A whole-ship control method based on inter-device direct communication

Abstract

The application discloses a whole-ship control method based on direct communication between devices, belongs to the technical field of direct communication between devices, and comprises the following steps: receiving a high-level control task, establishing a direct communication link, and forming a distributed cooperative network; performing distributed negotiation by exchanging respective state information and capability information, and generating a cooperative strategy; according to the generated cooperative strategy, each ship device intelligent node autonomously executes an assigned subtask; a twin model is established for each ship device intelligent node, a health state index thereof is calculated and updated in real time, in the distributed negotiation process, each node exchanges the health state index thereof, and a task load is dynamically distributed based on the health state index. In the technical scheme implementation process of the application, the direct communication link is established between the ship device intelligent nodes, the distributed cooperative network is formed, the distributed negotiation is performed through the network, and the cooperative strategy is generated, so that the control dependence of the central node is reduced, and the ship control efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of direct communication technology between devices, specifically a ship control method based on direct communication between devices. Background Technology

[0002] With the improvement of ship intelligence, the various subsystems of ships are becoming increasingly complex. Modern ships are comprehensive platforms that integrate multiple complex subsystems such as propulsion, navigation, electricity, and ballast.

[0003] In existing technologies, ship control systems mainly adopt a centralized or hierarchical architecture, such as using a PLC or industrial computer as the control core and connecting the underlying devices through fieldbuses such as CAN bus and Modbus. The advantage of this is that it can achieve unified scheduling and management, thereby ensuring the coordination and stability of system operation to a certain extent, and facilitating fault diagnosis and maintenance.

[0004] However, in the practical application of the above technical solutions, the central processing equipment becomes a single point of failure. Once it fails, it may cause the entire ship to lose control, resulting in low reliability. Secondly, the system scalability of this architecture is poor. Adding new equipment requires reconfiguring the network topology and relying on the central node for scheduling, which is not flexible enough. At the same time, control commands need to be processed by the central node before being issued, resulting in a long control response path, which makes it difficult to meet the requirements of high dynamic response in real time, especially in emergency situations where control delays are likely to occur.

[0005] Therefore, it is necessary to provide a ship control method based on direct communication between devices that can fully leverage the intelligence of the devices and achieve efficient and reliable collaborative control to solve the above problems.

[0006] It should be noted that the information disclosed in this background section is only for understanding the background technology of this application concept, and therefore may include information that does not constitute prior art. Summary of the Invention

[0007] Based on the aforementioned problems in the existing technology, the problem to be solved by this application is to provide a ship control method based on direct communication between devices, which improves ship control efficiency by eliminating the central node and relying on direct communication between devices.

[0008] The technical solution adopted by this application to solve its technical problem is: a ship control method based on direct communication between devices, comprising:

[0009] It receives high-level control tasks and establishes direct communication links between intelligent nodes of ship equipment related to the task, forming a distributed collaborative network;

[0010] In a distributed collaborative network, intelligent nodes of ship equipment conduct distributed negotiation by exchanging their respective status and capability information to generate collaborative strategies for executing high-level control tasks.

[0011] According to the generated collaborative strategy, each ship equipment intelligent node autonomously executes the assigned sub-tasks, and exchanges execution status and environmental perception data in real time through the established communication link, and dynamically adjusts the execution parameters based on real-time feedback to form closed-loop control.

[0012] A twin model is established for each intelligent node of the ship's equipment to calculate and update its health status index in real time. During the distributed negotiation process, each node exchanges its health status index and dynamically allocates task load based on the health status index.

[0013] In the implementation of the technical solution of this application, a direct communication link is established between intelligent nodes of ship equipment to form a distributed collaborative network. Through this network, distributed negotiation is carried out to generate collaborative strategies, thereby reducing the control dependence of the central node and improving the ship control efficiency.

[0014] The operation process of a distributed collaborative network includes:

[0015] Furthermore, the high-level control task is decomposed into at least one sub-task and broadcast to the distributed collaborative network. Then, response information for the sub-task is received from one or more ship equipment intelligent nodes. Based on the response information, the target node set for executing the sub-task is determined from one or more ship equipment intelligent nodes.

[0016] Furthermore, the response information includes at least the node's current location, working status, load, available communication bandwidth, and historical task execution success rate. The determination of the target node set is based on a multi-objective optimization algorithm, which is completed after comprehensively evaluating the node's current location, working status, load, available communication bandwidth, and historical task execution success rate.

[0017] Furthermore, the generation of collaborative strategies further includes:

[0018] The first ship equipment intelligent node sends its planned action command or the changing trend of the action command to the second ship equipment intelligent node through the communication link. The second ship equipment intelligent node calculates the compensation amount for the high-level control task based on the received action command or the changing trend of the action command using a feedforward control algorithm.

[0019] Furthermore, the feedforward control algorithm consists of a composite control structure, which is as follows:

[0020] The final control command = basic control quantity + k × feedforward control quantity + feedback error. The basic control quantity is generated by the task decomposition module and reflects the basic execution requirements of the current sub-task. k is the feedforward coefficient. The feedforward control quantity is determined based on the action command or change trend sent by the first ship equipment intelligent node. The feedback error is based on the deviation between the actual output of the system and the expected target. It is collected in real time by the state observer and participates in the adjustment.

[0021] Furthermore, the collaborative strategy refers to the distributed consensus reached by each node on task allocation, resource scheduling and execution timing. The collaborative strategy includes subtask attribution, communication cycle, fault tolerance mechanism and exception handling plan.

[0022] Furthermore, before generating the collaborative strategy, anomaly analysis is performed on the status of each node. Anomaly analysis of each node is achieved through real-time verification of data consistency and multi-source information fusion judgment. Redundant nodes are established for each intelligent node. During task execution, key status parameters are synchronized to the redundant nodes. When an intelligent node failure is detected, the redundant node immediately takes over its subtask and broadcasts the status switching information.

[0023] Furthermore, when multiple ship equipment intelligent nodes request to simultaneously access the negotiation process and execute parallel tasks, the node with the higher health status index will act as the execution node.

[0024] Furthermore, the method is applied to the automatic berthing and unberthing control scenario of ships;

[0025] The high-level task is a berthing / unberthing command;

[0026] The intelligent nodes of the ship equipment include at least: a side thruster node, a steering gear node, a propeller node, and a positioning system node;

[0027] The coordinated control strategy includes a control sequence of the resultant force direction and magnitude determined through negotiation among the side thruster node, servo node, and propeller node.

[0028] Furthermore, the method is applied to the dynamic positioning scenario of ships;

[0029] The aforementioned high-altitude task is to maintain the ship's position and heading stability under the disturbance of ocean currents and waves;

[0030] Intelligent nodes for ship equipment include at least multiple propeller nodes, multiple servo motor nodes, and position and attitude reference system nodes;

[0031] The collaborative control strategy includes a thrust distribution scheme, generated through negotiation by the multiple thruster nodes and servo nodes, to resist environmental disturbances.

[0032] The beneficial effects of this application are: This application provides a ship control method based on direct communication between devices, which establishes direct communication links between intelligent nodes of ship equipment and forms a distributed collaborative network. Through this network, distributed negotiation is carried out to generate collaborative strategies, thereby reducing the control dependence of the central node and improving the efficiency of ship control.

[0033] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0035] Figure 1 This is a schematic diagram of the overall process of a ship control method based on direct communication between devices in this application. Detailed Implementation

[0036] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0038] like Figure 1 As shown, this application provides a ship control method based on direct communication between devices. This method, applied to a ship control system, decentralizes control to each device node, enabling direct communication and distributed collaborative decision-making between devices to jointly complete various tasks. This effectively improves system response speed and reliability, avoiding the risk of system-wide failure due to central controller malfunction. The method includes the following steps:

[0039] Step 01: Receive high-level control tasks and establish direct communication links between intelligent nodes of ship equipment related to the task to form a distributed collaborative network;

[0040] Intelligent nodes for ship equipment refer to ship equipment equipped with embedded intelligent agents. These intelligent agents include at least a direct communication module between devices, a local microprocessor, a storage unit, and a dedicated driver interface for the devices. This enables the ship equipment to have the ability to perceive the environment, process information, make intelligent decisions, and communicate collaboratively. It also enables real-time data exchange and status synchronization through a preset communication protocol.

[0041] The operation process of a distributed collaborative network includes:

[0042] The high-level control task is decomposed into at least one sub-task and broadcast to the distributed cooperative network. Then, response information for the sub-task is received from one or more ship equipment intelligent nodes. Based on the response information, the target node set for executing the sub-task is determined from one or more ship equipment intelligent nodes.

[0043] The response information includes, but is not limited to, the node's current location, working status, load, available communication bandwidth, and historical task execution success rate. The determination of the target node set is based on a multi-objective optimization algorithm, which is completed after a comprehensive evaluation of the node's current location, working status, load, available communication bandwidth, and historical task execution success rate.

[0044] The application of multi-objective optimization algorithms can refer to existing technologies, and will not be described in detail in this embodiment;

[0045] The distributed collaborative network is dynamically constructed by multiple intelligent nodes of ship equipment through Time Sensitive Network (TSN). Each node achieves millisecond-level data interaction based on time synchronization mechanism and bandwidth reservation protocol to ensure real-time transmission of control commands and status feedback. In the distributed collaborative network, each intelligent node of ship equipment independently executes local decisions and participates in group cooperation. When a node fails, the other nodes can quickly reconstruct the communication path and reallocate tasks to maintain the overall stability of the system.

[0046] The high-level control task is issued by the shipboard central management unit or remote command center. It includes the task objective, execution conditions and priority identifier. For example, the task objective may be berthing, departure or emergency response. The execution conditions include environmental parameter thresholds and equipment status requirements. The priority identifier is used to make resource scheduling decisions when multiple tasks are concurrent. When a high-level control task is received, the task is first parsed to determine the set of intelligent nodes of the ship's equipment participating in the high-level control task. The task is then decomposed into several sub-tasks according to the task objective and assigned to the corresponding nodes, thereby reducing communication redundancy between unrelated nodes and improving task execution efficiency.

[0047] For example, when the high-level control task is a berthing operation, the system analyzes the set of nodes that need to participate, including the propeller, servo, side thruster and positioning sensor, and decomposes the berthing task into four sub-tasks: speed adjustment, heading adjustment, position calibration and berthing execution, which are taken over by the corresponding nodes. In this task, other equipment such as air conditioning, lighting or cargo hold control systems do not participate in communication and execution, so as to avoid waste of resources.

[0048] Step 02: In the distributed collaborative network, ship equipment intelligent nodes exchange their respective status information and capability information to conduct distributed negotiation and generate a collaborative strategy for executing high-level control tasks.

[0049] After the distributed collaborative network is established, each ship equipment intelligent node broadcasts its own operating status, available resources and functional capabilities, such as the thrust range of the propeller, the response accuracy of the steering gear or the sensor update frequency, and receives broadcast information from other nodes. Based on the consensus algorithm, the task allocation scheme is negotiated in a distributed manner.

[0050] Consensus algorithms are distributed computing protocols used to reach consensus decisions in decentralized systems. They ensure that nodes can still synchronously confirm the collaborative strategy even in the event of partial failure or communication delay. For example, using an improved Byzantine fault-tolerant algorithm, after receiving the task decomposition results, each node evaluates its own capabilities and current load, votes to select the optimal execution plan, and after multiple rounds of message passing and verification, finally reaches a globally consistent collaborative strategy. This process does not require a central node to make decisions, effectively improving the system's response speed.

[0051] The collaborative strategy refers to the distributed consensus reached by each node on task allocation, resource scheduling and execution timing. The collaborative strategy includes sub-task attribution, communication cycle, fault tolerance mechanism and abnormal handling plan. This strategy can dynamically adapt to the real-time status of the ship and environmental changes, and optimize overall energy efficiency while ensuring the integrity of the task.

[0052] Before generating the collaborative strategy, it is also necessary to perform anomaly analysis on the status of each node. Anomaly analysis of each node is achieved by real-time verification of data consistency and multi-source information fusion judgment. Redundant nodes are established for each intelligent node. During task execution, key status parameters are synchronized to the redundant nodes. When an intelligent node failure is detected, the redundant node immediately takes over its subtasks and broadcasts the status switching information to ensure the continuous execution of the collaborative strategy.

[0053] For example, if a sensor's feedback value exceeds a preset threshold or deviates from other observation sources beyond the tolerance range, it will be marked as abnormal. Once a node is marked as abnormal, it needs to be switched to a redundant node immediately, and the negotiation process for the collaborative strategy needs to be carried out again.

[0054] The generation of collaborative strategies further includes:

[0055] The first ship equipment intelligent node sends its planned action command or the changing trend of the action command to the second ship equipment intelligent node through the communication link. The second ship equipment intelligent node calculates the compensation amount for the high-level control task based on the received action command or the changing trend of the action command using a feedforward control algorithm.

[0056] During the collaboration of various intelligent nodes, communication delays or data packet loss will inevitably occur. When there are too many intelligent nodes, these communication delays and data packet loss will cause cumulative errors in the execution results of high-level control tasks, thereby affecting the overall stability of the system. Therefore, the compensation amount of the receiving intelligent node to the high-level control task is calculated by the feedforward control algorithm in order to predict and offset the impact caused by communication delays or data loss in advance.

[0057] Specifically, the feedforward control algorithm consists of a composite control structure, which is as follows:

[0058] The final control command = basic control quantity + k × feedforward control quantity + feedback error. The basic control quantity is generated by the task decomposition module and reflects the basic execution requirements of the current sub-task, such as the ship's propulsion direction (angle value), execution sequence (time value), and expected resource usage (load rate). k is the feedforward coefficient. The feedforward control quantity is determined based on the action command or change trend sent by the first ship equipment intelligent node. It is used to adjust the strength of the feedforward action to ensure that the compensation quantity can effectively offset the impact of communication delay without causing system oscillation due to overcompensation. The feedback error (PID) is based on the deviation between the actual output of the system and the expected target. It is collected in real time by the state observer and participates in the adjustment.

[0059] This composite control structure effectively suppresses dynamic disturbances and communication uncertainties in the multi-node collaborative process, reduces the cumulative error caused by communication delays and data packet loss, and improves the system response accuracy.

[0060] For example, in berthing missions, the coordination strategy clearly defines the linkage sequence of the thrusters and servo motors, the intervention timing of the side thrusters, and the data update frequency of the positioning sensors (distributed negotiation process) to ensure coordinated actions. Once a node's state is abnormal, the strategy automatically triggers a renegotiation mechanism, allowing neighboring nodes to take over key functions. This improves decision-making efficiency while ensuring the overall reliability and safety of the system. For instance, if positioning sensor data is lost during a berthing mission, the system immediately switches to a redundant observation node and recalculates the berthing trajectory correction, thereby ensuring that the safety and accuracy of the berthing process are not affected. At the same time, abnormal information is fed back to the main control interface for crew monitoring.

[0061] Step 03: According to the generated collaborative strategy, each ship equipment intelligent node autonomously executes the assigned sub-tasks, and exchanges execution status and environmental perception data in real time through the established communication link, and dynamically adjusts the execution parameters based on real-time feedback to form closed-loop control;

[0062] After distributed negotiation and the generation of a collaborative strategy, each intelligent node autonomously executes sub-tasks based on the consensus results. These sub-tasks are specific functional units undertaken by each node, such as adjusting thrust output of the thruster, adjusting steering angle of the servo, and increasing sampling frequency of the sensor. During execution, the node also needs to continuously exchange execution status and environmental perception data through the established communication link to achieve dynamic monitoring and collaborative optimization of task progress. During the execution phase, each node runs according to the instructions allocated by the collaborative strategy. When environmental disturbances or execution deviations exceed the threshold are detected, the node will autonomously trigger local replanning and update the collaborative network synchronously without waiting for instructions from the central node, thereby improving response speed.

[0063] Step 04: Establish a twin model for each ship equipment intelligent node, calculate and update its health status index in real time, and exchange its health status index during the distributed negotiation process, and dynamically allocate task load based on the health status index.

[0064] To facilitate real-time assessment and sharing of the health status of each node, a digital twin model is established for each ship's intelligent equipment node. This model maps the operating status of the physical nodes in real time. By combining historical data and machine learning algorithms, potential faults are dynamically predicted and a health status index is calculated. During the distributed negotiation process, each node broadcasts its own health status index to the collaborative network. The system prioritizes task allocation based on the health status. Nodes with excellent health status will be assigned tasks with higher weight, while nodes with low health indices will automatically reduce their load or enter a standby state, thus optimizing the task load allocation strategy.

[0065] When multiple ship equipment intelligent nodes request to simultaneously access the negotiation process and execute parallel tasks, the node with the higher health status index will be the execution node.

[0066] The above method can be applied to various scenarios, as illustrated below:

[0067] Automatic berthing and unberthing control scenario for ships: The high-level task is berthing and unberthing commands. The intelligent nodes of the ship's equipment include at least the side thruster node, the steering gear node, the propeller node, and the positioning system node. The collaborative control strategy includes the resultant force direction and magnitude control sequence determined by the side thruster node, the steering gear node, and the propeller node through negotiation.

[0068] Dynamic positioning scenario for ships: The high-level mission is to maintain the stability of the ship's position and heading under the disturbance of ocean currents and waves. The intelligent nodes of the ship's equipment include at least multiple thruster nodes, multiple servo motor nodes, and position / attitude reference system nodes. The cooperative control strategy includes a thrust distribution scheme generated by the multiple thruster nodes and servo motor nodes through negotiation to resist environmental disturbances.

[0069] Ship energy efficiency management scenario: The high-level task is energy efficiency optimization command. The ship equipment intelligent nodes include at least: generator set nodes, switchboard nodes, and high-power load nodes. The collaborative control strategy includes the optimized start-stop combination and load distribution scheme determined by the generator set nodes through negotiation.

[0070] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for whole-ship control based on inter-device direct communication, characterized by: The method comprises the following steps: receiving a high-level control task and establishing a direct communication link between intelligent nodes of ship equipment related to the task to form a distributed collaborative network; in the distributed collaborative network, the intelligent nodes of ship equipment perform distributed negotiation by exchanging their respective state information and capability information to generate a collaborative strategy for executing the high-level control task; according to the generated collaborative strategy, each intelligent node of ship equipment autonomously executes the assigned subtask, exchanges the execution status and environmental perception data in real time through the established communication link, and dynamically adjusts the execution parameters according to the real-time feedback to form a closed-loop control; a twin model is established for each intelligent node of ship equipment to calculate and update its health state index in real time, and in the distributed negotiation process, each node exchanges its health state index and dynamically allocates task load based on the health state index; the operation process of the distributed collaborative network comprises: decomposing the high-level control task into at least one subtask and broadcasting it to the distributed collaborative network, then receiving response information from one or more intelligent nodes of ship equipment for the subtask, and based on the response information, determining a target node set for executing the subtask from the one or more intelligent nodes of ship equipment; wherein the response information at least includes the current position, working state, load condition, available communication bandwidth and historical task execution success rate of the node, and the determination of the target node set is based on a multi-objective optimization algorithm to complete the comprehensive evaluation of the current position, working state, load condition, available communication bandwidth and historical task execution success rate of the node; in the distributed collaborative network, each intelligent node of ship equipment independently executes local decision and participates in group collaboration, and when a node fails, the remaining nodes quickly reconfigure the communication path and reassign the task; the generation of the collaborative strategy further comprises: the first intelligent node of ship equipment sends the action instruction planned to be executed or the trend of the change of the action instruction to the second intelligent node of ship equipment through the communication link, and the second intelligent node of ship equipment calculates the compensation amount for the high-level control task according to the action instruction or the trend of the change of the action instruction received by it using a feedforward control algorithm; the feedforward control algorithm is composed of a composite control structure, and the composite control structure is as follows: final control instruction = basic control amount + k × feedforward control amount + feedback error, wherein the basic control amount is generated by a task decomposition module and reflects the basic execution requirement of the current subtask, k is a feedforward coefficient, the feedforward control amount is determined based on the action instruction or the trend of the change of the action instruction sent by the first intelligent node of ship equipment, and the feedback error is the deviation between the actual output and the expected target of the system, which is collected and adjusted in real time by a state observer; the method is applied to the automatic berthing and unberthing control scene of a ship; the high-level control task is a berthing or unberthing instruction; the intelligent nodes of ship equipment at least include: a side thruster node, a rudder node, a propeller node, and a positioning system node; The cooperative strategy includes a control sequence of the direction and size of the resultant force determined by negotiation among the side thruster node, the rudder node and the thruster node, and the cooperative strategy explicitly defines the linkage timing of the thruster and the rudder, the intervention timing of the side thruster and the data update frequency of the positioning system, and once the state of a node is abnormal, the strategy automatically triggers the re-negotiation mechanism.

2. The whole-ship control method based on direct communication between devices according to claim 1, characterized in that: The cooperative strategy refers to the distributed consensus reached by the nodes on task allocation, resource scheduling and execution timing, and the cooperative strategy includes subtask attribution, communication cycle, fault tolerance mechanism and exception handling plan.

3. The whole-ship control method based on direct communication between devices according to claim 1, characterized in that: Before generating the cooperative strategy, the state of each node is analyzed for abnormality, the abnormality of each node is realized through real-time checking of data consistency and multi-source information fusion judgment, a redundant node is established for each intelligent node, critical state parameters are synchronized to the redundant node during the execution of the task, and when the intelligent node is monitored to be faulty, the redundant node immediately takes over the subtask and broadcasts the state switching information.

4. The whole-ship control method based on direct communication between devices according to claim 1, characterized in that: When multiple ship equipment intelligent nodes request to access the negotiation process at the same time and execute parallel tasks, the node with a higher health state index will be the execution node.

5. The method of claim 1, wherein the method further comprises: The method is applied to the dynamic positioning scene of a ship; The high-level control task is to maintain the position and heading of the ship stable under the disturbance of sea currents and waves; The ship equipment intelligent nodes at least include multiple thruster nodes, multiple rudder nodes and a position and attitude reference system node; The cooperative strategy includes a thrust distribution scheme for resisting environmental disturbance generated by negotiation among the multiple thruster nodes and the rudder nodes.

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