A multi-tugboat cooperative thrust intelligent distribution and compensation method and system

Abstract

The application provides a multi-tug cooperative thrust intelligent distribution and compensation method and system. The method comprises the following steps: obtaining multi-tug formation record data, mother ship record data, port environment record data and ship equipment record data, obtaining longitudinal total expected thrust, transverse total expected thrust and bow swing total expected moment through hierarchical model predictive control analysis and processing, distributing to each tug, and performing coupling wake interference constraint analysis combined with the multi-tug formation record data to obtain corresponding initial thrust instructions, and performing multi-layer dynamic compensation correction combined with the mother ship record data, the port environment record data and the ship equipment record data to obtain optimized thrust instructions. The application realizes multi-tug cooperative thrust intelligent distribution and compensation by constructing a hierarchical MPC and a quadratic programming thrust distribution architecture, integrating shallow water resistance dynamic compensation, wake interference coupling compensation, crosswind feedforward compensation, propeller silt loss compensation and mother ship inertia adaptive compensation.

Description

Technical Field

[0001] This application relates to the field of intelligent tugboat operation technology in ports, and more specifically, to a method and system for intelligent distribution and compensation of collaborative thrust among multiple tugboats. Background Technology

[0002] Large vessels are greatly affected by wind, waves, and currents during berthing and unberthing operations in harbors. This necessitates the coordinated output of thrust and torque from multiple tugboats to achieve precise attitude control and trajectory tracking. Currently, foreign technologies primarily include: upper-level MPC trajectory tracking and lower-level fixed secondary planning thrust distribution, which are only suitable for conventional open sea areas and lack compensation for shallow water dynamic drag and wake interference; adaptive sliding mode controllers are only optimized for low-speed berthing of a single tugboat and lack multi-tugboat collaborative reconfiguration logic; mode switching control only achieves smooth parameter transitions and does not dynamically compensate for thruster losses in strong winds or silty bottoms. All of these solutions suffer from the following drawbacks: lack of modeling of wake interference between tugboats during multi-tugboat collaborative operations, resulting in a thrust loss of over 10% for the tugboats ahead due to their wake; fixed drag coefficients that cannot adapt to dynamic changes in silty and shallow water; and long thrust reconfiguration response times after a single tugboat thruster failure, hindering rapid balancing of the fleet's combined force. Existing domestic technologies mainly include: shallow water thrust distribution transfer learning models, but these are only for single vessels and do not support multi-tugboat formation coordination; MPC+PID composite control, which only solves the trajectory tracking of a single vessel and does not consider thrust coupling between multiple tugboats; existing research has not combined measured data on strong winds, tides, and silt accumulation in port areas to build compensation models. Under extreme crosswind conditions of level 10 or above, the thrust distribution deviation exceeds 10%, and the mother ship's drift speed can reach 0.6 m / s, which can easily lead to collisions and grounding accidents.

[0003] The existing technology has the following main problems: First, the thrust distribution model is static: the shallow water drag coefficient is fixed and cannot be dynamically corrected according to real-time water depth and silt bottom, resulting in a trajectory tracking error of more than 2m in shallow water areas; Second, there is no multi-tugboat wake interference coupling model: during formation operations, the wake of the leading ship causes the thrust of the following ship to decrease, and the collaborative force deviation is up to 15%; Third, there is a lack of multi-dimensional dynamic compensation mechanisms: crosswind feedforward compensation, propeller silt loss compensation, and mother ship inertial load adaptive compensation are not integrated synchronously; Fourth, the real-time performance of fault reconstruction is poor: when the thrust of a single tugboat propeller decreases or fails, the time for the formation to redistribute thrust exceeds 500ms, and the risk of mother ship drift increases sharply; Fifth, the operation mode switching is oscillating: the thrust parameters change abruptly during the switching process between free navigation, coordinated berthing, and coordinated departure, causing large hull swaying, and there is no smooth transition and emergency collision avoidance linkage logic.

[0004] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for intelligent distribution and compensation of collaborative thrust for multiple tugboats. By constructing a hierarchical MPC and a secondary planning thrust distribution architecture, it integrates shallow water resistance dynamic compensation, wake interference coupling compensation, crosswind feedforward compensation, propeller mud accumulation loss compensation, and mother ship inertia adaptive compensation, thereby realizing intelligent distribution and compensation of collaborative thrust for multiple tugboats.

[0006] In the first aspect, this application provides a method for intelligent distribution and compensation of collaborative thrust among multiple tugboats, including the following steps:

[0007] Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0008] Based on the mother ship record data and port area environmental record data, the longitudinal total expected thrust, lateral total expected thrust and bow total expected torque are obtained through hierarchical model predictive control analysis.

[0009] The total expected longitudinal thrust, total expected lateral thrust, and total expected bow pitch torque are allocated to each tugboat, and coupled wake interference constraint analysis is performed in conjunction with the multi-tugboat formation data to obtain the initial thrust command corresponding to each tugboat's propeller.

[0010] The initial thrust command is combined with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction, thereby obtaining an optimized thrust command.

[0011] Optionally, in the multi-tugboat cooperative thrust intelligent allocation and compensation method described in this application, the acquisition of multi-source operating condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data, includes:

[0012] Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0013] The multi-tugboat formation data includes real-time position and attitude, real-time speed, relative distance between tugboats, and relative speed between tugboats;

[0014] The mother ship recorded data includes mother ship tonnage, mother ship acceleration, mother ship real-time turning angular velocity, ship lateral drift, and target trajectory sequence data;

[0015] The port area environmental record data includes real-time water depth and real-time wind field data;

[0016] The ship's equipment records data including the thruster output torque and the real-time tension of the towing cable.

[0017] Optionally, in the multi-tugboat cooperative thrust intelligent allocation and compensation method described in this application, the step of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected torque through hierarchical model predictive control analysis based on the mother ship record data and port area environmental record data includes:

[0018] The real-time water depth is subjected to water depth mean filtering to obtain an effective reference water depth, which is then compared with a preset reference water depth threshold. The dynamic silt shallow water resistance coefficient is determined based on the threshold comparison result.

[0019] The real-time wind field data is decomposed to obtain longitudinal wind disturbance force, lateral wind disturbance force, and yaw disturbance moment;

[0020] Based on the tonnage of the mother ship, an equivalent inertia matrix of the mother ship is constructed. Matrix operations are then performed using the acceleration and real-time turning angular velocity of the mother ship to obtain the inertial force and inertial torque.

[0021] The waypoints are subjected to third-order smooth interpolation based on the target trajectory sequence data to generate a set of reference trajectory points with equal time intervals.

[0022] By obtaining longitudinal and lateral water flow velocities from a pre-set port area tidal real-time database and combining them with the real-time navigation speed, longitudinal water flow disturbance force and lateral water flow drift disturbance force are obtained.

[0023] The dynamic silt shallow water resistance coefficient is embedded into the preset towed formation dynamics model for model correction. The longitudinal wind disturbance force, lateral wind disturbance force, yaw disturbance moment, inertial force, inertial moment, longitudinal water flow disturbance force, and lateral water flow drift disturbance force are loaded as external time-varying disturbance terms into the dynamic equation. A trajectory tracking cost function with energy consumption weight is constructed using the reference trajectory point set as the tracking benchmark. Preset channel geometry, preset thrust extremum, and preset port area speed limit constraints are loaded. Sequential quadratic programming is used to solve the upper-level trajectory tracking MPC rolling optimization, and the total expected longitudinal thrust, total expected lateral thrust, and total expected yaw moment of the multi-tug formation are output.

[0024] Optionally, in the multi-tugboat cooperative thrust intelligent allocation and compensation method described in this application, the step of allocating the total longitudinal expected thrust, the total lateral expected thrust, and the total expected bow roll moment to each tugboat, and combining the multi-tugboat formation recording data with coupled wake interference constraint analysis to obtain the initial thrust command corresponding to each tugboat's thruster, includes:

[0025] Based on the real-time pose determination of the formation's front and rear arrangement relationship, and combined with the relative spacing and relative speed of the tugboats, wake loss analysis is performed to obtain the thrust loss coefficient corresponding to each tugboat.

[0026] Obtain the azimuth angle and rotation arm of the thruster, embed the thrust loss coefficient into the formation resultant force balance equation constraint, and construct a basic quadratic programming model by combining the longitudinal total expected thrust, lateral total expected thrust and yaw total expected torque.

[0027] By optimizing both the total thrust distribution error and the propeller energy consumption, and by applying physical hard constraints on the thrust amplitude, azimuth range, and turning rate of the propeller, the basic quadratic programming model is solved and analyzed to obtain the initial thrust commands corresponding to each propeller of the tugboat.

[0028] Optionally, in the multi-tugboat cooperative thrust intelligent allocation and compensation method described in this application, the step of combining the initial thrust command with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction to obtain an optimized thrust command includes:

[0029] The silt loss ratio coefficient is obtained by analyzing and processing the output torque of the propeller in combination with the preset rated torque of the propeller.

[0030] The initial thrust command is optimized based on the silt loss ratio coefficient to obtain silt compensation gain thrust.

[0031] Vector decomposition is performed on the real-time wind field data to obtain the transverse wind speed of the vertical channel;

[0032] The crosswind thrust compensation increment is obtained by analyzing and processing the crosswind speed of the vertical channel in combination with the preset crosswind compensation ratio coefficient and the preset crosswind critical speed. This increment is then summed with the silt compensation gain thrust to obtain the crosswind compensation thrust.

[0033] The crosswind compensation thrust is used as the setpoint of the adaptive PID controller. The output torque of the thruster and the lateral drift of the ship are used as closed-loop feedback quantities for fine-tuning and correction. An optimized thrust command is generated and the target speed and target azimuth angle of the thruster are mapped and output.

[0034] Optionally, the multi-tugboat cooperative thrust intelligent allocation and compensation method described in this application further includes:

[0035] The trajectory tracking correction weight is obtained by analyzing and processing the mother ship tonnage and real-time tow cable tension in combination with the preset mother ship tonnage threshold and the preset port area benchmark tow cable tension.

[0036] The trajectory tracking correction weights are sent back to the upper-level MPC controller to dynamically update the trajectory tracking weights.

[0037] Secondly, this application provides a multi-tugboat cooperative thrust intelligent distribution and compensation system, the system comprising:

[0038] The module consists of a working condition perception and acquisition module, an upper-level trajectory tracking MPC module, a middle-level coupled wake secondary planning thrust allocation module, and a lower-level multi-compensation adaptive PID execution module.

[0039] The working condition sensing and acquisition module is used to acquire multi-source working condition recording data;

[0040] The upper-level trajectory tracking MPC module incorporates a dynamic correction model for silt and shallow water resistance and an adaptive compensation model for mother ship inertia, outputting the total expected longitudinal thrust, the total expected lateral thrust, and the total expected bow pitch torque.

[0041] The mid-level coupled wake secondary planning thrust allocation module pre-stores a mathematical model of wake interference of multiple tugboats, and solves the initial thrust command corresponding to each tugboat thruster with the physical limit of the thruster as a constraint.

[0042] The lower-level multi-compensation adaptive PID execution module integrates a silt accumulation loss compensation unit, a crosswind feedforward compensation unit, and a mother ship inertial load compensation unit, and outputs a corrected optimized thrust command.

[0043] Optionally, in the multi-tugboat cooperative thrust intelligent allocation and compensation system described in this application, the system further includes a memory and a processor. The memory includes a program for a multi-tugboat cooperative thrust intelligent allocation and compensation method. When the program for the multi-tugboat cooperative thrust intelligent allocation and compensation method is executed by the processor, it implements the following steps:

[0044] Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0045] Based on the mother ship record data and port area environmental record data, the longitudinal total expected thrust, lateral total expected thrust and bow total expected torque are obtained through hierarchical model predictive control analysis.

[0046] The total expected longitudinal thrust, total expected lateral thrust, and total expected bow pitch torque are allocated to each tugboat, and coupled wake interference constraint analysis is performed in conjunction with the multi-tugboat formation data to obtain the initial thrust command corresponding to each tugboat's propeller.

[0047] The initial thrust command is combined with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction, thereby obtaining an optimized thrust command.

[0048] Optionally, in the multi-tugboat cooperative thrust intelligent distribution and compensation system described in this application, the acquisition of multi-source operating condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data, includes:

[0049] Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0050] The multi-tugboat formation data includes real-time position and attitude, real-time speed, relative distance between tugboats, and relative speed between tugboats;

[0051] The mother ship recorded data includes mother ship tonnage, mother ship acceleration, mother ship real-time turning angular velocity, ship lateral drift, and target trajectory sequence data;

[0052] The port area environmental record data includes real-time water depth and real-time wind field data;

[0053] The ship's equipment records data including the thruster output torque and the real-time tension of the towing cable.

[0054] Optionally, in the multi-tugboat cooperative thrust intelligent distribution and compensation system described in this application, the step of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected torque through hierarchical model predictive control analysis based on the mother ship recorded data and port area environmental recorded data includes:

[0055] The real-time water depth is subjected to water depth mean filtering to obtain an effective reference water depth, which is then compared with a preset reference water depth threshold. The dynamic silt shallow water resistance coefficient is determined based on the threshold comparison result.

[0056] The real-time wind field data is decomposed to obtain longitudinal wind disturbance force, lateral wind disturbance force, and yaw disturbance moment;

[0057] Based on the tonnage of the mother ship, an equivalent inertia matrix of the mother ship is constructed. Matrix operations are then performed using the acceleration and real-time turning angular velocity of the mother ship to obtain the inertial force and inertial torque.

[0058] The waypoints are subjected to third-order smooth interpolation based on the target trajectory sequence data to generate a set of reference trajectory points with equal time intervals.

[0059] By obtaining longitudinal and lateral water flow velocities from a pre-set port area tidal real-time database and combining them with the real-time navigation speed, longitudinal water flow disturbance force and lateral water flow drift disturbance force are obtained.

[0060] The dynamic silt shallow water resistance coefficient is embedded into the preset towed formation dynamics model for model correction. The longitudinal wind disturbance force, lateral wind disturbance force, yaw disturbance moment, inertial force, inertial moment, longitudinal water flow disturbance force, and lateral water flow drift disturbance force are loaded as external time-varying disturbance terms into the dynamic equation. A trajectory tracking cost function with energy consumption weight is constructed using the reference trajectory point set as the tracking benchmark. Preset channel geometry, preset thrust extremum, and preset port area speed limit constraints are loaded. Sequential quadratic programming is used to solve the upper-level trajectory tracking MPC rolling optimization, and the total expected longitudinal thrust, total expected lateral thrust, and total expected yaw moment of the multi-tug formation are output.

[0061] As can be seen from the above, the method and system for intelligent distribution and compensation of multi-tugboat collaborative thrust provided in this application realizes intelligent distribution and compensation of multi-tugboat collaborative thrust by constructing a hierarchical MPC and secondary planning thrust distribution architecture, integrating shallow water resistance dynamic compensation, wake interference coupling compensation, crosswind feedforward compensation, propeller mud accumulation loss compensation and mother ship inertia adaptive compensation.

[0062] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0063] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 A flowchart illustrating a method for intelligent allocation and compensation of collaborative thrust among multiple tugboats, provided in an embodiment of this application;

[0065] Figure 2 A flowchart illustrating the process of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected torque in a multi-tugboat cooperative thrust intelligent allocation and compensation method provided in this application embodiment;

[0066] Figure 3 A flowchart illustrating the process of obtaining the initial thrust command corresponding to each thruster of a tugboat in a multi-tugboat cooperative thrust intelligent allocation and compensation method provided in this application embodiment;

[0067] Figure 4 This is a system block diagram of a multi-tugboat cooperative thrust intelligent distribution and compensation system provided in an embodiment of this application. Detailed Implementation

[0068] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0069] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0070] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a method for intelligent allocation and compensation of multi-tugboat cooperative thrust in some embodiments of this application. This method is used in terminal devices, such as computers and mobile terminals. The method includes the following steps:

[0071] S11. Acquire multi-source operating condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0072] S12. Based on the mother ship record data and port area environmental record data, the longitudinal total expected thrust, lateral total expected thrust and bow total expected torque are obtained through hierarchical model predictive control analysis.

[0073] S13. Distribute the total expected longitudinal thrust, total expected lateral thrust, and total expected bow pitch torque to each tugboat, and perform coupled wake interference constraint analysis processing in conjunction with the multi-tugboat formation record data to obtain the initial thrust command corresponding to each tugboat's propeller.

[0074] S14. The initial thrust command is combined with the mother ship record data, port area environmental record data and ship equipment record data to perform multi-layer dynamic compensation and correction to obtain an optimized thrust command.

[0075] It is important to note that, in order to achieve intelligent distribution and compensation of thrust from multiple tugboats, a four-layer progressive control architecture is constructed, comprising a data acquisition layer, a global planning layer, a formation allocation layer, and an execution compensation layer. Firstly, multi-source operational condition data, including multi-tugboat formation data, mother ship data, port environment data, and ship equipment data, is collected in real-time with a control cycle of 200ms using various types of physical sensors and ship-to-shore communication equipment. This data is then transmitted via a shipborne 700M... The 5G main link completes the synchronous data transmission between multiple tugboats, ensuring that the data synchronization delay does not exceed 100ms and is uniformly aligned to a 200ms control cycle. Then, the upper-level trajectory tracking model predictive controller (MPC) uses the high-precision electronic nautical chart of the port area and the dynamic tidal current field as spatial constraints, and integrates real-time water depth data, mother ship inertial parameters and wind field vectors to perform rolling optimization solutions for the overall thrust requirements of the formation. Next, the mid-level thrust distributor introduces a mathematical model of wake interference between multiple tugboats to distribute the total expected force and torque of the formation output by the upper level to all full-rotation thrusters of each tugboat. Finally, the lower-level adaptive PID controller performs triple parallel online compensation correction on the initial thrust command output by the mid-level controller, including silt accumulation loss compensation, crosswind feedforward compensation and mother ship inertial load compensation.

[0076] According to an embodiment of the present invention, the acquisition of multi-source operational condition record data includes multi-tugboat convoy record data, mother ship record data, port area environmental record data, and ship equipment record data, including:

[0077] Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data;

[0078] The multi-tugboat formation data includes real-time position and attitude, real-time speed, relative distance between tugboats, and relative speed between tugboats;

[0079] The mother ship recorded data includes mother ship tonnage, mother ship acceleration, mother ship real-time turning angular velocity, ship lateral drift, and target trajectory sequence data;

[0080] The port area environmental record data includes real-time water depth and real-time wind field data;

[0081] The ship's equipment records data including the thruster output torque and the real-time tension of the towing cable.

[0082] It is important to note that real-time attitude (longitude, latitude, heading angle) and real-time speed (including longitudinal and lateral speed relative to the ground) are obtained through the GNSS-RTK positioning unit (sampling frequency 5-20Hz, positioning accuracy in the centimeter range). The relative distance and relative speed of the tugboats are obtained through real-time ranging and Doppler velocity measurement using a 77GHz 4D millimeter-wave radar (ranging accuracy 0.1m, velocity accuracy 0.1m / s). The mother ship's tonnage is obtained by reading the displacement tonnage field from the mother ship's static information using the AIS receiver unit. The mother ship's acceleration (including longitudinal and lateral acceleration) is obtained by fusing AIS motion state data with the ship's IMU inertial measurement unit. The mother ship's real-time turning angular velocity is obtained by the AIS heading change rate or the ship's gyroscope. The ship's lateral drift is obtained in real-time through the lateral deviation between the GNSS-RTK attitude and the target track. The target track sequence data is calculated and obtained by reading the global waypoint sequence pre-planned from the electronic chart system; real-time water depth is obtained by collecting channel bottom depth data through a multibeam echo sounder, and the effective reference water depth is output after mean filtering to remove instantaneous wave fluctuations; real-time wind field data is obtained by collecting the real-time wind speed and wind direction angle in the port area through ultrasonic wind speed and wind direction sensors; the thruster output torque is obtained in real time by collecting the actual output torque of each azimuth thruster through strain gauge torque sensors, and the real-time tension of the tow cable is collected by a fiber optic tension sensor at a sampling frequency of 10Hz.

[0083] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the process of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected moment using a multi-tugboat cooperative thrust intelligent allocation and compensation method according to some embodiments of this application. According to an embodiment of the present invention, obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected moment through hierarchical model predictive control analysis processing based on the mother ship recorded data and port area recorded data includes:

[0084] S211. The real-time water depth is subjected to water depth mean filtering to obtain an effective reference water depth, and compared with a preset reference water depth threshold. The dynamic silt shallow water resistance coefficient is determined based on the threshold comparison result.

[0085] S212. The real-time wind field data is decomposed to obtain longitudinal wind disturbance force, lateral wind disturbance force and yaw disturbance moment;

[0086] S213. Construct an equivalent inertia matrix for the mother ship based on its tonnage, and perform matrix operations using the mother ship's acceleration and real-time turning angular velocity to obtain the inertial force and inertial torque.

[0087] S214. Perform third-order smooth interpolation on waypoints based on the target trajectory sequence data to generate a set of reference trajectory points with equal time intervals;

[0088] S215. The longitudinal and lateral water flow velocities are obtained by using a preset port area tidal real-time database and combined with the real-time navigation speed for analysis and processing to obtain the longitudinal water flow disturbance force and the lateral water flow drift disturbance force.

[0089] S22. The dynamic silt shallow water resistance coefficient is embedded into the preset towed formation dynamic model for model correction. The longitudinal wind disturbance force, lateral wind disturbance force, yaw disturbance moment, inertial force, inertial moment, longitudinal water flow disturbance force, and lateral water flow drift disturbance force are loaded into the dynamic equation as external time-varying disturbance terms. A trajectory tracking cost function with energy consumption weight is constructed using the reference trajectory point set as the tracking benchmark. The preset channel geometry, preset thrust extremum, and preset port area speed limit constraints are loaded. The upper-level trajectory tracking MPC rolling optimization solution is obtained by using sequential quadratic programming, and the longitudinal total expected thrust, lateral total expected thrust, and yaw total expected moment of the multi-tug formation are output.

[0090] It is important to note that the effective reference water depth is obtained after the multi-point water depth data from the echo sounder is filtered by mean. Set two reference water depth thresholds. and ,when At that time, the dynamic silt shallow water resistance coefficient The value is 1.2, when At that time, the dynamic silt shallow water resistance coefficient The value is 1.3, and a linear interpolation formula is used when the water depth is between the two values. Real-time calculations show that the shallower the water, the greater the frictional resistance of the silt and the wave-making resistance in shallow water, leading to a corresponding increase in the calculated MPC thrust requirement. Real-time wind speed and direction absolute vectors are obtained from real-time wind field data analysis. Vector decomposition is then performed using the fixed channel orientation angle to separate the longitudinal wind speed parallel to the channel. and crosswind speed of vertical channel The longitudinal wind disturbance force is calculated based on the wind speed and the pre-defined windward area of ​​the ship. ,in, For longitudinal wind disturbance force, The longitudinal drag coefficient is preset (based on the actual ship / wind tunnel calibration). The longitudinal windward projection area of ​​the ship (as indicated by the ship's static drawings). To preset standard air density, Longitudinal wind speed parallel to the channel; lateral wind disturbance force ,in, For lateral wind disturbance, The lateral drag coefficient is preset (based on the actual ship / wind tunnel calibration). The projected lateral windward area of ​​the ship is preset (based on the ship's static drawings). For the transverse wind speed and bow roll disturbance moment perpendicular to the channel ,in, To preset the bow sway torque, The longitudinal wind disturbance force, lateral wind disturbance force, and bow roll disturbance moment are used as inputs to the dynamic model as external continuous disturbance terms of the MPC, based on the preset wind pressure center to the mother ship's center of gravity (calibrated according to the ship's static drawings).

[0091] Based on the tonnage of the mother ship Construct a third-order diagonal equivalent inertia matrix ,in, To predetermine the equivalent bow roll inertia of the mother ship, it is obtained by fitting data from a table using the mother ship's tonnage, length, and beam. A motion state vector is then defined. ,in, For longitudinal acceleration, For lateral acceleration, The longitudinal inertial force is calculated in real time using matrix multiplication to obtain the mother ship's real-time turning angular velocity (bow angular velocity). Lateral inertial force and bow roll inertia torque ;

[0092] Read the pre-planned global waypoint sequence from the preset electronic nautical chart, perform third-order (cubic spline) smooth interpolation on the waypoints, and generate a reference trajectory point set with equal time intervals (aligned with the MPC control cycle). ( (For MPC prediction time domain length), synchronously bind static constraints such as speed limits, one-way channels, and no-navigation zones at each waypoint;

[0093] Retrieve the real-time tidal database of the port area and extract the longitudinal water flow velocity based on the current time and channel position. and lateral water flow velocity Combined with longitudinal speed to ground and lateral speed on ground Calculate the longitudinal relative water flow velocity Lateral relative water flow velocity A hydrodynamic drag model was adopted, and the longitudinal underwater wetted surface area of ​​the formation (pre-stored based on draft and ship length static calculations), the transverse underwater wetted surface area of ​​the formation (pre-stored based on ship beam and draft), the longitudinal water resistance coefficient (the overall longitudinal hydrodynamic coefficient of the mother ship and tugboat formation, calibrated by tank tests), and the transverse sideflow water resistance coefficient were used to calculate the longitudinal water flow disturbance force of the foundation. ,in, Based on longitudinal water flow disturbance, To preset seawater density, To preset the longitudinal water resistance coefficient, To preset the longitudinal underwater wetted surface area of ​​the formation, The longitudinal relative water flow velocity and the lateral drift disturbance force of the foundation. ,in, Based on the lateral water flow disturbance force, To preset the lateral water resistance coefficient, The preset horizontal underwater wetted surface area of ​​the formation. To obtain the longitudinal and lateral flow velocity relative to the flow path, the longitudinal and lateral flow disturbance forces of the foundation are multiplied by the real-time silt shallow water resistance coefficient. This amplifies and corrects the two types of flow disturbance forces, resulting in the longitudinal and lateral flow disturbance forces, which serve as the external disturbance force inputs for the MPC dynamics model. Downstream navigation lowers the lower limit of thrust demand, while upstream navigation increases the lower limit of thrust constraint. In areas with significant crossflow, the lateral thrust constraint range is expanded.

[0094] Construct a dual-objective weighted cost function ,in, To predict the actual position of the formation after step i at time t. For reference to the target's track position, To preset the trajectory error weights, To preset energy consumption weights, To predict the total thrust modulus of the formation in the time domain (based on the prediction time domain length of model predictive control, solving for the longitudinal and lateral resultant forces of the formation at each prediction step in the time domain, calculating the instantaneous thrust modulus of a single step using the plane L2 norm, and summing the instantaneous thrust modulus at all moments in the prediction time domain), characterizing the energy consumption, dynamic equality constraints (integrating shallow water drag coefficient, mother ship inertia, wind disturbance force, and tidal current), thrust inequality constraints, channel geometry constraints, and velocity constraints are applied. A rolling optimization using the SQP solver is employed, executing only the first set of control variables in each control cycle, re-collecting data and updating parameters in the next cycle, and repeating the solution to output the expected total longitudinal thrust. Horizontal total expected thrust Total expected torque of bow roll .

[0095] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the process of obtaining the initial thrust command corresponding to each thruster of a tugboat in a multi-tugboat cooperative thrust intelligent allocation and compensation method according to some embodiments of this application. According to an embodiment of the present invention, the step of allocating the total expected longitudinal thrust, the total expected lateral thrust, and the total expected bow pitch moment to each tugboat, and combining this with the multi-tugboat formation recording data for coupled wake interference constraint analysis to obtain the initial thrust command corresponding to each thruster of the tugboat, includes:

[0096] S31. Determine the formation arrangement relationship based on the real-time pose, and perform wake loss analysis and processing based on the relative spacing and relative speed of the tugboats to obtain the thrust loss coefficient corresponding to each tugboat.

[0097] S32. Obtain the azimuth angle and rotation arm of the thruster, embed the thrust loss coefficient into the formation resultant force balance equation constraint, and construct a quadratic programming basic model by combining the longitudinal total expected thrust, lateral total expected thrust and yaw total expected torque.

[0098] S33. The basic model of the quadratic programming is solved and analyzed by optimizing the total thrust distribution error and the propeller energy consumption balance, and by loading physical hard constraints on the thrust amplitude, azimuth range and turning rate of the propeller, so as to obtain the initial thrust command corresponding to each propeller of the tugboat.

[0099] It is important to note that the formation is distinguished by the arrangement of tugboats at the front and rear. Along the course of navigation, the high-speed rotation of the propellers of the leading tugboats generates a low-pressure wake region. When the following tugboats enter this region, the propeller's water intake efficiency decreases, resulting in a reduction in actual thrust output at the same throttle. Thrust loss is calculated only for tugboats located within the wake coverage area of ​​other tugboats. The front-to-back arrangement of each tugboat in the formation is determined based on the real-time GNSS-RTK attitude (i.e., which tugboats are located in the wake regions behind which tugboats), combined with the relative spacing acquired by 4D millimeter-wave radar. and relative speed Substitute the wake loss function calibrated based on measured towing test data from the port area. The smaller the distance, the greater the speed difference. The smaller the size (the more severe the thrust decay), the less wake coverage the tugboat. The function model can be pre-calibrated offline through multi-ship convoy towing tests in the port area and then solidified into the controller program; the nominal output thrust is based on a convoy of n tugboats, each equipped with m azimuth thrusters. To optimize the variables, a dual optimization objective is constructed: ;in, Let be the azimuth angle of the i-th thruster. For the rotational lever arm of the thrusters relative to the formation, the first term is the resultant force / torque and the expected value of the upper-level MPC ( , , The goal is to minimize the L2 norm error of the thrusters. The second term is the energy balance term, which is the weighted sum of the squares of the thrust of each thruster. Weighting coefficients To control the uniformity of thrust load distribution and prevent single thrusters from being fully loaded or overloaded; three types of hard constraints are set and all are included in the feasible region of the quadratic programming solution, including: thrust amplitude constraints. ( (Rated maximum thrust of the thruster), azimuth constraint (The azimuth thruster can adjust its direction within the full circumference), azimuth rate of change constraint. (Physical limit of maximum steering rate of mechanical steering mechanism); The objective function is rearranged into standard QP form, and iteratively solved within a 200ms control cycle using the interior point method or active set QP solver, outputting the uncompensated nominal thrust of each thruster. and matching azimuth reference values This refers to the initial thrust command corresponding to each propeller of the tugboat.

[0100] According to an embodiment of the present invention, the step of combining the initial thrust command with the mother ship record data, port area environmental record data, and ship equipment record data to perform multi-layer dynamic compensation correction to obtain an optimized thrust command includes:

[0101] The silt loss ratio coefficient is obtained by analyzing and processing the output torque of the propeller in combination with the preset rated torque of the propeller.

[0102] The initial thrust command is optimized based on the silt loss ratio coefficient to obtain silt compensation gain thrust.

[0103] Vector decomposition is performed on the real-time wind field data to obtain the transverse wind speed of the vertical channel;

[0104] The crosswind thrust compensation increment is obtained by analyzing and processing the crosswind speed of the vertical channel in combination with the preset crosswind compensation ratio coefficient and the preset crosswind critical speed. This increment is then summed with the silt compensation gain thrust to obtain the crosswind compensation thrust.

[0105] The crosswind compensation thrust is used as the setpoint of the adaptive PID controller. The output torque of the thruster and the lateral drift of the ship are used as closed-loop feedback quantities for fine-tuning and correction. An optimized thrust command is generated and the target speed and target azimuth angle of the thruster are mapped and output.

[0106] It is important to note that the actual output torque of the thruster is collected in real time via a thruster torque sensor. With respect to the manufacturer's specified preset thruster rated torque Compare and calculate the sludge accumulation loss ratio coefficient. (Due to the silty bottom of the port area, the mud accumulation on the surface of the propeller increases the rotational resistance, resulting in lower rotational speeds.) Below Calculate the compensation gain based on the loss ratio. Basic thrust command output to the middle layer Perform dynamic magnification correction (Limited) The silt compensation gain thrust is obtained, and the steady-state error is controlled within 5%. The real-time wind speed and direction vector (obtained from real-time wind field data analysis, and decomposed by combining the fixed channel orientation angle to separate the transverse wind speed perpendicular to the channel) is decomposed into two orthogonal components by combining the channel azimuth angle: the longitudinal wind speed parallel to the channel. (Input only the MPC dynamics model) and the lateral wind speed of the vertical channel. Only when Crosswind compensation is triggered when the preset critical crosswind speed corresponds to Beaufort level 10, using a piecewise linear model. Calculate the crosswind thrust compensation increment of a single upwind thruster ( This is based on a preset crosswind compensation ratio coefficient determined by the actual ship calibration of the windward area of ​​large bulk carriers in the port area. Simultaneously, an upper limit for the compensation increment is set; if the calculated value exceeds the upper limit, the maximum compensation value is used. The total compensation thrust is distributed among the tugboats on the windward side according to their lateral lever arm weights, while the compensation increment on the leeward side is set to zero; silt compensation increases the thrust. Increased crosswind thrust compensation The summation yields the crosswind-compensated thrust, which serves as the setpoint input for the adaptive PID controller. The PID controller outputs the thruster torque. The ship's lateral drift (calculated in real time between GNSS-RTK attitude and target trajectory lateral deviation) is used as a closed-loop feedback quantity for fine-tuning and correction to eliminate residual errors. Finally, the target speed and target azimuth angle of each azimuth thruster are mapped and output, which is the optimized thrust command.

[0107] According to an embodiment of the present invention, it further includes:

[0108] The trajectory tracking correction weight is obtained by analyzing and processing the mother ship tonnage and real-time tow cable tension in combination with the preset mother ship tonnage threshold and the preset port area benchmark tow cable tension.

[0109] The trajectory tracking correction weights are sent back to the upper-level MPC controller to dynamically update the trajectory tracking weights.

[0110] It is important to note that, firstly, the tonnage of the mother ship... Piecewise interpolation is used to solve for the inertia coefficient of the foundation at different tonnage levels. :when At 10,000 tons, ,when 10,000 tons per hour Intermediate value linear interpolation The larger the tonnage, the higher the foundation inertia; then, the real-time tension of the towing cable... (Fiber Bragg grating tension sensor sampling at 10Hz) and preset port area reference towing cable tension (50,000-ton no-load standard tension, calibration constant) Calculate the tension correction coefficient (Limited to 0.7-1.3), if , ,like , Amplifying the inertial effect, if , To mitigate the inertial effect, the greater the tension, the stronger the rigid coupling between the tugboat and the mother ship, and the more significant the inertial coupling effect. Finally, the total inertial correction coefficient is obtained through coupling. (Limited to 0.42-1.3) to prevent hull vibration caused by sudden changes in MPC weights under extreme operating conditions; trajectory tracking corrects weights. ,when (For large tonnage vessels with high towing cable tension) increasing the tracking weight allows the MPC to output thrust more quickly to counteract the inertia of the large ship. (For small tonnage or slack towing cable) the tracking weight is reduced to save energy, and the tracking weight is updated synchronously every 200ms control cycle.

[0111] It is worth mentioning that, according to embodiments of the present invention, it further includes:

[0112] The output torque of the thruster is compared with the preset theoretical output torque of the thruster to obtain the thruster torque reduction rate for a single tug wheel;

[0113] The thruster torque reduction rate is compared with a preset thruster torque reduction rate threshold.

[0114] If the thruster torque reduction rate is less than a preset thruster torque reduction rate threshold, monitoring continues.

[0115] If the thruster torque reduction rate is greater than or equal to the preset thruster torque reduction rate threshold, the tugboat thruster is determined to be faulty, and the upper limit constraint of the thruster thrust is set to zero and removed from the set of quadratic programming optimization variables. The quadratic programming basic model of the coupled wake interference constraint is reconstructed with all the thrusters of the remaining normal tugboats as the new set of optimization variables, and the quadratic programming reconstruction basic model is obtained.

[0116] Based on the reconstructed basic model of the quadratic programming, the thrust output ratio of the normal tugboat propeller is dynamically adjusted upwards. The reconstructed propeller thrust distribution scheme maintains the error between the total force and total torque of the formation and the expected value of the upper MPC, and generates the reconstructed thrust command.

[0117] It is important to note that the thruster torque sensor continuously collects the actual output torque of each thruster with a control cycle of 200ms. The theoretical output torque of the thruster is preset according to the MPC theory allocation during the current control cycle. The thruster torque reduction rate refers to When the thruster torque decreases by 20%, the tugboat's thruster is deemed to have malfunctioned (possible causes include foreign objects entangled in the propeller, motor overload protection, or drive shaft breakage). After the malfunction is detected, the system immediately sets the upper limit of thrust constraints for all thrusters of the malfunctioning tugboat to zero, removes its nominal thrust variable from the set of optimization variables in the QP solution, and uses all thrusters of the remaining normal tugboats as the new set of optimization variables. The system uses the total expected force and torque of the formation output by the upper-level MPC as the equality constraint objective, and the upper limit of thrust, azimuth range, and turning rate of the normal thrusters as the inequality constraints. The QP model is then reconstructed and solved. After reconstruction, the QP solution still aims to minimize the error between the overall resultant force / torque of the formation and the expected value of the upper-level MPC. By dynamically increasing the thrust output of the remaining normal tugboats, the system compensates for the thrust gap of the malfunctioning tugboat, maintains the stability of the total resultant force and torque of the formation, and avoids drifting or grounding accidents of the mother ship due to a sudden drop in resultant force.

[0118] It is worth mentioning that, according to embodiments of the present invention, it further includes:

[0119] Obtain the work mode switching path and determine the compliance status of the work mode switching path;

[0120] If the compliance status is switch compliance, then security checks are performed on the communication link packet loss rate, relative distance, and relative speed; if the compliance status is switch non-compliance, then mode switching is prohibited.

[0121] If all safety checks pass, a second-order low-pass filter with a preset time constant is used to smoothly interpolate the MPC trajectory tracking weights and thrust allocation parameters; otherwise, mode switching is prohibited.

[0122] Within the mode switching transition state window, the closest encounter distance and the closest encounter time between the target vessel and the tugboat formation are calculated in real time.

[0123] The nearest encounter distance is compared with the preset collision safety warning distance, and the nearest encounter time is compared with the preset safety time threshold. Based on the threshold comparison, the execution mode is either paused or executed normally.

[0124] It is important to note that the four operational modes—freedom of navigation, coordinated berthing, coordinated departure, and coordinated turning—are defined as four mutually exclusive state nodes. The state machine internally stores compliant switching paths (e.g., switching from free of navigation to coordinated berthing is a compliant path, while switching from coordinated departure to coordinated berthing is a non-compliant path). Only mode transitions along preset compliant paths are allowed to prevent mode jumps caused by operator error. Three safety checks are performed before mode switching: First, communication link packet loss rate check: within a preset time window, the packet loss rate of the ship-shore communication link is statistically analyzed. If the packet loss rate exceeds 10%, communication is deemed unreliable, and switching is prohibited to prevent disruption during the switching process. The following measures were implemented: 1) Loss of command caused formation loss of control; 2) Relative distance verification: Real-time calculation of the relative distance between each tugboat and the mother ship using 4D millimeter-wave radar; switching is prohibited when the distance between any tugboat and the mother ship is less than a preset safe distance threshold to prevent collisions caused by sudden thrust changes during mode switching; 3) Relative speed verification: Real-time calculation of the relative speed between each tugboat and the mother ship; switching is prohibited when the relative speed exceeds a preset safe speed threshold to prevent tail-end collision risks; During the switching process, key control quantities such as MPC weights and thrust distribution parameters are smoothly interpolated using a second-order low-pass filter with a time constant of 0.5s. The transfer function of the second-order filter is... ,in, This is the complex frequency domain transfer function of a second-order low-pass filter. The input is the old mode parameters, and the output is the new mode parameters after a smooth transition. Let Laplace be a complex variable, and let it be a differential operator. The preset damping ratio is set to 0.707. The preset time constant is set to 0.5s, allowing parameters to smoothly transition from the old mode value to the new mode value, avoiding abrupt changes that could cause fluctuations in the ship's position. Within the mode switching transition window (usually lasting 1-2 seconds), based on the real-time attitude, speed, and heading data of each tugboat in the formation and third-party vessels around the channel, the closest encounter distance (CPA) and closest encounter time (TCPA) between each target vessel and the formation are continuously calculated. When CPA < 0.3 nautical miles but TCPA < 0: the two vessels are moving away from each other, there is no risk of collision, and there is no need to interrupt the mode switching. When CPA < 0.3 nautical miles and TCPA ≥ 5 minutes: the danger point is close, but it will take a long time to arrive, and a smooth switch and avoidance can be completed. When CPA < 0.3 nautical miles and 0 ≤ TCPA < 5 minutes: the vessel will meet at close range in a short time, and the switch must be stopped immediately and collision avoidance thrust distribution must be executed.

[0125] Please refer to Figure 4 , Figure 4 This is a system block diagram of a multi-tugboat cooperative thrust intelligent distribution and compensation system in some embodiments of this application.

[0126] This invention also discloses a multi-tugboat cooperative thrust intelligent distribution and compensation system, comprising:

[0127] The module consists of a working condition perception and acquisition module, an upper-level trajectory tracking MPC module, a middle-level coupled wake secondary planning thrust allocation module, and a lower-level multi-compensation adaptive PID execution module.

[0128] The working condition sensing and acquisition module is used to acquire multi-source working condition recording data;

[0129] The upper-level trajectory tracking MPC module incorporates a dynamic correction model for silt and shallow water resistance and an adaptive compensation model for mother ship inertia, outputting the total expected longitudinal thrust, the total expected lateral thrust, and the total expected bow pitch torque.

[0130] The mid-level coupled wake secondary planning thrust allocation module pre-stores a mathematical model of wake interference of multiple tugboats, and solves the initial thrust command corresponding to each tugboat thruster with the physical limit of the thruster as a constraint.

[0131] The lower-level multi-compensation adaptive PID execution module integrates a silt accumulation loss compensation unit, a crosswind feedforward compensation unit, and a mother ship inertial load compensation unit, and outputs a corrected optimized thrust command.

[0132] It is important to note that the operational condition perception and acquisition module consists of seven types of physical sensors: an echo sounder, wind speed and direction sensors, thruster torque sensors, fiber optic tension sensors, a GNSS-RTK positioning unit, a 4D millimeter-wave radar, and a mother ship AIS receiver unit. This module is responsible for the real-time acquisition and spatiotemporal synchronization of multi-source operational condition data. The upper-level trajectory tracking MPC module is deployed on the shipboard industrial control computer and incorporates a port area electronic nautical chart database, a tidal current database, a dynamic correction model for silt and shallow water resistance, and a mother ship inertial adaptive compensation model. It uses the SQP solver rolling... The system optimizes the output of the total expected force and torque of the formation; the mid-level coupled wake secondary programming thrust allocation module pre-stores the wake loss function of multiple tugboats based on the measured calibration in the port area, and solves the basic thrust allocation value of each thruster by means of the physical limit of the thruster through the interior point method or the active set QP solver; the lower-level multi-compensation adaptive PID execution module integrates three parallel compensation units, including a silt accumulation loss compensation unit, a crosswind feedforward compensation unit, and a mother ship inertial load compensation unit, and outputs the target speed and azimuth control command of the thruster after hierarchical correction of the basic thrust command.

[0133] It is worth mentioning that, according to embodiments of the present invention, it further includes:

[0134] Fault monitoring and thrust reconfiguration module, smooth switching control module for operation mode, and redundant ship-to-shore communication interaction module;

[0135] The fault monitoring and thrust reconfiguration module includes a torque comparison unit, a fault determination unit, and a thrust reconfiguration unit. The torque comparison unit collects the actual output torque of each thruster and calculates the difference with the theoretically allocated torque. The fault determination unit determines the fault based on the torque reduction rate of the thruster of a single tugboat. After the fault determination, the thrust reconfiguration unit sets the upper limit constraint of the faulty thruster to zero and re-executes the secondary planning to complete the formation thrust redistribution.

[0136] The operation mode smooth switching control module includes a state machine management unit, a safety verification unit, a parameter smoothing unit, and an emergency collision avoidance unit. The state machine management unit stores the compliant operation mode switching path. The safety verification unit verifies three safety thresholds before switching: communication packet loss rate, relative distance, and relative speed. The parameter smoothing unit uses a second-order low-pass filter with a preset time constant to smooth the MPC control parameters. The emergency collision avoidance unit calculates the closest encounter distance and the closest encounter time in the transition state, and suspends the mode switching or performs normal mode switching based on the threshold comparison results.

[0137] The ship-shore redundant communication interaction module connects to the 700M 5G main link and the 5.8GHz marine self-organizing network backup link, and monitors the packet loss rate and bandwidth of the main link in real time. When the packet loss rate exceeds the threshold or the bandwidth does not meet the requirements, it automatically switches to the backup link.

[0138] It is important to note the following modules: Fault Monitoring and Thrust Reconfiguration Module: Provides the system with automatic detection of single tug propeller faults and millisecond-level thrust redistribution capability for the formation. The torque comparison unit performs difference calculations based on torque sensor data. The fault judgment unit uses a 20% thrust reduction as the judgment threshold. The thrust reconfiguration unit completes QP model reconstruction and solution within 300ms and outputs the redistributed thrust scheme. Operation Mode Smooth Switching Control Module: Provides the system with safe switching and emergency collision avoidance capabilities for four types of operation modes. The state machine management unit solidifies compliant switching paths. The safety verification unit performs three-threshold verification. The parameter smoothing unit uses a second-order low-pass filter with a 0.5-second time constant to eliminate switching oscillations. The emergency collision avoidance unit uses a collision risk judgment threshold of 0.3 nautical miles CPA and 5 minutes TCPA to achieve automatic collision avoidance during the transition phase. Ship-Shore Redundant Communication Interaction Module: Provides highly reliable and low-latency data communication guarantees for multi-tug formations. The 700M 5G main link provides high-speed transmission within the port area coverage, the 5.8GHz maritime self-organizing network backup link solves the 5G blind spot problem in the outer channel, the LZ4 compression algorithm reduces the data packet size, and the dual-link automatic switching ensures that the thrust data synchronization delay is <=100ms.

[0139] This invention discloses a method and system for intelligent distribution and compensation of collaborative thrust among multiple tugboats. By constructing a hierarchical MPC and secondary planning thrust distribution architecture, it integrates shallow water resistance dynamic compensation, wake interference coupling compensation, crosswind feedforward compensation, propeller mud accumulation loss compensation, and mother ship inertia adaptive compensation, thereby realizing intelligent distribution and compensation of collaborative thrust among multiple tugboats.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0141] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0143] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for intelligent distribution and compensation of cooperative thrust from multiple tugboats, characterized in that, Includes the following steps: Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data; Based on the mother ship record data and port area environmental record data, the longitudinal total expected thrust, lateral total expected thrust and bow total expected torque are obtained through hierarchical model predictive control analysis. The total expected longitudinal thrust, total expected lateral thrust, and total expected bow pitch torque are allocated to each tugboat, and coupled wake interference constraint analysis is performed in conjunction with the multi-tugboat formation data to obtain the initial thrust command corresponding to each tugboat's propeller. The initial thrust command is combined with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction, thereby obtaining an optimized thrust command.

2. The method for intelligent distribution and compensation of multi-tugboat cooperative thrust according to claim 1, characterized in that, The acquisition of multi-source operational condition record data includes multi-tugboat convoy record data, mother ship record data, port area environmental record data, and ship equipment record data, including: Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data; The multi-tugboat formation data includes real-time position and attitude, real-time speed, relative distance between tugboats, and relative speed between tugboats; The mother ship recorded data includes mother ship tonnage, mother ship acceleration, mother ship real-time turning angular velocity, ship lateral drift, and target trajectory sequence data; The port area environmental record data includes real-time water depth and real-time wind field data; The ship's equipment records data including the thruster output torque and the real-time tension of the towing cable.

3. The method for intelligent distribution and compensation of multi-tugboat cooperative thrust according to claim 2, characterized in that, The step of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected moment through hierarchical model predictive control analysis based on the mother ship record data and port area environmental record data includes: The real-time water depth is subjected to water depth mean filtering to obtain an effective reference water depth, which is then compared with a preset reference water depth threshold. The dynamic silt shallow water resistance coefficient is determined based on the threshold comparison result. The real-time wind field data is decomposed to obtain longitudinal wind disturbance force, lateral wind disturbance force, and yaw disturbance moment; Based on the tonnage of the mother ship, an equivalent inertia matrix of the mother ship is constructed. Matrix operations are then performed using the acceleration and real-time turning angular velocity of the mother ship to obtain the inertial force and inertial torque. The waypoints are subjected to third-order smooth interpolation based on the target trajectory sequence data to generate a set of reference trajectory points with equal time intervals. By obtaining longitudinal and lateral water flow velocities from a pre-set port area tidal real-time database and combining them with the real-time navigation speed, longitudinal water flow disturbance force and lateral water flow drift disturbance force are obtained. The dynamic silt shallow water resistance coefficient is embedded into the preset towed formation dynamics model for model correction. The longitudinal wind disturbance force, lateral wind disturbance force, yaw disturbance moment, inertial force, inertial moment, longitudinal water flow disturbance force, and lateral water flow drift disturbance force are loaded as external time-varying disturbance terms into the dynamic equation. A trajectory tracking cost function with energy consumption weight is constructed using the reference trajectory point set as the tracking benchmark. Preset channel geometry, preset thrust extremum, and preset port area speed limit constraints are loaded. Sequential quadratic programming is used to solve the upper-level trajectory tracking MPC rolling optimization, and the total expected longitudinal thrust, total expected lateral thrust, and total expected yaw moment of the multi-tug formation are output.

4. The method for intelligent distribution and compensation of multi-tugboat cooperative thrust according to claim 3, characterized in that, The process of allocating the total expected longitudinal thrust, total expected lateral thrust, and total expected bow roll moment to each tugboat, and combining this with the multi-tugboat formation data for coupled wake interference constraint analysis, to obtain the initial thrust command corresponding to each tugboat's propeller, includes: Based on the real-time pose determination of the formation's front and rear arrangement, and combined with the relative spacing and relative speed of the tugboats, wake loss analysis is performed to obtain the thrust loss coefficient corresponding to each tugboat. Obtain the azimuth angle and rotation arm of the thruster, embed the thrust loss coefficient into the formation resultant force balance equation constraint, and construct a basic quadratic programming model by combining the longitudinal total expected thrust, lateral total expected thrust and yaw total expected torque. By optimizing both the total thrust distribution error and the propeller energy consumption, and by applying physical hard constraints on the thrust amplitude, azimuth range, and turning rate of the propeller, the basic quadratic programming model is solved and analyzed to obtain the initial thrust commands corresponding to each propeller of the tugboat.

5. The method for intelligent distribution and compensation of multi-tugboat cooperative thrust according to claim 4, characterized in that, The step of combining the initial thrust command with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction to obtain an optimized thrust command includes: The silt loss ratio coefficient is obtained by analyzing and processing the output torque of the propeller in combination with the preset rated torque of the propeller. The initial thrust command is optimized based on the silt loss ratio coefficient to obtain silt compensation gain thrust. Vector decomposition is performed on the real-time wind field data to obtain the transverse wind speed of the vertical channel; The crosswind thrust compensation increment is obtained by analyzing and processing the crosswind speed of the vertical channel in combination with the preset crosswind compensation ratio coefficient and the preset crosswind critical speed. This increment is then summed with the silt compensation gain thrust to obtain the crosswind compensation thrust. The crosswind compensation thrust is used as the setpoint of the adaptive PID controller. The output torque of the thruster and the lateral drift of the ship are used as closed-loop feedback quantities for fine-tuning and correction. An optimized thrust command is generated and the target speed and target azimuth angle of the thruster are mapped and output.

6. The method for intelligent distribution and compensation of multi-tugboat cooperative thrust according to claim 2, characterized in that, Also includes: The trajectory tracking correction weight is obtained by analyzing and processing the mother ship tonnage and real-time tow cable tension in combination with the preset mother ship tonnage threshold and the preset port area benchmark tow cable tension. The trajectory correction weights are sent back to the upper-level MPC controller to dynamically update the trajectory tracking weights.

7. A multi-tugboat cooperative thrust intelligent distribution and compensation system, wherein the multi-tugboat cooperative thrust intelligent distribution and compensation system implements the multi-tugboat cooperative thrust intelligent distribution and compensation method according to any one of claims 1-6, characterized in that, include: The module consists of a working condition perception and acquisition module, an upper-level trajectory tracking MPC module, a middle-level coupled wake secondary planning thrust allocation module, and a lower-level multi-compensation adaptive PID execution module. The working condition sensing and acquisition module is used to acquire multi-source working condition recording data; The upper-level trajectory tracking MPC module incorporates a dynamic correction model for silt and shallow water resistance and an adaptive compensation model for mother ship inertia, outputting the total expected longitudinal thrust, the total expected lateral thrust, and the total expected bow pitch torque. The mid-level coupled wake secondary planning thrust allocation module pre-stores a mathematical model of wake interference of multiple tugboats, and solves the initial thrust command corresponding to each tugboat thruster with the physical limit of the thruster as a constraint. The lower-level multi-compensation adaptive PID execution module integrates a silt accumulation loss compensation unit, a crosswind feedforward compensation unit, and a mother ship inertial load compensation unit, and outputs a corrected optimized thrust command.

8. A multi-tugboat cooperative thrust intelligent distribution and compensation system, characterized in that, Also includes: The system includes a memory and a processor. The memory contains a program for a method of intelligent allocation and compensation of multi-tugboat cooperative thrust. When the program for intelligent allocation and compensation of multi-tugboat cooperative thrust is executed by the processor, it performs the following steps: Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data; Based on the mother ship record data and port area environmental record data, the longitudinal total expected thrust, lateral total expected thrust and bow total expected torque are obtained through hierarchical model predictive control analysis. The total expected longitudinal thrust, total expected lateral thrust, and total expected bow pitch torque are allocated to each tugboat, and coupled wake interference constraint analysis is performed in conjunction with the multi-tugboat formation data to obtain the initial thrust command corresponding to each tugboat's propeller. The initial thrust command is combined with the mother ship's recorded data, port area environmental recorded data, and ship equipment recorded data to perform multi-layer dynamic compensation and correction, thereby obtaining an optimized thrust command.

9. The intelligent distribution and compensation system for multi-tugboat cooperative thrust according to claim 8, characterized in that, The acquisition of multi-source operational condition record data includes multi-tugboat convoy record data, mother ship record data, port area environmental record data, and ship equipment record data, including: Acquire multi-source operational condition record data, including multi-tugboat formation record data, mother ship record data, port area environmental record data, and ship equipment record data; The multi-tugboat formation data includes real-time position and attitude, real-time speed, relative distance between tugboats, and relative speed between tugboats; The mother ship recorded data includes mother ship tonnage, mother ship acceleration, mother ship real-time turning angular velocity, ship lateral drift, and target trajectory sequence data; The port area environmental record data includes real-time water depth and real-time wind field data; The ship's equipment records data including the thruster output torque and the real-time tension of the towing cable.

10. The intelligent distribution and compensation system for multi-tugboat cooperative thrust according to claim 9, characterized in that, The step of obtaining the longitudinal total expected thrust, lateral total expected thrust, and bow total expected moment through hierarchical model predictive control analysis based on the mother ship record data and port area environmental record data includes: The real-time water depth is subjected to water depth mean filtering to obtain an effective reference water depth, which is then compared with a preset reference water depth threshold. The dynamic silt shallow water resistance coefficient is determined based on the threshold comparison result. The real-time wind field data is decomposed to obtain longitudinal wind disturbance force, lateral wind disturbance force, and yaw disturbance moment; Based on the tonnage of the mother ship, an equivalent inertia matrix of the mother ship is constructed. Matrix operations are then performed using the acceleration and real-time turning angular velocity of the mother ship to obtain the inertial force and inertial torque. The waypoints are subjected to third-order smooth interpolation based on the target trajectory sequence data to generate a set of reference trajectory points with equal time intervals. By obtaining longitudinal and lateral water flow velocities from a pre-set port area tidal real-time database and combining them with the real-time navigation speed, longitudinal water flow disturbance force and lateral water flow drift disturbance force are obtained. The dynamic silt shallow water resistance coefficient is embedded into the preset towed formation dynamics model for model correction. The longitudinal wind disturbance force, lateral wind disturbance force, yaw disturbance moment, inertial force, inertial moment, longitudinal water flow disturbance force, and lateral water flow drift disturbance force are loaded as external time-varying disturbance terms into the dynamic equation. A trajectory tracking cost function with energy consumption weight is constructed using the reference trajectory point set as the tracking benchmark. Preset channel geometry, preset thrust extremum, and preset port area speed limit constraints are loaded. Sequential quadratic programming is used to solve the upper-level trajectory tracking MPC rolling optimization, and the total expected longitudinal thrust, total expected lateral thrust, and total expected yaw moment of the multi-tug formation are output.

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