Multi-tug cooperative manipulation control method and system, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a multi-tug cooperative manipulation control method and system, electronic equipment and a storage medium, and belongs to the technical field of ship control. According to the method, current state information, multi-tug manipulation information and environment information of a ship are input into a ship state prediction model to obtain ship prediction state information, the ship prediction state information comprises a prediction trajectory, an optimization function is determined according to the ship prediction state information, the optimization function comprises a trajectory deviation function and a collision risk function, and the trajectory deviation function and the collision risk function are optimized. Secondly, with the minimization of the optimization function as the target, the multi-tug acting force distribution scheme is optimized to obtain the optimal multi-tug acting force distribution scheme, the multi-tug acting force distribution scheme comprises tug manipulation parameters of tugs, the tug manipulation parameters comprise tug power and tug postures, and the tug power and the tug postures are optimized to obtain the optimal multi-tug acting force distribution scheme. And according to the tug operation scheme, single tug instruction adaptation is carried out to obtain a single tug instruction. The safety of the multi-tug cooperative auxiliary ship operation can be improved.

Description

Technical Field

[0001] The present application relates to the field of ship control technology, and in particular to a method, system, electronic device and storage medium for coordinated maneuvering control of multiple tugboats. Background Art

[0002] Tugboats are special vessels that provide professional water services such as towing, pushing, escorting, and rescue for large cargo ships. They are an important safety guarantee for cargo ships entering and leaving ports, and are an indispensable part of port production. Tugboats are usually operated by drivers. When multiple tugboats work together with large cargo ships, due to the different experience levels of the drivers, it is difficult to coordinate and control the large cargo ships during the operation, resulting in the inability to control the cargo ships to follow the route or driving accidents. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a multi-tugboat collaborative maneuvering control method, system, electronic device and storage medium, aiming to improve the safety of multi-tugboat collaborative auxiliary ship operations.

[0004] To achieve the above objectives, one aspect of an embodiment of the present application provides a multi-tugboat coordinated maneuvering control method, comprising the following steps:

[0005] Obtain the current status information of the target ship, multi-tugboat maneuvering information and environmental information;

[0006] Inputting the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into a ship state prediction model to obtain ship predicted state information, wherein the ship predicted state information includes a predicted trajectory;

[0007] Determining an optimization function according to the predicted ship state information, wherein the optimization function includes a trajectory deviation function and a collision risk function;

[0008] Optimizing the multi-tugboat force distribution scheme to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat attitude;

[0009] The single tugboat instruction is adapted according to the multi-tugboat force distribution scheme to obtain a single tugboat instruction.

[0010] In some embodiments, determining the optimization function according to the predicted ship state information comprises the following steps:

[0011] Determining a trajectory deviation function according to the predicted trajectory and the target trajectory in the predicted state information of the ship;

[0012] Determine the collision risk function based on the tugboat position, obstacle position and tugboat force;

[0013] An optimization function is determined according to the trajectory deviation function and the collision risk function.

[0014] In some embodiments, determining the optimization function according to the trajectory deviation function and the collision risk function comprises the following steps:

[0015] determining an energy consumption function based on the tugboat force and the tugboat speed;

[0016] The trajectory deviation function, the collision risk function and the energy consumption function are integrated in a weighted manner to determine an optimization function.

[0017] In some embodiments, the method of optimizing the multi-tugboat force distribution scheme with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme with the goal of minimizing the optimization function includes the following steps:

[0018] Initialize the multi-tugboat force distribution plan;

[0019] Inputting the multi-tugboat force distribution scheme into the ship state prediction model and the tugboat dynamics model respectively to obtain the predicted trajectory of the target ship and the tugboat force of each tugboat;

[0020] determining a value of an optimization function based on the predicted trajectory, the tugboat position of each tugboat, and the tugboat force;

[0021] According to the mapping relationship of the values ​​of the optimization function under different multi-tugboat force distribution schemes, the multi-tugboat force distribution scheme is optimized to obtain the optimal multi-tugboat force distribution scheme.

[0022] In some embodiments, the method of optimizing the multi-tugboat force distribution scheme with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme includes the following steps:

[0023] Taking minimizing the optimization function as the goal, optimizing the multi-tugboat force distribution scheme;

[0024] Inputting the optimized multi-tugboat force distribution scheme and the current state information and environmental information of the ship into the ship state prediction model to obtain new ship prediction state information, wherein the ship prediction state information also includes predicted speed and predicted attitude;

[0025] determining a berthing risk according to the predicted speed and the predicted attitude;

[0026] When the berthing risk is greater than a risk threshold, the multi-tugboat force distribution scheme is adjusted until the berthing risk is less than the risk threshold, thereby obtaining an optimal multi-tugboat force distribution scheme.

[0027] In some embodiments, the step of adapting a single tugboat instruction according to the multi-tugboat force distribution scheme to obtain a single tugboat instruction comprises the following steps:

[0028] Get the current maneuvering parameters of each tugboat;

[0029] determining a steering parameter adjustment amount according to current steering parameters of the target tugboat and steering parameters of the tugboat in the multi-tugboat force distribution scheme;

[0030] A single tugboat instruction is determined according to the manipulation parameter adjustment amount.

[0031] In some embodiments, determining the collision risk function according to the tugboat position, the obstacle position and the tugboat force comprises the following steps:

[0032] Determine the distance between each tugboat and the obstacle based on the tugboat position and the obstacle position;

[0033] Determine the collision risk calculation formula for each tugboat based on the distance between each tugboat and the obstacle and the corresponding tugboat force;

[0034] The collision risk function is obtained by combining the collision risk calculation formulas of various tugboats.

[0035] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides a multi-tugboat coordinated maneuvering control system, comprising:

[0036] The first module is used to obtain the current status information of the target ship, multi-tugboat maneuvering information and environmental information;

[0037] The second module is used to input the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into a ship state prediction model to obtain ship predicted state information, wherein the ship predicted state information includes a predicted trajectory;

[0038] A third module is used to determine an optimization function according to the predicted ship state information, wherein the optimization function includes a track deviation function and a collision risk function;

[0039] The fourth module is used to optimize the multi-tugboat force distribution scheme to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat posture;

[0040] The fifth module is used to adapt the single tugboat instruction according to the multi-tugboat force distribution plan to obtain the single tugboat instruction.

[0041] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program implements the method described in the above embodiment when executed by the processor.

[0042] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage, and the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the above embodiment.

[0043] The multi-tugboat collaborative maneuvering control method, system, electronic device and storage medium proposed in the present application input the current state information of the ship, the maneuvering information of multiple tugboats and the environmental information into the ship state prediction model to obtain the ship predicted state information, the ship predicted state information includes the predicted trajectory, the optimization function is determined according to the ship predicted state information, the optimization function includes the trajectory deviation function and the collision risk function, and then the multi-tugboat force distribution scheme is optimized with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes the tugboat maneuvering parameters of each tugboat, the tugboat maneuvering parameters include the tugboat power and the tugboat posture, and the single tugboat instruction is adapted according to the tugboat maneuvering scheme to obtain the single tugboat instruction. The present application constructs an optimization function based on the predicted state of the ship, and then searches for the optimal multi-tugboat force distribution scheme with the optimization function as the goal, thereby providing a single tugboat instruction for each tugboat, so that the tugboat sails according to the target trajectory during the auxiliary ship operation, reducing the risk of collision. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flow chart of a multi-tugboat coordinated maneuvering control method provided in an embodiment of the present application;

[0045] Figure 2 It is a schematic diagram of an application scenario of the multi-tugboat collaborative maneuvering control method provided in an embodiment of the present application;

[0046] Figure 3 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] It should be noted that, although the functional modules are divided in the system and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] The embodiments of the present application provide a method, system, electronic device and storage medium for controlling coordinated maneuvering of multiple tugboats, aiming to improve the safety of coordinated auxiliary ship operations of multiple tugboats.

[0051] The multi-tugboat collaborative maneuvering control method, system, electronic device and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the multi-tugboat collaborative maneuvering control method in the embodiments of the present application is described.

[0052] The multi-tugboat collaborative maneuvering control method provided in the embodiment of the present application relates to the field of ship control technology. The multi-tugboat collaborative maneuvering control method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the multi-tugboat collaborative maneuvering control method, etc., but is not limited to the above forms.

[0053] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0054] In some embodiments, please refer to Figure 2 The multi-tugboat collaborative maneuvering control method of the embodiment of the present application can be applied to the following scenarios.

[0055] The shore-based distance measurement module (such as a laser radar distance measurement group) and the shore-based communication module are provided on the shore. The ship-based communication module and the central processing unit can be provided on the ship-based end. The central processing unit can execute the multi-tugboat cooperative manipulation control method of the embodiment of the present application to output a single tugboat instruction. The tugboat is also provided with a ship-based communication module, which can receive single tugboat instructions. The ship-side, shore-side and tugboat-side ends can all selectively be provided with an enhanced display module. During operation, the shore-based laser radar distance measurement group transmits the collected information to the shore-based communication module, and the shore-based communication module transmits it to the ship-side central processing unit module to solve the position of the large wheel and give tugboat manipulation suggestions. The shore-based distance measurement module can be distributed on the sea side of the dock wall in an array manner and electrically connected to the shore-based communication module. The shore-based communication module collects the measurement information of the shore-based distance measurement module and sends the information to the ship-based communication module. The ship-based communication module is electrically connected to the central processing unit module deployed on the ship-side. The shore-based distance measurement module can monitor in real time the distance from the bow to the stern of the ship to each point from the extension of the dock (respectively d 艏 ,d 艉 and the middle points of the ship d i ), berthing and unberthing speeds, berthing and unberthing angles, etc., and the information is summarized to the shore-based communication module.

[0056] After receiving information such as the distance to the large-wheelbase terminal, berthing and unberthing speeds, and berthing and unberthing angles, the central processing unit module can calculate the real-time posture and historical posture trajectory of the large ship in this berthing and unberthing operation, and combine the current port area's wind speed, wind direction, tide level, flow velocity and flow direction environmental data as well as the large ship's hydrodynamic model to predict the future state of the large ship's berthing and unberthing operation through the built-in BP neural network algorithm. Then, combined with the adaptive learning neural network, it continuously deduces appropriate maneuvering suggestions, thereby giving a reasonable maneuvering plan, which can be converted into a single-ship maneuvering instruction suitable for the tugboat and output to the enhanced display module.

[0057] Figure 1 is an optional flow chart of a multi-tugboat coordinated maneuvering control method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0058] Step S101, obtaining the current state information of the target ship, multi-tugboat maneuvering information and environmental information;

[0059] Step S102, inputting the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into the ship state prediction model to obtain the ship predicted state information, wherein the ship predicted state information includes the predicted trajectory;

[0060] Step S103, determining an optimization function according to the predicted ship state information, wherein the optimization function includes a track deviation function and a collision risk function;

[0061] Step S104, with the goal of minimizing the optimization function, optimizing the multi-tugboat force distribution scheme to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat attitude;

[0062] Step S105, adapting the single tugboat instruction according to the multi-tugboat force distribution scheme to obtain the single tugboat instruction.

[0063] In step S101 of some embodiments, the target ship is a ship that needs tugboat assistance. The current state information of the ship includes information such as the speed, attitude, position and self-control amount of the target ship. The multi-tugboat maneuvering information includes the current tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat attitude. The environmental information includes wind speed, wind direction, tide level, water flow speed and water flow direction.

[0064] In step S102 of some embodiments, the current state information of the ship, the multi-tugboat maneuvering information and the environmental information are input into the ship state prediction model to obtain the predicted state information of the ship. The ship state prediction model can be obtained based on the deep learning of the long-term and short-term neural network. The ship state prediction model can reflect the mapping relationship between the current state information of the ship, the multi-tugboat maneuvering information and the environmental information and the future state through deep learning. Specifically, the BP neural network is used for real-time ship state prediction. The BP neural network processes the input current posture and environmental data through a multi-layer perceptron structure to predict the future state of the ship. The training process uses a large amount of historical data to optimize the weights and biases of the neural network, thereby minimizing the prediction error. During the prediction process, the neural network can output the future posture, speed and trajectory of the ship, providing a basis for subsequent power distribution optimization.

[0065] In steps S103 to S104 of some embodiments, a multi-objective optimization function capable of characterizing trajectory deviation and collision risk is constructed according to the predicted state information of the ship, and the optimization function can indirectly characterize the mapping relationship between the tugboat maneuvering parameters and the characteristic trajectory deviation and collision risk. With the goal of minimizing the optimization function, that is, minimizing the trajectory deviation and the collision risk, the multi-tugboat force distribution scheme is optimized to obtain the optimal tugboat maneuvering scheme, wherein the tugboat maneuvering scheme includes the optimal tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include the required tugboat power and tugboat attitude, and the tugboat power and tugboat attitude can characterize the magnitude and direction of the force of the tugboat on the ship. The constraints of the optimization function may include actual restrictions such as the maximum thrust and response time of each tugboat. The optimization method adopts a genetic algorithm or a particle swarm optimization algorithm, iterates the calculation under the constraints, and gradually approaches the optimal solution. The genetic algorithm finds the global optimal tugboat force distribution scheme through selection, crossover and mutation operations, and the particle swarm optimization algorithm finds the global optimal tugboat force distribution scheme through updating and optimization of the particle swarm.

[0066] In step S105 of some embodiments, since the control panel designs and current control amounts of each tugboat are different, single tugboat command adaptation can be performed according to the optimal multi-tugboat force distribution plan to convert the tugboat control plan into a simple single tugboat command to facilitate the driver's operation.

[0067] In some embodiments, in step S103, determining the optimization function according to the predicted ship state information may include but is not limited to the following steps:

[0068] Step S201, determining a trajectory deviation function according to the predicted trajectory and the target trajectory in the ship predicted state information;

[0069] Step S202, determining a collision risk function according to the tugboat position, the obstacle position and the tugboat force;

[0070] Step S203: determining an optimization function according to the trajectory deviation function and the collision risk function.

[0071] In some embodiments, the trajectory deviation function in step S201 is expressed as:

[0072]

[0073] Among them, q i is the i-th target position in the target trajectory, p i is the corresponding predicted position in the predicted trajectory, R is the rotation matrix, and t is the translation vector. By minimizing the trajectory deviation function, the future trajectory of the ship under the tugboat maneuvering scheme can be made as close to the ideal target trajectory as possible.

[0074] In some embodiments, in step S202, determining the collision risk function according to the tugboat position, the obstacle position and the tugboat force may include but is not limited to the following steps:

[0075] Step S301, determining the distance between each tugboat and the obstacle according to the tugboat position and the obstacle position;

[0076] Step S302, determining a collision risk calculation formula for each tugboat according to the distance between each tugboat and the obstacle and the corresponding tugboat force;

[0077] Step S303, obtaining a collision risk function by integrating the collision risk calculation formulas of each tugboat.

[0078] In this embodiment, the collision risk function is expressed as:

[0079]

[0080] Among them, γ k is the risk weight coefficient, d k is the distance between the tugboat and the obstacle (such as the dock or other ships), R k is the force exerted by the kth tugboat (including magnitude and direction).

[0081] In some embodiments, in step S203, determining the optimization function according to the trajectory deviation function and the collision risk function may include but is not limited to the following steps:

[0082] Step S401, determining an energy consumption function according to the tugboat force and the tugboat speed;

[0083] Step S402: Determine an optimization function by integrating the trajectory deviation function, the collision risk function and the energy consumption function in a weighted manner.

[0084] In this embodiment, the energy consumption function is expressed as:

[0085]

[0086] Among them, α j and β j is the energy consumption coefficient of the tugboat, which is related to the tugboat force and tugboat speed, respectively. j is the force of the jth tugboat, v j is the speed of the jth tugboat.

[0087] The energy consumption minimization function aims to reduce the energy consumed by the tugboat during operation, and to minimize fuel consumption and operating costs without affecting safety and accuracy. The main considerations include the optimal distribution of force (pulling or thrust), speed control, and energy consumption control during non-operating periods. For example, the optimal distribution of thrust means that considering the different thrust efficiency of each tugboat, the system will make the optimal distribution according to the thrust characteristics of each tugboat. The greater the thrust, the higher the energy consumption, so the system will give priority to tugboats with a high thrust-to-energy consumption ratio. Speed ​​control means that the system adjusts the thrust of the tugboat so that the ship can operate at the most energy-efficient speed. Energy consumption control during non-operating periods means that when the tugboat is not actually maneuvering, the system will put the tugboat into low-power mode to reduce unnecessary energy consumption.

[0088] In some embodiments, an optimization function for tugboat force distribution under multi-objective constraints is established. Multi-objective optimization needs to construct a comprehensive optimization function based on minimizing energy consumption, minimizing collision risk, and maximizing maneuvering accuracy. Since there may be conflicts between various objectives (for example, increasing thrust to reduce risk will lead to increased energy consumption), it is necessary to weight each objective. The comprehensive optimization objective function can be expressed as: total =w1J1+w2J2+w3J3, where w1, w2, and w3 are weight coefficients of the above three objectives respectively, and the above weights can be adjusted dynamically according to the specific operating environment. Generally speaking, safety parameters are placed in the most important position in tugboat operations. For the optimization solution of the above multi-objective constraints, since there are only three constraints, traditional methods such as Newton's method and weighted sum method can be used to solve them more conveniently, so as to find the global optimal control parameters of each tugboat.

[0089] In some embodiments, in step S104, the multi-tugboat force distribution scheme is optimized to obtain the optimal multi-tugboat force distribution scheme with the goal of minimizing the optimization function, which may include but is not limited to the following steps:

[0090] Step S501, initializing a multi-tugboat force distribution scheme;

[0091] Step S502, inputting the multi-tugboat force distribution scheme into the ship state prediction model and the tugboat dynamics model respectively, to obtain the predicted trajectory of the target ship and the tugboat force of each tugboat;

[0092] Step S503, determining the value of the optimization function according to the predicted trajectory, the tugboat position of each tugboat, and the tugboat force;

[0093] Step S504: According to the mapping relationship between the values ​​of the optimization function under different multi-tugboat force distribution schemes, the multi-tugboat force distribution scheme is optimized to obtain the optimal multi-tugboat force distribution scheme.

[0094] In this embodiment, the initialized multi-tugboat force distribution scheme is respectively input into the ship state prediction model and the tugboat dynamics model, the ship state prediction model outputs the predicted trajectory of the target ship, the tugboat dynamics model includes the dynamics sub-models of each tugboat, and the tugboat dynamics model can output the tugboat force of each tugboat based on the tugboat maneuvering parameters of each tugboat in the multi-tugboat force distribution scheme, wherein the dynamics sub-model of the tugboat characterizes the mapping relationship between the tugboat's angle attitude and output power and the tugboat force generated on the ship. According to the ship position in the predicted trajectory, combined with the fixed position distribution relationship between the ship and the tugboat, the tugboat position of each tugboat can be determined. The value of the optimization function is determined according to the predicted trajectory, the tugboat force of each tugboat and the tugboat position. Through multiple iterative updates of the above-mentioned multi-tugboat force distribution scheme, the value of the optimization function under different multi-tugboat force distribution schemes can be obtained, and according to the mapping relationship between the values ​​of the optimization functions of different multi-tugboat force distribution schemes, the corresponding multi-tugboat force distribution scheme when the optimization function is minimum is determined, thereby obtaining the optimal multi-tugboat force distribution scheme.

[0095] In some embodiments, in step S104, the multi-tugboat force distribution scheme is optimized to obtain the optimal multi-tugboat force distribution scheme with the goal of minimizing the optimization function, which may include but is not limited to the following steps:

[0096] Step S601, optimizing the multi-tugboat force distribution scheme with the goal of minimizing the optimization function;

[0097] Step S602, inputting the optimized multi-tugboat force distribution scheme and the current state information and environmental information of the ship into the ship state prediction model to obtain new ship prediction state information, wherein the ship prediction state information also includes predicted speed and predicted attitude;

[0098] Step S603, determining the berthing risk according to the predicted speed and predicted attitude;

[0099] Step S604: when the berthing risk is greater than the risk threshold, the multi-tugboat force distribution scheme is adjusted until the berthing risk is less than the risk threshold, thereby obtaining an optimal multi-tugboat force distribution scheme.

[0100] In this embodiment, in the ship berthing scenario, after optimizing the multi-tugboat force distribution scheme, the berthing collision risk can be further evaluated to improve the safety of the maneuvering suggestions. The risk during berthing mainly refers to the collision accident caused by the posture of the berthing ship not being parallel to the shoreline. The berthing operation of the ship is mainly calculated using the key indicator of the normal berthing speed. For ships with different full load displacement, there are different normal berthing speed requirements, which are defined as the standard speed V stand (For example, the normal berthing speed of a seagoing vessel with a full load displacement of 1,000 to 5,000 tons at an unprotected dock should be 0.2-0.4 m / s). The risk of a ship berthing at any time is the value of the speed at any time and the standard speed. The greater the Risk, the greater the risk value. In this embodiment, the multi-tugboat maneuvering information corresponding to the multi-tugboat force distribution scheme obtained by the above optimization, as well as the current state information and environmental information of the ship are input into the ship state prediction model to obtain new ship prediction state information, wherein the ship prediction state information also includes predicted speed and predicted attitude, and the normal berthing speed can be determined according to the predicted speed and predicted attitude, and the berthing risk is determined according to the normal berthing speed and the standard speed of the ship. When the berthing risk is greater than the risk threshold, it indicates that the tugboat thrust to the shore is too large. Therefore, the maneuvering power of each tugboat can be reduced on the basis of the multi-tugboat force distribution scheme obtained by the optimization, and then the berthing risk assessment and scheme adjustment are re-performed until the berthing risk is less than the risk threshold, and the optimal multi-tugboat force distribution scheme is obtained.

[0101] In some embodiments, the tugboat maneuvering scheme includes tugboat maneuvering parameters of each tugboat. In step S105, a single tugboat instruction is adapted according to the tugboat maneuvering scheme to obtain a single tugboat instruction, which may include but is not limited to the following steps:

[0102] Step S701, obtaining the current maneuvering parameters of each tugboat;

[0103] Step S702, determining a manipulation parameter adjustment amount according to the current manipulation parameters of the target tugboat and the tugboat manipulation parameters in the multi-tugboat force distribution scheme;

[0104] Step S703, determining the single tugboat instruction according to the control parameter adjustment amount.

[0105] In this embodiment, the multi-tugboat force distribution scheme includes the optimal tugboat maneuvering parameters of each tugboat. After analyzing and obtaining the optimal multi-tugboat force distribution scheme, the current maneuvering parameters of each tugboat are obtained, and the maneuvering parameter adjustment amount is determined according to the difference between the current maneuvering parameters of the target tugboat and the tugboat maneuvering parameters of the tugboat in the multi-tugboat force distribution scheme, and then converted into a single tugboat instruction according to the mapping of the maneuvering parameter adjustment amount, and the single tugboat instruction is used to indicate the power amount and tugboat angle amount that the tugboat needs to adjust. By converting the multi-tugboat force distribution scheme into a single tugboat instruction, it is simplified into an easy-to-understand instruction, which conforms to the driver's cognitive habits. The enhanced display module in the tugboat displays the single tugboat instruction and the current posture of the ship detected by the shore-based ranging module to the tugboat driver and the shore-based dispatching command center in the form of graphics or text, thereby improving the display effect of the instruction.

[0106] The embodiment of the present application also proposes a multi-tugboat coordinated maneuvering control system, including:

[0107] The first module is used to obtain the current status information of the target ship, multi-tugboat maneuvering information and environmental information;

[0108] The second module is used to input the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into the ship state prediction model to obtain the ship prediction state information, wherein the ship prediction state information includes the prediction trajectory;

[0109] The third module is used to determine an optimization function according to the predicted state information of the ship, wherein the optimization function includes a track deviation function and a collision risk function;

[0110] The fourth module is used to optimize the multi-tugboat force distribution scheme with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme, wherein the tugboat maneuvering parameters include tugboat power and tugboat attitude;

[0111] The fifth module is used to adapt the single tugboat command according to the tugboat maneuvering plan to obtain the single tugboat command.

[0112] It can be understood that the contents of the above-mentioned multi-tugboat collaborative maneuvering control method embodiment are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above-mentioned multi-tugboat collaborative maneuvering control method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned multi-tugboat collaborative maneuvering control method embodiment.

[0113] The embodiment of the present application also provides an electronic device, the electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, and the program is executed by the processor to realize the above-mentioned multi-tugboat coordinated maneuvering control method. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0114] See also Figure 3 , Figure 3 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0115] The processor 301 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0116] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the multi-tugboat cooperative maneuvering control method of the embodiment of this application;

[0117] Input / output interface 303, used to implement information input and output;

[0118] The communication interface 304 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0119] A bus 305 that transmits information between the various components of the device (e.g., the processor 301, the memory 302, the input / output interface 303, and the communication interface 304);

[0120] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0121] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned multi-tugboat collaborative manipulation control method.

[0122] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0124] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0125] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0127] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0128] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0129] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0130] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0131] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0132] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0133] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for controlling coordinated maneuvering of multiple tugboats, characterized in that: The following steps are involved: Obtain the current status information of the target ship, multi-tugboat maneuvering information and environmental information; Inputting the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into a ship state prediction model to obtain ship predicted state information, wherein the ship predicted state information includes a predicted trajectory; Determining an optimization function according to the predicted ship state information, wherein the optimization function includes a trajectory deviation function and a collision risk function; With the goal of minimizing the optimization function, optimizing the multi-tugboat force distribution scheme to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat attitude; The single tugboat instruction is adapted according to the multi-tugboat force distribution scheme to obtain a single tugboat instruction.

2. The multi-tugboat coordinated maneuvering control method according to claim 1, characterized in that: Determining the optimization function according to the predicted ship state information comprises the following steps: Determining a trajectory deviation function according to the predicted trajectory and the target trajectory in the predicted state information of the ship; Determine the collision risk function based on the tugboat position, obstacle position and tugboat force; An optimization function is determined according to the trajectory deviation function and the collision risk function.

3. The multi-tugboat coordinated maneuvering control method according to claim 2, characterized in that: Determining the optimization function according to the trajectory deviation function and the collision risk function comprises the following steps: determining an energy consumption function based on the tugboat force and the tugboat speed; The trajectory deviation function, the collision risk function and the energy consumption function are integrated in a weighted manner to determine an optimization function.

4. The multi-tugboat coordinated maneuvering control method according to claim 2, characterized in that: The method of optimizing the multi-tugboat force distribution scheme with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme comprises the following steps: Initialize the multi-tugboat force distribution plan; Inputting the multi-tugboat force distribution scheme into the ship state prediction model and the tugboat dynamics model respectively to obtain the predicted trajectory of the target ship and the tugboat force of each tugboat; determining a value of an optimization function based on the predicted trajectory, the tugboat position of each tugboat, and the tugboat force; According to the mapping relationship of the optimization function values ​​under different multi-tugboat force distribution schemes, the multi-tugboat force distribution scheme is optimized to obtain the optimal multi-tugboat force distribution scheme.

5. The method for controlling coordinated maneuvering of multiple tugboats according to claim 1, characterized in that: The method of optimizing the multi-tugboat force distribution scheme with the goal of minimizing the optimization function to obtain the optimal multi-tugboat force distribution scheme comprises the following steps: Taking minimizing the optimization function as the goal, optimizing the multi-tugboat force distribution scheme; Inputting the optimized multi-tugboat force distribution scheme and the current state information and environmental information of the ship into the ship state prediction model to obtain new ship prediction state information, wherein the ship prediction state information also includes predicted speed and predicted attitude; determining a berthing risk according to the predicted speed and the predicted attitude; When the berthing risk is greater than a risk threshold, the multi-tugboat force distribution scheme is adjusted until the berthing risk is less than the risk threshold, thereby obtaining an optimal multi-tugboat force distribution scheme.

6. The multi-tugboat coordinated maneuvering control method according to claim 1, characterized in that: The step of adapting a single tugboat instruction according to the multi-tugboat force distribution scheme to obtain a single tugboat instruction comprises the following steps: Get the current maneuvering parameters of each tugboat; Determining a steering parameter adjustment amount according to current steering parameters of the target tugboat and steering parameters of the tugboats in the multi-tugboat force distribution scheme; A single tugboat instruction is determined according to the manipulation parameter adjustment amount.

7. The multi-tugboat coordinated maneuvering control method according to claim 2, characterized in that: Determining the collision risk function according to the tugboat position, the obstacle position and the tugboat force comprises the following steps: Determine the distance between each tugboat and the obstacle based on the tugboat position and the obstacle position; Determine the collision risk calculation formula for each tugboat based on the distance between each tugboat and the obstacle and the corresponding tugboat force; The collision risk function is obtained by combining the collision risk calculation formulas of various tugboats.

8. A multi-tugboat coordinated maneuvering control system, characterized in that: include: The first module is used to obtain the current status information of the target ship, multi-tugboat maneuvering information and environmental information; The second module is used to input the current state information of the ship, the multi-tugboat maneuvering information and the environmental information into a ship state prediction model to obtain ship predicted state information, wherein the ship predicted state information includes a predicted trajectory; A third module is used to determine an optimization function according to the predicted ship state information, wherein the optimization function includes a track deviation function and a collision risk function; The fourth module is used to optimize the multi-tugboat force distribution scheme to obtain the optimal multi-tugboat force distribution scheme, wherein the multi-tugboat force distribution scheme includes tugboat maneuvering parameters of each tugboat, and the tugboat maneuvering parameters include tugboat power and tugboat posture; The fifth module is used to adapt the single tugboat instruction according to the multi-tugboat force distribution plan to obtain the single tugboat instruction.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method described in any one of claims 1 to 7 are realized.

10. A storage medium, the storage medium being a computer-readable storage medium, used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 7.

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