Multi-tugboat thrust distribution method and system based on health weighting and flow field coupling

Abstract

The disclosure provides a multi-tug thrust distribution method and system based on health degree weighting and flow field coupling, relating to the technical field of ship intelligent control, comprising: acquiring real-time system state data and tug instruction tracking error historical data; constructing a real-time health degree evaluation model; determining a flow field coupling interference coefficient according to the geometric position relationship of each tug relative to the ship and adjacent tugs, and constructing a geometric constraint model; based on the geometric constraint model and the health degree evaluation model, taking the minimization of system total energy consumption, error minimization and control quantity smoothing as the goal, determining multi-constraint conditions, and constructing a target cost function based on health degree weighting; using an alternating direction multiplier method-based solving algorithm to solve the target cost function under the multi-constraint conditions, mapping the solved torque vector to the optimal thrust instruction and issuing it to each tug actuator for operation. The disclosure improves the utilization rate and turnover rate of the port berth.

Description

Technical Field

[0001] This disclosure relates to the field of ship intelligent control technology, specifically to a method and system for multi-tugboat thrust distribution based on health weighting and flow field coupling. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] With the increasing trend towards larger ships, the maneuverability of ultra-large vessels during port entry, exit, and berthing has significantly increased. To overcome the reduced rudder effectiveness at low speeds and the interference of wind, waves, and currents, multiple azimuth tugboats are usually required to work in coordination to provide precise lateral thrust and yaw moment.

[0004] Existing multi-tug cooperative control technologies mainly rely on thrust distribution algorithms, which map the total generalized force calculated by the upper-level motion controller into the thrust amplitude and azimuth commands of each tug. The current mainstream approach is based on quadratic programming or pseudo-inverse matrix methods, aiming to minimize total energy consumption through optimization.

[0005] However, existing thrust distribution strategies have two significant technical drawbacks in practical engineering applications: (1) First, there is a lack of dynamic adaptability to the "sub-healthy" state of the actuator. Existing methods usually treat tugboats as either "fully functional" or "fully faulty". When a tugboat exhibits "sub-healthy" characteristics such as overheating of the propeller, sluggish hydraulic response, or increased communication packet loss rate, existing algorithms will still allocate standard rated thrust to it. This will not only lead to insufficient actual output force of the tugboat, causing a decrease in the control accuracy of large ships, but may even accelerate equipment damage and cause safety accidents due to sudden thrust changes.

[0006] (2) Second, it ignores the flow field coupling interference during multi-tugboat operations. During berthing operations, multiple tugboats often gather on one side of the hull, in close proximity. When the propeller wake of one tugboat impacts the hull of an adjacent tugboat or the hull of a larger ship, it will generate significant parasitic drag or thrust reduction effects. Existing algorithms assume that the thrust of each tugboat acts independently and do not consider this flow field coupling effect, resulting in the calculated thrust command being greatly reduced in the actual physical field, causing various ineffective energy consumptions due to "force against force". Summary of the Invention

[0007] To address the aforementioned issues, this disclosure proposes a multi-tug thrust allocation method and system based on health-weighted and flow field coupling. By introducing a health assessment model and a flow field coupling constraint mechanism, it achieves smooth soft unloading of faulty tugs and automatic avoidance of ineffective thrust sectors, thereby improving the robustness, safety, and energy efficiency of the multi-tug cooperative control system.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions: A multi-tug thrust allocation method based on health-weighted and flow-field coupled methods includes: Obtain real-time system status data and historical data on tugboat command tracking errors; A real-time health assessment model is constructed based on system status data and historical data on tugboat command tracking errors. Based on the geometric positional relationships of each tugboat relative to the ship and between adjacent tugboats, the flow field coupling interference coefficient is determined, and a geometric constraint model is constructed. Based on the geometric constraint model and the health assessment model, with the objectives of minimizing the total energy consumption, minimizing the error, and smoothing the control quantity, multiple constraints are determined, and a health-weighted objective cost function is constructed. An algorithm based on the alternating direction multiplier method is used to solve the objective cost function under multiple constraints. The obtained torque vector is mapped into the optimal thrust command and sent to each tugboat actuator.

[0009] According to some embodiments, the present disclosure adopts the following technical solutions: A multi-tug thrust distribution system based on health-weighted and flow-field coupled systems includes: The data acquisition module is used to acquire real-time system status data and historical data on tugboat command tracking errors; The health assessment module is used to build a real-time health assessment model based on system status data and historical data on tugboat command tracking errors. The flow field coupling constraint determination module is used to determine the flow field coupling interference coefficient and construct the geometric constraint model based on the geometric positional relationship between each tugboat and the ship and between adjacent tugboats. The objective function construction module is used to determine multiple constraints and construct a health-weighted objective cost function based on the geometric constraint model and the health assessment model, with the objectives of minimizing the total energy consumption, minimizing the error, and smoothing the control quantity. The optimization solution module is used to solve the objective cost function under multiple constraints using a solution algorithm based on the alternating direction multiplier method. The obtained torque vector is mapped into the optimal thrust command and sent to each tugboat actuator for operation.

[0010] According to some embodiments, the present disclosure adopts the following technical solutions: A computer program product includes a computer program that, when executed by a processor, implements the multi-tug thrust distribution method based on health-weighted and flow field coupling.

[0011] According to some embodiments, the present disclosure adopts the following technical solutions: A non-transitory computer-readable storage medium is provided for storing computer instructions, which, when executed by a processor, implement the multi-tug thrust allocation method based on health-weighted and flow field coupling.

[0012] According to some embodiments, the present disclosure adopts the following technical solutions: An electronic device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-tug thrust distribution method based on health weighting and flow field coupling.

[0013] Compared with the prior art, the beneficial effects of this disclosure are as follows: This disclosure presents a multi-tugboat thrust allocation method based on health-weighted and flow-field coupling. By introducing a health assessment model and a flow-field coupling constraint mechanism, and considering the evolution of actuator health status and hydrodynamic interference between tugboats, it achieves smooth soft unloading of faulty tugboats and automatic avoidance of ineffective thrust sectors, thereby improving the robustness, safety, and energy efficiency of the multi-tugboat cooperative control system. It is applicable to automatic or assisted berthing operations for ultra-large container ships, tankers, and bulk carriers in complex and confined waters.

[0014] This disclosed multi-tug thrust distribution method based on health-weighted and flow field coupling differs from the traditional "all or nothing" hard switching logic. It achieves a smooth transition during fault processes through health-weighted calculation. When the tug's performance slightly degrades, the algorithm automatically reduces its load, ensuring control continuity and preventing further equipment damage, thus improving the system's fault tolerance and safety.

[0015] The multi-tug thrust allocation method based on health weighting and flow field coupling disclosed herein can automatically avoid invalid angles that cause "thrust reduction" through flow field coupling constraints, reduce mutual interference between tugs, make the theoretically calculated thrust more consistent with the actual thrust acting on the hull, and improve the actual thrust efficiency.

[0016] The multi-tug thrust allocation method based on health-weighted and flow field coupling disclosed herein incorporates the uncertainty (health) of the actuator into closed-loop optimization, making the thrust allocation result more consistent with the actual capability boundary of the physical system, reducing trajectory tracking errors caused by actuator saturation or response lag, and enhancing control accuracy.

[0017] The disclosed method for multi-tug thrust distribution based on health-weighted and flow field coupling introduces shore-based sensing data and anti-collision fender constraints, effectively eliminating the risk of scraping of terminal facilities caused by blind pushing during traditional berthing, and improving the safety of port operations. At the same time, precise thrust distribution reduces the time spent on repeated adjustments of vessel position, significantly shortens the berthing time of a single vessel, and improves the utilization and turnover rate of port berths. Attached Figure Description

[0018] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0019] Figure 1 This is a schematic diagram of the multi-tug thrust distribution method based on health weighting and flow field coupling according to an embodiment of the present disclosure. Detailed Implementation

[0020] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] Example 1 One embodiment of this disclosure provides a method for multi-tugboat thrust allocation based on health-weighted and flow field coupling, the method steps including: Step 1: Obtain real-time system status data and historical data on tugboat command tracking errors; Step 2: Based on system status data and historical data on tugboat command tracking errors, construct a real-time health assessment model; Step 3: Based on the geometric positional relationships of each tugboat relative to the ship and between adjacent tugboats, determine the flow field coupling interference coefficient and construct a geometric constraint model; Step 4: Based on the geometric constraint model and health assessment model, with the objectives of minimizing total system energy consumption, minimizing error, and smoothing control input, determine multiple constraints and construct a health-weighted objective cost function. Step 5: The objective cost function under multiple constraints is solved using a solution algorithm based on the alternating direction multiplier method. The obtained torque vector is then mapped to the optimal thrust command and sent to each tugboat actuator.

[0024] As one embodiment, the multi-tug thrust allocation method based on health-weighted and flow-field coupling disclosed herein improves the robustness, safety, and energy efficiency of the multi-tug cooperative control system by introducing a health assessment model and a flow-field coupling constraint mechanism, thereby achieving smooth soft unloading of faulty tugs and automatic avoidance of ineffective thrust sectors. The specific implementation process is as follows: Step 1: Obtain real-time system status data and historical data on tugboat command tracking errors; First, system initialization and status awareness are performed. System status data is collected in real time via the ship-to-shore communication link, including the ship's position obtained through onboard sensors. and speed The relative distance between ships and the quay wall is obtained through the port shore-based sensing system. Approach speed Real-time tidal and instantaneous wind field data; and thrust feedback transmitted back via the tugboat's bottom-level controller. Main unit speed and communication delay These actuator operating parameters are collected. This shore-based data is exchanged with the tugboat formation via a low-latency 5G port private network, serving as the global environmental constraint input for the thrust allocation algorithm.

[0025] Step 2: Based on system status data and historical data on tugboat command tracking errors, construct a real-time health assessment model; Based on historical command tracking error data and equipment operating parameters of each tugboat, a normalized health index is calculated. ,in 1 indicates complete health, and 0 indicates complete failure.

[0026] Specifically, a forgetting factor is introduced to regulate the degree of memory for historical health states, and performance decay causation and equipment state penalty terms are introduced. A first-order forgetting filter algorithm is used to calculate the... tugboats Real-time health index at any moment The real-time health assessment model is calculated using the following formula:

[0027] in, It is a forgetting factor used to regulate the degree of memory of historical health status; These are the thrust command value from the previous moment and the thrust feedback value from the current moment, respectively. For the first The rated maximum thrust of a tugboat; This is the residual normalized mapping function, which characterizes the performance degradation caused by response bias; This is a penalty item for equipment status; it applies when abnormal oil temperature or pressure is detected. The weight will be reduced accordingly.

[0028] Step 3: Based on the geometric positional relationships of each tugboat relative to the ship and between adjacent tugboats, determine the flow field coupling interference coefficient and construct a geometric constraint model; This disclosure obtains the geodetic coordinates of the ship and each tugboat through multi-source sensor fusion, and after coordinate system transformation, accurately calculates the relative position vector of each tugboat in the local coordinate system of the ship and the relative azimuth angle between adjacent tugboats. Secondly, the propeller wake interference region is modeled as a dynamic two-dimensional sector in geometric space, with the current position of the force-applying tugboat as the vertex and its thrust direction angle as the [missing information]. The central axis is defined by a pre-set propeller wake diffusion half-angle. With the effective radius of the flow field disturbance Together, we define the boundaries of their function.

[0029] Subsequently, the system calculates the flow field disturbance criterion in real time based on the above geometric model. This is to detect whether adjacent tugboats have fallen into the wake impact sector. When it is determined that there is flow field coupling interference (i.e., To ensure the effectiveness of subsequent convex optimization solutions, the normal vector of the hyperplane is calculated. This strictly linearizes the original dynamic sector into a thrust vector. Projective inequality constraints .

[0030] Furthermore, based on the relative positions of each tugboat in the large ship's coordinate system and its current thrust direction, a tugboat coordinate system is established. For adjacent tugboats Flow field disturbance criterion function :

[0031] in, Tugs and The position coordinate vector; For tugboats The thrust direction angle; For from tugboat Pointing to the tug The relative azimuth angle; The propeller wake diffusion half-angle threshold; Let be the effective radius of the flow field disturbance. If flow field interference is detected, thrust direction taboo constraints need to be introduced in subsequent optimization.

[0032] Step 4: Based on the geometric constraint model and health assessment model, with the objectives of minimizing total system energy consumption, minimizing error, and smoothing control input, determine multiple constraints and construct a health-weighted objective cost function. This disclosure introduces a health-weighted matrix into the objective function, which aims to minimize total system energy consumption, minimize error, and smooth control input. The construction of this matrix directly depends on the health index output by the aforementioned real-time health assessment model. Its diagonal elements are The mapping shows that the lower the health of the tugboat, the greater its penalty weight in the optimization objective.

[0033] The coupling logic between the above evaluation model and constraint model is as follows: the exponent output by the real-time health evaluation model is used to dynamically update the weighting matrix in the cost function (changing the thrust distribution tendency) and the physical thrust upper limit constraint (shrinking the capability boundary); simultaneously, the flow field disturbance criterion result output by the geometric constraint model is used to determine whether to add additional flow field collision avoidance inequality constraints to the solver. The objective cost function and all the above dynamic constraints together constitute the standard constrained quadratic programming model to be solved, and its specific objective cost function is as follows:

[0034] in, Let be the thrust amplitude vector of each tugboat to be solved; It is a basic energy consumption weight matrix used to balance the load distribution among the tugboats; For a health-weighted matrix, its diagonal elements ,in This is the gain coefficient. It is a tiny positive number. This item ensures health. The lower the tug's weight, the greater the penalty weight in the objective cost function; This is a vector of slack variables used to handle small errors in the dynamic equations; The penalty matrix for slack variables; This is the thrust rate vector; This is the smoothness weight matrix.

[0035] Step 5: The objective cost function under multiple constraints is solved using a solution algorithm based on the alternating direction multiplier method. The obtained torque vector is then mapped to the optimal thrust command and sent to each tugboat actuator.

[0036] This disclosure transforms the problem into a constrained quadratic programming problem under multiple constraints. To meet the millisecond-level real-time requirements of ship berthing control systems, this disclosure employs an OSQP numerical solver based on the alternating direction multiplier method to solve the constrained quadratic programming problem under multiple constraints.

[0037] The above multi-constraint optimization problem is solved iteratively to obtain the total demand force / torque vector output by the upper-level motion controller. The mapping to the optimal thrust command must satisfy the following constraints during the solution process: (1) Dynamic equilibrium constraints: ,in For thrust configuration matrix; (2) Physical constraints based on health status: That is, the upper limit of thrust decreases dynamically with health status; (3) Simultaneously, safety protection constraints for wharf facilities are added. Based on the maximum allowable pressure and shear force limits of the anti-collision fenders provided by the port, the following inequality constraints are established:

[0038] in, The angle between the tugboat thrust and the normal to the shore wall. As for berthing distance The function of maximum permissible impact force for varying quay fenders; (4) For the judgment in step 3 as Interference conditions (i.e., tugboats) The thrust was directed at the tugboat. (The wake-sensitive region), constructing thrust projection constraints based on the separating hyperplane:

[0039] in, tugboat thrust vector ; The normal vector of the boundary of the flow field disturbance sector. It can force the tugboat to move. The thrust vector projection is located at the Centered on, half-angle as Outside the cone, thus achieving active avoidance of flow field disturbances within the convex optimization framework.

[0040] Furthermore, command smoothing and issuance are performed. The optimal thrust command obtained from the solution is smoothed in the time domain and converted into control signals for the underlying actuators.

[0041] At the same time, the system will calculate the real-time health index of the tugboat. Real-time feedback is uploaded to the port's intelligent dispatch cloud platform. This feedback is based on the health status of a particular tugboat. When the load remains below the preset safety threshold, the system not only performs soft unloading of thrust at the algorithm level, but also automatically sends a capacity degradation warning to the port dispatch center. Based on this, the port dispatch system determines whether to dispatch backup tugboats or adjust the berth allocation plan.

[0042] Example 2 One embodiment of this disclosure provides a multi-tug thrust distribution method based on health-weighted and flow field coupling, using a multi-tug thrust distribution system consisting of four azimuth-rotor tugs (labeled as...). Taking the scenario of collaboratively assisting a 200,000-ton large container ship in berthing operations as an example, the port side is equipped with a shore-based lidar monitoring system and a 5G intelligent dispatch network. The specific implementation process of the method in this scenario is as follows: Step (1): System dynamics description and problem definition.

[0043] Let the position of the large ship in the horizontal plane and its bow vector be... The velocity vector is The total desired control force / torque for the three degrees of freedom calculated by the upper-level motion controller (such as Model Predictive Control, MPC) is: The core of the thrust distribution problem is to solve for the thrust command vector of each tugboat. and angle vector This makes the resultant torque generated by each tugboat approach the desired value:

[0044] in, This is the thrust amplitude vector; This is the thrust angle vector; The thrust configuration matrix is ​​defined by the positions of the action points of each tugboat in the ship's coordinate system. and thrust direction Decide; This is a vector of slack variables used to handle small physical infeasibility errors in the dynamic equations.

[0045] Step (2): Real-time health assessment of the tugboat.

[0046] To quantify the degree of performance degradation of tugboat actuators, this disclosure defines the following... tugboats Normalized Health Index at any given moment First, calculate the thrust response residuals. :

[0047] in, The thrust is the force generated by the command issued in the previous moment. The measured feedback thrust at the current moment (estimated using a shaft power or speed model). Define a health iterative function based on forgetting filtering:

[0048] in, The forgetting factor (set to 0.9) is used to smooth historical state fluctuations. For the first i The rated maximum thrust of a tugboat; For nonlinear mapping functions (such as Sigmoid variants), the normalized residuals are mapped to efficiency decay coefficients; This is a penalty item based on equipment status. When sensors send alarms (such as excessively high oil temperature or packet loss), Normal time .

[0049] Furthermore, The range of values ​​is strictly limited to between: (1) The tugboat is in optimal condition.

[0050] (2) The tugboat is in a sub-healthy state (e.g., damaged propeller, insufficient power).

[0051] (3) The tugboat is completely unusable.

[0052] Step (3): Modeling of flow field coupling interference.

[0053] When multiple tugboats are operating in close formation, the "wake effect" must be avoided. Assuming the tugboats... The position is tugboat The position is Definition from point to The relative azimuth angle is Establish flow field disturbance criteria. :

[0054] in, The effective radius of the flow field disturbance; The propeller wake diffusion half-angle (usually taken as 15°-20°).

[0055] like Then it is considered a tugboat The wake will impact the tugboat. To avoid this situation, this disclosure introduces a separating hyperplane linear constraint in the optimization problem, forcing the thrust vector to be projected into the safe region:

[0056] in, For thrust vector, This is the normal vector of the boundary of the interference sector.

[0057] Furthermore, based on health-weighted optimization, this disclosure constructs a quadratic programming (QP) problem of the following form, with the objective function being:

[0058] in, This represents the energy consumption term, namely the sum of squares of thrust. The basic energy consumption weight matrix (identity matrix); As a core innovation item; This is a slack variable penalty term, which allows for a small deviation in the total force under extreme conditions, ensuring that the algorithm has a solution; This is a penalty term for the rate of change of thrust, to prevent drastic fluctuations in the thruster output. For a health-weighted matrix, its diagonal elements Defined as:

[0059] When a tugboat's health When it decreases, the corresponding weighting coefficient The sharp increase forces the optimizer to minimize the tugboat's thrust while maintaining overall force balance. This allows for automatic "soft uninstallation".

[0060] Furthermore, the constraints are as follows: (1) Equality constraints (force balance):

[0061] (2) Physical constraints based on health status:

[0062] Among them, the upper limit of thrust depends on health. Dynamic reduction prevents overloading of malfunctioning tugboats.

[0063] (3) Rate of change constraint:

[0064] (4) Port facility safety protection constraints: To protect the wharf fenders, a total thrust limit based on shore-based distance data is introduced:

[0065] in The angle between the thrust and the normal to the shore wall. This is the allowable impact force function that decreases with berthing distance.

[0066] (5) Flow field coupling constraints: For all satisfied For the tug pair, apply the linear inequality constraint described in step (3).

[0067] As one example, the specific algorithm implementation process of the system is as follows: like Figure 1 As shown, at the start of the control cycle (100ms), the system executes the following process: a. Data Acquisition: The ship's motion status is read through onboard sensors, and the berthing distance is read from the shore-based lidar via the port's 5G network. and the operating parameters of each tugboat.

[0068] b. Health Assessment: Calculate the health vector of each tugboat according to the formula. For example, detected Response lag, calculated .

[0069] c. Parameter update: (1) Update the weight matrix in the QP objective function ,make The weight becomes three times the normal value; (2) The upper limit of thrust is revised to 35% of the rated value; (3) According to Update port fender allowable thrust .

[0070] d. Interference detection: Calculate the relative positions of each tugboat; if interference is detected... The thrust direction may cause scouring Then, the corresponding linear inequality constraint row is generated.

[0071] e. Solver execution: Call an interior-point solver (such as OSQP) to solve the above QP problem.

[0072] f. Results Output and Feedback: (1) The smaller thrust task was automatically assigned to the healthy tugboat. Share the remaining load; (2) The thrust angle automatically avoids the interference zone; (3) The system will The sub-health status of the vessels is uploaded to the port dispatch center in real time, triggering a capacity warning.

[0073] Through the above-described implementation methods, this disclosure not only solves the problem of poor adaptability of traditional allocation strategies, but also realizes proactive protection and green and efficient operation of port facilities through ship-shore collaboration.

[0074] Example 3 One embodiment of this disclosure provides a multi-tugboat thrust distribution system based on health-weighted and flow field coupling, comprising: The data acquisition module is used to acquire real-time system status data and historical data on tugboat command tracking errors; The health assessment module is used to build a real-time health assessment model based on system status data and historical data on tugboat command tracking errors. The flow field coupling constraint determination module is used to determine the flow field coupling interference coefficient and construct the geometric constraint model based on the geometric positional relationship between each tugboat and the ship and between adjacent tugboats. The objective function construction module is used to determine multiple constraints and construct a health-weighted objective cost function based on the geometric constraint model and the health assessment model, with the objectives of minimizing the total energy consumption, minimizing the error, and smoothing the control quantity. The optimization solution module is used to solve the objective cost function under multiple constraints using a solution algorithm based on the alternating direction multiplier method. The obtained torque vector is mapped into the optimal thrust command and sent to each tugboat actuator for operation.

[0075] Example 4 One embodiment of this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the multi-tug thrust distribution method based on health-weighted and flow field coupling.

[0076] Example 5 One embodiment of this disclosure provides a non-transitory computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, they implement the multi-tug thrust allocation method based on health-weighted and flow field coupling.

[0077] Example 6 One embodiment of this disclosure provides an electronic device, including a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-tug thrust distribution method based on health weighting and flow field coupling.

[0078] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A multi-tugboat thrust distribution method based on health-weighted and flow-field coupling, characterized in that, include: Obtain real-time system status data and historical data on tugboat command tracking errors; A real-time health assessment model is constructed based on system status data and historical data on tugboat command tracking errors. Based on the geometric positional relationships of each tugboat relative to the ship and between adjacent tugboats, the flow field coupling interference coefficient is determined, and a geometric constraint model is constructed. Based on the geometric constraint model and the health assessment model, with the objectives of minimizing the total energy consumption, minimizing the error, and smoothing the control quantity, multiple constraints are determined, and a health-weighted objective cost function is constructed. An algorithm based on the alternating direction multiplier method is used to solve the objective cost function under multiple constraints. The obtained torque vector is mapped into the optimal thrust command and sent to each tugboat actuator.

2. The multi-tug thrust distribution method based on health-weighted and flow field coupling as described in claim 1, characterized in that, The system status data and historical data on tugboat command tracking errors are obtained in real time. The system status data includes the vessel's position, speed, relative distance between the vessel and the quay wall, approach speed, real-time tide and instantaneous wind field data, tugboat thrust feedback, main engine speed, and communication delay data. The system status data is exchanged with the tugboat formation through the port's private network with low latency.

3. The multi-tug thrust distribution method based on health-weighted and flow field coupling as described in claim 1, characterized in that, Based on system status data and historical data on tugboat command tracking errors, a real-time health assessment model is constructed, including: Based on historical command tracking error data and system status data for each tugboat, a normalized health index is calculated, where 1 represents complete health and 0 represents complete failure. A forgetting factor is introduced to adjust the degree of memory for historical health states. Performance decay inheritance and equipment status penalty terms are also incorporated. A first-order forgetting filter algorithm is used to calculate the... i tugboats k The real-time health index at any given moment is used to obtain the real-time health assessment model.

4. The multi-tug thrust distribution method based on health-weighted and flow field coupling as described in claim 1, characterized in that, The process of determining the flow field coupling interference coefficient and constructing a geometric constraint model based on the geometric positional relationships of each tugboat relative to the ship and between adjacent tugboats includes: The geodetic coordinates of the ship and each tugboat are obtained by multi-source sensor fusion. After coordinate system transformation, the relative position vector of each tugboat in the local coordinate system of the ship and the relative azimuth angle between adjacent tugboats are accurately calculated. Secondly, the propeller wake interference area is modeled as a dynamic two-dimensional sector in geometric space. The sector takes the current position of the force-applying tugboat as the vertex, its thrust direction angle as the central axis, and its boundary is defined by the preset propeller wake diffusion half angle and the effective radius of the flow field interference. Based on the geometric model, the flow field interference criterion is calculated in real time to detect whether adjacent tugboats fall into the wake impact sector. When the flow field coupling interference is determined to exist, in order to ensure the effectiveness of the subsequent convex optimization solution, the normal vector of the hyperplane is calculated to strictly linearize the original dynamic sector into the projection inequality constraint of the thrust vector.

5. The multi-tug thrust distribution method based on health-weighted and flow field coupling as described in claim 1, characterized in that, Based on the geometric constraint model and health assessment model, with the objectives of minimizing total system energy consumption, minimizing error, and smoothing control input, multiple constraints are determined, and a health-weighted objective cost function is constructed, including: In the objective function aimed at minimizing total system energy consumption, minimizing error, and smoothing control input, a health-weighted matrix is ​​introduced. The diagonal elements of the health-weighted matrix are negatively correlated with the health index, meaning that the lower the health of the tugboat, the greater its penalty weight in the optimization objective. The objective cost function is: in, Let be the thrust amplitude vector of each tugboat to be solved; It is a basic energy consumption weight matrix used to balance the load distribution among the tugboats; For a health-weighted matrix, its diagonal elements ,in This is the gain coefficient. If it is a tiny positive number, this item ensures health. The lower the tug's weight, the greater the penalty weight in the objective cost function; This is a vector of slack variables used to handle small errors in the dynamic equations; The penalty matrix for slack variables; This is the thrust rate vector; This is the smoothness weight matrix.

6. The multi-tug thrust distribution method based on health-weighted and flow field coupling as described in claim 1, characterized in that, The multi-constraint optimization problem is solved iteratively by mapping the torque vector output by the upper motion controller to the optimal thrust command. The constraints that need to be satisfied during the solution process include dynamic equilibrium constraints, physical constraints based on health, safety protection constraints for wharf facilities, and thrust projection constraints.

7. A multi-tugboat thrust distribution system based on health-weighted and flow-field coupling, characterized in that, include: The data acquisition module is used to acquire real-time system status data and historical data on tugboat command tracking errors; The health assessment module is used to build a real-time health assessment model based on system status data and historical data on tugboat command tracking errors. The flow field coupling constraint determination module is used to determine the flow field coupling interference coefficient and construct the geometric constraint model based on the geometric positional relationship between each tugboat and the ship and between adjacent tugboats. The objective function construction module is used to determine multiple constraints and construct a health-weighted objective cost function based on the geometric constraint model and the health assessment model, with the objectives of minimizing the total energy consumption, minimizing the error, and smoothing the control quantity. The optimization solution module is used to solve the objective cost function under multiple constraints using a solution algorithm based on the alternating direction multiplier method. The obtained torque vector is mapped into the optimal thrust command and sent to each tugboat actuator for operation.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-tug thrust distribution method based on health weighting and flow field coupling as described in any one of claims 1-6.

9. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the multi-tug thrust allocation method based on health weighting and flow field coupling as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to enable the electronic device to implement the multi-tug thrust distribution method based on health weighting and flow field coupling as described in any one of claims 1-6.

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