A hybrid unmanned aerial vehicle charging intelligent regulation system

Abstract

The application discloses a kind of mixed unmanned plane charging intelligent regulation and control systems, belong to charging regulation and control field, comprising: information acquisition component, for obtaining the current power information and current task information of multiple unmanned planes;Charging determination component, for determining the best charging opportunity and charging mode of each unmanned plane;Target determination component, for obtaining target charging opportunity and target charging mode;Quick charging execution component, the target battery of target unmanned plane is charged;Battery replacement execution component, for the current battery on target unmanned plane is quickly replaced by preferred battery.The present application solves the technical problems that the unmanned plane charging lacks intelligent scheduling and flexible charging mode in the prior art, leading to low unmanned plane charging efficiency and poor task execution capability, achieves efficient management of unmanned plane charging process through intelligent scheduling and mixed charging mode, improves the technical effects of unmanned plane charging efficiency and optimization task execution capability.

Description

Technical Field

[0001] This invention relates to the field of charging regulation, and more specifically to an intelligent regulation system for hybrid drone charging. Background Technology

[0002] With the development of drone technology, drones are increasingly widely used in surveying, monitoring, logistics, and other fields. However, the endurance of drones has always been a key factor limiting their long-term operation. To ensure continuous operation, timely and effective charging is essential. Existing drone charging technologies typically employ fixed charging methods and preset charging times, lacking the ability to flexibly adjust according to actual conditions. This simple charging management approach often fails to meet the actual needs of drones in complex and ever-changing working environments. For example, existing technology cannot dynamically determine the optimal charging time based on the drone's real-time battery status and current task status, leading to unnecessary charging wait times or interruptions to ongoing critical tasks. Furthermore, existing charging systems only provide wired charging, further limiting the efficiency of the charging process. Therefore, the inability to accurately determine the charging timing and select the optimal charging method results in a long charging time, reducing the overall working efficiency of the drone and hindering its mission execution capabilities. Summary of the Invention

[0003] This application provides an intelligent control system for hybrid drone charging, aiming to solve the technical problems of low charging efficiency and poor mission execution capabilities caused by the lack of intelligent scheduling and flexible charging methods in existing drone charging technologies.

[0004] This application discloses an intelligent control system for hybrid drone charging, comprising: an information acquisition component for acquiring current battery level and current task information of multiple drones; a charging determination component for determining the optimal charging time and charging method for each drone based on the current battery level and current task information, wherein the charging method includes fast charging and battery swapping; a target determination component for acquiring the target charging time and target charging method of a target drone, wherein the target charging time is the optimal charging time for the target drone, and the target charging method is the charging method of the target drone; a fast charging execution component for controlling the target drone to the charging area when the target charging time is triggered if the target charging method is fast charging, and charging the target battery of the target drone according to the battery characteristics and remaining battery level of the target drone; and a battery swapping execution component for controlling the target drone to the battery swapping area when the target charging time is triggered if the target charging method is battery swapping, and quickly swapping the current battery on the target drone by selecting the optimal battery.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] By employing an information acquisition component to obtain current battery and task information from multiple drones, the system can monitor the real-time operating status of each drone, providing foundational data for subsequent operations. A charging determination component, based on the acquired current battery and task information, determines the optimal charging time and method for each drone, achieving intelligent and personalized charging strategies. A target determination component acquires the target drone's target charging time and method, specifying the charging strategy for each individual drone to prepare for charging execution. When the target charging method is determined to be fast charging, the fast charging execution component, upon triggering the target charging time, controls the target drone to the charging area and, based on the target drone's battery characteristics and remaining charge, performs targeted charging for the target drone. The battery charging process enables precise control of fast charging, allowing for optimized charging based on the specific conditions of the drone. If the target charging method is battery replacement, the battery swapping execution component controls the target drone to the battery swapping area when the target charging timing is triggered. By selecting the optimal battery, the current battery on the target drone is quickly replaced, providing another efficient charging method suitable for situations requiring rapid resumption of work. This solution addresses the technical problems of low charging efficiency and poor mission execution capabilities caused by the lack of intelligent scheduling and flexible charging methods in existing drone charging technologies. It achieves the technical effect of efficient management of the drone charging process through intelligent scheduling and hybrid charging methods, improving drone charging efficiency and optimizing mission execution capabilities.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0008] Figure 1 This application provides a schematic diagram of a smart control system for hybrid drone charging.

[0009] Figure 2 This application provides a schematic diagram of a process for determining the optimal charging time and charging method in an intelligent control system for hybrid drone charging.

[0010] Explanation of reference numerals in the attached diagram: Information acquisition component 11, charging determination component 12, target determination component 13, fast charging execution component 14, battery swapping execution component 15. Detailed Implementation

[0011] The overall concept of the technical solution provided in this application is as follows:

[0012] This application provides an intelligent control system for hybrid drone charging. First, an information acquisition component acquires real-time data on the current battery level and current task information of multiple drones, providing a data foundation for subsequent decision-making. Then, a charging determination component uses this information to determine the optimal charging time and method for each drone, achieving personalized and intelligent charging strategies. Next, a target determination component specifies the charging strategy for individual drones, preparing for the execution phase. For fast charging, the fast charging execution component guides the drone to a designated charging area and performs precise charging based on battery characteristics and remaining battery power. For battery replacement, the battery swapping execution component guides the drone to a battery swapping area and performs rapid battery replacement through optimal battery selection.

[0013] By intelligently controlling hybrid drone charging, the entire drone charging process is comprehensively optimized. It can dynamically adjust charging strategies based on real-time conditions, flexibly selecting the most suitable charging method and effectively improving charging efficiency. Simultaneously, due to the more rational charging process, the drone's mission execution capabilities are significantly enhanced, enabling it to complete tasks more continuously and efficiently.

[0014] After introducing the basic principles of this application, the non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0015] like Figure 1 As shown in the figure, this application provides an intelligent control system for hybrid drone charging, the system comprising:

[0016] Information acquisition component 11 is used to acquire the current battery level and current mission information of multiple drones.

[0017] Specifically, the information acquisition component 11 establishes a connection with multiple drones via wireless communication to acquire real-time status data for each drone, including current battery level and current task information. The current battery level information includes parameters such as the remaining battery percentage and actual remaining battery power; the current task information includes data such as task type, estimated completion time, and task progress. The information acquisition component 11 sends data requests to the drones through periodic polling or based on specific trigger conditions to obtain the current battery level and current task information for multiple drones.

[0018] By acquiring real-time and accurate information on the current battery level and mission status of multiple drones, the information acquisition component 11 provides comprehensive data support for the system, enabling subsequent charging decisions and scheduling to be based on a full understanding of the current status of the drones, thereby improving the intelligence level and operational efficiency of the entire system.

[0019] The charging determination component 12 is used to determine the optimal charging time and charging method for each drone based on the current power information and the current task information. The charging method includes fast charging and battery replacement.

[0020] Specifically, the charging determination component 12 analyzes the current battery level and current task information of each drone to calculate and determine the most suitable charging time for each drone, i.e., the optimal charging time. This intelligent charging timing determination mechanism can effectively prevent drones from interrupting their missions due to depleted battery power, while also maximizing drone efficiency. Furthermore, the charging determination component 12 is responsible for selecting the most suitable charging method for each drone. Two charging methods are available: fast charging and battery replacement. Fast charging is suitable when the battery level is not too low and there is sufficient time to charge, while battery replacement is more suitable for emergency situations requiring a rapid resumption of work. The charging determination component 12 intelligently selects the optimal charging method based on the specific conditions of the drone, such as remaining battery power and task completion status. This flexible charging method selection mechanism can meet the charging needs of different scenarios, further improving the adaptability and efficiency of the entire system.

[0021] Through an intelligent mechanism for determining charging timing and method, the charging determination component 12 provides crucial decision support for the entire hybrid UAV charging control system, effectively improving the system's intelligence level and operational efficiency.

[0022] The target determination component 13 is used to obtain the target charging time and target charging method of the target drone, wherein the target charging time is the optimal charging time of the target drone, and the target charging method is the charging method of the target drone.

[0023] Specifically, after the charging determination component 12 determines the optimal charging time and method for multiple drones, the target determination component 13 selects the target drone that needs to be charged. For this target drone, the target determination component 13 obtains its corresponding target charging time and target charging method. The target charging time is the optimal charging time determined by the charging determination component 12 for the target drone; it is the most suitable time to charge. Similarly, the target charging method refers to the charging method selected by the charging determination component 12 for the target drone, which may be either a fast charging method or a battery replacement method. The selection of the charging method is based on the specific situation of the target drone and the current mission requirements.

[0024] The target determination component 13 concretizes the decision results of the charging determination component 12 onto a single drone, providing clear targets and guidance for subsequent charging execution, which helps the system manage the charging process of each drone more accurately and efficiently.

[0025] The fast charging execution component 14 is used to control the target drone to the charging area when the target charging timing is triggered if the target charging mode is fast charging mode, and to charge the target battery of the target drone according to the battery characteristics and remaining power of the target drone.

[0026] Specifically, when the target charging method determined by the target determination component 13 is fast charging, the fast charging execution component 14 will begin operating when the system triggers the target charging timing. First, the fast charging execution component 14 controls the target drone to fly to a preset charging area. Once the target drone arrives at the charging area, the fast charging execution component 14 formulates the optimal charging strategy based on the target drone's battery characteristics and current remaining power. Battery characteristics include parameters such as battery type, capacity, and charging curve, while the remaining power is a precise value obtained through real-time monitoring. Based on this information, the fast charging execution component 14 determines the most suitable charging current, voltage, and charging time to achieve a fast and safe charging process. During charging, the fast charging execution component 14 continuously monitors the charging status, including but not limited to parameters such as charging current, voltage, and temperature. If any abnormality is detected, the fast charging execution component 14 can promptly adjust the charging parameters or, if necessary, interrupt the charging process to protect the battery and the drone's safety.

[0027] The fast charging execution component 14 can minimize the charging time of drones while ensuring charging safety and battery life, thereby improving the working efficiency of the entire drone fleet.

[0028] The battery swapping execution component 15 is used to control the target drone to the battery swapping area when the target charging time is triggered if the target charging method is battery swapping. The current battery on the target drone is then quickly swapped using a preferred battery.

[0029] Specifically, when the target charging method determined by the target determination component 13 is battery replacement, the battery swapping execution component 15 will begin operation when the system triggers the target charging timing. First, the battery swapping execution component 15 controls the target drone to fly to the preset battery swapping area. Once the target drone arrives at the swapping area, the battery swapping execution component 15 initiates the battery swapping process. Specifically, first, it selects the optimal spare battery, or preferred battery, from the battery bank. The selection of the preferred battery is based on multiple factors, such as battery capacity, cycle count, and health status, to ensure that the replaced battery can maximally meet the target drone's endurance requirements. Next, the battery swapping execution component 15 controls automated equipment or a robotic arm to perform the battery swapping operation, removing the current battery from the target drone and installing the preferred battery. After the battery swapping is completed, the battery swapping execution component 15 performs a series of safety checks and system tests to ensure that the newly installed preferred battery is compatible with the drone and functions properly.

[0030] The battery swapping assembly 15 enables drone battery replacement in a very short time, significantly reducing charging wait time and greatly improving drone operating efficiency. This rapid battery swapping method is suitable for scenarios with urgent missions or requiring long-term continuous operation, thereby enhancing the overall drone platooning capability.

[0031] Furthermore, the information collection component 11 includes the following execution steps:

[0032] Establish a pre-defined correspondence between the identification information of drones and their communication addresses;

[0033] Based on the identification information of multiple drones, the communication addresses of the multiple drones are obtained by querying the corresponding relationship;

[0034] Send power query commands and mission query commands to the communication addresses of multiple drones;

[0035] The system receives current battery level information and current task information reported by each drone. The current battery level information is the battery data obtained in real time by each drone through the battery detection unit based on the received battery query command. The current task information is the task parameters obtained by each drone from the task execution unit based on the received task query command.

[0036] In one feasible implementation, firstly, a mapping relationship between the identification information of drones and their communication addresses is pre-established. This mapping relationship is stored and managed using a database, mapping table, or other appropriate data structure. The identification information is a unique identifier for each drone, capable of uniquely identifying it, while the communication address may be an IP address, MAC address, or an address format used in other network communication protocols. Secondly, when acquiring information about multiple drones, the information acquisition component 11 queries the pre-established mapping relationship based on the known identification information of the multiple drones to obtain their communication addresses. Then, the information acquisition component 11 sends power query commands and task query commands to the obtained communication addresses of the multiple drones. The power query commands and task query commands use a predefined communication protocol format to ensure that the drones can correctly parse and respond to these query requests.

[0037] Subsequently, the information acquisition component 11 receives the current battery level and current task information reported by each drone. Specifically, the current battery level information is the battery data obtained in real time by each drone through its onboard battery detection unit based on the received battery query command, ensuring the accuracy and timeliness of the battery level information. The current task information is the task parameters obtained by each drone from its task execution unit based on the received task query command, including key information such as the current task type, task progress, and estimated completion time. Through a structured and automated information acquisition process, the information acquisition component 11 can efficiently and accurately acquire real-time status information of multiple drones, providing reliable data support for subsequent charging decisions and scheduling.

[0038] Furthermore, such as Figure 2 As shown, the charging determination component 12 includes the following execution steps:

[0039] Iterate through multiple drones, identify the first drone, and extract the current battery information and current task information of the first drone as the first battery information and the first task information;

[0040] Based on the first power information and the first task information, the remaining task load and power consumption rate of the first UAV are determined.

[0041] Based on the remaining task load and power consumption rate, obtain the predicted remaining power of the first UAV when the current task is completed.

[0042] The optimal charging time and charging method for the first drone are determined based on the predicted remaining battery power.

[0043] In one feasible implementation, firstly, the charging determination component 12 traverses multiple drones and selects one drone as the first drone. For the first drone, its current battery level and current task information are extracted and defined as first battery level information and first task information, respectively, laying the foundation for subsequent personalized charging strategy formulation. Secondly, based on the acquired first battery level information and first task information, the charging determination component 12 performs data analysis and calculation to determine the remaining task load and battery consumption rate of the first drone. Specifically, to determine the remaining task load, the charging determination component 12 first extracts the total task load parameter from the first task information, such as the total flight distance or the estimated total flight time; then, it extracts the current task completion rate, such as the percentage of the flight distance already flown relative to the total flight distance; then, it calculates the actual remaining task load by subtracting the completed portion from the total task load. Regarding the determination of the battery consumption rate, the charging determination component 12 first extracts the initial battery level and current battery level from the first battery level information, and extracts the task start time and current time from the first task information; then, it calculates the consumed battery load (i.e., initial battery load minus current battery load) and the executed time, dividing the consumed battery load by the executed time to obtain the battery consumption rate.

[0044] Then, the charging determination component 12 calculates the remaining task load and power consumption rate, for example, by constructing a power consumption prediction model, to obtain the predicted remaining power of the first drone when the current task is completed. Afterwards, based on the predicted remaining power and factors such as preset power thresholds, the charging determination component 12 determines the optimal charging time and method for the first drone. The optimal charging time may be immediately after the task ends, or it may be at an appropriate point during the task execution; the choice of charging method involves a trade-off between fast charging and battery replacement to meet the drone's endurance requirements and task requirements.

[0045] Through a refined analysis and decision-making process, the charging determination component 12 can formulate personalized and intelligent charging strategies for each drone, effectively balancing charging needs and mission execution efficiency, thereby optimizing the energy management and mission execution performance of the entire drone fleet.

[0046] Furthermore, the charging determination component 12 also includes the following execution steps:

[0047] Obtain the current remaining battery power of the first drone;

[0048] Based on the remaining task load of the first UAV, calculate the remaining task execution time for the first UAV to perform the current task;

[0049] Based on the power consumption rate of the first UAV and the remaining mission execution time, calculate the total expected power consumption of the first UAV during the remaining mission execution time.

[0050] The predicted remaining battery power of the first drone is obtained by subtracting the estimated total battery consumption from the current remaining battery power of the first drone when the current task is completed.

[0051] In a preferred embodiment, when determining the predicted remaining battery power of the first drone upon completion of the current task based on the remaining task load and power consumption rate, the charging determination component 12 first obtains the current remaining battery power of the first drone, expressed as a battery capacity percentage or actual battery power value, providing accurate basic data for subsequent calculations. Secondly, the charging determination component 12, using the previously determined remaining task load and combining it with the drone's current flight speed and task type, calculates the remaining time for the first drone to execute the current task. For tasks measured in distance units, the remaining distance is divided by the current flight speed; for tasks measured in time units, the remaining task load is used directly; and for complex tasks, segmented calculations followed by summation are required to ensure accurate time estimation for different task types. Next, the charging determination component 12, combining the known power consumption rate and the calculated remaining execution time, estimates the total expected power consumption of the first drone to complete the remaining tasks. During the calculation, the influence of environmental factors such as wind speed and temperature on power consumption is considered, and fine-tuning is performed using preset correction coefficients to improve prediction accuracy. Subsequently, the charging determination component 12 subtracts the expected total power consumption from the current remaining power to obtain the predicted remaining power of the first drone when the mission is completed. This reflects the power status of the drone after completing the mission and provides a basis for formulating a charging strategy.

[0052] By using the charging determination component 12, the energy requirements of the drone are comprehensively assessed, providing accurate and reliable data support for subsequent charging decisions, improving the energy management efficiency of a single drone, helping to optimize drone charging scheduling, and enhancing the drone's operational efficiency and reliability.

[0053] Furthermore, the charging determination component 12 also includes the following execution steps:

[0054] Obtain the termination position of the first drone when the current task is completed, and obtain the return landing position of the first drone, wherein the return landing position includes a charging area and a battery swapping area;

[0055] Calculate the return battery threshold of the first UAV based on the termination position and the return landing position;

[0056] When the predicted remaining power meets the return power threshold, the moment when the first UAV completes its current task is determined as the optimal charging time, and fast charging is determined as the charging method.

[0057] When the predicted remaining power does not meet the return power threshold, the first UAV is backtracked to the mission execution position based on the termination position to determine the target return position that meets the requirements for reaching the return landing position. The moment when the first UAV reaches the target return position is determined as the optimal charging time, and the battery replacement method is determined as the charging method.

[0058] In a preferred embodiment, when determining the optimal charging time and method for the first UAV based on the predicted remaining battery power, the charging determination component 12 first analyzes the task data of the current task, extracts the coordinates of the task target point, or analyzes the endpoint of the preset flight path to obtain the termination position of the first UAV when the current task is completed. Simultaneously, it obtains the return landing position of the first UAV, which includes a charging area and a battery swapping area for charging the UAV; this is a pre-set fixed point. Next, the charging determination component 12 calculates the return battery power threshold for the first UAV based on the obtained termination position and return landing position. Specifically, it first calculates the distance from the termination position to the return landing position, then estimates the flight time based on the average flight speed of the target UAV, and finally combines this with the average energy consumption rate of the UAV to obtain the minimum battery power required to complete this flight, i.e., the return battery power threshold. After calculation, the obtained predicted remaining battery power is compared with the return battery power threshold. When the predicted remaining battery power meets (is greater than or equal to) the return battery power threshold, the charging determination component 12 directly determines the moment when the first UAV completes the current task as the optimal charging time and selects fast charging as the charging method. In this scenario, the drone can safely complete its mission and return to its charging station.

[0059] However, when the predicted remaining battery power does not meet the return flight battery power threshold, the charging determination component 12 initiates a mission execution position backtracking process. Starting from the termination position, it checks each position point step by step along the preset flight path. For each position point, it calculates the battery power required to reach the return landing position from that point and compares it with the predicted remaining battery power at that point. When the first position point that meets the conditions is found, it is determined as the target return flight position. Subsequently, the charging determination component 12 determines the moment when the first UAV arrives at this target return flight position as the optimal charging time and selects battery replacement as the charging method.

[0060] Through precise calculation and judgment, the charging determination component 12 can intelligently determine the optimal charging strategy based on the actual situation of the drone, which not only ensures the maximum completion of the mission, but also guarantees the safe recovery and timely charging of the drone.

[0061] Furthermore, the charging determination component 12 also includes the following execution steps:

[0062] Based on the current battery information and current task information of the first UAV, the flight trajectory information of the first UAV is determined. The flight trajectory information includes the position, speed and battery data of the first UAV during the execution of the current task.

[0063] Based on the termination position, the flight trajectory information is traversed in reverse to obtain the battery data of the first UAV at each location point;

[0064] For each first position point traversed, calculate the amount of electricity required for the first drone to fly from the first position point to the return landing position point;

[0065] Compare the power data at the location point with the power required at the location point;

[0066] When the power data at the location point is greater than or equal to the power required at the location point, the first location point is determined as the target return position;

[0067] If the power data at a given location is less than the power required for that location, continue traversing to the next location until the target return position is found.

[0068] In a preferred embodiment, when the first UAV is traced back to its mission execution position based on the termination position to determine a target return landing position that is reachable, firstly, the charging determination component 12 determines the flight trajectory information of the first UAV based on its current battery information and current mission information. Specifically, it analyzes the mission planning data and real-time flight data in the current battery information and current mission information to generate a detailed trajectory information set containing the UAV's position, speed, and battery data during the execution of the current mission. Next, the charging determination component 12 traverses the flight trajectory information in reverse from the termination position. During this process, the battery data of the first UAV at each position point is acquired one by one according to a preset time or distance interval. Then, for each traversed position point, the charging determination component 12 calculates the battery power required for the first UAV to fly from that position point to the return landing position. By considering factors such as the distance from the position point to the return landing position, the estimated flight time, and the UAV's average energy consumption rate, combined with real-time environmental data (such as wind speed and temperature), the required battery power for each position point is obtained. After the calculation is completed, the charging determination component 12 compares the position point battery power data with the calculated required battery power for the position point. This comparison process is performed point-by-point to find the furthest safe return point. When the battery data at a certain location is greater than or equal to the required battery power at that point, the charging determination component 12 designates that location as the target return position, meaning that from this point, the drone has enough battery power to safely return to the charging or battery swapping area. If the battery data at a location is less than the required battery power at that location, the charging determination component 12 continues to traverse the next location closer to the starting point, repeating the above calculation and comparison process until a target return position that meets the conditions is found.

[0069] Through a precise, point-by-point analysis process, the charging determination component 12 can maximize the mission execution range of the UAV and find the optimal return position while ensuring safety, thereby improving the operating efficiency and reliability of the entire system.

[0070] Furthermore, the fast charging method is a wireless charging method, including:

[0071] A wireless charging platform is set up in the charging area;

[0072] Control the target drone to land on the wireless charging platform;

[0073] Energy is transferred through the coupling between the receiving coil on the wireless charging platform and the charging coil on the target drone, thereby enabling wireless charging of the target drone.

[0074] In a preferred embodiment, to further achieve energy supply and convenient charging for the drone, wireless charging is selected as the fast charging method. First, a wireless charging platform is set up within the charging area to ensure the drone can land safely and stably. When fast charging of the target drone is required, the drone is controlled to land precisely on the wireless charging platform. For example, a visual positioning system or other precise navigation technology is used to ensure the drone accurately aligns with the charging position. After successful landing, electromagnetic coupling is formed between the receiving coil on the wireless charging platform and the pre-installed charging coil on the target drone, allowing energy to be efficiently transferred from the wireless charging platform to the drone. During charging, the energy transfer efficiency and the charging status of the drone's battery are continuously monitored, and charging parameters are adjusted as necessary to ensure the safety and efficiency of the charging process. Wireless charging enables fast and convenient charging of the target drone, completing the entire charging process without manual intervention. This not only improves charging efficiency but also enhances the automation and operational flexibility of drone charging.

[0075] Furthermore, wireless charging of the target drone includes:

[0076] High-power laser energy is transmitted using large-diameter optical fibers;

[0077] The high-power laser energy is divided into multiple low-power optical energies using a beam splitter.

[0078] The low-power light energy is projected onto a photovoltaic cell matrix for photoelectric conversion;

[0079] The photoelectric converted electrical energy is transferred to the energy storage system through the boost module to power the wireless charging platform.

[0080] In a preferred embodiment, large-diameter optical fibers are used to transmit high-power laser energy. For example, a 500m large-diameter optical fiber with a diameter of 440 micrometers is used, capable of carrying and transmitting high-power laser energy. The purpose of choosing large-diameter optical fibers is to meet the requirements of long-distance transmission of high-power lasers while minimizing transmission loss. Next, a beam splitter is used to divide the transmitted high-power laser energy into multiple smaller-power optical energy components. Specifically, a 1-to-19 beam splitter is used to evenly divide the high-power optical energy into nineteen smaller-power optical energy components, thereby better managing and utilizing the energy. Then, these smaller-power optical energy components are projected onto a photovoltaic array for photoelectric conversion. The photovoltaic array consists of multiple high-efficiency photoelectric conversion units, which can efficiently convert light energy into electrical energy, improving energy conversion efficiency and enhancing system stability and reliability. Subsequently, a boost module transmits the photoelectric converted electrical energy to the energy storage system to power the wireless charging platform. The boost module's function is to convert the relatively low-voltage electrical energy after photoelectric conversion into high-voltage electrical energy suitable for energy storage and wireless transmission. The energy storage system is used to temporarily store the converted electrical energy, ensuring that the wireless charging platform can continuously and stably provide charging services for drones.

[0081] This energy transmission and conversion method achieves long-distance, high-efficiency energy transmission, overcoming the limitations of traditional cable power supply. Simultaneously, through beam splitting and matrix design, it can safely and efficiently handle high-power energy, providing drones with fast and reliable charging services. It is suitable for drone operations in long-distance, complex environments, such as power line inspections, improving the efficiency and applicability of the drone system.

[0082] Furthermore, while the target drone is wirelessly charging, the information collected by the target drone is uploaded to the server via wireless communication.

[0083] In a preferred embodiment, while the target drone lands on the wireless charging platform for charging, a data transmission channel is simultaneously established between the drone and the server via wireless communication. For example, a 4G / 5G network or a dedicated wireless network ensures the stability and reliability of data transmission. Through wireless communication, the drone can upload various types of information collected during its mission to the server in real time, including high-resolution images, video data, infrared thermal imaging data, and laser scanning data. By simulating both drone charging and data transmission, the drone's efficiency is maximized, and data transmission is completed during charging time, ensuring data timeliness.

[0084] In summary, the intelligent control system for hybrid drone charging provided in this application has the following technical effects:

[0085] The information acquisition component acquires current battery and task information for multiple drones, providing fundamental data for determining the optimal charging time and method for each drone. The charging determination component, based on current battery and task information, determines the optimal charging time and method for each drone, including fast charging and battery replacement, enabling intelligent and personalized charging strategies and allowing for the creation of optimal charging plans tailored to each drone's specific needs. The target determination component acquires the target charging time and method for a specific drone, specifying the optimal charging time and method for that drone, thus concretizing the charging strategy for individual drones and preparing for subsequent execution phases. The fast charging execution component, if the target charging method is fast charging, controls the target drone to the charging area when the target charging time is triggered. Based on the target drone's battery characteristics and remaining battery power, it charges the target battery, achieving precise control of fast charging and optimizing charging based on the drone's specific situation. The battery swapping execution component, when the target charging method is battery replacement, controls the target drone to the battery swapping area when the target charging opportunity is triggered. It then quickly replaces the current battery on the target drone by selecting the optimal battery, providing an alternative and efficient charging method suitable for situations requiring rapid resumption of operations. Through intelligent scheduling and hybrid charging methods, it achieves efficient management of the drone charging process, improving drone charging efficiency and optimizing mission execution capabilities.

[0086] In summary, any step of the system described above can be stored as a computer instruction or program in an unrestricted computer memory, and can be called and recognized by an unrestricted computer processor to implement any of the systems in the embodiments of this application, without any additional restrictions.

[0087] Furthermore, the "first" or "second" mentioned above may not only represent a sequential relationship, but may also represent a specific concept, and / or refer to the individual or collective selection of multiple elements. Clearly, those skilled in the art can make various modifications and variations to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A smart control system for hybrid drone charging, characterized in that, include: An information acquisition component is used to acquire the current battery level and current task information of multiple drones; A charging determination component is used to determine the optimal charging time and charging method for each drone based on the current power information and the current task information. The charging method includes fast charging and battery replacement. A target determination component is used to obtain the target charging time and target charging method of a target drone, wherein the target charging time is the optimal charging time for the target drone, and the target charging method is the charging method of the target drone; A fast charging execution component is used to control the target drone to the charging area when the target charging opportunity is triggered if the target charging method is fast charging. The component then charges the target battery of the target drone according to the battery characteristics and remaining power of the target drone. A battery swapping execution component is used to control the target drone to the battery swapping area when the target charging time is triggered if the target charging method is battery swapping. The component then quickly swaps the current battery on the target drone by selecting the appropriate battery. The charging determination component includes the following execution steps: Iterate through multiple drones, identify the first drone, and extract the current battery information and current task information of the first drone as the first battery information and the first task information; Based on the first power information and the first task information, the remaining task load and power consumption rate of the first UAV are determined. Based on the remaining task load and power consumption rate, obtain the predicted remaining power of the first UAV when the current task is completed. The optimal charging time and charging method for the first drone are determined based on the predicted remaining battery power.

2. The intelligent control system for hybrid drone charging according to claim 1, characterized in that, The information collection component includes the following execution steps: Establish a pre-defined correspondence between the identification information of drones and their communication addresses; Based on the identification information of multiple drones, the communication addresses of the multiple drones are obtained by querying the corresponding relationship; Send power query commands and mission query commands to the communication addresses of multiple drones; The system receives current battery level information and current task information reported by each drone. The current battery level information is the battery data obtained in real time by each drone through the battery detection unit based on the received battery query command. The current task information is the task parameters obtained by each drone from the task execution unit based on the received task query command.

3. The intelligent control system for hybrid drone charging according to claim 1, characterized in that, The charging determination component also includes the following execution steps: Obtain the current remaining battery power of the first drone; Based on the remaining task load of the first UAV, calculate the remaining task execution time for the first UAV to perform the current task; Based on the power consumption rate of the first UAV and the remaining mission execution time, calculate the total expected power consumption of the first UAV during the remaining mission execution time. The predicted remaining battery power of the first drone is obtained by subtracting the estimated total battery consumption from the current remaining battery power of the first drone when the current task is completed.

4. The intelligent control system for hybrid drone charging according to claim 1, characterized in that, The charging determination component also includes the following execution steps: Obtain the termination position of the first drone when the current task is completed, and obtain the return landing position of the first drone, wherein the return landing position includes a charging area and a battery swapping area; Calculate the return battery threshold of the first UAV based on the termination position and the return landing position; When the predicted remaining power meets the return power threshold, the moment when the first UAV completes its current task is determined as the optimal charging time, and fast charging is determined as the charging method. When the predicted remaining power does not meet the return power threshold, the first UAV is backtracked to the mission execution position based on the termination position to determine the target return position that meets the requirements for reaching the return landing position. The moment when the first UAV reaches the target return position is determined as the optimal charging time, and the battery replacement method is determined as the charging method.

5. The intelligent control system for hybrid drone charging according to claim 4, characterized in that, The charging determination component also includes the following execution steps: Based on the current battery information and current task information of the first UAV, the flight trajectory information of the first UAV is determined. The flight trajectory information includes the position, speed and battery data of the first UAV during the execution of the current task. Based on the termination position, the flight trajectory information is traversed in reverse to obtain the battery data of the first UAV at each location point; For each first position point traversed, calculate the amount of electricity required for the first drone to fly from the first position point to the return landing position point; Compare the power data at the location point with the power required at the location point; When the power data at the location point is greater than or equal to the power required at the location point, the first location point is determined as the target return position; If the power data at a given location is less than the power required for that location, continue traversing to the next location until the target return position is found.

6. The intelligent control system for hybrid drone charging according to claim 1, characterized in that, The fast charging method is a wireless charging method, including: A wireless charging platform is set up in the charging area; Control the target drone to land on the wireless charging platform; Energy is transferred through the coupling between the receiving coil on the wireless charging platform and the charging coil on the target drone, thereby enabling wireless charging of the target drone.

7. The intelligent control system for hybrid drone charging according to claim 6, characterized in that, Wireless charging of the target drone includes: High-power laser energy is transmitted using large-diameter optical fibers; The high-power laser energy is divided into multiple low-power optical energies using a beam splitter. The low-power light energy is projected onto a photovoltaic cell matrix for photoelectric conversion; The photoelectric converted electrical energy is transferred to the energy storage system through the boost module to power the wireless charging platform.

8. The intelligent control system for hybrid drone charging according to claim 6, characterized in that, While the target drone is wirelessly charging, the information collected by the target drone is uploaded to the server via wireless communication.

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