Unmanned ship control system and method for loading unmanned aerial vehicle and unmanned ship

Abstract

The invention discloses a control system and method for an unmanned ship loaded with an unmanned aerial vehicle and the unmanned ship, and the system comprises an environment sensing module which is disposed on the unmanned ship and the unmanned aerial vehicle, and is used for collecting the external environment data, such as the wind speed, the wave height, the pressure, and the temperature, in real time; the autonomous decision module is arranged on the unmanned ship and controls the unmanned ship and the unmanned aerial vehicle to act based on the data of the environment sensing module; and the communication module is arranged on the unmanned ship and the unmanned aerial vehicle and is used for communication between the unmanned ship and the unmanned aerial vehicle, and the unmanned ship is communicated with a land control center. According to the unmanned ship control system and method loaded with the unmanned aerial vehicle and the unmanned ship, in the task execution process, the external environment can be automatically judged, floating task execution or diving risk avoiding can be automatically determined, whether the unmanned aerial vehicle is released or not can be automatically judged, and the unmanned ship and the unmanned aerial vehicle do not need to be manually controlled.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned boats and drones, and in particular to a control system and method for an unmanned boat loaded with a drone, and the unmanned boat. Background Art

[0002] In recent years, with the continued growth in global demand for marine resource development, environmental monitoring, and maritime safety and protection, unmanned surface vehicles (USVs), as the core vehicles of intelligent marine equipment, have become a research focus in the field of marine technology. With their autonomous navigation, remote control, and long endurance, USVs play a key role in scenarios such as hydrographic data collection, coastline patrols, disaster warning, and deep-sea exploration. Compared to traditional manned vessels, USVs overcome the time and space limitations of human operations, making them particularly suitable for high-risk, harsh environments, or long-duration missions. They provide important support for automated and intelligent operations in the marine sector.

[0003] However, the current practical application of unmanned aerial vehicle (UAV) technology still faces significant limitations. Existing UAVs generally lack intelligence and focus on a single function (such as surface monitoring or water quality sampling). They are also not integrated with drones, resulting in a smaller surface monitoring range compared to aerial drones. Even when some UAVs carry drones, they typically only accommodate a single type, preventing the flexibility to switch equipment configurations based on mission requirements. Traditional systems rely heavily on manual remote control and lack intelligent coordination between the UAV and drone, making it difficult to autonomously select the type of drone and when to release it. Furthermore, in the face of severe weather or complex sea conditions, existing UAVs require manual intervention for evasive maneuvers, resulting in weak autonomous judgment capabilities, a high risk of mission interruption, and low execution efficiency. Furthermore, their energy management and communication link designs lack dynamic optimization mechanisms, making communication interruptions and energy waste more likely in extreme environments. This severely restricts the expanded application of UAVs in complex marine scenarios. Summary of the Invention

[0004] In view of the above-mentioned problem that existing unmanned boats and drones rely on human intervention, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: including an unmanned boat, which is equipped with a small drone and a medium-sized drone, and also includes an environmental perception module, which is arranged on the unmanned boat and the drone, and is used to collect external environmental data such as wind speed, wave height, pressure, and temperature in real time; an autonomous decision-making module, which is arranged on the unmanned boat, and controls the actions of the unmanned boat and the drone based on the data of the environmental perception module; a communication module, which is arranged on the unmanned boat and the drone, and is used for communication between the unmanned boat and the drone, and communication between the unmanned boat and the land control center.

[0006] As a preferred solution of the unmanned boat control system loaded with a drone described in the present invention, the environmental perception module includes a pressure sensor arranged on the outside of the unmanned boat for monitoring the external water pressure of the unmanned boat, several temperature sensors arranged inside and outside the unmanned boat for detecting the internal and external temperatures of the unmanned boat, an anemometer arranged on the top of the unmanned boat for monitoring the external wind speed when the unmanned boat floats on the sea surface, and a wave monitor arranged on the outside of the unmanned boat for detecting the height of sea waves; the temperature sensor and wind speed sensor integrated on the drone are used to detect the temperature and wind speed in the air, and the laser rangefinder arranged at the bottom of the drone is used to measure the height from the sea surface vertically downward; it also includes a multi-sensor verification mechanism for calibrating when the sensor data deviation of the unmanned boat and the drone exceeds the allowable range; and a wave spectrum analysis device arranged inside the unmanned boat for analyzing real-time wave frequency and amplitude.

[0007] As a preferred solution of the unmanned boat control system loaded with drones described in the present invention, the communication module includes a satellite communication link arranged in the unmanned boat, which is used for communication between the unmanned boat and the land control center, and also includes a wireless network transceiver and a wired optical fiber communication equipment arranged in the unmanned boat, which are used for communication between the unmanned boat and the drone; the small drone and the unmanned boat use wireless network communication, the medium-sized drone and the unmanned boat use wireless network or wired optical fiber communication, and the unmanned boat communicates with the land control center.

[0008] As a preferred solution of the unmanned boat control system loaded with drones described in the present invention, it also includes an energy management module that dynamically switches the energy supply mode according to environmental conditions; the energy management module includes a power storage device, a solar charging device, and a drone charging device arranged on the unmanned boat.

[0009] As a preferred solution of the unmanned boat control system loaded with drones described in the present invention, the autonomous decision-making module uses data from the environmental perception module to perform the following operations: judge the sea conditions to trigger the unmanned boat to surface or dive; select to release a small drone or a medium-sized drone based on mission information; and control the medium-sized drone to return and charge.

[0010] As a preferred solution of the unmanned boat control system loaded with drones described in the present invention, it also includes: a dynamic decision optimization module, which is arranged inside the unmanned boat and connected to the autonomous decision module, and dynamically adjusts the decision threshold and action priority through a machine learning algorithm based on historical data and real-time data; the dynamic decision optimization module also includes predicting the severe weather window period in advance based on the meteorological forecast model, and adjusting the diving or surfacing trigger timing through confidence weighting when the forecast result conflicts with the real-time data; and optimizing the drone selection strategy according to the mission history data.

[0011] As a preferred solution of the unmanned boat control system loaded with drones described in the present invention, it also includes a task adaptive interruption and recovery module arranged in the unmanned boat. When the unmanned boat dives to avoid bad weather, it automatically saves the current task progress and environmental snapshot data, and re-evaluates the task feasibility and resumes execution based on the latest environmental status after surfacing.

[0012] The present invention also provides a method for controlling an unmanned boat loaded with a drone, comprising the following steps:

[0013] S1. Environmental data collection to determine the external environment of the unmanned boat;

[0014] S2, making mission decisions and selecting and releasing drones based on environmental data;

[0015] S3. Dynamically controlling the unmanned boat to float / dive according to environmental conditions;

[0016] S4. Energy management according to the energy conditions of the unmanned boat and the unmanned aerial vehicle and the external environment.

[0017] As a preferred solution of the control method of the unmanned boat loaded with a drone described in the present invention, wherein: in S1, environmental data such as wind speed and wave height are collected in real time by onboard sensors, and weather warnings from a land control center are received via satellite communication to generate a comprehensive environmental status assessment report; in S2, based on the comprehensive environmental status assessment report, it is determined whether the current sea conditions allow the execution of the mission, and the corresponding drone is selected according to the mission type.

[0018] The present invention also provides an unmanned boat loaded with a drone, which has the above-mentioned unmanned boat control system and / or the above-mentioned unmanned boat control method, and also includes a boat body, an equipment compartment arranged at the head of the boat body, an unmanned boat cabin arranged at the upper part of the boat body, and a pressurized cabin arranged at the lower part of the boat body, wherein the tail of the unmanned boat cabin is provided with an openable and closable door.

[0019] The beneficial effects of the present invention are as follows: by setting up an environmental perception module and an autonomous decision-making module, the present invention can independently judge the external environment during the execution of the mission, independently decide whether to surface to perform the mission or dive to avoid risks, and can independently judge whether to launch the drone. There is no need for manual control of the unmanned boat and drone. It is not easy to be damaged by communication interruption during the execution of the mission. Even in the absence of control, it can go to the sea area far away from the control center to perform the mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a schematic diagram of the control system of an unmanned boat equipped with a drone according to the present invention.

[0022] Figure 2 This is a decision diagram of the autonomous decision module of the unmanned boat control system equipped with a drone according to the present invention.

[0023] Figure 3 This is a schematic diagram of the multi-sensor calibration mechanism of the unmanned boat control system equipped with a drone according to the present invention.

[0024] Figure 4 This is a schematic diagram of the dynamic decision-making optimization module of the unmanned boat control system equipped with a drone according to the present invention.

[0025] Figure 5 This is a schematic diagram of the communication module of the unmanned boat control system equipped with a drone according to the present invention.

[0026] Figure 6 This is an overall schematic diagram of the unmanned boat control system equipped with a drone according to the present invention.

[0027] Figure 7 The figure is a schematic diagram of the overall structure of the unmanned boat loaded with a UAV according to the present invention.

[0028] Figure 8 The figure is a schematic cross-sectional view of the unmanned boat loaded with a UAV according to the present invention.

[0029] In the figure: 100, environmental perception module; 200, autonomous decision-making module; 300, communication module; 400, energy management module; 500, dynamic decision optimization module; 600, mission adaptive interruption and recovery module; 700, hull; 701, equipment compartment; 702, unmanned aerial vehicle compartment; 702a, hatch; 702b, machine nest; 703, pressurized cabin;. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] 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. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0032] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0033] Furthermore, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, when describing the embodiments of the present invention, cross-sectional views illustrating device structures may be partially enlarged and not to scale. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0034] Example 1

[0035] Reference Figure 1-Figure 3 , which is the first embodiment of the present invention, provides an unmanned boat control system equipped with a drone. This device includes an unmanned boat, which is equipped with a small drone and a medium-sized drone. An environmental perception module 100 is provided on the unmanned boat and the drone, and is used to collect external environmental data such as wind speed, wave height, pressure, and temperature in real time; an autonomous decision-making module 200 is provided on the unmanned boat, and controls the actions of the unmanned boat and the drone based on the data from the environmental perception module 100; and a communication module 300 is provided on the unmanned boat and the drone, and is used for communication between the unmanned boat and the drone, and between the unmanned boat and the land control center.

[0036] Reference Figure 2, detailing how the autonomous decision-making module 200 determines and makes decisions. The autonomous decision-making module 200 uses data from the environmental perception module 100 to assess sea conditions and trigger the unmanned vehicle to surface or dive. The autonomous decision-making module 200 first receives real-time environmental data (wind speed, wave height, temperature, etc.) collected by the environmental perception module 100, mission instructions transmitted by the communication module 300, and power status feedback from the energy management module 400. The decision-making center performs parallel processing based on three types of input data. By analyzing wind speed and wave height data, it determines whether the current environment is dangerous (e.g., wind speed > 15m / s and wave height > 1.5m). If dangerous, a dive avoidance command is triggered; if the environment is safe, the ascent process begins. The type of mission instruction distinguishes between emergency tasks (e.g., disaster emergency monitoring) and long-term tasks (e.g., marine ecological inspections). Emergency tasks prioritize the release of small drone swarms for rapid response, while long-term tasks select medium-sized drones for continuous operation. The battery level of unmanned boats and drones is monitored in real time. If the drone battery level is detected to be lower than the threshold (e.g. the remaining battery level of a medium-sized drone is less than 30%), the return-to-home charging mechanism is forcibly activated.

[0037] The environmental sensing module 100 includes a pressure sensor provided on the outside of the unmanned boat for monitoring the external water pressure of the unmanned boat, several temperature sensors provided inside and outside the unmanned boat for detecting the internal and external temperatures of the unmanned boat, an anemometer provided on the top of the unmanned boat for monitoring the external wind speed when the unmanned boat is floating on the sea surface, and a wave monitor provided on the outside of the unmanned boat 700 for detecting the height of the sea waves.

[0038] The temperature sensor and wind speed sensor integrated on the drone are used to detect the temperature and wind speed in the air. The laser rangefinder installed at the bottom of the drone is used to measure the height from the sea surface vertically downward.

[0039] During use, when the environmental perception module 100 detects that the wind speed or wave height exceeds a preset threshold, the autonomous decision-making module 200 automatically controls the unmanned boat to retract the drone and fill the pressurized cabin 703 with water to perform a diving action, while closing the equipment compartment door 702a, thereby diving into the sea to avoid the wind and waves on the sea surface. When the environmental perception module 100 detects that the waves in the sea are decreasing, and in conjunction with the weather forecast data, it is judged that the wind and waves have ended, the autonomous decision-making module 200 automatically controls the unmanned boat to surface, and continuously detects external environmental data during the surface process to prevent misjudgment.

[0040] The following table illustrates how the unmanned boat responds to different wind speed and wave height thresholds. The actual control data can be adjusted according to the sea conditions and special needs.

[0041] Table 1

[0042]

[0043]

[0044] As shown in Table 1 above, when the environmental perception module 100 detects an external wind speed of 12m / s and a wave height of 1.3m, the self-service decision module compares the environmental data according to the preset threshold, and then controls the unmanned boat to use the communication module 300 to send a signal to the drone to retrieve the drone performing the mission outside. The unmanned boat closes the hatch 702a and floats on the sea surface, shuts down unnecessary equipment (such as power equipment), reduces its own power consumption, and continues to monitor the external wind speed and wave height to determine whether to proceed to the next response or return to normal tasks. If the environmental perception module 100 detects an external wind speed of 16m / s and a wave height of 2m, it will dive to avoid the wind and waves on the sea surface after retrieving the drone. At this time, in addition to shutting down the power equipment, it also shuts down air detection equipment such as wind speed, only detecting underwater surges, and maintaining low data communication to transmit data and receive commands. If the wave height continues to increase to more than 3m, the unmanned boat will increase its diving depth, further shut down its equipment, disconnect communications, and only retain monitoring of the surge. It will also switch from real-time monitoring to monitoring at regular intervals, further reducing power consumption and extending standby time until it surfaces when the water calms down.

[0045] The UAV control system also includes a multi-sensor calibration mechanism, which is used to calibrate sensor data from the UAV and drone's environmental perception modules 100 when deviations exceed allowable limits. This mechanism is used to determine whether to dive or avoid the surface. A wave spectrum analyzer, installed within the UAV, analyzes real-time wave frequency and amplitude to determine surge type (e.g., wind or swell) and dynamically adjust the dive depth threshold.

[0046] Refer to Table 2 and Figure 3 , which gives examples of how to calibrate multiple sensors. In actual use, sensors can be added or reduced, and the data thresholds mentioned are only examples and can be adjusted according to the sea climate.

[0047] Table 2

[0048]

[0049] As shown in Table 2, during use, the launched drone transmits environmental data to the unmanned boat, which then compares the data and performs hardware data cross-checks. For example, if the unmanned boat detects a surface wind speed of 5 m / s, but the drone detects a wind speed of 10 m / s, the drone can verify this by restarting the unmanned boat's anemometer and checking the wind speeds of the drones against each other. If the deviation is still outside the range, satellite meteorological data can be transmitted as a benchmark for calibration.

[0050] Figure 3The multi-sensor calibration mechanism is detailed in the following flowchart. First, after the environmental perception module 100 acquires data from each sensor in real time, it simultaneously initiates a dual data comparison mechanism to cross-validate data from multiple sensors in the same location (e.g., comparing multiple temperature sensors on the exterior of a boat) and to perform a horizontal comparison of data from the same type of sensor (e.g., comparing the temperature sensor on the exterior of an unmanned boat with that on a drone).

[0051] Secondly, the system performs difference calculation and threshold judgment. It calculates the real-time data difference between the boat-borne sensor and the airborne sensor (such as wind speed deviation and wave height deviation). If the difference exceeds the preset threshold (such as wind speed deviation > ±5m / s for 5 consecutive minutes), the subsequent verification process is triggered.

[0052] During calibration, the calibration analysis engine is started, and based on the stability analysis of historical data and the consistency check of related sensors (such as the linkage between wave monitor data and drone laser rangefinder data), the confidence weight of each sensor data is evaluated; if a single abnormal sensor is located, the automatic calibration process begins; if the abnormality cannot be located or multiple sensors are abnormal, the faulty device is marked and the backup sensor is activated.

[0053] Automatic calibration is performed on abnormal sensors (such as calling satellite meteorological data as a baseline value). After the calibration is completed, the autonomous decision-making module 200 verifies the validity of the data; if the verification passes, the data is marked as credible and the task continues; if the verification fails, the system sends a manual intervention request to the land control center and synchronously updates the sensor status to the historical database.

[0054] Example 2

[0055] Reference Figure 1 、 Figure 5 、 Figure 6 , which is the second embodiment of the present invention. This embodiment differs from the first embodiment in that: the communication module 300 includes a satellite communication link arranged in the unmanned boat, which is used for communication between the unmanned boat and the land control center, and also includes a wireless network transceiver device and a wired optical fiber communication device arranged in the unmanned boat, which are used for communication between the unmanned boat and the drone, and communication between the unmanned boat and the land control center.

[0056] Small UAVs and unmanned boats use wireless network communication, and medium-sized UAVs and unmanned boats can choose to use wireless network or wired fiber optic communication. When using fiber optic communication, the unmanned boat is equipped with a reel for reeling in the optical fiber to prevent the optical fiber from being entangled.

[0057] It uses satellite communication links to communicate with the land control center to transmit commands and positioning data, and can also transmit meteorological data. The unmanned boat itself can send wireless network signals to conduct wireless network data transmission with small and medium-sized drones, and when releasing medium-sized drones, it can also use wired optical fiber to conduct wired data transmission with medium-sized drones. It will not be interfered with by signals, the control is more stable, and the data transmission efficiency and stability are higher. The optical fiber length is set to 120% of the maximum operating radius of the medium-sized drone.

[0058] During use, the unmanned boat goes to the mission site based on the positioning data and meteorological data from the land control center. The unmanned boat maintains real-time monitoring of the external environment and is ready to dive and hide at any time in conjunction with the received meteorological data.

[0059] Once the unmanned boat arrives at the mission location, it will follow the mission instructions from the ground control center: For high-risk areas or high-urgency missions, a swarm of disposable small drones will be launched for rapid data collection. For continuous duty missions, a medium-sized drone will be released and the return-to-recharge mechanism will be activated for long-term monitoring. After release, the drones will execute their mission and transmit various data to the unmanned boat for aggregation.

[0060] The small drone has the function of self-organizing network, adopts LoRaWAN protocol self-organizing network, the single node communication distance is ≥500 meters, and the relay node is dynamically elected by the autonomous decision module 200.

[0061] The control system of the unmanned boat loaded with drones also includes an energy management module 400, which dynamically switches the energy supply mode according to environmental conditions. The energy management module 400 includes a power storage device, a solar charging device, and a drone charging device installed on the unmanned boat.

[0062] When the unmanned boat is at sea, it can charge its energy storage device via solar charging equipment, prioritizing solar power for power supply and using the drone charging equipment to charge various drones. In inclement weather, the unmanned boat can retrieve the drone and dive for shelter, using the energy stored in the energy storage device to power it. The drone can then be disconnected from the boat and non-essential equipment can be shut down to reduce energy consumption.

[0063] Example 3

[0064] Reference Figure 4 , which is the third embodiment of the present invention. This embodiment differs from the second embodiment in that it also includes a dynamic decision optimization module 500, which is arranged on the unmanned boat. Based on historical data and real-time data, the decision threshold and action priority are dynamically adjusted through a machine learning algorithm. The dynamic decision optimization module 500 assigns priorities to various actions and tasks, and adjusts decisions in real time according to the action priority.

[0065] Specifically, the dynamic decision-making optimization module 500 receives real-time environmental data (such as wind speed, wave height, temperature, and pressure) from the environmental perception module 100, obtains mission instructions and feedback information from the communication module 300, and obtains the current battery status of the unmanned boat and drone from the energy management module 400. Simultaneously, the dynamic decision-making optimization module 500 also receives weather forecast data from the land-based control center.

[0066] The dynamic decision optimization module 500 utilizes an LSTM neural network (Long Short-Term Memory Recurrent Neural Network). This LSTM neural network includes an input layer that receives standardized environmental data (such as wind speed, wave height, temperature, and pressure); a hidden layer containing multiple LSTM units that extract long-term dependencies within the time series; and an output layer that outputs optimized decision thresholds and action priorities. The training data includes historical data on wave height, wind speed, and task success rates. Based on this data, a dynamic loss (Loss) is calculated: Loss = α × dynamic task failure rate + β × task execution time + γ × energy consumption. Here, the dynamic task failure rate is: Dynamic task failure rate = α × historical task failure rate + (1-α) × real-time task failure rate, where α is a weighting factor used to balance the influence of historical and real-time data. This factor is typically adjusted dynamically based on the urgency of the task and the severity of environmental changes. For example, in the case of drastic external environmental changes, the coefficient α can be appropriately reduced to increase the weight of real-time data.

[0067] In the historical mission failure rate and real-time mission failure rate, the historical mission failure rate is obtained by dividing the number of historical mission failures by the total number of missions. The real-time mission failure rate is predicted by a machine learning model based on current environmental conditions (wind speed, wave height, temperature, etc.) and the status of the unmanned boat and drone (battery power, communication status, etc.).

[0068] According to the calculated dynamic loss, task urgency, and environmental adaptability, each task is prioritized and the task priority is adjusted dynamically.

[0069] The dynamic decision optimization module 500 dynamically adjusts the operational priorities of the UAV and drone based on the power information provided by the energy management module 400. For example, if the drone's battery is low, the module prioritizes its return to recharge and adjusts the UAV's energy allocation strategy based on the mission priority.

[0070] Among them, the dynamic decision optimization module 500 also includes predicting the severe weather window period in advance based on the meteorological forecast model, and adjusting the diving or surfacing trigger timing through confidence weighting when the forecast result conflicts with the real-time data; optimizing the drone selection strategy according to the mission history data, and the confidence weight is calculated based on the historical accuracy of the meteorological forecast model and the deviation range of the real-time sensor data.

[0071] Furthermore, the control system of the unmanned boat equipped with a drone also includes a mission adaptive interruption and recovery module 600 arranged in the unmanned boat. When the unmanned boat dives to avoid bad weather, it automatically saves the current mission progress and environmental snapshot data, and re-evaluates the mission feasibility and resumes execution based on the latest environmental status after surfacing.

[0072] The dynamic decision optimization module 500 interacts with the land control center in real time through the communication module 300, receives weather forecast data and mission instructions, and transmits the optimized decision results and status information back to the control center. When an equipment cabin failure or drone anomaly is detected, the current task is automatically terminated and the self-test program is triggered. At the same time, a fault code is sent to the control center. After the decision is executed, the action results (such as actual avoidance time, energy consumption increment) are transmitted back through the communication module 300 to dynamically update the LSTM training data set. When communication is interrupted, the dynamic decision optimization module 500 enables the local emergency decision mode. The data generated during the communication interruption is stored locally and updated after the communication is restored. The preset task process is autonomously executed based on the last received instruction and the current environmental data.

[0073] During use, the dynamic decision optimization module 500 receives in real time the mission instructions and weather forecast data transmitted by the communication module 300 from the land control center; the real-time environmental data such as wind speed, wave height, temperature, etc. collected by the environmental perception module 100; and the power status of the unmanned boat and drone fed back by the energy management module 400.

[0074] Then, the severe weather window period is predicted based on the meteorological forecast model, and the execution risk of the current decision is evaluated through the task success rate prediction model; task priority sorting: based on the task urgency (such as urgent / critical tasks), energy consumption cost (high energy consumption tasks) and historical task data, a task priority sequence is generated (such as priority 1 corresponds to high-urgency tasks).

[0075] If the weather forecast results conflict with the real-time environmental data (e.g., the predicted wave height is <1.5m but the real-time monitoring is >2m), the timing of the dive / surfacing trigger will be adjusted through a confidence weighting mechanism. The confidence weight is calculated based on the historical accuracy of the prediction model and the deviation range of the sensor data. If there is no conflict, the preset strategy will be executed (such as diving to avoid danger or releasing a drone).

[0076] The optimized decision threshold and action priority are sent to the autonomous decision module 200 to drive the unmanned boat to perform operations such as snorkeling control and drone scheduling; the task execution results (such as snorkeling avoidance time and energy consumption increase) are transmitted back to the dynamic decision optimization module 500 through the communication module 300 to update the historical decision library and optimize the subsequent task sequence.

[0077] The remaining structures are the same as those of Example 1.

[0078] Example 4

[0079] The present invention also provides a method for controlling an unmanned boat loaded with a drone, comprising the following steps:

[0080] S1. Environmental data collection to determine the external environment of the unmanned boat;

[0081] S2, making mission decisions and selecting and releasing drones based on environmental data;

[0082] S3, dynamically control the unmanned boat to float / dive according to environmental conditions;

[0083] S4. Energy management based on the energy status of unmanned boats and drones and external environmental conditions.

[0084] Furthermore, in S1, environmental data such as wind speed and wave height are collected in real time through onboard sensors, and weather warnings from the land control center are received through satellite communications to generate a comprehensive environmental status assessment report.

[0085] S1.1. When the deviation between the anemometer and the drone wind speed data exceeds the allowable range, the redundant sensor calibration procedure is started and the weighted average value is used as the basis for decision making.

[0086] In S2, based on the comprehensive environmental status assessment report, it is determined whether the current sea conditions allow the mission to be performed, and the corresponding UAV is selected according to the mission type.

[0087] For long-term monitoring missions, reusable medium-sized drones are released and high-bandwidth communication links are established; for high-risk or high-urgency missions, disposable small drones are released and low-power one-way command transmission is adopted.

[0088] S2.1. Before releasing a drone, assess the status of the equipment compartment and the drone's battery level. If the battery level of a medium-sized drone is lower than a preset value, prioritize releasing a small drone and send a low-battery alarm to the ground control center.

[0089] In S3, based on the comprehensive environmental status assessment report of the unmanned boat and the drone, the current sea conditions are judged. If the wind speed or wave height exceeds the preset threshold, the drone is retracted, the pressurized cabin 703 is triggered to take on water and dive, and the equipment compartment door 702a is closed; if the sea surface is calm and there is sufficient light, the surfacing action is triggered and solar charging is started.

[0090] S3.1. Identify the surge type (wind wave or swell wave) through wave spectrum analysis and dynamically adjust the diving depth threshold.

[0091] In S4, solar energy is used first to charge unmanned boats and drones. When bad weather or a diving state is detected, it switches to backup battery power and shuts down non-essential equipment to reduce energy consumption.

[0092] S4.1. In the submerged state, the backup battery power is dynamically allocated according to the depth and mission load, with priority given to ensuring the power supply of the communication module 300 and the core control unit.

[0093] S4.2. When a medium-sized UAV is returning to recharge, if the solar power supply is insufficient, non-critical equipment will be suspended according to the urgency of the mission to prioritize charging.

[0094] S5. Mission Execution and Exception Handling: After completing its mission, the medium-sized drone is automatically controlled to return to the equipment bay and initiate charging. If a cabin failure or drone anomaly is detected, the current mission is terminated, a self-test is triggered, and a fault code is sent to the control center.

[0095] S5.1. When communication is interrupted, the local emergency decision-making mode is activated, and the preset mission process is autonomously executed based on the last received instruction and current environmental data. When the loss of contact exceeds 30 minutes and the wave height is greater than 1.5 meters, the dive procedure is forcibly started; during the loss of contact, an attempt is made to send a status code via Beidou short message every 5 minutes.

[0096] S5.2. When the UAV dives to avoid bad weather, the current mission progress and environmental snapshot data are automatically saved.

[0097] S5.3. After surfacing, reassess the feasibility of the mission based on the latest environmental conditions. If the original mission is recoverable, continue execution. If not, generate an alternative plan and submit it to the land control center for confirmation.

[0098] S6. Adaptive decision optimization based on environmental conditions, task execution and energy conditions.

[0099] S6.1. Dynamically adjust decision thresholds and task priorities using machine learning algorithms based on historical environmental data and mission execution results. Combined with weather forecast models, anticipate severe weather windows and optimize the timing of dive or ascent triggers.

[0100] S6.2. Based on energy data such as solar charging efficiency, remaining power of the unmanned boat, and remaining power of the drone, determine whether to launch a new drone, whether to recover a drone, and whether to prioritize charging.

[0101] The remaining structures are the same as those of Example 2.

[0102] Example 5

[0103] Reference Figure 7 、 Figure 8 , which is the fifth embodiment of the present invention, provides an unmanned boat carrying a drone, including a hull 700, an equipment cabin 701 arranged at the head of the hull 700, an unmanned aerial vehicle cabin 702 arranged at the upper part of the hull 700, and a pressurized cabin 703 arranged at the lower part of the hull 700.

[0104] Among them, the tail of the unmanned aerial vehicle cabin 702 is provided with an openable and closable door 702a. When the door 702a is unfolded, it can serve as a take-off and landing platform for the unmanned aerial vehicle. When closed, it seals the unmanned aerial vehicle cabin 702. The equipment cabin 701 is isolated from the unmanned aerial vehicle cabin 702 and the pressurized cabin 703 to prevent seawater from entering the equipment cabin 701. The equipment cabin 701 is used to install electronic equipment, the unmanned aerial vehicle cabin 702 is used to install a small unmanned aerial vehicle nest 702b and place medium-sized unmanned aerial vehicles, and the pressurized cabin 703 is used to control the pressurization to control the ascent and descent of the unmanned boat.

[0105] There is also a propeller at the tail of the unmanned boat to drive the boat to move.

[0106] Furthermore, various equipment such as navigation, satellite positioning, processors, and power storage equipment of the unmanned boat are installed in the equipment cabin 701. The unmanned aerial vehicle cabin 702 is used to store the unmanned aerial vehicle and install unmanned aerial vehicle charging equipment. It is also equipped with a unmanned aerial vehicle releasing device. The pressurized cabin 703 is used to pump out seawater to control the ascent and descent of the unmanned aerial vehicle. The hatch 702a at the rear of the unmanned aerial vehicle cabin 702 can be used as a unmanned aerial vehicle airport when unfolded, for taking off and landing unmanned aerial vehicles.

[0107] Furthermore, the small drone is installed in the drone cabin 702 via a machine nest 702b, which is electrically connected to the power storage device of the unmanned boat, thereby enabling the small drone to be charged. The medium-sized drone moves and is fixed in the drone cabin 702 via a track equipped with a charging device.

[0108] Furthermore, the track also controls the launch and retraction of the medium-sized drone. During launch, the track transports the drone to the open hatch 702a. The track then unlocks and retracts, allowing the drone to take off on its own. During retraction, the track is positioned at hatch 702a. After the drone lands, the track clamps onto it and then drives it back to the drone bay 702.

[0109] The small UAV takes off directly from the machine nest 702b and flies out of the UAV cabin 702. Since the small UAV is disposable, it does not need to be recovered.

[0110] Importantly, it should be noted that the construction and arrangement of the present application shown in a plurality of different exemplary embodiments are only exemplary. Although only several embodiments are described in detail in this disclosure, it should be readily understood by those having reference to this disclosure that, without departing substantially from the novel teachings and advantages of the subject matter described in this application, many modifications are possible (e.g., the size, scale, structure, shape and ratio of various elements, and parameter values ​​(e.g., temperature, pressure, etc.), installation arrangements, use of materials, color, directional changes, etc.). For example, the element shown as integrally formed can be made up of multiple parts or elements, the position of the element can be inverted or otherwise changed, and the property or number or position of the discrete element can be changed or changed. Therefore, all such modifications are intended to be included within the scope of the present invention. The order or sequence of any process or method step can be changed or reordered according to alternative embodiments. Therefore, the present invention is not limited to specific embodiments, but extends to the multiple modifications still falling within the scope of the appended claims.

[0111] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An unmanned boat control system equipped with a drone, characterized by: Including unmanned boats, the unmanned boats are equipped with small drones and medium drones; include, An environmental perception module (100), provided on the unmanned boat and the unmanned aerial vehicle, for collecting external environmental data such as wind speed, wave height, pressure, and temperature in real time; An autonomous decision-making module (200), provided on the unmanned boat, controls the actions of the unmanned boat and the drone based on data from the environment perception module (100); A communication module (300) is provided on the unmanned boat and the drone, and is used for communication between the unmanned boat and the drone, and for communication between the unmanned boat and the land control center.

2. The unmanned boat control system equipped with a drone according to claim 1, characterized in that: The environmental sensing module (100) includes a pressure sensor arranged outside the unmanned boat for monitoring the water pressure outside the unmanned boat, a plurality of temperature sensors arranged inside and outside the unmanned boat for detecting the temperature inside and outside the unmanned boat, an anemometer arranged on the top of the unmanned boat for monitoring the external wind speed when the unmanned boat floats on the sea surface, and a wave monitor arranged outside the unmanned boat for detecting the height of sea waves; The temperature sensor and wind speed sensor integrated on the drone are used to detect the temperature and wind speed in the air, and the laser rangefinder set on the bottom of the drone is used to measure the height from the sea surface vertically downward; It also includes a multi-sensor calibration mechanism for calibrating when the sensor data deviation of the unmanned boat and the drone exceeds the allowable range; a wave spectrum analysis device arranged inside the unmanned boat analyzes the real-time wave frequency and amplitude.

3. The unmanned boat control system equipped with a drone according to claim 1 or 2, characterized in that: The communication module (300) includes a satellite communication link arranged in the unmanned boat, which is used for the unmanned boat to communicate with a land control center, and also includes a wireless network transceiver and a wired optical fiber communication device arranged in the unmanned boat, which are used for the unmanned boat to communicate with a drone and the unmanned boat to communicate with a land control center; The small UAV and the unmanned boat communicate with each other via a wireless network, and the medium UAV and the unmanned boat communicate with each other via a wireless network or wired optical fiber.

4. The unmanned boat control system equipped with a drone according to claim 3, characterized in that: It also includes an energy management module (400) for dynamically switching energy supply modes according to environmental conditions. The energy management module (400) includes power storage equipment, solar charging equipment, and drone charging equipment arranged on the unmanned boat.

5. The unmanned boat control system equipped with a drone according to any one of claims 1, 2, and 4, characterized in that: The autonomous decision-making module (200) uses data from the environment perception module (100) to perform the following operations: Determine sea conditions and trigger the unmanned boat to surface or dive; Selecting to release the small UAV or the medium UAV according to the mission information; Control the medium-sized UAV to return home and charge.

6. The unmanned boat control system equipped with a drone according to claim 5, characterized in that: It also includes a dynamic decision optimization module (500), which is arranged inside the unmanned boat and connected to the autonomous decision module (200), and dynamically adjusts the decision threshold and action priority through a machine learning algorithm based on historical data and real-time data; The dynamic decision optimization module (500) further includes predicting the bad weather window period in advance based on the meteorological prediction model, and adjusting the diving or surfacing triggering timing by confidence weighting when the prediction result conflicts with the real-time data; Optimize drone selection strategy based on mission history data.

7. The unmanned boat control system equipped with a drone according to any one of claims 1, 2, 4, and 6, characterized in that: It also includes a task adaptive interruption and recovery module (600) arranged in the unmanned boat, which automatically saves the current task progress and environmental snapshot data when the unmanned boat dives to avoid bad weather, and re-evaluates the feasibility of the task based on the latest environmental status and resumes execution after surfacing.

8. A method for controlling an unmanned boat loaded with a drone, characterized in that: The unmanned boat control system for loading a drone according to any one of claims 1 to 7 further comprises the following steps: S1. Environmental data collection to determine the external environment of the unmanned boat; S2, making mission decisions and selecting and releasing drones based on environmental data; S3. Dynamically controlling the unmanned boat to float / dive according to environmental conditions; S4. Energy management according to the energy conditions of the unmanned boat and the unmanned aerial vehicle and the external environment.

9. The control method of an unmanned boat loaded with a drone according to claim 8, characterized in that: In S1, environmental data such as wind speed and wave height are collected in real time through onboard sensors, and weather warnings from a land control center are received through satellite communications to generate a comprehensive environmental status assessment report; In S2, based on the comprehensive environmental status assessment report, it is determined whether the current sea conditions allow the mission to be performed, and a corresponding UAV is selected according to the mission type.

10. An unmanned boat loaded with a drone, characterized in that: It has the unmanned boat control system according to any one of claims 1 to 7 and / or the unmanned boat control method according to any one of claims 8 and 9, and further includes: A hull (700), an equipment cabin (701) provided at the head of the hull (700), an unmanned aircraft cabin (702) provided at the upper portion of the hull (700), and a pressurized cabin (703) provided at the lower portion of the hull (700). The tail of the unmanned aerial vehicle cabin (702) is provided with an openable and closable cabin door (702a).

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