Intelligent regulation and control system for charging of hybrid unmanned aerial vehicle

Abstract

The invention discloses a hybrid unmanned aerial vehicle charging intelligent regulation and control system, and belongs to the field of charging regulation and control, and the system comprises an information collection assembly which is used for obtaining the current electric quantity information and the current task information of a plurality of unmanned aerial vehicles; the charging determining assembly is used for determining the optimal charging opportunity and the optimal charging mode of each unmanned aerial vehicle; the target determining component is used for acquiring a target charging opportunity and a target charging mode; the fast charging execution assembly is used for charging a target battery of the target unmanned aerial vehicle; and the battery replacement execution assembly is used for rapidly replacing the current battery on the target unmanned aerial vehicle through the optimized battery. The technical problems of low unmanned aerial vehicle charging efficiency and poor task execution capability caused by lack of intelligent scheduling and flexible charging modes in unmanned aerial vehicle charging in the prior art are solved, efficient management of the unmanned aerial vehicle charging process is realized through intelligent scheduling and hybrid charging modes, and the unmanned aerial vehicle charging efficiency is improved. The charging efficiency of the unmanned aerial vehicle is improved; and the task execution capability is optimized.

Description

Technical Field

[0001] The present invention relates to the field of charging control, and in particular to an intelligent control system for charging a hybrid unmanned aerial vehicle. Background Art

[0002] With the development of drone technology, drones are increasingly used in surveying, monitoring, logistics and other fields. However, the endurance of drones has always been a key factor restricting their long-term work. In order to ensure the continuous work of drones, they need to be charged in a timely and effective manner. Existing drone charging technologies usually adopt fixed charging methods and preset charging times, and lack the ability to flexibly adjust according to actual conditions. This simple charging management method often cannot meet the actual needs of drones when facing complex and changeable actual working environments. For example, the existing technology cannot dynamically determine the best charging time based on the real-time power status of the drone and the current mission situation, resulting in unnecessary charging waiting time or interruption of important tasks being performed. In addition, the existing charging system only provides wired charging, which further limits the efficiency of the charging process. Therefore, due to the inability to accurately grasp the charging time and select the optimal charging method, the charging process takes a long time, which reduces the overall work efficiency of the drone and makes the drone's mission execution capability poor. Summary of the invention

[0003] The present application provides an intelligent control system for hybrid drone charging, aiming to solve the technical problems in the prior art that drone charging lacks intelligent scheduling and flexible charging methods, resulting in low drone charging efficiency and poor mission execution capability.

[0004] The present application discloses an intelligent control system for charging a hybrid drone, including: an information collection component for obtaining current power information and current mission information of multiple drones; a charging determination component for determining the optimal charging time and charging method for each drone based on the current power information and current mission information, wherein the charging methods include a fast charging method and a battery replacement method; a target determination component for obtaining a target charging time and a target charging method for 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 for the target drone; a fast charging execution component for controlling the target drone to a charging area when the target charging time is triggered if the target charging method is a fast charging method, and charging the target battery of the target drone according to the battery characteristics and remaining power of the target drone; a battery replacement execution component for controlling the target drone to a battery replacement area when the target charging time is triggered if the target charging method is a battery replacement method, and quickly replacing the current battery on the target drone by selecting a preferred battery.

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

[0006] By adopting the information collection component to obtain the current power information and current mission information of multiple drones, the working status of each drone can be mastered in real time to provide basic data for the follow-up; the charging determination component is used to determine the best charging time and charging method for each drone based on the current power information and current mission information obtained, so as to realize the intelligent and personalized charging strategy; the target determination component obtains the target charging time and target charging method of the target drone, and concretizes the charging strategy to a single drone to prepare for charging execution; when the target charging method is determined to be the fast charging method, the fast charging execution component controls the target drone to the charging area when the target charging time is triggered, and targets the target drone according to the battery characteristics and remaining power of the target drone. The battery is charged, and precise control of fast charging is achieved, which can optimize charging according to the specific situation of the UAV; if the target charging method is battery replacement, the battery replacement execution component controls the target UAV to the battery replacement area when the target charging opportunity is triggered, and quickly replaces the current battery on the target UAV by optimizing the battery, providing another efficient charging method, which is suitable for technical solutions that require rapid resumption of work, and solves the technical problems of lack of intelligent scheduling and flexible charging methods for UAV charging in the prior art, resulting in low UAV charging efficiency and poor mission execution capability, and achieves the technical effect of realizing efficient management of the UAV charging process through intelligent scheduling and hybrid charging methods, improving UAV charging efficiency and optimizing mission execution capabilities.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A structural schematic diagram of an intelligent control system for charging a hybrid drone is provided for an embodiment of the present application;

[0009] Figure 2 A flow chart of determining the best charging time and charging method in an intelligent control system for charging a hybrid drone is provided for an embodiment of the present application.

[0010] Explanation of the reference numerals: information collection component 11, charging determination component 12, target determination component 13, fast charging execution component 14, battery replacement execution component 15. DETAILED DESCRIPTION

[0011] The overall idea of ​​the technical solution provided by this application is as follows:

[0012] An embodiment of the present application provides an intelligent control system for charging a hybrid drone. First, the current power information and current mission information of multiple drones are obtained in real time through the information collection component to provide a data basis for subsequent decision-making. Then, based on this information, the charging determination component determines the best charging time and charging method for each drone to achieve personalization and intelligence of the charging strategy. Next, the target determination component concretizes the charging strategy to a single drone to prepare for the execution stage. For fast charging, the fast charging execution component will control the drone to reach the designated charging area and accurately charge it according to the battery characteristics and remaining power. For battery replacement, the battery swap execution component will guide the drone to the battery swap area and quickly replace the battery by optimizing it.

[0013] Through the intelligent control of hybrid drone charging, the drone charging process is fully optimized, and the charging strategy can be dynamically adjusted according to the real-time situation, and the most suitable charging method can be flexibly selected, which effectively improves the charging efficiency. At the same time, due to the more reasonable charging process, the drone's mission execution capability is significantly improved, and it can complete the mission more continuously and efficiently.

[0014] After introducing the basic principles of the present application, the non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the drawings in the specification.

[0015] like Figure 1 As shown, the embodiment of the present application provides an intelligent control system for charging a hybrid drone, the system comprising:

[0016] The information collection component 11 is used to obtain the current power information and current mission information of multiple drones.

[0017] Specifically, the information collection component 11 establishes a connection with multiple drones through wireless communication, and obtains the status data of each drone in real time, including the current power information and the current task information. The current power information includes parameters such as the remaining battery power percentage and the actual remaining power value; the current task information includes relevant data such as the task type, the estimated completion time, and the task progress. The information collection component 11 uses periodic polling or based on specific trigger conditions to send data requests to the drones, thereby obtaining the current power information and current task information of multiple drones.

[0018] By acquiring the current power information and current mission information of multiple drones in real time and accurately, the information collection component 11 provides comprehensive data support for the system, so that subsequent charging decisions and scheduling can be based on a full understanding of the current status of the drones, thereby improving the intelligence level and operating efficiency of the entire system.

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

[0020] Specifically, the charging determination component 12 calculates and determines the most suitable time for each drone to charge, that is, the best charging time, by analyzing the current power information and current mission information of each drone. This intelligent charging timing determination mechanism can effectively prevent the drone from interrupting its mission due to exhaustion of power, and can also maximize the work efficiency of the drone. In addition, the charging determination component 12 is also responsible for selecting the most suitable charging method for each drone. Among them, there are two charging methods: fast charging method and battery replacement method. The fast charging method is suitable for situations where the power has not dropped too low and there is enough time to charge, while the battery replacement method is more suitable for emergency situations where work needs to be resumed quickly. The charging determination component 12 intelligently selects the optimal charging method based on the specific situation of the drone, such as the remaining power, the degree of mission completion, and other factors. This flexible charging method selection mechanism can meet the charging needs in different scenarios, further improving the adaptability and efficiency of the entire system.

[0021] Through the intelligent charging timing and method determination mechanism, the charging determination component 12 provides key decision support for the entire hybrid UAV charging control system, effectively improving the intelligence level and operation efficiency of the system.

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

[0023] Specifically, after the charging determination component 12 completes the determination of the optimal charging time and charging method for multiple drones, the target determination component 13 selects a target drone that needs to be charged. For the target drone, the target determination component 13 will obtain its corresponding target charging time and target charging method. Among them, the target charging time is the optimal charging time determined by the charging determination component 12 for the target drone, which is the most suitable time point for charging. Similarly, the target charging method refers to the charging method selected by the charging determination component 12 for the target drone, which may be one of the fast charging method or the battery replacement method, wherein the choice of charging method is determined based on the specific situation of the target drone and the current mission requirements.

[0024] Through the target determination component 13, the decision result of the charging determination component 12 is concretized to a single UAV, providing clear goals and guidance for subsequent charging execution, which helps the system to manage the charging process of each UAV more accurately and efficiently.

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

[0026] Specifically, when the target charging method determined by the target determination component 13 is a fast charging method, the fast charging execution component 14 will start working when the system triggers the target charging timing. First, the fast charging execution component 14 controls the target UAV to fly to the preset charging area. When the target UAV arrives at the charging area, the fast charging execution component 14 formulates the optimal charging strategy based on the battery characteristics and the current remaining power of the target UAV. Battery characteristics, including parameters such as battery type, capacity, and charging curve, and the remaining power is an accurate value obtained through real-time detection. 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 the charging process, 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 adjust the charging parameters in time or interrupt the charging process when necessary to protect the safety of the battery and the UAV.

[0027] Through the fast charging execution component 14, the charging time of the drone can be shortened to the maximum extent while ensuring charging safety and battery life, thereby improving the working efficiency of the entire drone fleet.

[0028] The battery replacement execution component 15 is used to control the target UAV to the battery replacement area when the target charging time is triggered if the target charging method is the battery replacement method, and quickly replace the current battery on the target UAV by selecting the preferred battery.

[0029] Specifically, when the target charging method determined by the target determination component 13 is a battery replacement method, the battery replacement execution component 15 will start working when the system triggers the target charging timing. First, the battery replacement execution component 15 controls the target UAV to fly to the preset battery replacement area. When the target UAV arrives at the battery replacement area, the battery replacement execution component 15 starts the battery replacement process. Specifically, first, select the best backup battery, that is, the preferred battery, from the battery library. The selection of the preferred battery is based on multiple factors, such as battery capacity, number of cycles, health status, etc., to ensure that the replaced battery can meet the endurance requirements of the target UAV to the greatest extent. Next, the battery replacement execution component 15 controls the automated equipment or robotic arm to perform the battery replacement operation, remove the current battery on the target UAV, and install the preferred battery. After the battery replacement is completed, the battery replacement execution component 15 performs a series of safety checks and system tests to ensure that the newly installed preferred battery is compatible with the UAV and works properly.

[0030] The battery replacement execution component 15 can complete the replacement of the drone's battery in a very short time, significantly reducing the charging waiting time and greatly improving the drone's working efficiency. This fast battery replacement method is suitable for scenarios where the mission is urgent or requires long-term continuous work, thereby improving the entire drone fleet's operating capacity.

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

[0032] Establish a correspondence between the identification information of the drone and its communication address in advance;

[0033] According to the identification information of the multiple drones, query and obtain the communication addresses of the multiple drones from the corresponding relationship;

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

[0035] Receive the current power information and current task information reported by each UAV, wherein the current power information is the power data obtained by each UAV through real-time detection by a power detection unit according to the received power query instruction, and the current task information is the task parameters obtained by each UAV from the task execution unit according to the received task query instruction.

[0036] In a feasible implementation, first, a correspondence between the identification information of the drone and its communication address is pre-established. This correspondence is stored and managed using a database, a mapping table or other appropriate data structure. Among them, the identification information is the unique number of the drone, which can uniquely identify a drone, and the communication address may be an IP address, a MAC address or an address format used in other network communication protocols. Secondly, when obtaining information of multiple drones, the information collection component 11 queries the pre-established correspondence based on the known identification information of multiple drones, and obtains the communication addresses of these drones. Then, the information collection component 11 sends power query instructions and task query instructions to the communication addresses of the acquired multiple drones. Among them, the power query instructions and task query instructions use a predefined communication protocol format to ensure that the drone can correctly parse and respond to these query requests.

[0037] Afterwards, the information collection component 11 receives the current power information and current task information reported by each drone. Specifically, the current power information is the power data obtained by each drone through real-time detection by its onboard power detection unit according to the received power query instruction. The accuracy and timeliness of the power information are ensured through real-time detection; the current task information is the task parameters obtained by each drone from its task execution unit according to the received task query instruction, including key information such as the current task type, task progress, and estimated completion time. Through a structured and automated information collection process, the information collection component 11 can efficiently and accurately obtain the real-time status information of multiple drones, providing reliable data support for subsequent charging decisions and scheduling.

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

[0039] Traversing multiple drones, determining a first drone, and extracting current power information and current mission information of the first drone as first power information and first mission information;

[0040] Determine a remaining mission amount and a power consumption rate of the first UAV based on the first power information and the first mission information;

[0041] According to the remaining task amount and the power consumption rate, obtaining 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 according to the predicted remaining power.

[0043] In a feasible implementation, first, the charging determination component 12 traverses multiple drones and determines a drone as the first drone. For the first drone, its current power information and current task information are extracted, and these information are defined as the first power information and the first task information, respectively, which lays the foundation for the subsequent personalized charging strategy formulation. Secondly, based on the acquired first power information and first task information, the charging determination component 12 performs data analysis and calculation to determine the remaining task volume and power consumption rate of the first drone. Specifically, to determine the remaining task volume, the charging determination component 12 first extracts the total task volume parameter from the first task information, such as the total flight distance or the estimated total flight time; then, extracts the current task completion degree, such as the percentage of the flight distance to the total flight distance; then, the actual remaining task volume is calculated by subtracting the completed part from the total task volume. In terms of determining the power consumption rate, the charging determination component 12 first extracts the initial power and current power from the first power information, and extracts the task start time and current time from the first task information; then, calculates the consumed power (i.e., the initial power minus the current power) and the executed time, and divides the consumed power by the executed time to obtain the power consumption rate.

[0044] Then, the charging determination component 12 obtains the predicted remaining power of the first UAV when the current task is completed based on the calculated remaining task amount and power consumption rate, for example, by constructing a power consumption prediction model. After that, the charging determination component 12 determines the best charging time and charging method for the first UAV based on the predicted remaining power, combined with factors such as the preset power threshold. Among them, the best charging time may be charging immediately after the task is completed, or it may be at an appropriate point in the task execution process; the choice of charging method is a trade-off between fast charging and battery replacement to meet the UAV's endurance needs and mission requirements.

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

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

[0047] Get the current remaining power of the first drone;

[0048] Calculating the remaining task execution time of the first UAV for executing the current task according to the remaining task amount of the first UAV;

[0049] Calculate the estimated total power consumption of the first UAV within the remaining mission execution time according to the power consumption rate of the first UAV and the remaining mission execution time;

[0050] The estimated total power consumption is subtracted from the current remaining power of the first drone to obtain the predicted remaining power of the first drone when the current task is completed.

[0051] In a preferred embodiment, when the predicted remaining power of the first drone when the current task is completed is obtained based on the remaining task amount and the power consumption rate, first, the charging determination component 12 obtains the current remaining power of the first drone, expressed as a percentage of battery capacity or actual power value, to provide accurate basic data for subsequent calculations. Secondly, the remaining task amount previously determined by the charging determination component 12 is combined with the current flight speed and task type of the drone to calculate the remaining time for the first drone to perform the current task. For tasks in distance units, the remaining distance is divided by the current flight speed; for tasks in time units, the remaining task amount is directly used; and for complex tasks, it is necessary to calculate in sections and then sum them to ensure accurate time estimation for different types of tasks. Next, the charging determination component 12 combines the known power consumption rate and the calculated remaining execution time to calculate the total expected power consumption of the first drone to complete the remaining tasks. When calculating, the influence of environmental factors such as wind speed and temperature on power consumption is considered, and fine-tuning is performed through a preset correction coefficient to improve the prediction accuracy. Afterwards, the charging determination component 12 subtracts the estimated total power consumption from the current remaining power to obtain the predicted remaining power of the first UAV when the mission is completed, which reflects the power status of the UAV after completing the mission and provides a basis for formulating a charging strategy.

[0052] Through the charging determination component 12, the energy demand of the UAV is comprehensively evaluated, and accurate and reliable data support is provided for subsequent charging decisions, thereby improving the energy management efficiency of a single UAV, helping to optimize the charging scheduling of the UAV, and improving the operating efficiency and reliability of the UAV.

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

[0054] Obtaining the end position of the first UAV when the current mission is completed, and obtaining the return landing position of the first UAV, wherein the return landing position includes a charging area and a battery replacement area;

[0055] Calculating a return power threshold of the first UAV according to 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 mission is determined as the best charging time, and the fast charging mode is determined as the charging mode;

[0057] When the predicted remaining power does not meet the return power threshold, the mission execution position of the first UAV is backtracked according to the termination position to determine a target return position that can reach 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 best charging time and charging method of the first drone based on the predicted remaining power, first, the charging determination component 12 extracts the coordinates of the mission target point or analyzes the end point of the preset flight path by analyzing the task data of the current task, and obtains the terminal position of the first drone when the current task is completed. At the same time, the return landing position of the first drone is obtained, and the return landing position includes a charging area and a battery replacement area to charge the drone, which is a pre-set fixed point. Then, the charging determination component 12 calculates the return power threshold of the first drone based on the obtained terminal position and return landing position. Specifically, first calculate the distance from the terminal position to the return landing position, then estimate the flight time in combination with the average flight speed of the target drone, and finally combine the average energy consumption rate of the drone to obtain the minimum power required to complete this flight, that is, the return power threshold. After the calculation is completed, the predicted remaining power is compared with the return power threshold. When the predicted remaining power meets (is greater than or equal to) the return power threshold, the charging determination component 12 directly determines the moment when the current task of the first drone is completed as the best charging time, and selects the fast charging method as the charging method. In this case, the drone is able to complete its mission safely and return to recharge.

[0059] However, when the predicted remaining power does not meet the return power threshold, the charging determination component 12 starts the task execution position backtracking process. Starting from the end position, each position point is checked step by step along the preset flight path. For each position point, the power required from the point to the return landing position is calculated and compared with the estimated remaining power at the point. When the first position point that meets the conditions is found, it is determined as the target return position. Subsequently, the charging determination component 12 determines the moment when the first drone arrives at this target return position as the best charging time, and selects the battery replacement method as the charging method.

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

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

[0062] Determine the flight trajectory information of the first drone according to the current power information and the current mission information of the first drone, wherein the flight trajectory information includes the position, speed and power data of the first drone during the execution of the current mission;

[0063] Based on the termination position, reversely traverse the flight trajectory information to obtain the power data of the first UAV at each position point;

[0064] For each first position point traversed, calculate the power required for the first UAV to fly from the first position point to the position point where the return flight lands;

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

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

[0067] When the power data of the location point is less than the power required by the location point, continue to traverse the next location point until the target return location is found.

[0068] In a preferred embodiment, when the first UAV performs task execution position backtracking according to the termination position and determines the target return position that can reach the return landing position, first, the charging determination component 12 determines the flight trajectory information of the first UAV according to the current power information and current task information of the first UAV, and specifically, analyzes the task planning data and real-time flight data in the current power information and the current task information to generate a detailed trajectory information set containing the position, speed and power data of the UAV during the execution of the current task. Next, the charging determination component 12 reversely traverses the flight trajectory information starting from the termination position. In this process, the power data of the first UAV at each position point is obtained one by one according to the preset time or distance interval. Then, for each traversed position point, the charging determination component 12 calculates the power required for the first UAV to fly from the 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, the average energy consumption rate of the UAV, and combining real-time environmental data (such as wind speed and temperature), the required power of each position point is obtained. After the calculation is completed, the charging determination component 12 compares the power data of the position point with the calculated power required for the position point. This comparison process is performed point by point, aiming to find the farthest safe return point. When the power data of a certain location point is greater than or equal to the power required for the point, the charging determination component 12 determines the location point as the target return position, which means that from this point on, the drone has enough power to safely return to the charging area or battery replacement area. If the power data of the location point is less than the power required for the location point, the charging determination component 12 will continue to traverse the next location point 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 drone 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] Setting a wireless charging platform in the charging area;

[0072] Controlling the target UAV to land on the wireless charging platform;

[0073] Energy is transferred by coupling between the receiving coil on the wireless charging platform and the charging coil on the target UAV, thereby realizing wireless charging of the target UAV.

[0074] In a preferred embodiment, in order to further realize the energy supply and convenient charging of the drone, the fast charging method uses a wireless charging method. First, a wireless charging platform is set up in the charging area to ensure that the drone can land safely and stably. When the target drone needs to be quickly charged, the target drone is controlled to land accurately on the wireless charging platform. For example, a visual positioning system or other precise navigation technology is used to ensure that the drone can accurately align with the charging position. After the drone successfully lands, an electromagnetic coupling is formed between the receiving coil on the wireless charging platform and the charging coil pre-installed on the target drone, allowing energy to be efficiently transmitted from the wireless charging platform to the drone. During the charging process, the energy transmission efficiency and the charging status of the drone battery are continuously monitored, and the charging parameters are adjusted when necessary to ensure the safety and efficiency of the charging process. Through the wireless charging method, the target drone can be charged quickly and conveniently, and the entire charging process can be completed without human intervention, which not only improves the charging efficiency, but also enhances the automation and operational flexibility of drone charging.

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

[0076] Use large diameter optical fiber to transmit high power laser energy;

[0077] Using a beam splitter to split the high-power laser energy into multiple low-power light energies;

[0078] Projecting the low-power light energy onto a photocell matrix for photoelectric conversion;

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

[0080] In a preferred embodiment, a large-diameter optical fiber is used to transmit high-power laser energy. For example, a 500m large-diameter optical fiber with a fiber diameter of 440 microns is used to carry and transmit high-power laser energy. The purpose of selecting a large-diameter optical fiber is to meet the needs of long-distance transmission of high-power lasers while minimizing transmission losses. Next, a beam splitter is used to divide the transmitted high-power laser energy into multiple portions of low-power light energy. Specifically, a one-to-nineteen beam splitter is used to evenly divide the high-power light energy into nineteen portions of low-power light energy, so as to better manage and utilize energy. Then, these low-power light energies are projected onto a photocell matrix for photoelectric conversion. The photocell matrix is ​​composed of a plurality of efficient photoelectric conversion units, which can efficiently convert light energy into electrical energy, improve the efficiency of energy conversion, and enhance the stability and reliability of the system. Afterwards, the electrical energy after photoelectric conversion is transferred to the energy storage system through a boost module to power the wireless charging platform. The function of the boost module 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. At the same time, through beam splitting and matrix design, it can safely and efficiently process high-power energy, providing fast and reliable charging services for drones, which is suitable for drone operations in long-distance and complex environments, such as power line inspections, and improves the work efficiency and scope of application of drone systems.

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

[0083] In a preferred embodiment, when the target drone lands on the wireless charging platform for charging, a data transmission channel is established between the drone and the server through wireless communication. For example, through a 4G / 5G network or a dedicated wireless network, the stability and reliability of data transmission are ensured. Through wireless communication, the drone can upload all kinds of information collected during the mission to the server in real time, including high-resolution images, video data, infrared thermal imaging data, laser scanning data, etc. By charging and transmitting data in parallel, the working efficiency of the drone is maximized, and the charging time is used to complete the data transmission, ensuring the timeliness of the data.

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

[0085] The information collection component is used to obtain the current power information and current mission information of multiple drones, providing basic data for determining the optimal charging time and charging method for each drone. The charging determination component is used to determine the optimal charging time and charging method for each drone based on the current power information and current mission information. The charging methods include fast charging method and battery replacement method, realizing the intelligent and personalized charging strategy, and being able to formulate the optimal charging plan according to the specific situation of each drone. The target determination component is used to obtain the target charging time and target charging method of the target drone, where 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, and the charging strategy is concretized to a single drone to prepare for the subsequent execution stage. The fast charging execution component is used to control the target drone to the charging area when the target charging time is triggered if the target charging method is the fast charging method, and charge the target battery of the target drone according to the battery characteristics and remaining power of the target drone, realizing precise control of fast charging, and being able to optimize charging according to the specific situation of the drone. The battery swap execution component is used to control the target drone to the battery swap area when the target charging time is triggered if the target charging method is battery replacement. It quickly replaces the current battery on the target drone by optimizing the battery, providing another efficient charging method suitable for situations where work needs to be resumed quickly. Through intelligent scheduling and hybrid charging methods, efficient management of the drone charging process is achieved, improving drone charging efficiency and optimizing mission execution capabilities.

[0086] Any steps of the system described above may be stored as computer instructions or programs in an unlimited computer memory, and may be called and recognized by an unlimited computer processor to implement any system in the embodiments of the present application, without any unnecessary restrictions.

[0087] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these changes and variations.

Claims

1. An intelligent control system for charging a hybrid drone, characterized in that: include: An information collection component, which is used to obtain current power information and current mission information of multiple drones; A charging determination component, the charging determination component is used to determine the best charging time and charging method for each drone based on the current power information and the current mission information, the charging method including a fast charging method and a battery replacement method; A target determination component, the target determination component is used to obtain a target charging time and a target charging method of the target UAV, wherein the target charging time is an optimal charging time for the target UAV, and the target charging method is a charging method for the target UAV; A fast charging execution component, wherein the fast charging execution component is used to control the target UAV to a charging area when the target charging opportunity is triggered if the target charging mode is a fast charging mode, and to charge the target battery of the target UAV according to the battery characteristics and the remaining power of the target UAV; A battery replacement execution component is used to control the target UAV to a battery replacement area when the target charging opportunity is triggered if the target charging method is a battery replacement method, and to quickly replace the current battery on the target UAV by selecting a preferred battery.

2. The intelligent control system for charging a hybrid drone according to claim 1, characterized in that: The information collection component includes the following execution steps: Establish a correspondence between the identification information of the drone and its communication address in advance; According to the identification information of the multiple drones, query and obtain the communication addresses of the multiple drones from the corresponding relationship; Send power query commands and mission query commands to the communication addresses of multiple drones; Receive the current power information and current task information reported by each UAV, wherein the current power information is the power data obtained by each UAV through real-time detection by a power detection unit according to the received power query instruction, and the current task information is the task parameters obtained by each UAV from the task execution unit according to the received task query instruction.

3. The intelligent control system for charging a hybrid drone according to claim 1, characterized in that: The charging determination component includes the following execution steps: Traversing multiple drones, determining a first drone, and extracting current power information and current mission information of the first drone as first power information and first mission information; Determine a remaining mission amount and a power consumption rate of the first UAV based on the first power information and the first mission information; According to the remaining task amount and the power consumption rate, obtaining 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 according to the predicted remaining power.

4. The intelligent control system for charging a hybrid drone according to claim 3 is characterized in that: The charging determination component also includes the following execution steps: Get the current remaining power of the first drone; Calculating the remaining task execution time of the first UAV for executing the current task according to the remaining task amount of the first UAV; Calculate the estimated total power consumption of the first UAV within the remaining mission execution time according to the power consumption rate of the first UAV and the remaining mission execution time; The estimated total power consumption is subtracted from the current remaining power of the first drone to obtain the predicted remaining power of the first drone when the current task is completed.

5. The intelligent control system for charging a hybrid drone according to claim 3 is characterized in that: The charging determination component also includes the following execution steps: Obtaining the end position of the first UAV when the current mission is completed, and obtaining the return landing position of the first UAV, wherein the return landing position includes a charging area and a battery replacement area; Calculating a return power threshold of the first UAV according to 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 mission is determined as the best charging time, and the fast charging mode is determined as the charging mode; When the predicted remaining power does not meet the return power threshold, the mission execution position of the first UAV is backtracked according to the termination position to determine a target return position that can reach 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.

6. The intelligent control system for charging a hybrid drone according to claim 5, characterized in that: The charging determination component also includes the following execution steps: Determine the flight trajectory information of the first drone according to the current power information and the current mission information of the first drone, wherein the flight trajectory information includes the position, speed and power data of the first drone during the execution of the current mission; Based on the termination position, reversely traverse the flight trajectory information to obtain the power data of the first UAV at each position point; For each first position point traversed, calculate the power required for the first UAV to fly from the first position point to the position point where the return flight lands; Compare the power data of the location point with the power required by the location point; When the power data of the location point is greater than or equal to the power required by the location point, the first location point is determined as the target return location; When the power data of the location point is less than the power required by the location point, continue to traverse the next location point until the target return location is found.

7. The intelligent control system for charging a hybrid drone according to claim 1, characterized in that: The fast charging method is a wireless charging method, including: Providing a wireless charging platform in the charging area; Controlling the target UAV to land on the wireless charging platform; Energy is transferred by coupling between the receiving coil on the wireless charging platform and the charging coil on the target UAV, thereby realizing wireless charging of the target UAV.

8. The intelligent control system for charging a hybrid drone according to claim 7, characterized in that: Wireless charging of target drones, including: Use large diameter optical fiber to transmit high power laser energy; Using a beam splitter to split the high-power laser energy into multiple low-power light energies; Projecting the low-power light energy onto a photocell matrix for photoelectric conversion; The electrical energy converted from photoelectricity is transferred to the energy storage system through the boost module to power the wireless charging platform.

9. The intelligent control system for charging a hybrid drone according to claim 7, characterized in that: While the target UAV is being wirelessly charged, the information collected by the target UAV is uploaded to the server via wireless communication.

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