate of ; is the nonlinear state transfer function, describing the system dynamics; For the moment The posterior state estimate of ; is the control input at the previous moment; is the estimated hydrodynamic disturbance at the previous moment; is the prior estimated covariance matrix; is the Jacobian matrix of the state transfer function; For the moment The posterior covariance matrix of ; is the process noise covariance matrix.

7. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 6, characterized in that: The update phase of the extended Kalman filter fuses the pose observation data and updates the state estimate. The formula is: ； Where, is the Kalman gain matrix; is the Jacobian matrix of the observation function; is the identity matrix; For the moment The posterior state estimate of ; is the updated posterior covariance matrix; is the observation noise covariance matrix; is the prior state estimate; is the actual observed value; is the predicted observation value; Online calibration of model parameters: Estimation from updated state Extract parameter vector from , update the initial reconstruction matrix according to the preset mapping relationship , generate the calibrated reconstruction model ,satisfy: ； Where, For the moment The effective strain vector of is the reconstruction matrix after calibration; is the equivalent six-degree-of-freedom hydrodynamic load vector.

8. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 7, characterized in that: The step S5 comprises: Based on the disturbance estimate output by the calibrated reconstructed model, the feedforward compensation term is calculated as follows: ； Where, is the feedforward control vector used to directly offset the hydrodynamic disturbance; Assign a matrix to the control; for The pseudo-inverse matrix of is the reconstructed equivalent six-degree-of-freedom hydrodynamic load; Based on the state estimation results of the extended Kalman filter, the closed-loop feedback term is calculated as follows: ； Where, is the feedback control vector used to eliminate the deviation between the target and the estimated state; is the proportional gain matrix, the preset weight parameters; is the differential gain matrix and the preset damping parameters; is the target state vector; is the estimated motion state vector; is the target state change rate; To estimate the state change rate; The feedforward and feedback control quantities are superimposed to generate the overall control instruction and drive the actuator. The formula is: ； Where, is the total control instruction vector, which is sent to the actuator; is the feedforward control term; is the feedback control item; Real-time communication protocol Sent to the following actuators: Dynamic positioning thruster array: according to thrust command Adjust the speed of each thruster to generate six-degree-of-freedom control force; Anchor winch system: Assists in suppressing low-frequency drift through cable tension control; Hydraulic leveling mechanism: fine-tuning the pitch angle of components and roll angle , ensure the installation surface is level.

9. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 8, characterized in that: The control allocation matrix The construction method is: For each thruster , define the control allocation matrix No. Listed as The thrust contribution of each thruster is: ； in: For the The contribution vector of each actuator to the six-degree-of-freedom control force; For the The unit vector of the thrust direction of each propeller; For the The position vector of each thruster installation position relative to the center of mass of the component; is the vector cross product operator.

10. The method for installing and positioning prefabricated components under complex hydrological conditions according to claim 9, characterized in that: The executive mechanism comprises: Dynamic positioning thruster array, whose thrust direction covers six degrees of freedom; Anchor cable winch system, which controls the position and posture of auxiliary components through tension control; Hydraulic leveling mechanism, used to fine-tune the pitch and roll attitude of the component.

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