Energy supplement method, energy supplement device, storage medium and computer device

Abstract

The application discloses an energy supplement method, an energy supplement device, a storage medium and a computer device. The energy supplement method comprises the following steps: acquiring the current power of an energy supplement robot; determining the energy supplement urgency of the energy supplement robot according to the current power; when the energy supplement urgency meets preset urgency conditions, controlling the energy supplement robot to go to a base station to perform charging energy supplement; and when the energy supplement urgency does not meet the preset urgency conditions and the current weather condition meets preset weather conditions, controlling the energy supplement robot to generate power on the sea surface by using a photovoltaic device and a fan of the energy supplement robot. In this way, the energy supplement urgency is autonomously evaluated based on the power of the energy supplement robot, the energy supplement robot directly goes to the base station to perform charging energy supplement to quickly recover the power when the energy supplement is urgent, and the energy supplement robot generates power on the sea surface by using the photovoltaic device and the fan of the energy supplement robot when the energy supplement is not urgent and the weather condition is good, so that the power is slowly recovered by using natural energy while waiting for subsequent tasks, and the energy supplement efficiency and the task continuity are considered.

Description

Technical Field

[0001] This application relates to the field of marine robots, and more specifically, to a power replenishment method, power replenishment device, storage medium, and computer equipment. Background Technology

[0002] With the development of the marine economy, the electricity demand of marine equipment such as yachts and offshore vessels is increasing daily. In some technologies, when marine equipment runs low on power, it can call for help from a coastal base station, allowing power to be manually supplied to the equipment for recharging. Of course, in some technologies, robots can automatically fulfill the recharging needs of marine equipment. After performing a recharging mission, the marine recharging robot consumes power and needs to recharge itself at an appropriate time. Therefore, how to recharge the robot in a way that better balances mission continuity and recharging efficiency has become a technical problem to be solved. Summary of the Invention

[0003] This application provides a power replenishment method, a power replenishment device, a computer-readable storage medium, and a computer device to solve at least one technical problem in the related art.

[0004] This application provides a power replenishment method for a power replenishment robot. The power replenishment method includes: acquiring the current power level of the power replenishment robot; determining the power replenishment urgency of the power replenishment robot based on the current power level; when the power replenishment urgency meets a preset urgency condition, controlling the power replenishment robot to go to a base station for charging; when the power replenishment urgency does not meet the preset urgency condition and the current weather conditions meet the preset weather conditions, controlling the power replenishment robot to generate electricity on the sea surface using its own photovoltaic device and wind turbine for power replenishment.

[0005] In the above implementation, the power replenishment robot autonomously assesses the urgency of power replenishment based on its own power level. When power replenishment is urgent, it goes directly to the base station to recharge and replenish power quickly. When power replenishment is not urgent and weather conditions are good, it uses its own photovoltaic devices and wind turbines to generate power on the sea surface. While waiting for subsequent tasks, it slowly restores power using natural energy. In this way, the power replenishment robot can make adaptive choices between "rapidly charging to replenish power to cope with urgent tasks" and "slowly replenishing power using natural energy to save round-trip energy consumption", which takes into account both power replenishment efficiency and task continuity.

[0006] In some embodiments, the power replenishment method further includes: when the urgency of power replenishment does not meet a preset urgency condition and the current weather conditions do not meet preset weather conditions, controlling the power replenishment robot to go to the base station for charging and power replenishment.

[0007] In the above implementation, when the urgency of energy replenishment does not meet the preset urgency conditions but the weather conditions are bad, the energy replenishment robot may have extra time, but the efficiency of energy replenishment by generating electricity on the sea surface is low or not feasible. The energy replenishment robot is then controlled to go to the base station for charging to quickly restore the power, ensuring that it can still complete self-replenishment in an efficient manner under the constraints of weather conditions.

[0008] In some implementations, determining the urgency of recharging the robot includes: obtaining the number of recharging tasks currently to be executed by the robot, and determining that the urgency of recharging meets a preset urgency condition when the number of recharging tasks currently to be executed is greater than a first preset number.

[0009] In the above implementation, when the power replenishment robot has a large backlog of power replenishment tasks, it indicates that the demand for external power replenishment is strong. The power replenishment robot needs to complete its self-power replenishment as soon as possible to engage in subsequent power replenishment tasks. At this time, it is determined that power replenishment is urgent, and the base station is selected for charging to replenish power in the shortest time.

[0010] In some implementations, determining the urgency of recharging the robot includes: acquiring an emergency marker carried in the recharging task to be performed by the robot, and determining that the urgency of recharging meets a preset urgency condition when the recharging task carries an emergency marker.

[0011] In the above implementation, the emergency flag for the refueling mission reflects the urgency of the maritime target object 300's need for refueling services. When the mission to be performed involves an emergency flag, the refueling robot needs to prioritize ensuring its own sufficient power to quickly respond to the emergency refueling demand. At this time, it is determined that refueling is urgent, and it selects a base station for charging to quickly restore its power.

[0012] In some implementations, the urgency of recharging is determined based on at least one of the following factors: the current battery level of the recharging robot, the lower the current battery level, the higher the urgency; the number of recharging tasks currently pending to be performed by the recharging robot, the more tasks, the higher the urgency; whether the recharging tasks pending to be performed by the recharging robot carry an emergency marker, the urgency increases when an emergency marker is carried; and the distance between the recharging robot and the next recharging task point, the closer the distance, the higher the urgency.

[0013] In the above implementation, the determination of the urgency of recharging does not rely on a single fixed factor, but comprehensively considers multiple dimensions such as power level, number of tasks, urgency of tasks and task distance, so that the judgment of urgency is closer to the actual task load and energy status of the recharging robot, avoiding one-sided judgment caused by a single factor.

[0014] In some implementations, the current weather conditions satisfying preset weather conditions include: the current light intensity is not lower than a first light threshold, or the current wind speed is not lower than a first wind speed threshold.

[0015] In the above implementation, by setting minimum thresholds for light intensity and wind speed as weather conditions, when there is sufficient sunlight or abundant sea breeze, the energy replenishment robot is highly efficient at generating electricity on the sea surface using its own photovoltaic devices and wind turbines. Choosing a sea surface energy replenishment route allows for continuous power restoration without interrupting the mission's standby status. When neither sunlight nor wind speed meets the thresholds, the efficiency of sea surface energy replenishment is too low, and returning to the base station for recharging is more efficient.

[0016] In some implementations, controlling the power replenishment robot to travel to the base station for charging includes: acquiring the power consumption required for the power replenishment robot to travel to the base station; based on the current power level and the power consumption for the return trip, controlling the power replenishment robot to return directly to the base station for charging when the power replenishment robot meets the conditions for direct return; if the power replenishment robot does not meet the conditions for direct return, controlling the power replenishment robot to generate electricity using its own photovoltaic device and wind turbine on the nearby sea surface, and controlling the power replenishment robot to return to the base station for charging after the current power level meets the conditions for direct return.

[0017] In the above implementation, when the recharge robot decides to return to the base station for charging, it does not return immediately without conditions. Instead, it first assesses whether its current power level is sufficient to support the journey back to the base station from its current location. If the power level is insufficient for a direct return, it first uses photovoltaic devices and wind turbines on the nearby sea surface to generate electricity to replenish its power. It then returns to the base station only after the power level has been restored to meet the return requirements. This two-stage return strategy avoids the risk of the recharge robot being stranded on the sea surface due to depleted power during the return journey, ensuring the safety of the return process.

[0018] In some implementations, during the process of controlling the energy replenishment robot to generate electricity using its own photovoltaic devices and wind turbines on the sea surface, the energy replenishment method further includes: obtaining weather forecast information; when it is determined that the current weather conditions will become unsatisfactory within a preset time period, determining whether the current power of the energy replenishment robot meets the power requirements for returning to the base station for charging; and when the power requirements for returning to the base station for charging are met, stopping the power generation and energy replenishment on the sea surface and controlling the energy replenishment robot to return to the base station for charging.

[0019] In the above implementation, the power replenishment robot continuously monitors weather forecast information during the process of generating electricity on the sea surface. If the forecast indicates that the current good weather window is about to close (for example, the approaching clouds will cause a sharp drop in sunlight intensity, or the wind will weaken), and the current power is sufficient to support the return trip, it will actively stop the inefficient or unsustainable power generation on the sea surface and return to the base station in advance to recharge, so as to avoid being in a dilemma of being unable to generate electricity on the sea surface and unable to return due to sudden weather changes.

[0020] In some implementations, the energy replenishment method further includes: obtaining demand forecast information for energy replenishment tasks to be performed in the current sea surface area; adjusting the judgment of energy replenishment urgency based on the demand forecast information, so as to perform charging and energy replenishment in advance when the predicted demand for energy replenishment tasks increases.

[0021] In the above implementation, the power replenishment robot not only judges the urgency of power replenishment based on the current task status, but also incorporates the prediction of future power replenishment task demand in the area. When it is predicted that the demand for subsequent power replenishment tasks will increase, even if the current urgency is not high, the power replenishment robot can return to the base station to charge and replenish power in advance, ensuring that it has sufficient power reserves when the peak demand for tasks arrives.

[0022] This application provides a power replenishment device for a power replenishment robot. The power replenishment device includes: an acquisition module for acquiring the current power level of the power replenishment robot; a determination module for determining the power replenishment urgency of the power replenishment robot based on the current power level; and a control module for controlling the power replenishment robot to go to a base station for charging when the power replenishment urgency meets a preset urgency condition; and controlling the power replenishment robot to generate electricity on the sea surface using its own photovoltaic device and wind turbine when the power replenishment urgency does not meet the preset urgency condition and the current weather conditions meet the preset weather conditions.

[0023] This application provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the steps of any of the above-described power replenishment methods.

[0024] This application provides a computer device, including: a memory storing a computer program; and a processor communicatively connected to the memory, wherein the processor executes the computer program to implement the steps of any of the above-described power replenishment methods.

[0025] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0026] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is a schematic diagram of the energy replenishment robot according to an embodiment of this application; Figure 2 This is a schematic diagram of the application state of the energy replenishment robot according to the embodiments of this application; Figure 3 This is a schematic diagram of a scenario for the energy replenishment method according to an embodiment of this application; Figure 4 This is a flowchart illustrating the energy replenishment method according to an embodiment of this application; Figure 5 This is a schematic diagram of the power replenishment device according to an embodiment of this application; Figure 6 This is a schematic diagram of the computer device according to an embodiment of this application; Figure 7 This is another schematic flowchart of the energy replenishment method according to the embodiments of this application; Figure 8 This is another schematic diagram of the energy replenishment method according to the embodiments of this application.

[0027] Explanation of reference numerals in the attached figures: 100 - Energy replenishment robot; 10 - Photovoltaic device; 20 - Wind turbine; 30 - Energy storage unit; 200 - Base station; 300 - Maritime target object; 400 - Energy replenishment device; 410 - Acquisition module; 420 - Determination module; 430 - Control module; 600 - Computer equipment; 610 - Memory; 620 - Processor. Detailed Implementation

[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0029] The following disclosure provides many different embodiments or examples for implementing different structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. In addition, various specific examples of processes and materials are provided in this application, but those skilled in the art will recognize the application of other processes and / or the use of other materials.

[0030] Please see Figures 1-3 This application provides a power replenishment method. This method can be applied to a power replenishment robot 100. The power replenishment robot 100 may include a photovoltaic device 10, a wind turbine 20, and an energy storage unit 30. The energy storage unit 30 can store electrical energy, powering the power replenishment robot 100 itself and charging external devices. The power replenishment robot 100 can travel between a base station 200 and a target object 300 at sea. The base station 200 can be a fixed charging facility located on the coastline or nearshore area.

[0031] like Figure 4 As shown, the energy replenishment method of this application includes: S10, obtain the current battery level of the power replenishment robot 100; S20, based on the current battery level, determine the urgency of recharging the power replenishment robot 100; S30, when the urgency of energy replenishment meets the preset urgency conditions, control the energy replenishment robot 100 to go to the base station 200 for charging and energy replenishment; S40, if the urgency of energy replenishment does not meet the preset urgency conditions and the current weather conditions meet the preset weather conditions, control the energy replenishment robot 100 to generate electricity and replenish energy on the sea surface using its own photovoltaic device 10 and wind turbine 20.

[0032] like Figure 5 As shown, in some embodiments, the method can be implemented by a power replenishment device 400, which includes an acquisition module 410, a determination module 420, and a control module 430. The acquisition module 410 acquires the current battery level of the power replenishment robot 100. The determination module 420 determines the urgency of power replenishment for the power replenishment robot 100 based on the current battery level. The control module 430 controls the power replenishment robot 100 to proceed to the base station 200 for charging when the urgency meets a preset urgency condition; and controls the power replenishment robot 100 to generate electricity on the sea surface using its own photovoltaic device 10 and wind turbine 20 when the urgency does not meet the preset urgency condition and the current weather conditions meet the preset weather conditions.

[0033] like Figure 6 As shown, in some embodiments, the power replenishment method can be implemented by a computer device 600, which includes a memory 610 and a processor 620. The memory 610 stores computer program instructions, which, when executed by the processor 620, cause the processor 620 to execute the power replenishment method of the present application. The processor 620 is used to obtain the current power level of the power replenishment robot 100; to determine the power replenishment urgency of the power replenishment robot 100 based on the current power level; to control the power replenishment robot 100 to go to the base station 200 for charging when the power replenishment urgency meets a preset urgency condition; and to control the power replenishment robot 100 to generate electricity on the sea surface using its own photovoltaic device 10 and wind turbine 20 when the power replenishment urgency does not meet the preset urgency condition and the current weather conditions meet the preset weather conditions.

[0034] In the above implementation, the power replenishment robot 100 autonomously assesses the urgency of power replenishment based on its current power level, and then intelligently selects between two routes: "power replenishment via sea surface power generation" and "power replenishment via base station charging" based on the urgency and weather conditions. When power replenishment is urgent, the power replenishment robot 100 goes directly to the base station 200 for charging. The base station 200 can provide high-power fast charging, enabling the power replenishment robot 100 to restore its power in a short time and engage in subsequent power replenishment tasks.

[0035] When energy replenishment is not urgent and weather conditions are favorable (i.e., the weather conditions meet the preset weather conditions), photovoltaic devices 10 and wind turbines 20 are used to generate electricity on the current sea surface to replenish energy, slowly restoring the power supply using natural energy without interrupting the standby status in the mission sea area.

[0036] During the implementation of the above-mentioned energy replenishment method, the current power level of the energy replenishment robot 100 can be read in real time from the Battery Management System (BMS) of the energy storage unit 30, and can be expressed as a percentage (State of Charge, SOC) or an energy value (such as kilowatt-hour, kWh). The urgency of energy replenishment can be determined based on the comparison between the current power level and a preset power threshold, or it can be determined by considering other factors such as task load. The preset urgency condition can be a standard for judging whether the energy replenishment robot 100 needs to quickly restore its power, such as the current power level being lower than a certain preset power threshold or a large backlog of tasks to be executed.

[0037] Charging refers to the process of rapidly charging the energy storage unit 30 of the energy replenishment robot 100 at high power via shore-based power source at base station 200. Power generation refers to the process by which the energy replenishment robot 100 at sea uses its own photovoltaic device 10 (e.g., solar panels) to convert solar energy into electrical energy and uses wind turbine 20 to convert wind energy into electrical energy, and then charges the energy storage unit 30.

[0038] The photovoltaic device 10 can be installed on the upper surface of the power replenishment robot 100 or other locations exposed to sunlight. The wind turbine 20 can serve as the power unit for the power replenishment robot 100, and can switch between different states to achieve horizontal navigation and vertical lifting. In non-navigation mode, the wind turbine 20 can be driven by sea breezes to generate wind power. The base station 200 can include multiple charging berths, which can simultaneously provide charging and power replenishment services for multiple power replenishment robots 100.

[0039] In one example of one implementation, the power replenishment robot 100 is performing a power replenishment mission in a certain sea area, with a current SOC of 42%. The power replenishment robot 100 determines that the current power is still sufficient and the urgency of power replenishment does not meet the preset urgency conditions. It further obtains the current weather conditions—sunny weather, real-time light intensity of about 750W / m², and wind speed of about 9m / s—which meet the preset weather conditions (e.g., light intensity not less than 500W / m², or wind speed not less than 5m / s). It then controls the power replenishment robot 100 to generate electricity on the sea surface using the photovoltaic device 10 and the wind turbine 20 to slowly restore the power with a combined power generation of about 1.2kW, while continuing to stand by in the mission sea area.

[0040] In another example, the current SOC of the power replenishment robot 100 is 19%. It is determined that the power is low and there are three power replenishment tasks waiting to be executed. The power replenishment urgency meets the preset urgency conditions, so the power replenishment robot 100 is controlled to go directly to the base station 200 for charging.

[0041] In this application embodiment, the power replenishment robot 100 can be understood as a marine mobile device with autonomous navigation capabilities and energy storage and release capabilities. It also needs to replenish external equipment and charge its own energy storage unit 30. The preset urgent condition can be understood as one or more judgment criteria used to determine whether the power replenishment robot 100 needs to restore its power as soon as possible.

[0042] Preset weather conditions can be understood as one or more criteria for determining whether the current sea surface weather is suitable for efficient power generation and energy replenishment using photovoltaic devices 10 and wind turbines 20. Charging replenishment can be understood as a method of rapid charging of the energy replenishment robot 100 at the base station 200 via shore-based power. Power generation replenishment can be understood as a method of energy replenishment where the energy replenishment robot 100 uses its own photovoltaic devices 10 and wind turbines 20 at sea to convert natural energy into electrical energy and charge the energy storage unit 30.

[0043] In some embodiments, the energy replenishment method further includes: when the urgency of energy replenishment does not meet a preset urgency condition and the current weather conditions do not meet preset weather conditions, controlling the energy replenishment robot 100 to go to the base station 200 for charging and energy replenishment.

[0044] In some implementations, the control module 430 is also used to control the charging robot 100 to go to the base station 200 for charging when the urgency of charging does not meet the preset urgency condition and the current weather conditions do not meet the preset weather conditions.

[0045] In some implementations, the processor 620 is also configured to control the charging robot 100 to go to the base station 200 for charging when the charging urgency does not meet the preset urgency condition and the current weather conditions do not meet the preset weather conditions.

[0046] In the above embodiments, when the urgency of the energy replenishment robot 100 does not meet the preset urgency conditions (i.e., energy replenishment is not urgent and there is sufficient time) but the weather conditions are bad, although the energy replenishment robot 100 does not need to rush to restore power, the efficiency of energy replenishment by generating electricity on the sea surface is limited by the weather and is low or even infeasible (e.g., insufficient sunlight, no wind or light wind on cloudy or rainy days), and the effect of continuing to generate electricity by using photovoltaic device 10 and wind turbine 20 at sea is minimal.

[0047] At this time, control the energy replenishment robot 100 to go to the base station 200 for charging and energy replenishment. Utilize the high-power fast charging capability of the base station 200 to restore the power reserve in a short time. It can complete the self-replenishment in the most efficient way under weather conditions, avoiding the waste of a lot of time on the sea with little effect.

[0048] In one example, the current SOC of the power replenishment robot 100 is 55%, indicating sufficient power and only one routine power replenishment task to be performed, meaning the urgency of power replenishment does not meet the preset urgency condition. However, the current weather is cloudy with weak real-time sunlight and slow wind speed, both of which do not meet the preset weather conditions. Therefore, it is determined that the efficiency of power generation from the sea surface is too low, and the power replenishment robot 100 is controlled to travel to the base station 200 for charging.

[0049] In this embodiment, weather conditions not meeting preset weather conditions can be understood as the current light intensity and wind speed being lower than their respective thresholds, meaning that neither light nor wind resources are sufficient to support the photovoltaic device 10 and wind turbine 20 in effectively generating electricity. In this weather scenario, the power output of electricity generation from the sea surface is too low or zero. The charging robot 100 prioritizes returning to the base station 200 for recharging, using the high efficiency of recharging to compensate for the inefficiency or impracticality of electricity generation from the sea surface.

[0050] In some implementations, determining the urgency of recharging the power replenishment robot 100 includes: Obtain the number of energy replenishment tasks currently pending to be executed by the energy replenishment robot 100. If the number of energy replenishment tasks currently pending to be executed is greater than the first preset number, determine that the energy replenishment urgency meets the preset urgency condition.

[0051] In some implementations, the acquisition module 410 is used to acquire the number of energy replenishment tasks currently to be executed by the energy replenishment robot 100. The control module 430 is used to determine that the energy replenishment urgency meets a preset urgency condition when the number of currently to be executed energy replenishment tasks is greater than a first preset number.

[0052] In some implementations, the processor 620 is used to obtain the number of recharge tasks currently to be executed by the recharge robot 100; and to determine that the recharge urgency meets a preset urgency condition when the number of currently to be executed recharge tasks is greater than a first preset number.

[0053] In the above implementation, the urgency of the power replenishment robot 100 is directly related to the number of currently backlogged power replenishment tasks. When there are a large number of pending power replenishment tasks, it indicates that the demand for external power replenishment is strong, and the power replenishment robot 100 needs to complete its self-power replenishment as soon as possible to engage in subsequent power replenishment tasks. At this time, it is determined that power replenishment is urgent, and the base station 200 is selected for charging to restore power in the shortest time, so as to avoid a large backlog of subsequent power replenishment tasks due to excessive self-power replenishment time.

[0054] During the execution of the above-described implementation, the refueling task can be generated and assigned to the refueling robot 100 by the scheduling system based on the refueling requirements of the maritime target object 300. The refueling robot 100 can maintain a task queue, recording a list of refueling tasks currently to be executed. The processor 620 can read the number of tasks in the task queue and compare this number with a first preset number. The first preset number can be set according to the refueling capability of the refueling robot 100 and the task density of the service sea area.

[0055] For example, if the normal task throughput of the power replenishment robot 100 is to complete 2 to 3 power replenishment tasks per hour, the first preset number can be set to 3. When the number of queued tasks exceeds 3, it is determined that power replenishment is urgent.

[0056] In one example, the refueling robot 100 has four refueling tasks (refueling requests from four different yachts) waiting to be executed in its task queue during a holiday afternoon, with a current SOC of 38%. If the number of tasks read is four, which is greater than the first preset number of three, the refueling urgency is determined to meet the preset urgency condition, and the refueling robot 100 is controlled to go to the base station 200 for charging. The base station 200 charges the SOC from 38% to over 85% in a short time using high-power fast charging, enabling the refueling robot 100 to return to the mission area as soon as possible to handle the backlog of four refueling tasks.

[0057] In this embodiment, the power replenishment task to be executed can be understood as a power replenishment service request that has been assigned to the power replenishment robot 100 but has not yet started execution. The first preset quantity can be understood as the threshold number of tasks that triggers the power replenishment urgency determination. The task queue can be understood as the set of power replenishment tasks to be executed in the power replenishment robot 100 arranged in a certain order (such as time order or priority order).

[0058] In some implementations, determining the urgency of recharging the power replenishment robot 100 includes: Obtain the emergency marker carried by the energy replenishment robot 100 in the energy replenishment task to be performed. When the energy replenishment task carries an emergency marker, determine that the urgency of energy replenishment meets the preset urgency conditions.

[0059] In some implementations, the acquisition module 410 is used to acquire the emergency marker carried by the energy replenishment robot 100 in the energy replenishment task to be performed. The control module 430 is used to determine that the energy replenishment urgency meets a preset urgency condition when the energy replenishment task carries an emergency marker.

[0060] In some implementations, the processor 620 is used to acquire an emergency marker carried in a recharging task to be performed by the recharging robot 100; and to determine, when an emergency marker is carried in a recharging task, that the urgency of recharging meets a preset urgency condition.

[0061] In the above implementation, the emergency flag reflects the urgency of the power replenishment task, such as routine power replenishment or emergency rescue. When there is a power replenishment task with an emergency flag in the task queue, it indicates that a maritime target object 300 is in urgent need of power replenishment. The power replenishment robot 100 needs to prioritize ensuring its own power is sufficient in order to quickly respond to the emergency power replenishment demand. At this time, it is determined that power replenishment is urgent, and the base station 200 is selected for charging to quickly restore power.

[0062] During the execution of the above-described implementation, the emergency identifier can be specified by the user of the maritime target object 300 that issues the replenishment request when initiating the request, for example, by providing "normal options" and "emergency options" in the user interface. The emergency identifier can also be automatically identified by the scheduling system based on the specific content of the replenishment request, such as recognizing emergency status descriptions like "power system failure" or "personnel in distress" attached to the replenishment request. The processor 620 traverses each replenishment task in the task queue, checking whether it carries an emergency identifier.

[0063] For example, the power replenishment robot 100 currently has two power replenishment tasks in its task queue: one is a routine power replenishment task (routine power replenishment for a yacht), and the other is an emergency rescue power replenishment task (a fishing boat's power system malfunctions and requires emergency power replenishment to restore power). Although the total number of tasks is only two (not exceeding the first preset number, such as three), if the emergency rescue power replenishment task is found to carry an emergency identifier, and the power replenishment urgency is determined to meet the preset urgency conditions, the power replenishment robot 100 is controlled to go to the base station 200 for charging and power replenishment, ensuring that it can quickly respond to the fishing boat's power system malfunction with sufficient power.

[0064] In this application's embodiments, an emergency identifier can be understood as an information mark attached to a refueling mission, used to indicate that the refueling mission is urgent in nature. A routine refueling mission can be understood as a refueling mission initiated under normal navigation or operational conditions that is not urgent in nature. An emergency rescue refueling mission can be understood as a refueling mission initiated under emergency or dangerous circumstances that requires priority handling.

[0065] In some implementations, the urgency of energy replenishment is determined based on at least one of the following factors: 1. The current battery level of the power replenishment robot 100. The lower the current battery level, the higher the urgency of power replenishment. 2. The number of recharge tasks currently pending for the recharge robot 100. The more tasks there are, the higher the urgency of recharging. 3. Does the energy replenishment robot 100 carry an emergency tag in the energy replenishment task to be performed? If it carries an emergency tag, the urgency of energy replenishment is increased. IV. The distance between the energy replenishment robot 100 and the next energy replenishment task point. The closer the distance, the higher the urgency of energy replenishment.

[0066] In some embodiments, the acquisition module 410 is used to acquire at least one of the following information: the current battery level of the power replenishment robot 100, the number of power replenishment tasks to be executed, the emergency flag in the power replenishment tasks, and the distance between the power replenishment robot 100 and the next power replenishment task point. The control module 430 is used to comprehensively determine the urgency of power replenishment based on the information acquired by the acquisition module 410.

[0067] In some implementations, the processor 620 is used to acquire at least one of the following information: the current power level of the power replenishment robot 100, the number of power replenishment tasks to be executed, the emergency indicator in the power replenishment task, and the distance between the power replenishment robot 100 and the next power replenishment task point; and to comprehensively determine the urgency of power replenishment based on the acquired information.

[0068] In the above implementation, the determination of the urgency of recharging can be comprehensively considered from multiple dimensions, making the urgency judgment more consistent with the actual working conditions of the recharging robot 100. The lower the battery level, the less energy the recharging robot 100 has available reserves, and the higher the urgency of recharging; the more tasks there are, the greater the pressure on subsequent external recharging, and the higher the urgency of recharging; tasks with an urgent marker indicate that there is a target object that urgently needs recharging, increasing the urgency of recharging; the closer to the next task point, the closer the time to start executing the task, and the higher the urgency of recharging. By comprehensively considering multi-dimensional information, the judgment of the urgency of recharging avoids the limitations of a single dimension.

[0069] During the implementation of the above-described methods, the urgency of refueling can be determined using a weighted scoring or tiered assessment. For example, the four dimensions of battery power, number of tasks, emergency signage, and distance can be quantified into scores. Each dimension's score is multiplied by its corresponding weighting coefficient, and the sum of these scores yields a comprehensive score for the urgency of refueling. This score is then compared with preset urgency conditions. The weighting coefficients can be adjusted according to actual operating conditions. For instance, in areas with a high density of refueling tasks, the weight of the task quantity dimension can be increased; in areas with frequent emergencies, the weight of the emergency signage dimension can be increased.

[0070] For example, a refueling robot 100 faces the following situation in a certain sea area: its current SOC is 25% (low battery), it has 3 tasks to perform (many tasks), one of the tasks carries an emergency marker, and the next task location is 8 nautical miles away (moderate distance). The processor 620 obtains information from four dimensions and calculates a comprehensive score for the urgency of refueling: battery level contributes 0.7 (higher score for lower battery), number of tasks contributes 0.6, emergency marker contributes 0.9, and distance contributes 0.4. These are weighted equally (0.25 each) and summed to obtain a comprehensive urgency score of 0.65, which is higher than the preset urgency threshold of 0.5. Therefore, the urgency of refueling is determined to meet the preset urgency condition.

[0071] In another example, the current SOC of the power replenishment robot 100 is 70% (sufficient power), there is 1 task to be performed (easy task), there is no emergency indicator, and the distance to the next task point is 20 nautical miles (relatively far). The overall urgency of the four dimensions is only 0.15, which is far below the preset urgency threshold. Therefore, it is determined that the urgency of power replenishment does not meet the preset urgency conditions, and the power replenishment robot 100 can continue to use the wind and solar resources on the sea surface to generate electricity and replenish energy.

[0072] In this embodiment, the urgency of recharging can be understood as a quantitative indicator that comprehensively reflects the urgency of the recharging robot 100 in recharging itself, which can be derived based on information from multiple dimensions such as battery level, task load, urgency, and spatial distance. The recharging task point can be understood as the geographical location of the target object 300 at sea when it issues a recharging request, and the recharging robot 100 needs to go to that location to perform the recharging service.

[0073] In some implementations, the current weather conditions satisfy preset weather conditions, including: The current light intensity is not lower than the first light threshold, or the current wind speed is not lower than the first wind speed threshold.

[0074] In some implementations, the acquisition module 410 is used to acquire the current real-time light intensity and wind speed. The control module 430 is used to determine that the current weather conditions meet preset weather conditions when the real-time light intensity is not lower than a first light threshold or the wind speed is not lower than a first wind speed threshold.

[0075] In some implementations, the processor 620 is used to acquire the current real-time light intensity and wind speed; and to determine that the current weather conditions meet preset weather conditions when the real-time light intensity is not lower than a first light threshold or the wind speed is not lower than a first wind speed threshold.

[0076] In the above implementation, the preset weather conditions are determined by two indicators: sunlight intensity and wind speed. Meeting either indicator is sufficient for surface power generation and replenishment. When sunlight is abundant, the photovoltaic device 10 generates higher power; when wind speed is high, the wind turbine 20 generates higher power. This parallel determination of the two indicators (sunlight intensity or wind speed) expands the applicable weather window for surface power generation and replenishment, enabling the replenishment robot 100 to effectively generate power on the sea surface during most daytime hours or in windy sea areas, reducing unnecessary energy consumption during return trips.

[0077] During the aforementioned weather condition assessment process, real-time light intensity can be collected in real time by a light sensor mounted on the power replenishment robot 100, with the unit being watts per square meter (W / m²). Wind speed can be collected in real time by a wind speed sensor (such as an ultrasonic anemometer) mounted on the power replenishment robot 100, with the unit being meters per second (m / s) or knots.

[0078] The first light threshold can be set according to the photoelectric conversion efficiency and the minimum acceptable charging power of the photovoltaic device 10, for example, a value between 300W / m² and 500W / m². The first wind speed threshold can be set according to the cut-in wind speed of the wind turbine 20 (i.e., the minimum wind speed at which the wind turbine 20 begins to generate effective power), for example, a value between 3m / s and 5m / s.

[0079] In one example, the photovoltaic device 10 of the power replenishment robot 100 has an effective light-receiving area of ​​2m² and a photoelectric conversion efficiency of approximately 20%. Under the condition of a first illumination threshold of 400W / m², the power generation of the photovoltaic device 10 is approximately 400×2×20%≈160W. The cut-in wind speed of the wind turbine 20 is 3m / s. Under the condition of a first wind speed threshold of 4m / s and a wind speed of 4m / s, the power generation of the wind turbine 20 is approximately 80W.

[0080] If the current real-time irradiance is 650W / m² (higher than the first irradiance threshold of 400W / m²) and the wind speed is 2m / s (lower than the first wind speed threshold of 4m / s), the irradiance conditions meet the criteria, and the weather conditions are determined to meet the preset weather conditions. The energy replenishment robot 100 can then generate electricity to replenish its energy using the photovoltaic device 10. Similarly, if the current real-time irradiance is 150W / m² (lower than the first irradiance threshold of 400W / m²) and the wind speed is 8m / s (higher than the first wind speed threshold of 4m / s), the wind speed conditions meet the criteria, and the weather conditions are also determined to meet the preset weather conditions. The energy replenishment robot 100 can then generate electricity to replenish its energy using the wind turbine 20.

[0081] In another example, the energy replenishment robot 100 is located in a tropical sea area at noon, with a sunlight intensity of about 950W / m², a photovoltaic device 10 generating about 380W, a wind speed of about 10m / s, and a wind turbine 20 generating about 300W. At this time, both the sunlight and wind speed far exceed the threshold. The energy replenishment robot 100 can simultaneously utilize the photovoltaic device 10 and the wind turbine 20 to perform wind-solar complementary power generation for energy replenishment, with a total power generation of about 680W and a high charging efficiency.

[0082] In another example, a certain sea area was affected by a low-pressure system, with cloudy and rainy weather all day. The real-time light intensity remained below 80W / m² for a long time, and the wind speed was only about 1m / s. The light and wind speed did not meet the preset weather conditions. The energy replenishment robot 100 determined that it was not feasible to generate energy on the sea surface and went to the base station 200 to recharge.

[0083] In this embodiment, real-time illumination intensity can be understood as the solar radiation power received per unit area at the location of the power replenishment robot 100 on the sea surface. The first illumination threshold can be understood as the critical illumination intensity value for determining whether the photovoltaic device 10 can generate electricity with acceptable efficiency. The first wind speed threshold can be understood as the critical wind speed value for determining whether the wind turbine 20 can generate electricity with acceptable efficiency. Preset weather conditions can be understood as one or more sets of meteorological parameter threshold conditions for determining whether the current weather is suitable for generating electricity on the sea surface.

[0084] Please see Figure 7 In some implementations, controlling the charging robot 100 to travel to the base station 200 for charging (step S30) includes: S31, obtain the power consumption required for the power replenishment robot 100 to return to base station 200; S32, based on the current power level and the power consumption during the return trip, when the charging robot 100 meets the conditions for direct return, control the charging robot 100 to directly return to the base station 200 for charging and power replenishment; S33, when the power replenishment robot 100 does not meet the conditions for direct return, control the power replenishment robot 100 to generate electricity using its own photovoltaic device 10 and wind turbine 20 on the nearby sea surface, and control the power replenishment robot 100 to return to the base station 200 for charging after the current power meets the conditions for direct return.

[0085] In some implementations, the control module 430 is used to acquire the power consumption required for the recharge robot 100 to return to the base station 200. Based on the current power level and the power consumption required for the return trip, the control module 430 controls the recharge robot 100 to directly return to the base station 200 for recharging when the conditions for a direct return are met; when the conditions for a direct return are not met, the control module 430 controls the recharge robot 100 to generate electricity using its own photovoltaic device 10 and wind turbine 20 on the nearby sea surface, and controls the recharge robot 100 to return to the base station 200 for recharging once the current power level meets the conditions for a direct return.

[0086] In some embodiments, the processor 620 is configured to acquire the power consumption required for the recharge robot 100 to return to the base station 200; and to control the recharge robot 100 to directly return to the base station 200 for recharging when the current power consumption and the return power consumption meet the conditions for direct return; and to control the recharge robot 100 to generate electricity using its own photovoltaic device 10 and wind turbine 20 on the nearby sea surface when the recharge robot 100 does not meet the conditions for direct return, and to control the recharge robot 100 to return to the base station 200 for recharging after the current power consumption meets the conditions for direct return.

[0087] In the above implementation, when the power replenishment robot 100 decides to return to the base station 200 for charging, it does not issue a return command unconditionally. Instead, it first assesses whether the current power level is sufficient to support the entire return journey. If the power level is sufficient, it returns directly; if the power level is insufficient to support the entire return journey, it adopts a two-stage return strategy of "first generating power on the nearby sea surface to replenish energy → then returning after the power level reaches the required level". This strategy effectively avoids the extreme situation where the power replenishment robot 100 is stranded on the sea surface and loses its mobility due to power depletion during the return journey, ensuring the safety and reliability of the return process.

[0088] During the aforementioned return-to-home assessment, the power consumption for return-to-home can be estimated based on the distance between the current location of the power replenishment robot 100 and the base station 200, as well as the power consumption per unit distance of the power replenishment robot 100 in return-to-home mode. The direct return-to-home condition can be that the current power level is greater than or equal to the return-to-home power consumption, or that the current power level is greater than or equal to the return-to-home power consumption plus a certain safety margin (e.g., 120% of the return-to-home power consumption).

[0089] The nearest sea surface refers to the sea surface where the energy replenishment robot 100 is currently located or a suitable sea surface nearby for mooring. The energy replenishment robot 100 can generate electricity on-site using the photovoltaic device 10 and the wind turbine 20 without needing to travel an additional long distance. When the energy replenishment robot 100 does not meet the conditions for direct return, the processor 620 controls the energy replenishment robot 100 to stop sailing or maintain a low-speed drift, using the photovoltaic device 10 and the wind turbine 20 to generate electricity at its current location or a nearby sea surface, while continuously monitoring the current power level. When the current power level recovers to meet the conditions for direct return, the control module 430 controls the energy replenishment robot 100 to start its journey back to the base station 200.

[0090] For a concrete example, the recharge robot 100 decides to return to base station 200 to recharge because the urgency of recharging meets preset conditions. At this time, its current SOC is 22%, and it is about 18 nautical miles away from base station 200. The processor 620 estimates the power consumption for the return trip: the recharge robot 100 consumes about 1.5% of its SOC per nautical mile during cruise, requiring about 27% of its SOC for 18 nautical miles. Adding a 5% safety margin, the total power required for the return trip is 32% of its SOC.

[0091] The current SOC of 22% does not meet the conditions for direct return (22% < 32%). The control module 430 controls the energy replenishment robot 100 to generate electricity using the photovoltaic device 10 and wind turbine 20 on the nearby sea surface. After about 1.5 hours, the current SOC rises to 34%, meeting the conditions for direct return (34% > 32%). The control module 430 then controls the energy replenishment robot 100 to return to the base station 200 for charging and replenishment.

[0092] In another example, the power replenishment robot 100 is only 5 nautical miles away from the base station 200. The estimated power consumption for returning is 7.5% of the state of charge (SOC). With the safety margin, the total SOC required is about 10%. The current SOC is 25%, which meets the conditions for direct return (25%>10%). The control module 430 directly controls the power replenishment robot 100 to return.

[0093] In this embodiment, the power consumption for returning to base station 200 can be understood as the estimated power consumption required for the power replenishment robot 100 to return from its current location. The direct return condition can be understood as the condition for determining whether the power replenishment robot 100 has sufficient power to directly return to base station 200 from its current location. The nearby sea surface can be understood as a suitable sea surface area for power generation and replenishment at or near the current location of the power replenishment robot 100. The two-stage return can be understood as a segmented return process involving first generating power at sea surface to restore power, and then returning to the base station for recharging.

[0094] In some embodiments, during the process of controlling the energy replenishment robot 100 to generate electricity on the sea surface using its own photovoltaic device 10 and wind turbine 20, the energy replenishment method further includes: Get weather forecast information; When it is determined that the current weather conditions will become unsuitable for the preset weather conditions within a preset time period, it is determined whether the current power of the charging robot 100 meets the power requirements for returning to the base station 200 for charging. When the power requirements for returning to base station 200 for recharging are met, the power generation and recharging on the sea surface are stopped, and the recharging robot 100 is controlled to return to base station 200 for recharging.

[0095] In some implementations, the acquisition module 410 is used to acquire weather forecast information. The control module 430 is used to determine whether the current power level of the charging robot 100 meets the power requirements for returning to the base station 200 for charging when it is determined that the current weather conditions will become unsatisfactory within a preset time period; and when the power requirements for returning to the base station 200 for charging are met, to stop the surface power generation and control the charging robot 100 to return to the base station 200 for charging.

[0096] In some embodiments, the processor 620 is used to acquire weather forecast information; and to determine whether the current power of the charging robot 100 meets the power conditions for returning to the base station 200 for charging when it is determined that the current weather conditions will become unsatisfactory within a preset period of time; and to stop surface power generation and control the charging robot 100 to return to the base station 200 for charging when the power conditions for returning to the base station 200 for charging are met.

[0097] In the above implementation, the power replenishment robot 100 does not passively wait for weather changes during the process of generating electricity on the sea surface, but actively obtains weather forecast information and responds proactively to upcoming weather changes. When the weather forecast indicates that the current good weather window is about to close (for example, cloud cover will cause a sudden drop in sunlight, or the wind will weaken, causing a decrease in wind turbine power generation), if the current power is sufficient to support the return trip, the power replenishment robot 100 will proactively stop generating electricity on the sea surface and return to the base station 200 to recharge. This proactive termination mechanism allows the power replenishment robot 100 to proactively withdraw using the remaining power, avoiding the dual dilemma of unsustainable power generation on the sea surface due to sudden weather changes and inability to return due to excessive power consumption.

[0098] During the implementation of the aforementioned weather forecast suspension mechanism, weather forecast information can be obtained from meteorological service agencies or cloud-based meteorological data platforms through the wireless communication module of the power replenishment robot 100. Alternatively, the power replenishment robot 100 can perform local forecasts based on its onboard meteorological sensors (such as barometers, temperature and humidity sensors, etc.) combined with historical weather data. The preset time period can be set as needed, such as the next 30 minutes, the next hour, or the next 2 hours. The power condition can be, for example, that the current power level is greater than or equal to the power consumption for returning to base plus a safety margin.

[0099] For a specific example, the power replenishment robot 100 is generating electricity on the sea surface in a certain sea area. The current State of Charge (SOC) is 48%, the real-time solar irradiance is approximately 700W / m², and the combined power output of the photovoltaic device 10 and wind turbine 20 is approximately 500W. The acquisition module 410 obtains weather forecast information, which indicates that a large area of ​​cloud cover will cover the current sea area in about 30 minutes, and the solar irradiance is expected to drop below 100W / m². The processor 620 determines that the current weather conditions will become unsuitable for preset weather conditions (solar irradiance will fall below the first solar threshold) within a preset time period (30 minutes). The processor 620 further calculates: the distance to base station 200 is approximately 10 nautical miles, the return trip consumes approximately 15% of the SOC, and the current SOC of 48% is greater than the power required for the return trip, meeting the power requirements for returning to base station 200. The control module 430 stops the surface power generation and controls the power replenishment robot 100 to return to base station 200 for charging. The power replenishment robot 100 safely arrives at base station 200 approximately 25 minutes before the weather deteriorates.

[0100] In another example, during the recharge process at sea, the SOC of the power replenishment robot 100 was only 28%, and the weather forecast indicated that the weather would worsen in 30 minutes. The processor 620 determined that the current SOC of 28% barely covered the power consumption for the return trip (approximately 25% SOC), and was insufficient to continue recharge using the inefficient photovoltaic device 10 after the weather worsened. Although the return trip condition was critically met, the processor 620 still determined that the power requirement for returning to base station 200 was met (28% > 25%), and controlled the power replenishment robot 100 to stop recharge at sea and immediately return. The power replenishment robot 100 may have consumed approximately 25% SOC during the return trip, and upon arrival at base station 200, it would have approximately 3% SOC remaining, arriving safely.

[0101] In another example, during the power replenishment process at sea, the SOC of the power replenishment robot 100 was only 15%. The weather forecast indicated that the weather would worsen in 30 minutes, and the return trip would consume approximately 25% of the SOC, which was insufficient for the return trip. The control module 430 maintained the power replenishment at sea, continuing to utilize the remaining favorable weather conditions to restore as much power as possible while continuously monitoring the SOC. After approximately 40 minutes, the SOC rose to 26%, meeting the return trip requirements (26% > 25%). However, the weather had already begun to deteriorate (sunlight intensity dropped to 120 W / m²). The control module 430 still controlled the power replenishment robot 100 to return safely to the base station 200 during the remaining journey in worsening weather.

[0102] In this application's implementation, weather forecast information can be understood as predictions of weather conditions (such as light intensity, wind speed, cloud cover, and probability of rainfall) over a future period, and can originate from meteorological service agencies, cloud-based meteorological data platforms, or local meteorological sensors. The preset time period can be understood as a pre-defined forward-looking time window used to determine weather changes. The power condition can be understood as determining whether the current power of the charging robot 100 is sufficient to support its return from its current location to the base station 200 for recharging.

[0103] Please see Figure 8 In some implementations, the energy replenishment method further includes: S50: Obtain demand forecast information for energy replenishment tasks to be performed in the current sea surface area; S60 adjusts the assessment of the urgency of energy replenishment based on demand forecast information, so as to perform charging and energy replenishment in advance when the predicted demand for energy replenishment tasks increases.

[0104] In some implementations, the acquisition module 410 is used to acquire demand forecast information for the energy replenishment task to be performed in the current sea surface area. The control module 430 is used to adjust the determination of the urgency of energy replenishment based on the demand forecast information, so as to perform charging and energy replenishment in advance when the predicted demand for energy replenishment tasks increases.

[0105] In some implementations, the processor 620 is used to obtain demand forecast information for the energy replenishment task to be performed in the current sea surface area; and to adjust the determination of the urgency of energy replenishment based on the demand forecast information, so as to perform charging and energy replenishment in advance when the predicted demand for energy replenishment tasks increases.

[0106] In the above implementation, the power replenishment robot 100 not only judges the urgency of power replenishment based on the current power level and task status, but also incorporates the prediction of future power replenishment task demand, making the power replenishment decision forward-looking. When it is predicted that the demand for subsequent power replenishment tasks will increase significantly (for example, historical data analysis shows that a certain period in the future will be a peak period for power replenishment requests, or maritime activity forecasts show that a large number of ships will enter the service area), even if the current urgency has not yet reached the preset urgency condition, the power replenishment robot 100 can increase the power replenishment urgency in advance and actively return to the base station 200 to recharge, so as to prepare energy reserves before the peak demand arrives.

[0107] In the implementation of the aforementioned demand forecasting process, demand forecasting information can be generated by the scheduling system through comprehensive analysis of historical refueling request data, maritime activity forecasts (such as holiday yacht travel forecasts, event schedules, etc.), and regional vessel density monitoring data. Demand forecasting information can include the predicted number of refueling requests within a preset future time period, the predicted distribution of refueling request types, and the predicted spatial distribution of refueling requests. The control module 430 can adjust the currently calculated refueling urgency based on the demand forecasting information, for example, by multiplying the refueling urgency by a correction coefficient greater than 1 when the predicted demand increases, or by lowering the threshold for judging refueling urgency.

[0108] In one example, the current SOC of the power replenishment robot 100 is 45%, with only one task to be performed and no urgent indication. Normally, the urgency level for power replenishment does not meet the preset urgency condition, and it can continue to slowly restore power by generating electricity on the sea surface. However, the demand forecast information obtained by the acquisition module 410 shows that a sailing race will end in two hours, and approximately 10 participating sailboats are expected to send out power replenishment requests after the race. Furthermore, the race area happens to be within the service area of ​​the power replenishment robot 100. Based on the demand forecast information, the control module 430 increases the power replenishment urgency level, determines that the urgency level meets the preset urgency condition, and controls the power replenishment robot 100 to return to the base station 200 for rapid charging approximately 1.5 hours before the end of the race. This ensures that the robot will be fully charged (expected SOC to reach over 90% after charging) and ready to provide high-intensity power replenishment services when the race ends and the peak demand for power replenishment arrives.

[0109] In another example, on a weekday afternoon in its service area, historical data showed that the number of recharge requests was typically low (approximately 1 to 2 per hour), and demand forecasting also indicated no special events scheduled for the next 3 hours, with stable demand forecasts. The control module 430 determined that there was no need to increase the urgency of recharge, and the recharge robot 100 maintained its slow recharge strategy of generating power from the sea surface, saving travel time and energy consumption to and from the base station.

[0110] In this application's implementation, demand forecasting information can be understood as predictive data on the quantity and type of recharging tasks occurring in a certain sea area within a future period. An increased predicted demand for recharging tasks can be understood as the demand forecasting information indicating that the future volume of recharging tasks will exceed the current level or a preset demand threshold. Proactively performing charging and recharging can be understood as proactively performing base station charging and recharging in advance due to demand forecasting, even when the recharging urgency is not yet determined based solely on current information and has not yet reached a preset urgency condition.

[0111] In the implementation of the aforementioned energy replenishment device 400, the acquisition module 410 can be understood as a virtual functional unit within the energy replenishment device 400 responsible for collecting data required for decision-making from the sensors and communication interfaces of the energy replenishment robot 100. Its physical support is the processor 620 executing data acquisition program instructions. The acquisition module 410 communicates with the BMS of the energy storage unit 30 to obtain the current power level, communicates with the light sensor to obtain the real-time light intensity, communicates with the wind speed sensor to obtain the current wind speed, and communicates with the scheduling system to obtain the list of tasks to be executed, emergency indicators, and demand forecast information.

[0112] The determination module 420 can be understood as a virtual functional unit in the power replenishment device 400 responsible for processing the information collected by the acquisition module 410 to assess the urgency of power replenishment. Its physical support is the processor 620 executing the urgency assessment program instructions. The determination module 420 is data-connected to the acquisition module 410, receives the current power level and task-related information as input, and outputs a judgment result on whether the urgency of power replenishment meets the preset urgency conditions.

[0113] The control module 430 can be understood as a virtual functional unit within the energy replenishment device 400 responsible for generating and outputting navigation control commands and energy replenishment mode switching commands based on the energy replenishment urgency judgment result and weather judgment result output by the determination module 420. Its physical support is the processor executing control logic program instructions. The control module 430 communicates with the propulsion system of the energy replenishment robot 100 (such as the drive circuit of the wind turbine 20) to control the navigation direction and speed, and communicates with the charging management system to control the start and stop of sea surface power generation replenishment or base station charging replenishment.

[0114] In this embodiment, the acquisition module 410 can be understood as a virtual functional module in the power replenishment device 400 responsible for acquiring information required for decision-making, such as power level, task status, and weather. The determination module 420 can be understood as a virtual functional module in the power replenishment device 400 responsible for comprehensively processing the acquired information to determine the urgency of power replenishment. The control module 430 can be understood as a virtual functional module in the power replenishment device 400 responsible for selecting a power replenishment route based on the information provided by the determination module 420 and outputting corresponding control commands.

[0115] In some embodiments, the computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the steps of any of the above-described power-up methods. The storage medium may include at least one of read-only memory (ROM), random access memory (RAM), magnetic disk storage media, optical disk storage media, flash memory, etc.

[0116] In some embodiments, the processor 620 is configured to read computer program instructions from a computer-readable storage medium and execute the computer program instructions to implement the steps of any of the above-described power replenishment methods.

[0117] In the aforementioned application example of the storage medium, the manufacturer of the power replenishment robot 100 burns the firmware program containing the aforementioned power replenishment method into a flash memory chip (as the storage medium) and installs the flash memory chip on the main control board of the power replenishment robot 100. After the power replenishment robot 100 is powered on, the processor 620 loads the firmware program from the flash memory into RAM for execution, and can automatically run all decision logic from obtaining power to selecting the power replenishment route. When the version of the power replenishment algorithm needs to be upgraded, such as optimizing the calculation model of power replenishment urgency or adding support for more meteorological data sources, it is only necessary to push the new firmware to the power replenishment robot 100 via the wireless communication link and write it into the flash memory. After the power replenishment robot 100 restarts, it can load the new power replenishment method without having to return it to the factory to replace the hardware.

[0118] In some embodiments, the computer device 600 includes a memory 610 and a processor 620. The memory 610 stores a computer program, and the processor 620 is communicatively connected to the memory 610. When the processor 620 executes the computer program, it implements the steps of any of the above-described power replenishment methods.

[0119] The computer device 600 can be integrated into the onboard computing unit of the power replenishment robot 100, or it can be deployed on a remote server of a base station and send control commands to the power replenishment robot 100 via a wireless communication link. In the above embodiments, the power replenishment method is carried out by the standard hardware architecture of the memory 610 and the processor 620, which enables the power replenishment decision logic of this application to be reliably executed on general-purpose computer hardware, facilitating mass production and maintenance.

[0120] In the deployment example of the aforementioned computer device 600, the onboard computing unit of the power replenishment robot 100 employs an ARM-based embedded processor (as processor 620), coupled with DDR4 memory (as memory 610), and runs an embedded real-time operating system. The computer program for the power replenishment method is compiled into an ARM executable file using C language and stored in the solid-state drive of the onboard computing unit.

[0121] After the power replenishment robot 100 is powered on, the processor 620 loads the executable file from the solid-state drive into the DDR4 memory to run. It processes power data from the BMS, weather data from the meteorological sensor, and task data from the scheduling system in real time. It can complete the entire process decision-making from obtaining power to generating power replenishment control commands in a short time, meeting the real-time decision-making needs of the power replenishment robot 100 in the dynamic marine environment.

[0122] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0123] Although embodiments of this application have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the claims and their equivalents.

Claims

1. A power replenishment method for a power replenishment robot, characterized in that, The energy replenishment method includes: Obtain the current battery level of the power replenishment robot; Based on the current battery level, determine the urgency of recharging the power supply for the power replenishment robot; When the urgency of energy replenishment meets the preset urgency conditions, the energy replenishment robot is controlled to go to the base station for charging and energy replenishment. If the urgency of energy replenishment does not meet the preset urgency condition, and the current weather conditions meet the preset weather conditions, the energy replenishment robot is controlled to generate electricity and replenish energy on the sea surface using its own photovoltaic device and wind turbine.

2. The energy replenishment method according to claim 1, characterized in that, The energy replenishment method further includes: If the urgency of recharging does not meet the preset urgency condition, and the current weather conditions do not meet the preset weather conditions, the recharging robot is controlled to go to the base station for charging.

3. The energy replenishment method according to claim 1, characterized in that, Determining the urgency of recharging the robot includes: The number of recharge tasks currently pending execution by the recharge robot is obtained. When the number of currently pending recharge tasks is greater than a first preset number, the recharge urgency is determined to meet the preset urgency condition; and / or, Obtain the emergency identifier carried in the energy replenishment task to be performed by the energy replenishment robot, and when the emergency identifier is carried in the energy replenishment task, determine that the energy replenishment urgency meets the preset urgency condition.

4. The energy replenishment method according to claim 1, characterized in that, The urgency of energy replenishment is determined based on at least one of the following factors: The current battery level of the power replenishment robot is high or low; the lower the current battery level, the higher the urgency of power replenishment. The number of recharge tasks currently pending execution by the recharge robot; the more tasks there are, the higher the urgency of recharge. Does the energy replenishment robot carry an emergency marker in the energy replenishment task to be performed? If it carries an emergency marker, the urgency of the energy replenishment is increased. The closer the energy replenishment robot is to the next energy replenishment task point, the higher the urgency of the energy replenishment.

5. The energy replenishment method according to claim 1, characterized in that, The current weather conditions satisfying the preset weather conditions include: The current light intensity is not lower than the first light threshold; or The current wind speed is not lower than the first wind speed threshold.

6. The energy replenishment method according to claim 1, characterized in that, The control of the power replenishment robot to travel to the base station for charging includes: Obtain the power consumption required for the recharge robot to return to the base station; Based on the current power level and the power consumption during the return trip, when the charging robot meets the conditions for direct return, control the charging robot to directly return to the base station for charging. When the recharge robot does not meet the direct return conditions, the robot is controlled to generate electricity using its own photovoltaic device and wind turbine on the nearby sea surface. Once the current power level meets the direct return conditions, the robot is controlled to return to the base station for recharging.

7. The energy replenishment method according to claim 1, characterized in that, In the process of controlling the energy replenishment robot to generate electricity and replenish energy on the sea surface using its own photovoltaic device and wind turbine, the energy replenishment method further includes: Get weather forecast information; When it is determined that the current weather conditions will become unsatisfactory within a preset time period, it is determined whether the current power of the charging robot meets the power requirements for returning to the base station for charging. When the power conditions for returning to the base station for recharging are met, the surface power generation for recharging is stopped and the recharging robot is controlled to return to the base station for recharging.

8. The energy replenishment method according to claim 1, characterized in that, The energy replenishment method further includes: Obtain demand forecast information for energy replenishment tasks to be performed in the current sea surface area; The determination of the urgency of energy replenishment is adjusted based on the demand forecast information, so as to perform charging and energy replenishment in advance when the predicted demand for energy replenishment tasks increases.

9. A power replenishment device for a power replenishment robot, characterized in that, The energy replenishment device includes: The acquisition module is used to acquire the current battery level of the power replenishment robot; The determination module is used to determine the urgency of recharging the power replenishment robot based on the current power level; The control module is used to control the energy replenishment robot to go to the base station for charging when the energy replenishment urgency meets the preset urgency conditions; If the urgency of energy replenishment does not meet the preset urgency condition, and the current weather conditions meet the preset weather conditions, the energy replenishment robot is controlled to generate electricity and replenish energy on the sea surface using its own photovoltaic device and wind turbine.

10. A computer-readable storage medium, characterized in that, The medium stores computer program instructions, which, when executed by a processor, implement the steps of the energy replenishment method according to any one of claims 1 to 8.

11. A computer device, characterized in that, include: Memory, which stores computer programs; A processor, communicatively connected to the memory, executes the computer program to implement the steps of the energy replenishment method according to any one of claims 1 to 8.

Images