Intelligent fleet simulation navigation system based on remote control

Abstract

The invention discloses an intelligent fleet navigation simulation system based on remote control. The method comprises the following steps: setting a navigation task, simulating and generating a multi-ship path based on an A * algorithm, extracting an abnormal path section and environment data based on historical navigation data, comparing the abnormal path section and environment data with real-time data of a fleet, judging a potential control abnormal risk, and if an abnormal control judgment occurs, judging whether the ship is abnormal or not. And a sharing mechanism is started to carry out cooperative deviation analysis and decision judgment on multiple ships, so that control abnormality analysis on the whole fleet is effectively improved, and the cooperative control capability and the intelligent level of the fleet are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent ships, and more specifically, to an intelligent fleet simulation navigation system based on remote control. Background Art

[0002] With the rapid development of the global shipping industry, smart ships and unmanned fleet technologies have gradually become research hotspots. Based on the fleet's perception analysis of the target environment and related waters and the fleet's collaborative control, they can be comprehensively applied in related fields such as water monitoring, resource exploration, search and rescue analysis, and object transportation. In addition, remotely controlled smart fleets can improve navigation efficiency, reduce operating costs, and enhance safety in complex water environments. Through the fleet's collaborative analysis and environmental perception, combined with remote control, efficient mission planning can be carried out for the fleet.

[0003] However, in the existing technology, especially in the remote control of multiple ships, it is difficult to coordinate the control of multiple ships to navigate a complete path due to the influence of communication fluctuations, complex water environment, ship hardware, etc. There are often control anomalies or navigation deviations of one or more ships in certain waters or on certain navigation paths. In this case, it is often necessary to intervene with human experience, which makes it difficult to achieve effective synchronous control of multiple ships and complete execution of fleet tasks, and it is difficult to improve the level of fleet automation and intelligence. Summary of the invention

[0004] The invention overcomes the defects of the prior art and proposes an intelligent fleet simulation navigation system based on remote control.

[0005] A first aspect of the present invention provides a remote-controlled intelligent fleet navigation simulation method, comprising: S102: In the target waters, a navigation task of the intelligent fleet is set, and according to the navigation task, a path simulation is performed through the A* algorithm to generate a multi-ship path; S104: Setting a remote control solution based on multiple ship paths; S106: locating abnormal control paths and time nodes according to historical remote control parameters of each ship, and extracting corresponding environmental features and path features from historical environmental data and historical path data of each ship based on the abnormal control paths and time nodes to obtain abnormal path features and abnormal environmental features; S108: Performing fleet navigation control based on the remote control solution, and collecting environmental characteristics and path characteristics for each ship in real time, quantizing the real-time environmental characteristics, real-time path characteristics, abnormal path characteristics, and abnormal environmental characteristics based on each preset path length, and comparing the average difference of the feature vectors, performing linear fitting on the average difference, and performing abnormal control prediction through linear fitting; S110: If an abnormal control judgment is made, a shared data mechanism is initiated for the target ship. Through the shared data mechanism, the target ship and adjacent ships at a preset distance are positioned, perceived and evaluated for navigation, and the evaluation data is sent to the control center for decision analysis.

[0006] In this solution, the S102 is specifically: The smart fleet includes multiple ships, each of which includes a control terminal, a communication terminal and a display terminal; The navigation mission includes the navigation distance, navigation position and navigation time of each ship; The navigation waypoints of each ship are set according to the navigation mission, and the shortest path planning is performed for each ship based on the A* algorithm, and multi-ship paths are generated.

[0007] In this solution, the S104 is specifically: The paths of multiple ships are sent to the control center, and navigation control analysis is performed on the ship paths. Combined with the target waters, control parameter instructions for multiple ships are set and a remote control plan is generated.

[0008] In this solution, the S106 is specifically: Obtain historical remote control parameters for each ship; Through historical remote control parameters, locate the path segments and time points where abnormal operation occurs, and mark them as abnormal control paths and time nodes; Based on the abnormal control path, the path features are extracted from the historical path data. The path features include path length, path turning angle, path straight distance, path width, and traffic density. Based on the time node, the water environment characteristics of the corresponding period are extracted from the historical environmental data. The water environment characteristics include water temperature, water flow, wind speed, wind direction, and meteorological information; Through feature extraction, abnormal path features and abnormal environment features are obtained.

[0009] In this solution, the S108 is specifically: Fleet navigation control based on remote control solutions; Taking a ship as the analysis unit, the environmental characteristics and path characteristics of the ship are collected in real time and marked as real-time environmental characteristics and real-time path characteristics; The real-time environment features and the abnormal environment features are vectorized, and a multi-dimensional first environment feature vector and a second environment feature vector are generated respectively; Calculate the difference between the first environment feature vector and the second environment feature vector based on the Euclidean distance, and mark it as a first difference degree; Vectorization and difference calculation are performed based on the real-time path feature and the abnormal path feature to obtain a second difference; Calculate the mean of the first difference and the second difference to obtain an average difference; Based on the lengths of multiple preset paths that have been navigated, multiple average differences are calculated, and linear fitting is performed on the multiple average differences to obtain a prediction fitting curve; By predicting the fitting curve, the average difference in the next preset path length is predicted to obtain a predicted value; If the predicted value is greater than the preset abnormal value, the abnormal control judgment is set and the abnormal ship is marked.

[0010] In this solution, the S110 is specifically: When the data sharing mechanism is activated, the abnormal ship builds a dedicated sharing network with neighboring ships at a preset distance; generating first navigation data based on the current position and navigation status of the abnormal ship; The neighboring ship uses the environmental detection device to locate and evaluate the navigation status of the abnormal ship and generate the second navigation data; Through the shared network, the neighboring ships evaluate the abnormal ship positioning deviation and navigation anomaly based on the first navigation data and the second navigation data to generate real-time navigation evaluation data; The first navigation data, the second navigation data and the real-time navigation evaluation data are sent to the control center, and the control center performs real-time matching analysis on the current navigation state and the expected navigation state of the abnormal ship and sets the navigation decision.

[0011] The second aspect of the present invention further provides a remote-controlled intelligent fleet simulation navigation system, the system comprising: a memory, a processor, the memory comprising a remote-controlled intelligent fleet simulation navigation program, the remote-controlled intelligent fleet simulation navigation program being executed by the processor to implement the following steps: S102: In the target waters, a navigation task of the intelligent fleet is set, and according to the navigation task, a path simulation is performed through the A* algorithm to generate a multi-ship path; S104: Setting a remote control solution based on multiple ship paths; S106: locating abnormal control paths and time nodes according to historical remote control parameters of each ship, and extracting corresponding environmental features and path features from historical environmental data and historical path data of each ship based on the abnormal control paths and time nodes to obtain abnormal path features and abnormal environmental features; S108: Performing fleet navigation control based on the remote control solution, and collecting environmental characteristics and path characteristics for each ship in real time, quantizing the real-time environmental characteristics, real-time path characteristics, abnormal path characteristics, and abnormal environmental characteristics based on each preset path length, and comparing the average difference of the feature vectors, performing linear fitting on the average difference, and performing abnormal control prediction through linear fitting; S110: If an abnormal control judgment is made, a shared data mechanism is initiated for the target ship. Through the shared data mechanism, the target ship and adjacent ships at a preset distance are positioned, perceived and evaluated for navigation, and the evaluation data is sent to the control center for decision analysis.

[0012] The third aspect of the present invention also provides a computer-readable storage medium, which includes a remote-controlled intelligent fleet simulation navigation program. When the remote-controlled intelligent fleet simulation navigation program is executed by a processor, the steps of the remote-controlled intelligent fleet simulation navigation method as described in any one of the above items are implemented.

[0013] The present invention discloses a remote-controlled intelligent fleet simulation navigation system. By setting navigation tasks and simulating and generating multiple ship paths based on the A* algorithm, the path segments and environmental data with abnormalities are extracted based on historical navigation data, and compared with the real-time data of the fleet to judge the potential control abnormality risk. If an abnormal control judgment occurs, the sharing mechanism is activated to perform collaborative deviation analysis and decision judgment of multiple ships, thereby effectively improving the control abnormality analysis of the entire fleet, improving the fleet's collaborative control capabilities and the fleet's intelligence level. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of a remote-controlled intelligent fleet navigation simulation method according to the present invention is shown; Figure 2 A block diagram of a remote-controlled intelligent fleet simulation navigation system of the present invention is shown. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] Figure 1 The flowchart of the intelligent fleet simulation navigation method based on remote control of the present invention is shown.

[0018] like Figure 1 As shown, the first aspect of the present invention provides a method for simulating navigation of an intelligent fleet based on remote control, comprising: S102: Set the navigation tasks for the intelligent fleet in the target water area. According to the navigation tasks, perform path simulation through the A* algorithm to generate multi-ship paths. S104: Set a remote control scheme based on the multi-ship paths. S106: Locate the abnormal control paths and time nodes according to the historical remote control parameters of each ship. Based on the abnormal control paths and time nodes, extract the corresponding environmental features and path features from the historical environmental data and historical path data of each ship to obtain abnormal path features and abnormal environmental features. S108: Control the fleet navigation based on the remote control scheme. For each ship, collect environmental features and path features in real time. Based on each preset path length, vectorize the real-time environmental features, real-time path features, abnormal path features, and abnormal environmental features and compare the average difference degree of the feature vectors. Perform linear fitting on the average difference degree to predict abnormal control through linear fitting. S110: If an abnormal control determination occurs, activate the shared data mechanism for the target ship. Through the shared data mechanism, perform positioning sharing perception and navigation assessment on the target ship and the ships adjacent within the preset distance, and send the assessment data to the control center for decision-making analysis.

[0019] It should be noted that the A* algorithm is a heuristic algorithm that searches for the shortest path by setting path points.

[0020] According to an embodiment of the present invention, the S102 is specifically as follows: The intelligent fleet includes multiple ships, and each ship includes a control terminal, a communication terminal, and a display terminal. The navigation tasks include the navigation distance, navigation position points, and navigation time of each ship. Set the navigation passing points of each ship according to the navigation tasks. Based on the A* algorithm, perform the shortest path planning for each ship and generate multi-ship paths.

[0021] It should be noted that the control terminal is used to set the ship navigation control parameters, the communication terminal is used for remote control communication between the ship and the control center, generally for real-time communication based on the Internet of Things, and the display terminal is used to display the ship status.

[0022] According to an embodiment of the present invention, the S104 is specifically as follows: Send the multi-ship paths to the control center, perform navigation control analysis on the ship paths, combine with the target water area, set the control parameter instructions for the multi-ships, and generate a remote control scheme.

[0023] It should be noted that the remote control scheme is based on the overall operation control parameters of the fleet, including the control parameters of each ship.

[0024] According to the embodiment of the present invention, the S106 is specifically: Obtain historical remote control parameters for each ship; Through historical remote control parameters, locate the path segments and time points where abnormal operation occurs, and mark them as abnormal control paths and time nodes; Based on the abnormal control path, the path features are extracted from the historical path data. The path features include path length, path turning angle, path straight distance, path width, and traffic density. Based on the time node, the water environment characteristics of the corresponding period are extracted from the historical environmental data. The water environment characteristics include water temperature, water flow, wind speed, wind direction, and meteorological information; Through feature extraction, abnormal path features and abnormal environment features are obtained.

[0025] It should be noted that the historical remote control parameters are historical control data, navigation path data, etc. collected by the ship control terminal during the historical time period, which are used to mine the state data where abnormal remote control exists, extract the corresponding abnormal control path and time node, and extract the environmental characteristics and path characteristics for the abnormal path and time node. The path width can include the maximum, minimum and average widths, and the path characteristics can reflect the complexity of navigation. Water body characteristics can reflect the water environment during navigation. By analyzing the water body characteristics, the correlation between the changes in water body characteristics and the occurrence of abnormal control can be mined.

[0026] According to the embodiment of the present invention, the S108 is specifically: Fleet navigation control based on remote control solutions; Taking a ship as the analysis unit, the environmental characteristics and path characteristics of the ship are collected in real time and marked as real-time environmental characteristics and real-time path characteristics; The real-time environment features and the abnormal environment features are vectorized, and a multi-dimensional first environment feature vector and a second environment feature vector are generated respectively; Calculate the difference between the first environment feature vector and the second environment feature vector based on the Euclidean distance, and mark it as a first difference degree; Vectorization and difference calculation are performed based on the real-time path feature and the abnormal path feature to obtain a second difference; Calculate the mean of the first difference and the second difference to obtain an average difference; Based on the lengths of multiple preset paths that have been navigated, multiple average differences are calculated, and linear fitting is performed on the multiple average differences to obtain a prediction fitting curve; By predicting the fitting curve, the average difference in the next preset path length is predicted to obtain a predicted value; If the predicted value is greater than the preset abnormal value, the abnormal control judgment is set and the abnormal ship is marked.

[0027] It should be noted that setting the preset path length for analysis is used to perform segmented real-time anomaly assessment on the entire navigation path. The calculation methods of the first difference and the second difference are consistent, and both use the distance value as the difference. By predicting the fitting curve, abnormal control prediction can be effectively achieved.

[0028] The Euclidean distance is calculated as follows: , Among them, d is the Euclidean distance, N is the number of vector dimensions, , are the values ​​of the i-th dimension of the two calculation vectors.

[0029] According to the embodiment of the present invention, the S110 is specifically: When the data sharing mechanism is activated, the abnormal ship builds a dedicated sharing network with neighboring ships at a preset distance; generating first navigation data based on the current position and navigation status of the abnormal ship; The neighboring ship uses the environmental detection device to locate and evaluate the navigation status of the abnormal ship and generate the second navigation data; Through the shared network, the neighboring ships evaluate the abnormal ship positioning deviation and navigation anomaly based on the first navigation data and the second navigation data to generate real-time navigation evaluation data; The first navigation data, the second navigation data and the real-time navigation evaluation data are sent to the control center, and the control center performs real-time matching analysis on the current navigation state and the expected navigation state of the abnormal ship and sets the navigation decision.

[0030] It should be noted that the environmental detection device includes a radar device, a camera device, etc. The overall navigation system of the fleet includes multiple ships and a control center, and the control center and multiple ships establish a real-time communication network based on the Internet of Things. A shared network is established between ships based on demand.

[0031] It is worth noting here that in the unmanned fleet operation mission (especially the remote control operation of the unmanned fleet), the navigation mission content includes water monitoring, resource exploration, search and rescue analysis, object transportation, etc., which have high navigation requirements for ships in real time, precision, and safety. However, in the existing technology, especially in the remote control of multiple ships, due to the influence of communication fluctuations, complex water environment, ship hardware, etc., it is difficult to coordinate and control multiple ships to navigate the complete path. Often, one or more ships have control anomalies or navigation deviations in certain waters or certain navigation paths. In this case, it is often necessary to intervene with human experience, making it difficult to achieve effective synchronous control of multiple ships and complete execution of fleet tasks, and it is difficult to improve the level of fleet automation and intelligence.

[0032] Based on this, the present invention simulates the corresponding remote control scheme in the target waters, extracts the path segments and environmental data with abnormalities based on the historical navigation data, sets the corresponding abnormal path characteristics and abnormal environmental characteristics, and in the real-time remote control process, removes the real-time environment and path characteristics of each preset path segment in real time, and compares and analyzes them with the abnormal characteristics in real time, calculates the average difference, performs linear fitting on the average difference, and reasonably predicts whether there is a potential abnormal control risk, effectively improves the system's control abnormality analysis of the entire fleet, improves the accurate deviation and control abnormality assessment of individual transmission, and adopts a sharing mechanism in real time. Through the collaborative navigation analysis of multiple ships on the target ship, the corresponding shared data is collected and sent to the control center for evaluation of navigation decisions, effectively improving the stability of the fleet's collaborative operation of multiple ships in a complex environment.

[0033] In addition, starting the shared data mechanism often consumes certain computing resources and network resources, and continuously turning it on will affect the stable operation of the entire fleet. Therefore, the present invention performs selective startup based on real-time status, effectively improving the dynamic regulation capability of the ship.

[0034] According to an embodiment of the present invention, the data sharing mechanism further includes: When the shared data mechanism is activated, the ships within the preset distance range are judged in real time with the abnormal ship as the center, marked as neighboring ships, and a dedicated shared network is established with the neighboring ships; generating first navigation data based on the current position and navigation status of the abnormal ship; The neighboring ship uses the environmental detection device to locate and evaluate the navigation status of the abnormal ship and generate the second navigation data; In a neighboring ship, the navigation deviation and path deviation of the abnormal ship are analyzed by using the first navigation data and the second navigation data to generate operation decision information; Generate multiple operation decision information based on multiple adjacent ships, and send the operation decision information to the control center in real time; During the real-time operation cycle, the system cyclically determines the existence of neighboring ships. If the neighboring ships exceed the preset distance range, the system stops sending operation decision information.

[0035] It should be noted that the operation decision includes decision-making schemes such as termination of remote control, continued control, route adjustment, and adjustment of ship navigation parameters. In the operation of a fleet of multiple ships, starting a shared network for collaborative abnormality analysis often consumes too many resources, and there is also a certain amount of data analysis pressure on the control center. Based on this, the embodiment of the present invention identifies adjacent ships within a preset range in real time, performs network sharing collaborative decision analysis on adjacent ships, analyzes operation decision information with certain differences from the perspective of different adjacent ships, and sends the operation decision information to the control center in real time, effectively improving the decision analysis capability of the control center, and distributing the decision analysis pressure among multiple ship terminals, thereby improving the stability and feasibility of the comprehensive operation of the fleet.

[0036] According to an embodiment of the present invention, it also includes: When an abnormal control judgment occurs, the path segment of the preset path length corresponding to the current position of the abnormal ship is marked as an abnormal path segment; In the decision-making evaluation process of the control center, the path cost is set for the abnormal path segment based on the degree of abnormal ship control deviation; Path constraints are generated based on the path cost. In the next navigation mission, during the path simulation process of the A* algorithm, the path constraints are imported for path simulation.

[0037] It should be noted that the control deviation degree is obtained based on a comprehensive analysis of navigation deviation, path deviation and the operation decision information of adjacent ships, and is used to determine the degree of deviation from control of abnormal ships. The path cost is proportional to the degree of control deviation of abnormal ships. By generating path constraints, the path planning algorithm can be dynamically adjusted to adapt to complex waters with abnormal control and improve the comprehensive path planning capability.

[0038] Figure 2 A block diagram of a remote-controlled intelligent fleet simulation navigation system of the present invention is shown.

[0039] The second aspect of the present invention further provides a remote-controlled intelligent fleet simulation navigation system 2, the system comprising: a memory 21, a processor 22, the memory 21 comprising a remote-controlled intelligent fleet simulation navigation program, the remote-controlled intelligent fleet simulation navigation program being executed by the processor 22 to implement the following steps: S102: In the target waters, a navigation task of the intelligent fleet is set, and according to the navigation task, a path simulation is performed through the A* algorithm to generate a multi-ship path; S104: Setting a remote control solution based on multiple ship paths; S106: locating abnormal control paths and time nodes according to historical remote control parameters of each ship, and extracting corresponding environmental features and path features from historical environmental data and historical path data of each ship based on the abnormal control paths and time nodes to obtain abnormal path features and abnormal environmental features; S108: Performing fleet navigation control based on the remote control solution, and collecting environmental characteristics and path characteristics for each ship in real time, quantizing the real-time environmental characteristics, real-time path characteristics, abnormal path characteristics, and abnormal environmental characteristics based on each preset path length, and comparing the average difference of the feature vectors, performing linear fitting on the average difference, and performing abnormal control prediction through linear fitting; S110: If an abnormal control judgment is made, a shared data mechanism is initiated for the target ship. Through the shared data mechanism, the target ship and adjacent ships at a preset distance are positioned, perceived and evaluated for navigation, and the evaluation data is sent to the control center for decision analysis.

[0040] It should be noted that the A* algorithm is a heuristic algorithm that searches for the shortest path by setting path points.

[0041] According to an embodiment of the present invention, the S102 is specifically: The smart fleet includes multiple ships, each of which includes a control terminal, a communication terminal and a display terminal; The navigation mission includes the navigation distance, navigation position and navigation time of each ship; The navigation waypoints of each ship are set according to the navigation mission, and the shortest path planning is performed for each ship based on the A* algorithm, and multi-ship paths are generated.

[0042] It should be noted that the control terminal is used to set the ship's navigation control parameters, the communication terminal is used for remote control communication between the ship and the control center, generally based on the Internet of Things for real-time communication, and the display terminal is used to display the ship's status.

[0043] According to an embodiment of the present invention, the S104 is specifically: The paths of multiple ships are sent to the control center, and navigation control analysis is performed on the ship paths. Combined with the target waters, control parameter instructions for multiple ships are set and a remote control plan is generated.

[0044] It should be noted that the remote control scheme is based on the overall operational control parameters of the fleet, including control parameters based on each ship.

[0045] According to the embodiment of the present invention, the S106 is specifically: Obtain historical remote control parameters for each ship; Through historical remote control parameters, locate the path segments and time points where abnormal operation occurs, and mark them as abnormal control paths and time nodes; Based on the abnormal control path, the path features are extracted from the historical path data. The path features include path length, path turning angle, path straight distance, path width, and traffic density. Based on the time node, the water environment characteristics of the corresponding period are extracted from the historical environmental data. The water environment characteristics include water temperature, water flow, wind speed, wind direction, and meteorological information; Through feature extraction, abnormal path features and abnormal environment features are obtained.

[0046] It should be noted that the historical remote control parameters are historical control data, navigation path data, etc. collected by the ship control terminal during the historical time period, which are used to mine the state data where abnormal remote control exists, extract the corresponding abnormal control path and time node, and extract the environmental characteristics and path characteristics for the abnormal path and time node. The path width can include the maximum, minimum and average widths, and the path characteristics can reflect the complexity of navigation. Water body characteristics can reflect the water environment during navigation. By analyzing the water body characteristics, the correlation between the changes in water body characteristics and the occurrence of abnormal control can be mined.

[0047] According to the embodiment of the present invention, the S108 is specifically: Fleet navigation control based on remote control solutions; Taking a ship as the analysis unit, the environmental characteristics and path characteristics of the ship are collected in real time and marked as real-time environmental characteristics and real-time path characteristics; The real-time environment features and the abnormal environment features are vectorized, and a multi-dimensional first environment feature vector and a second environment feature vector are generated respectively; Calculate the difference between the first environment feature vector and the second environment feature vector based on the Euclidean distance, and mark it as a first difference degree; Vectorization and difference calculation are performed based on the real-time path feature and the abnormal path feature to obtain a second difference; Calculate the mean of the first difference and the second difference to obtain an average difference; Based on the lengths of multiple preset paths that have been navigated, multiple average differences are calculated, and linear fitting is performed on the multiple average differences to obtain a prediction fitting curve; By predicting the fitting curve, the average difference in the next preset path length is predicted to obtain a predicted value; If the predicted value is greater than the preset abnormal value, the abnormal control judgment is set and the abnormal ship is marked.

[0048] It should be noted that setting the preset path length for analysis is used to perform segmented real-time anomaly assessment on the entire navigation path. The calculation methods of the first difference and the second difference are consistent, and both use the distance value as the difference. By predicting the fitting curve, abnormal control prediction can be effectively achieved.

[0049] The Euclidean distance is calculated as follows: , Where d is the Euclidean distance, N is the number of vector dimensions, , are the values ​​of the i-th dimension of the two calculation vectors.

[0050] According to the embodiment of the present invention, the S110 is specifically: When the data sharing mechanism is activated, the abnormal ship builds a dedicated sharing network with neighboring ships at a preset distance; generating first navigation data based on the current position and navigation status of the abnormal ship; The neighboring ship uses the environmental detection device to locate and evaluate the navigation status of the abnormal ship and generate the second navigation data; Through the shared network, the neighboring ship evaluates the abnormal ship positioning deviation and navigation anomaly based on the first navigation data and the second navigation data to generate real-time navigation evaluation data; The first navigation data, the second navigation data and the real-time navigation evaluation data are sent to the control center, and the control center performs real-time matching analysis on the current navigation state and the expected navigation state of the abnormal ship and sets the navigation decision.

[0051] It should be noted that the environmental detection device includes a radar device, a camera device, etc. The overall navigation system of the fleet includes multiple ships and a control center, and the control center and multiple ships establish a real-time communication network based on the Internet of Things. A shared network is established between ships based on demand.

[0052] It is worth noting here that in the unmanned fleet operation mission (especially the remote control operation of the unmanned fleet), the navigation mission content includes water monitoring, resource exploration, search and rescue analysis, object transportation, etc., which have high navigation requirements for ships in real time, precision, and safety. However, in the existing technology, especially in the remote control of multiple ships, due to the influence of communication fluctuations, complex water environment, ship hardware, etc., it is difficult to coordinate and control multiple ships to navigate the complete path. Often, one or more ships have control anomalies or navigation deviations in certain waters or certain navigation paths. In this case, it is often necessary to intervene with human experience, making it difficult to achieve effective synchronous control of multiple ships and complete execution of fleet tasks, and it is difficult to improve the level of fleet automation and intelligence.

[0053] Based on this, the present invention simulates the corresponding remote control scheme in the target waters, extracts the path segments and environmental data with abnormalities based on the historical navigation data, sets the corresponding abnormal path characteristics and abnormal environmental characteristics, and in the real-time remote control process, removes the real-time environment and path characteristics of each preset path segment in real time, and compares and analyzes them with the abnormal characteristics in real time, calculates the average difference, performs linear fitting on the average difference, and reasonably predicts whether there is a potential abnormal control risk, effectively improves the system's control abnormality analysis of the entire fleet, improves the accurate deviation and control abnormality assessment of individual transmission, and adopts a sharing mechanism in real time. Through the collaborative navigation analysis of multiple ships on the target ship, the corresponding shared data is collected and sent to the control center for evaluation of navigation decisions, effectively improving the stability of the fleet's collaborative operation of multiple ships in a complex environment.

[0054] In addition, starting the shared data mechanism often consumes certain computing resources and network resources, and continuously turning it on will affect the stable operation of the entire fleet. Therefore, the present invention performs selective startup based on real-time status, effectively improving the dynamic regulation capability of the ship.

[0055] The third aspect of the present invention also provides a computer-readable storage medium, which includes a remote-controlled intelligent fleet simulation navigation program. When the remote-controlled intelligent fleet simulation navigation program is executed by a processor, the steps of the remote-controlled intelligent fleet simulation navigation method as described in any one of the above items are implemented.

[0056] The present invention discloses a remote-controlled intelligent fleet simulation navigation system. By setting navigation tasks and simulating and generating multiple ship paths based on the A* algorithm, the path segments and environmental data with abnormalities are extracted based on historical navigation data, and compared with the real-time data of the fleet to judge the potential control abnormality risk. If an abnormal control judgment occurs, the sharing mechanism is activated to perform collaborative deviation analysis and decision judgment of multiple ships, thereby effectively improving the control abnormality analysis of the entire fleet, improving the fleet's collaborative control capabilities and the fleet's intelligence level.

[0057] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the 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 can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0058] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; 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 present embodiment.

[0059] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0060] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0061] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0062] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A remote-controlled intelligent fleet navigation simulation method, characterized in that: include: S102: In the target waters, a navigation task of the intelligent fleet is set, and according to the navigation task, a path simulation is performed through the A* algorithm to generate a multi-ship path; S104: Setting a remote control solution based on multiple ship paths; S106: locating abnormal control paths and time nodes according to historical remote control parameters of each ship, and extracting corresponding environmental features and path features from historical environmental data and historical path data of each ship based on the abnormal control paths and time nodes to obtain abnormal path features and abnormal environmental features; S108: Performing fleet navigation control based on the remote control solution, and collecting environmental characteristics and path characteristics for each ship in real time, quantizing the real-time environmental characteristics, real-time path characteristics, abnormal path characteristics, and abnormal environmental characteristics based on each preset path length, and comparing the average difference of the feature vectors, performing linear fitting on the average difference, and performing abnormal control prediction through linear fitting; S110: If an abnormal control judgment is made, a shared data mechanism is initiated for the target ship. Through the shared data mechanism, the target ship and adjacent ships at a preset distance are positioned, perceived and evaluated for navigation, and the evaluation data is sent to the control center for decision analysis.

2. The method for simulating navigation of an intelligent fleet based on remote control according to claim 1, characterized in that: The S102 is specifically: The smart fleet includes multiple ships, each of which includes a control terminal, a communication terminal and a display terminal; The navigation mission includes the navigation distance, navigation position and navigation time of each ship; The navigation waypoints of each ship are set according to the navigation mission, and the shortest path planning is performed for each ship based on the A* algorithm, and multi-ship paths are generated.

3. The method for simulating navigation of an intelligent fleet based on remote control according to claim 1, characterized in that: The S104 is specifically: The paths of multiple ships are sent to the control center, and navigation control analysis is performed on the ship paths. Combined with the target waters, control parameter instructions for multiple ships are set and a remote control plan is generated.

4. The method for simulating navigation of an intelligent fleet based on remote control according to claim 1, characterized in that: The S106 is specifically: Obtain historical remote control parameters for each ship; Through historical remote control parameters, locate the path segments and time points where abnormal operation occurs, and mark them as abnormal control paths and time nodes; Based on the abnormal control path, the path features are extracted from the historical path data. The path features include path length, path turning angle, path straight distance, path width, and traffic density. Based on the time node, the water environment characteristics of the corresponding period are extracted from the historical environmental data. The water environment characteristics include water temperature, water flow, wind speed, wind direction, and meteorological information; Through feature extraction, abnormal path features and abnormal environment features are obtained.

5. The method for simulating navigation of an intelligent fleet based on remote control according to claim 1, characterized in that: The S108 is specifically: Fleet navigation control based on remote control solutions; Taking a ship as the analysis unit, the environmental characteristics and path characteristics of the ship are collected in real time and marked as real-time environmental characteristics and real-time path characteristics; The real-time environment features and the abnormal environment features are vectorized, and a multi-dimensional first environment feature vector and a second environment feature vector are generated respectively; Calculate the difference between the first environment feature vector and the second environment feature vector based on the Euclidean distance, and mark it as a first difference degree; Vectorization and difference calculation are performed based on the real-time path feature and the abnormal path feature to obtain a second difference; Calculate the mean of the first difference and the second difference to obtain an average difference; Based on the lengths of multiple preset paths that have been navigated, multiple average differences are calculated, and linear fitting is performed on the multiple average differences to obtain a prediction fitting curve; By predicting the fitting curve, the average difference in the next preset path length is predicted to obtain a predicted value; If the predicted value is greater than the preset abnormal value, the abnormal control judgment is set and the abnormal ship is marked.

6. The method for simulating navigation of an intelligent fleet based on remote control according to claim 1, characterized in that: The S110 is specifically: When the data sharing mechanism is activated, the abnormal ship builds a dedicated sharing network with neighboring ships at a preset distance; generating first navigation data based on the current position and navigation status of the abnormal ship; The neighboring ship uses the environmental detection device to locate and evaluate the navigation status of the abnormal ship and generate the second navigation data; Through the shared network, the neighboring ships evaluate the abnormal ship positioning deviation and navigation anomaly based on the first navigation data and the second navigation data to generate real-time navigation evaluation data; The first navigation data, the second navigation data and the real-time navigation evaluation data are sent to the control center, and the control center performs real-time matching analysis on the current navigation state and the expected navigation state of the abnormal ship and sets the navigation decision.

7. An intelligent fleet simulation navigation system based on remote control, characterized in that: The system includes: a memory and a processor, wherein the memory includes a remote-controlled intelligent fleet simulation navigation program, and when the remote-controlled intelligent fleet simulation navigation program is executed by the processor, the following steps are implemented: S102: In the target waters, a navigation task of the intelligent fleet is set, and according to the navigation task, a path simulation is performed through the A* algorithm to generate a multi-ship path; S104: Setting a remote control solution based on multiple ship paths; S106: locating abnormal control paths and time nodes according to historical remote control parameters of each ship, and extracting corresponding environmental features and path features from historical environmental data and historical path data of each ship based on the abnormal control paths and time nodes to obtain abnormal path features and abnormal environmental features; S108: Performing fleet navigation control based on the remote control solution, and collecting environmental characteristics and path characteristics for each ship in real time, quantizing the real-time environmental characteristics, real-time path characteristics, abnormal path characteristics, and abnormal environmental characteristics based on each preset path length, and comparing the average difference of the feature vectors, performing linear fitting on the average difference, and performing abnormal control prediction through linear fitting; S110: If an abnormal control judgment is made, a shared data mechanism is initiated for the target ship. Through the shared data mechanism, the target ship and adjacent ships at a preset distance are positioned, perceived and evaluated for navigation, and the evaluation data is sent to the control center for decision analysis.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a remote-controlled intelligent fleet simulation navigation program, and when the remote-controlled intelligent fleet simulation navigation program is executed by a processor, the steps of the remote-controlled intelligent fleet simulation navigation method as described in any one of claims 1 to 6 are implemented.

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