Sharing management method and system

Abstract

The invention provides a sharing management method and system, and relates to the technical field of sharing. The method comprises the steps that a plurality of unmanned aerial vehicle storage bins are spliced to form at least one shared unmanned aerial vehicle storage station, each unmanned aerial vehicle storage bin is used for containing at least one shared unmanned aerial vehicle, and at least one charging mode of battery replacement, wired charging and wireless charging of the shared unmanned aerial vehicles is set; each shared unmanned aerial vehicle storage station comprises three charging modes of battery replacement, wired charging and wireless charging; and in response to the situation that the first shared unmanned aerial vehicle is to be stored in the first shared unmanned aerial vehicle storage station, selecting a first unmanned aerial vehicle storage bin with a first charging mode in the first shared unmanned aerial vehicle storage station to store the first shared unmanned aerial vehicle according to the remaining electric quantity of the first shared unmanned aerial vehicle and the predicted shared flight task, the first shared unmanned aerial vehicle is charged in a first charging mode, and the first charging mode is one of battery replacement, wired charging and wireless charging. According to the invention, unmanned aerial vehicle sharing management is realized.

Description

Technical Field

[0001] The present application relates at least to the field of sharing technology, and in particular to a sharing management method and system. Background Art

[0002] The existing sharing economy model has yet to establish a complete intelligent operating system for drones. Regarding shared drone management, given the high power consumption of drones during flight missions, how to provide the required power within a sharing station is a pressing technical challenge in this area. Summary of the Invention

[0003] In response to the above-mentioned shortcomings, this application provides a sharing management method and system to solve the following technical problem: how to provide power to drones in a sharing station in a timely manner to meet the requirements of shared flight missions.

[0004] In a first aspect, the present application provides a sharing management method, the method comprising:

[0005] Several drone storage compartments are spliced ​​together to form at least one shared drone storage station. Each drone storage compartment is used to accommodate at least one shared drone and is equipped with at least one charging method for the shared drone: battery replacement, wired charging, or wireless charging. Each shared drone storage station includes three charging methods: battery replacement, wired charging, and wireless charging.

[0006] In response to the first shared drone being stored in the first shared drone storage station, based on the remaining power of the first shared drone and the predicted shared flight mission, the first drone storage warehouse with a first charging method of the first shared drone storage station is selected to store the first shared drone, and the first shared drone is charged using the first charging method, which is one of battery replacement, wired charging, and wireless charging.

[0007] Furthermore, in response to the first shared drone being stored in the first shared drone storage station, according to the remaining power of the first shared drone and the predicted shared flight mission, a first drone storage compartment with a first charging method in the first shared drone storage station is selected to store the first shared drone, and the first shared drone is charged using the first charging method, where the first charging method is one of battery replacement, wired charging, and wireless charging, specifically including:

[0008] In response to the first shared drone being stored in the first shared drone storage station, the first shared drone sends its remaining power to the shared management server;

[0009] The shared management server obtains a predicted shared flight mission for the first shared drone based on the historical shared flight missions and the reserved shared flight missions of the first shared drone storage station, obtains a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement, and based on the remaining power, and sends the first charging method to the first shared drone storage station;

[0010] The first shared drone storage station selects a first drone storage compartment with a first charging method within the station, for storing the first shared drone and charging the first shared drone using the first charging method.

[0011] Furthermore, the first shared drone storage station selects a first drone storage compartment with a first charging method within the station for storing the first shared drone and charging the first shared drone using the first charging method, specifically including:

[0012] The first shared drone lands in the guide area of ​​the first shared drone storage station, and the first shared drone storage station uses a first robotic arm or a slide rail to send the first shared drone in the guide area into a selected first drone storage bin that has a first charging method and is idle;

[0013] If the first charging method is battery replacement, the first drone storage warehouse uses the second robotic arm to replace the battery for the first shared drone. If the first charging method is wired charging, the first drone storage warehouse uses the third robotic arm to insert the charging connector for the first shared drone. If the first charging method is wireless charging, the first drone storage warehouse uses the fourth robotic arm to align the first shared drone with the wireless charging coil.

[0014] Furthermore, the method further comprises:

[0015] Each shared drone storage station is equipped with a drone self-test unit. Each drone storage compartment is equipped with a drone self-test unit's battery interface status sensor, voltage / current detection module, high-precision gyroscope calibration device, propeller torque sensor, and flight control system data interface.

[0016] In response to the first shared drone being stored in the first drone storage bin, the drone self-test unit of the first shared drone storage station obtains the battery charge and discharge cycle count, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone and uploads them to the shared management server;

[0017] The sharing management server maintains or modifies the predicted shared flight mission of the first shared drone based on the number of battery charge and discharge cycles, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone.

[0018] Furthermore, the method further comprises:

[0019] In response to the prediction that the existing second drone storage bins of the second shared drone storage station do not meet / exceed the demand for storing the second shared drone, the shared management server issues an instruction to add / reduce several second drone storage bins for the second shared drone storage station.

[0020] Furthermore, the method further comprises:

[0021] In response to receiving the shared drone request, obtaining a third shared drone that is compatible with the shared drone request, and formulating shared flight requirements for the third shared drone that meet the shared drone request based on power and performance data of the third shared drone, meteorological data of the area in which the shared drone request is to be executed, and airspace management data;

[0022] During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data. In response to determining that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements based on the shared flight mission execution data, the third shared drone is controlled to stop executing the shared flight mission.

[0023] Furthermore, in response to receiving the shared drone request, a third shared drone that is compatible with the shared drone request is obtained, and based on the power and performance data of the third shared drone, the meteorological data and airspace management data of the area where the shared drone request is executed, a shared flight requirement for the third shared drone that meets the shared drone request is formulated, specifically including:

[0024] In response to the shared management server receiving a shared drone request, the shared management server obtains several third shared drone storage stations within the shared drone request execution area. The several third shared drone storage stations include third shared drones of models and quantities that meet the shared drone request. Based on the power and performance data of each third shared drone, the location of each third shared storage station, and the meteorological data and airspace management data of the shared drone request execution area, the flight path requirements of each third shared drone that meets the shared drone request are planned.

[0025] Furthermore, the third shared drone uploads shared flight mission execution data during the execution of the shared flight mission requested by the shared drone. In response to determining, based on the shared flight mission execution data, that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements, the third shared drone is controlled to stop executing the shared flight mission, specifically including:

[0026] Each third shared drone is used as a blockchain light node. During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data carrying the block header to the blockchain. In response to the shared management server determining that the third shared drone's execution of the shared flight mission exceeds its shared flight requirements based on the shared flight mission execution data obtained from the blockchain, the shared management server controls the third shared drone to fly to and store the data in the fourth shared drone storage station.

[0027] In a second aspect, the present application provides a sharing management system, the system comprising:

[0028] A shared drone storage station, comprising at least one shared drone storage station formed by splicing a plurality of drone storage compartments, each drone storage compartment being used to accommodate at least one shared drone and being provided with at least one charging method for the shared drone: battery replacement, wired charging, or wireless charging. Each shared drone storage station includes three charging methods: battery replacement, wired charging, and wireless charging.

[0029] The charging management module is connected to the shared drone storage station and is used to respond to the first shared drone to be stored in the first shared drone storage station. According to the remaining power of the first shared drone and the predicted shared flight mission, the module selects the first drone storage warehouse with a first charging method in the first shared drone storage station to store the first shared drone, and uses the first charging method to charge the first shared drone. The first charging method is one of battery replacement, wired charging, and wireless charging.

[0030] Furthermore, the charging management module specifically includes at least one of the following:

[0031] A sharing management server is configured to receive the remaining power of a first shared drone sent in response to the first shared drone storage station to be stored, obtain a predicted shared flight mission for the first shared drone based on the historical shared flight missions and reserved shared flight missions of the first shared drone storage station, obtain a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the sequence of wireless charging, wired charging, and battery replacement and the remaining power, and send the first charging method to the first shared drone storage station so that the first shared drone storage station selects a first drone storage compartment within the station that has the first charging method for storing the first shared drone and charging the first shared drone using the first charging method;

[0032] The first shared drone storage station is used to receive the first charging method sent by the shared management server, select the first drone storage warehouse with the first charging method in the station, and store the first shared drone and charge the first shared drone using the first charging method. The first charging method is that the shared management server receives the remaining power of the first shared drone in response to the first shared drone being stored in the first shared drone storage station, obtains the predicted shared flight mission of the first shared drone based on the historical shared flight missions and booked shared flight missions of the first shared drone storage station, and obtains the charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement and the remaining power.

[0033] The present application provides a sharing management method and system, which forms a shared drone storage station by splicing several drone storage compartments. Each drone storage compartment is provided with at least one charging method of battery replacement, wired charging, and wireless charging. It can flexibly obtain a combination deployment of multiple charging methods for each shared drone storage station. When a shared drone needs to be stored in a shared drone storage station, the drone charging method can be determined based on the drone power and the predicted drone flight mission. The charging method that can meet the predicted drone shared flight mission requirements is selected to charge the drone to be stored, so that when a shared flight mission is received, a shared drone that can perform the mission can be obtained in a timely manner, thereby improving the level of intelligent management of drone sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a sharing management method according to an embodiment of the present application;

[0035] Figure 2 It is a structural diagram of a shared management system according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solution of the present application, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0037] It should be understood that the specific embodiments and drawings described herein are only used to explain the present application, rather than to limit the present application.

[0038] It can be understood that, in the absence of conflict, the various embodiments and features in the embodiments of the present application can be combined with each other.

[0039] It will be understood that, for the sake of ease of description, the drawings of this application only show the parts related to this application, while the parts not related to this application are not shown in the drawings.

[0040] It can be understood that each module and unit involved in the embodiments of the present application may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple modules and units may be integrated into one physical structure.

[0041] It is understood that, in the absence of conflict, the functions and steps marked in the flowcharts and block diagrams of the present application may occur in an order different from that marked in the drawings.

[0042] It is understood that the flowcharts and block diagrams of the present application illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present application. Each box in the flowchart or block diagram may represent a module, unit, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based device that implements the specified functions, or by a combination of hardware and computer instructions.

[0043] It can be understood that the modules and units involved in the embodiments of the present application can be implemented by software or hardware, for example, the modules and units can be located in a processor.

[0044] Example 1:

[0045] like Figure 1 As shown, the present application provides a sharing management method, the method comprising:

[0046] S1. Assemble several drone storage compartments to form at least one shared drone storage station. Each drone storage compartment is used to accommodate at least one shared drone and is equipped with at least one charging method for the shared drone: battery replacement, wired charging, or wireless charging. Each shared drone storage station includes three charging methods: battery replacement, wired charging, and wireless charging.

[0047] S2. In response to the first shared drone being stored in the first shared drone storage station, according to the remaining power of the first shared drone and the predicted shared flight mission, select the first drone storage warehouse with a first charging method of the first shared drone storage station to store the first shared drone, and use the first charging method to charge the first shared drone, where the first charging method is one of battery replacement, wired charging, and wireless charging.

[0048] In this embodiment, the method forms a shared drone storage station by splicing several drone storage compartments. Each drone storage compartment is provided with at least one charging method of battery replacement, wired charging, and wireless charging. The combined deployment of multiple charging methods of each shared drone storage station can be flexibly obtained. When a shared drone needs to be stored in a shared drone storage station, the drone charging method can be determined based on the drone power and the predicted drone flight mission. The charging method that can meet the predicted drone shared flight mission requirements is selected to charge the drone to be stored, so that when a shared flight mission is received, a shared drone that can perform the mission can be obtained in time, thereby improving the level of intelligent management of drone sharing.

[0049] Specifically, this embodiment provides a sharing and management method for realizing the sharing of drones and the management of shared drones.

[0050] With the rapid development and widespread application of drone technology, the traditional model of individual purchase or corporate leasing presents numerous challenges: high equipment acquisition costs and low resource utilization. Users or businesses are required to purchase drones on their own, resulting in high equipment idleness and increased operating costs. Complex operational management hinders scalable deployment, as drone maintenance, charging, dispatching, and flight management still rely on manual operations, making it difficult to meet the demands of intelligent drone operations at the city level. Limited flight endurance hinders mission execution. Current drones primarily rely on batteries, resulting in short flight times and difficulty performing extended missions, leading to increased demand for frequent battery replacement and charging. Airspace security and regulation are challenging, as shared drones must operate in urban airspace, public facilities, and sensitive areas, potentially leading to flight safety issues such as unauthorized intrusions and collisions with obstacles. The shared model, through centralized management, on-demand usage, and intelligent dispatching, enables centralized storage, automatic charging, and intelligent maintenance, eliminating the need for user-generated equipment maintenance and lowering the barrier to entry, effectively addressing these issues.

[0051] The existing sharing economy model (such as shared bicycles, shared cars, shared power banks, etc.) has not yet formed a complete intelligent operation system in the field of drones. Therefore, there is an urgent need for an innovative shared drone system that can realize intelligent storage and access, remote scheduling, intelligent charging, task management and safety control of drones, improve resource utilization, reduce operating costs, and ensure flight safety. This embodiment relates to an intelligent drone management system based on the sharing economy model and its supporting devices, which has functions such as automatic storage and access, intelligent scheduling, remote monitoring, fast charging and safety control. It can be widely used in urban logistics, security patrols, agricultural operations, environmental monitoring, emergency rescue and other scenarios to achieve an efficient, safe and intelligent drone sharing model.

[0052] This embodiment first provides a modular storage and charging unit (drone storage warehouse, with at least one charging method), and provides a storage device that integrates drone storage, automatic retrieval, intelligent charging, and status detection. It supports the simultaneous operation of multiple drones and ensures efficient operation and maintenance of the equipment. An expandable modular storage unit (drone storage warehouse) is used to accommodate drones. Each storage unit has independent charging and management capabilities, and is centrally scheduled, task-distributed, and status-monitored by the cloud. It has both independence and logical unified management, taking into account flexibility and controllability. The cloud predicts the demand for drone shared tasks and allocates the number, charging method, and distribution location of storage units according to demand, providing fully charged shared drones that meet sharing needs at any time in each shared drone storage station.

[0053] In one embodiment, S2, in response to a first shared drone being stored in a first shared drone storage station, selecting a first drone storage compartment with a first charging method in the first shared drone storage station to store the first shared drone based on the remaining power of the first shared drone and the predicted shared flight mission, and charging the first shared drone using the first charging method, where the first charging method is one of battery replacement, wired charging, and wireless charging, specifically including:

[0054] In response to the first shared drone being stored in the first shared drone storage station, the first shared drone sends its remaining power to the shared management server;

[0055] The shared management server obtains a predicted shared flight mission for the first shared drone based on the historical shared flight missions and the reserved shared flight missions of the first shared drone storage station, obtains a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement, and based on the remaining power, and sends the first charging method to the first shared drone storage station;

[0056] The first shared drone storage station selects a first drone storage compartment with a first charging method within the station, for storing the first shared drone and charging the first shared drone using the first charging method.

[0057] Specifically, traditional drone storage typically uses fixed racks or manual access, making it difficult to adapt to large-scale sharing needs. Existing drone storage methods may result in large space occupation, low equipment access efficiency, and incompatibility with drones of different sizes. Traditional shared equipment relies on centralized cabinets for unified deployment and management, but has poor flexibility and high expansion costs. Traditional battery replacement requires manual operation, which increases labor costs and maintenance difficulties, and battery monitoring is inconvenient. Wired charging requires physical connection, is easily affected by the environment, and the contact interface may age or be damaged. Although wireless charging can reduce contact loss, its charging efficiency is low and it is difficult to meet high power requirements.

[0058] In this embodiment, expandable modular storage units are used, each capable of independently accommodating a single drone, avoiding the drawbacks of centralized management of traditional shared equipment and improving maintenance efficiency. The storage units utilize a modular design, each consisting of an independent, enclosed cabin equipped with internal positioning rails, charging ports, and sensor modules, capable of accommodating and charging a single drone. A distributed modular deployment allows each storage unit to independently charge and manage, with centralized cloud-based scheduling, task distribution, and status monitoring. This delivers both independence and logically unified management, balancing flexibility and controllability. A cloud-based battery management system monitors battery charge, temperature, and health status in real time, predicting battery life and optimizing charging strategies. A hybrid wireless and wired charging mode is employed, with wireless charging used for short missions and wired charging used for longer missions, balancing charging efficiency and flexibility. A dual charging strategy combines fast and slow charging, with fast charging used when the battery is low to improve availability and slow charging used when in storage mode to extend battery life.

[0059] The system uses the Battery Management System (BMS) to collect the following parameters in real time: battery voltage, remaining capacity (SOC), current, temperature, cycle count, and internal resistance change. Thresholds are set: when SOC ≤ 30%, it is considered low battery and triggers the fast charge strategy; when SOC ≥ 90%, or after a long period of inactivity, the slow charge strategy is activated.

[0060] Fast charging mode utilizes a high-voltage, high-current, constant current-constant voltage (CC-CV) charging strategy. The specific operation is as follows: Once the BMS confirms that the battery's health permits fast charging, the charger (or wireless charging controller) increases the output power (e.g., to 48V / 6A). Constant current charging is first performed to quickly increase the battery's charge to approximately 80%, followed by switching to constant voltage mode, which gradually reduces the current to prevent overcharging. The system implements real-time temperature and voltage protection during the charging process to ensure safety.

[0061] Slow charging mode uses a low-current constant-current charging strategy, suitable for long-term storage or when the battery is nearly fully charged. It maintains a low charging voltage (e.g., 24V / 1A) and a stable current, interspersed with trickle charging to maintain a constant charge level. This slow charging process helps reduce battery aging and extend cycle life.

[0062] In one embodiment, the first shared drone storage station selects a first drone storage compartment with a first charging method within the station for storing the first shared drone and charging the first shared drone using the first charging method, specifically including:

[0063] The first shared drone lands in the guide area of ​​the first shared drone storage station, and the first shared drone storage station uses a first robotic arm or a slide rail to send the first shared drone in the guide area into a selected first drone storage bin that has a first charging method and is idle;

[0064] If the first charging method is battery replacement, the first drone storage warehouse uses the second robotic arm to replace the battery for the first shared drone. If the first charging method is wired charging, the first drone storage warehouse uses the third robotic arm to insert the charging connector for the first shared drone. If the first charging method is wireless charging, the first drone storage warehouse uses the fourth robotic arm to align the first shared drone with the wireless charging coil.

[0065] In this embodiment, a robotic arm or slide rail storage and retrieval system enables drones to automatically return to their designated locations or retrieve them, reducing manual operation and improving sharing efficiency. The system utilizes a multi-degree-of-freedom robotic arm or electric slide rail platform, which adjusts its motion path based on the storage location and drone status. During automatic homing, after the drone lands in the guidance area, the robotic arm / slide rail precisely moves it to its designated location through positioning recognition (e.g., visual recognition + location tags). Retrieval involves the reverse process, pushing the drone to the takeoff platform or transport exit, all without manual intervention. An automatic battery swap mechanism is integrated within the storage station, enabling the robotic arm to automatically remove and replace drone batteries, reducing charging wait times. A standardized battery compartment design allows different drone models to use standardized batteries with the same interface, enhancing compatibility. Resonant wireless charging technology provides higher energy transfer efficiency (>90%) and enables fast wireless charging. A magnetic self-alignment system on the drone's base ensures automatic alignment of the charging coil, improving charging efficiency. A magnetic self-alignment system on the drone's base automatically aligns the charging coil on the charging pad after landing. This structure typically includes a magnetic positioning assembly and a guide structure (such as a tapered guide groove or flexible limiter) that automatically corrects position within a small deviation range. The key to identifying battery charge levels and enabling fast and slow charging, whether used with dedicated drones or modified with existing models, lies in the collaborative work of a battery management system (BMS) and an intelligent charging control unit.

[0066] In one embodiment, the method further comprises:

[0067] Each shared drone storage station is equipped with a drone self-test unit. Each drone storage compartment is equipped with a drone self-test unit's battery interface status sensor, voltage / current detection module, high-precision gyroscope calibration device, propeller torque sensor, and flight control system data interface.

[0068] In response to the first shared drone being stored in the first drone storage bin, the drone self-test unit of the first shared drone storage station obtains the battery charge and discharge cycle count, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone and uploads them to the shared management server;

[0069] The sharing management server maintains or modifies the predicted shared flight mission of the first shared drone based on the number of battery charge and discharge cycles, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone.

[0070] Specifically, traditional storage methods cannot effectively detect the status of drones, resulting in the possibility of faulty drones being accidentally accessed. Traditional storage stations are not adaptable to outdoor environments (high and low temperatures, humidity), which may affect the performance of drones and batteries. Drone storage stations may become targets of theft or vandalism, and existing security measures are relatively limited.

[0071] In this embodiment, a drone self-test system is integrated within the storage station to monitor battery status, propeller wear, and flight control system health during storage. AI (artificial intelligence) is combined with analysis of fault trends to predict potential problems in advance and reduce operational interruptions caused by sudden failures. The system has anomaly detection and early warning capabilities to prevent battery overcharging, overheating, aging, and other issues, thereby improving battery safety. A variety of detection and control devices are integrated within the storage station, including a battery interface status sensor, a voltage / current detection module, a high-precision gyroscope calibration device, a propeller torque sensor, and a flight control system data interface. Through short-term wired or wireless communication with the drone, it automatically collects key operating parameters such as the remaining battery charge, number of charge and discharge cycles, whether the propeller is loose or worn, and sensor deviation. At the same time, it cooperates with the central control module to run a built-in self-test program to perform software-level diagnosis of the flight control system status, enabling comprehensive self-testing, fault warnings, and data reporting for the drone while it is parked.

[0072] In this embodiment, an intelligent temperature control system is used to maintain an appropriate storage temperature through heating or cooling in extreme weather conditions (below -10°C or above 40°C). A built-in humidity sensor and air filtration system prevent humid environments from affecting the safety of the drone's circuits or batteries. The temperature control system specifically consists of three components: Temperature detection utilizes high-precision digital temperature sensors, such as the DS18B20 (a commonly used digital temperature sensor), an NTC (Negative Temperature Coefficient) thermistor, and a digital infrared temperature sensor. These sensors are located in various locations within the storage compartment, particularly near the battery compartment and charging module, to sample and monitor the internal compartment temperature in real time. The sensor data is transmitted to a central control module, which determines whether to enter temperature control mode. Heating utilizes PTC (Positive Temperature Coefficient) ceramic heaters or carbon fiber heating film. PTC ceramic heaters have self-limiting properties to prevent overheating, making them suitable for stable heating in small, enclosed spaces. Carbon fiber heating film, located on the inside of the compartment wall, offers the advantages of planar heating, light weight, and high thermal efficiency, making it suitable for bulk storage. When the temperature is lower than the set lower limit (such as -10°C), the system starts the heating device, and the control circuit is linked to the temperature sensor to adjust the power to achieve constant temperature control; the cooling method adopts TEC (Thermoelectric cooler) semiconductor refrigeration plate + fan heat dissipation module. When the temperature in the cabin exceeds the upper limit (such as 40°C), the system starts the refrigeration unit, uses the TEC refrigeration plate to absorb heat and cool down, and cooperates with the aluminum heat sink + turbo fan to quickly dissipate heat. The refrigeration plate is in close contact with the inside of the cabin through the thermal conductive silicone pad to improve the heat conduction efficiency. The control module performs PWM (Pulse Width Modulation) speed regulation according to the real-time temperature signal to adjust the fan speed or cooling intensity. The heating and cooling elements are integrated into the inner structure of the cabin and isolated from the battery to ensure safety. The temperature control system is integrated with the central control board and has communication functions such as over-temperature protection, heating / cooling interlock, and abnormal alarm.

[0073] In this embodiment, a multi-layered anti-theft design employs multiple authentication methods, including facial recognition, fingerprint recognition, and password unlocking, ensuring that the drone is only accessible to authorized users. Combining GPS (Global Positioning System), cellular networks, and encrypted communications, remote location and one-click flight bans are possible even if the drone is stolen. Authorized users are those who have undergone real-name authentication on the platform and obtained system access control, typically including registered users, enterprise administrators, and platform operations and maintenance personnel. During user registration or enterprise authorization, the system collects facial images and fingerprint information, or sets a unique password, and stores this information in an encrypted cloud database. Each time a shared drone is used, the user authenticates using a local identification device (such as a camera or fingerprint sensor). The system compares this information with pre-recorded information, and only after verification is the drone allowed to be operated, effectively preventing unauthorized operation. Remote location uses the drone's built-in GPS module to obtain real-time location information. This data is then transmitted to the cloud platform via a cellular network (such as 4G / 5G, fourth / fifth generation mobile communication technology) or an IoT communication module, allowing the platform to query its precise coordinates at any time. In the event of an abnormal situation (such as illegal operation, overflight, or loss), the system administrator can issue a "no-fly" command through the backend, and the device will immediately execute an emergency landing or return. To prevent signal hijacking, the process uses end-to-end encrypted communication protocols (such as TLS, Transport Layer Security) to ensure the security of data and commands.

[0074] In one embodiment, the method further comprises:

[0075] In response to the prediction that the existing second drone storage bins of the second shared drone storage station do not meet / exceed the demand for storing the second shared drone, the shared management server issues an instruction to add / reduce several second drone storage bins for the second shared drone storage station.

[0076] In this embodiment, multiple units are expanded in parallel through standardized interfaces, and the number of units can be flexibly increased or decreased according to usage requirements, which is convenient for deployment in application scenarios of different scales. The distribution of drone storage warehouses can be allocated among multiple shared drone storage stations by the cloud (an implementation method of a shared management server). The allocation is based on the predicted drone sharing demand of each site. It is also predicted that it can correspond to the predicted shared flight mission of the first shared drone obtained based on the historical shared flight missions and booked shared flight missions of the first shared drone storage station. The drone sharing demand is predicted based on the historical shared flight missions and booked shared flight missions of each shared drone storage station. The historical shared flight missions may include drone sharing data based on time periodic characteristics, including the number of times different types of drones are rented by sharers on different dates (such as weekdays, holidays, during major events or different seasons, etc.).

[0077] In one embodiment, the method further comprises:

[0078] In response to receiving the shared drone request, obtaining a third shared drone that is compatible with the shared drone request, and formulating shared flight requirements for the third shared drone that meet the shared drone request based on power and performance data of the third shared drone, meteorological data of the area in which the shared drone request is to be executed, and airspace management data;

[0079] During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data. In response to determining that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements based on the shared flight mission execution data, the third shared drone is controlled to stop executing the shared flight mission.

[0080] Specifically, traditional scheduling models usually use fixed task allocation or manual intervention scheduling, which cannot cope with complex high-frequency, multi-task, and multi-drone operating environments. Existing drone task scheduling systems lack real-time optimization capabilities and are prone to mission failures or delays due to emergencies (such as weather changes, restricted flight paths, and drone failures). Traditional drone scheduling systems are usually based on single-machine single-task, and cannot achieve efficient coordination between multiple drones, resulting in low operational efficiency. When multiple drones perform tasks at the same time, route conflicts and uneven resource allocation are prone to occur, affecting overall scheduling efficiency. In traditional centralized scheduling systems, scheduling rules are controlled by the platform, and there is a possibility of bias or human intervention.

[0081] In this embodiment, a machine learning algorithm analyzes historical mission data to intelligently predict mission requirements and optimally match drones based on factors such as mission type, drone status, distance, and weather conditions. Combined with real-time data analysis (such as wind speed, rainfall, and airspace traffic), drone missions are dynamically adjusted to prevent mission failures due to external environmental changes. Blockchain technology is used to build a decentralized drone mission management system, ensuring transparency in task allocation and data security. Each drone can act as a distributed computing node, autonomously coordinating task execution through smart contracts, reducing reliance on centralized servers. A multi-agent reinforcement learning algorithm enables multiple drones to perceive each other and collaboratively plan routes, avoiding flight conflicts and improving airspace utilization. A dynamic task priority adjustment mechanism prioritizes urgent tasks (such as medical emergencies) or high-value tasks (such as high-precision inspections), improving overall scheduling efficiency. The blockchain + smart contract mechanism ensures automated task distribution, unmanned operation, and process traceability. This significantly lowers the trust barrier between users and operators and prevents fraudulent task allocation.

[0082] In one embodiment, in response to receiving a shared drone request, a third shared drone that is compatible with the shared drone request is obtained, and shared flight requirements for the third shared drone that meet the shared drone request are formulated based on power and performance data of the third shared drone, meteorological data of the area in which the shared drone request is executed, and airspace management data. Specifically, the requirements include:

[0083] In response to the shared management server receiving a shared drone request, the shared management server obtains several third shared drone storage stations within the shared drone request execution area. The several third shared drone storage stations include third shared drones of models and quantities that meet the shared drone request. Based on the power and performance data of each third shared drone, the location of each third shared storage station, and the meteorological data and airspace management data of the shared drone request execution area, the flight path requirements of each third shared drone that meets the shared drone request are planned.

[0084] In this embodiment, a drone is considered the optimal match for a task when it simultaneously meets the following conditions during task scheduling: A healthy state, with sufficient battery charge (e.g., SOC ≥ 70%), a functioning flight control system, and no sensor anomalies or historical fault records; optimal geographical distance, with its current location or docking station being the shortest distance from the task's starting point, and no restricted airspace along the route; high task adaptability, with the drone's performance indicators, such as payload, endurance, and flight accuracy, fully meeting the task requirements (e.g., load capacity for logistics, high-resolution image transmission for inspection); and minimal resource conflict, with the drone not reserved or occupied by other high-priority tasks, and its free scheduling window closely matching the current task's time. Assume that the system calculates a matching score based on the weighted weights of these multi-dimensional data, and the highest-scoring drone is the optimal match.

[0085] In this embodiment, real-time data analysis is accomplished through multi-source data collection and algorithm model calculation, which is mainly divided into the following steps:

[0086] 1) Data collection and real-time input. Assume that the system can access the following data sources in real time: the weather service platform API (Application Programming Interface): to obtain information such as wind speed, wind direction, rainfall, temperature and humidity, and thunderstorms; the airspace monitoring system: to obtain the open status of the current flight area, temporary no-fly zones, and route congestion; the drone's own sensors: to obtain onboard information such as location, battery level, attitude angular velocity, and flight control health status; the task scheduling system data: including task type, time window, take-off and landing points, execution priority, etc.

[0087] 2) Multi-dimensional comprehensive evaluation model. Based on the above input, the system runs a task adaptability scoring model, which mainly includes the core dimensions shown in Table 1:

[0088] Table 1 Dimension table of UAV shared mission planning

[0089]

[0090] Each dimension is converted into a score of 0 to 1, and finally the weighted sum is calculated. The drone with the highest score is the most suitable drone to perform the task, and flight path restrictions are planned for each drone.

[0091] In one embodiment, the third shared drone uploads shared flight mission execution data while executing the shared flight mission requested by the shared drone. In response to determining, based on the shared flight mission execution data, that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements, controlling the third shared drone to stop executing the shared flight mission specifically includes:

[0092] Each third shared drone is used as a blockchain light node. During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data carrying the block header to the blockchain. In response to the shared management server determining that the third shared drone's execution of the shared flight mission exceeds its shared flight requirements based on the shared flight mission execution data obtained from the blockchain, the shared management server controls the third shared drone to fly to and store the data in the fourth shared drone storage station.

[0093] In this embodiment, the system reruns the matching model every 30 seconds or when it receives a state change event (such as a sudden increase in wind speed or an aircraft failure); if the score of the current task drone is lower than the fault tolerance threshold, the scheduling is automatically terminated, and other drones are reallocated or the task execution is delayed; all scheduling decision-making processes are synchronously written to the scheduling log for subsequent manual or AI training to optimize the scheduling strategy. In this way, the system realizes real-time evaluation, risk prediction and dynamic optimization of task scheduling, ensuring that drones always perform tasks efficiently under appropriate and safe conditions. The lightweight blockchain node function is integrated into each shared drone, enabling it to have the ability to participate in decentralized collaboration and task status on-chain. A trusted task management network is built through blockchain technology to provide a trusted collaboration mechanism in a complex shared environment with multiple drones, multiple users, and multiple parties operating.

[0094] In this embodiment, light nodes are first deployed. A lightweight blockchain client runs on each drone or its associated ground base station (such as a charging and storage station). This client provides basic block verification, transaction signing, task hash submission, and contract invocation capabilities. These nodes do not need to store the entire blockchain history; they only need to download the necessary block headers and state tree information (e.g., using the ETH Light Client or Fabric SDK), significantly reducing computational and storage burdens. During each mission creation, acceptance, execution, and completion phase, the relevant status is written to the blockchain in the form of a hash digest, forming an immutable record of mission execution. Tasks are issued using smart contracts and automatically invoke matching rules. The contracts contain information such as task requirements, time windows, execution conditions, and acceptable aircraft specifications. The entire mission issuance, execution, and completion process is recorded on-chain. Any node (including operators, users, and regulators) can access the mission process, preventing data tampering by operators and ensuring data transparency and traceability. Whether a flight mission was executed as scheduled, whether it was replaced mid-flight, whether the flight path deviated, or whether it was aborted prematurely can all be verified. Based on a shared state view on the blockchain, multiple drones can autonomously determine whether to accept a mission and coordinate its execution. In the event of a failure, route conflict, or priority change, drones can collaborate based on on-chain mission status and contract logic, rather than relying entirely on a central controller, enhancing their collaborative autonomy. Furthermore, as a trusted ledger, blockchain can provide regulatory bodies with a basis for flight audits.

[0095] Specifically, traditional drones rely on ground stations or Wi-Fi (mobile hotspots) for communication. Limited by network coverage, they struggle to support large-scale remote dispatch. During missions, the central server's computational load is excessive, resulting in long response times and impacting dispatch efficiency. Existing drone path planning algorithms, primarily based on static maps, struggle to cope with unexpected obstacles (such as birds, unauthorized drone flights, and weather emergencies).

[0096] In this embodiment, a 5G low-latency network is used for drone data transmission, ensuring real-time synchronization of mission instructions and flight status. Incorporating edge computing nodes, some computing tasks are distributed to base stations or relay servers near the drone, reducing the burden on the central server and improving response speed. Through a cloud-based control platform, operators can remotely monitor the drone's mission status and manually intervene in scheduling when necessary. Incorporating AR (augmented reality) technology, the drone's flight path, airspace information, and environmental data are displayed in real time on the control interface, enhancing the visualization of mission management. A hybrid algorithm based on A* and Dijkstra is used to adjust the flight path in real time, ensuring that the drone avoids no-fly zones, congested airspace, and dynamic obstacles. Incorporating visual SLAM (Simultaneous Localization and Mapping) technology allows the drone to continuously update its surrounding environment model during flight, improving path planning accuracy. Considering that complex urban environments (such as areas with dense high-rise buildings) can easily affect drone signal reception and increase flight risks, this solution integrates multiple sensors such as lidar, ultrasonic, millimeter-wave radar, and infrared cameras to achieve 360-degree real-time obstacle avoidance. Using deep learning algorithms, it analyzes the types of obstacles ahead (such as buildings, birds, and drones) and adopts the optimal avoidance strategy to ensure flight safety.

[0097] Specifically, traditional interaction methods lack natural and intuitive control methods, performing poorly in remote operations, special operation scenarios, or emergency rescue situations, resulting in low interaction efficiency. Drones' feedback mechanisms are relatively simple, making it difficult for users to obtain timely information about the drone's status. In complex flight environments or when signals are unstable, operational lags or control failures are prone to occur. Furthermore, traditional drone interaction interfaces are relatively fixed, making it difficult to adapt to the needs of different users. This lack of personalized optimization impacts the overall user experience. Therefore, there is an urgent need for a more intelligent, efficient, and flexible interaction system to improve operational convenience, safety, and controllability.

[0098] In this embodiment, a multimodal intelligent interaction system is proposed. Users can use an app (application) to schedule and unlock drones by scanning a QR code, monitor mission status in real time, and automatically settle accounts. The app utilizes voice recognition, gesture control, augmented reality (AR), virtual reality (VR), and AI adaptive optimization technologies to create a more natural, intuitive, and intelligent way to interact with and control drones. Regarding multimodal interaction, the system supports voice control, gesture control, and hybrid interaction modes. Voice control utilizes natural language processing (NLP) technology, allowing users to directly control the drone through voice commands, such as "take off," "land," "auto follow," and "return to base station." It also supports multi-round dialogue interaction and provides real-time feedback on drone status information, such as battery level, flight speed, and mission progress, improving operational convenience and intelligence. Gesture control utilizes computer vision and AI recognition technologies, allowing users to remotely control the drone through gestures, such as raising a hand to indicate takeoff, waving to indicate return, and pointing to a target for autonomous following. This contactless control method is particularly suitable for outdoor scenarios, security patrols, and emergency rescue operations, and can be combined with voice interaction to achieve more flexible control modes. The system integrates augmented reality and virtual reality technologies to provide users with an immersive interactive experience. Through smart glasses or mobile devices, users can view the drone's flight path, environmental information, mission status, and more in real time in the AR interface, and make adjustments through gestures or clicks to enhance their control and perception of the drone. VR mode is suitable for remote control. Wearing a VR headset, users gain a first-person perspective (FPV), providing more precise control capabilities for tasks such as remote inspections and disaster surveys, allowing operators to control the drone in an immersive remote environment. To further optimize the interactive experience, the system also features AI-powered personalized optimization, automatically adjusting the interface and control mode based on user operating habits and task requirements. For example, it provides simplified guidance for novice users while opening up custom functions for expert users. Furthermore, through cloud-based data analysis, the system can provide intelligent mission recommendations, such as optimizing flight paths based on historical mission data or proactively recommending optimal response plans when an anomaly is detected, thereby improving the efficiency and safety of the drone's mission execution. Through multimodal interaction, immersive experience, and AI adaptive optimization, the system significantly enhances the usability and intelligence of shared drones, making them more widely applicable in diverse application scenarios, including logistics and distribution, patrol and security, environmental monitoring, and agricultural plant protection. VR headsets are optionally supported depending on the usage scenario.

[0099] Specifically, existing remote operation and safety management of drones mainly rely on manual inspections and regular maintenance, which is inefficient and difficult to detect faults in a timely manner. This is especially true in shared drone mode, where the equipment is used frequently and long-term operation is prone to problems such as battery degradation, sensor failure, and flight control system anomalies, leading to unstable operations. In addition, traditional drone safety management methods are relatively passive and rely on manual monitoring by ground stations, making it difficult to respond to emergencies in a timely manner, such as hacker attacks, GPS signal interference, illegal intrusion into airspace, or emergency obstacle avoidance failures. At the same time, current drone supervision methods mostly use fixed rule settings and lack intelligent real-time analysis and prediction capabilities, making it difficult to dynamically adjust according to different flight environments and mission requirements. Therefore, a more intelligent, automated, and efficient remote operation and safety management system is needed to improve the operational stability, safety, and compliance of shared drones.

[0100] In this embodiment, an intelligent remote operation and maintenance and safety management system is proposed. Combining AI diagnosis, cloud-based monitoring, blockchain traceability, and active defense technologies, it enables full lifecycle management and real-time safety protection for drones. Regarding remote operation and maintenance, the system employs an AI-driven health monitoring and predictive maintenance mechanism. Using onboard sensors, it collects key data such as flight control system, battery status, and engine performance in real time. Based on big data analysis and machine learning models, it predicts potential drone failure risks. For example, by monitoring battery voltage profiles and charge / discharge cycle counts, battery degradation trends can be identified in advance and replacement schedules can be planned. Flight stability analysis can identify abnormal drift in sensors such as gyroscopes and accelerometers, preventing uncontrolled flight. Furthermore, the system supports cloud-based operation and maintenance management, automatically dispatching drones back to maintenance stations for self-inspection, repair, or replacement of worn components, significantly reducing human intervention costs and improving equipment availability. Regarding safety management, the system integrates intelligent defense and airspace monitoring technologies, leveraging AI algorithms to monitor drone flight paths, mission execution, and surrounding environments in real time, automatically identifying abnormal behavior and implementing proactive protective measures. For example, the system can detect GPS signal interference or forged signals, and ensure accurate positioning by integrating visual SLAM, inertial navigation and other technologies to prevent drones from getting lost due to GPS spoofing. At the same time, to address the risk of hacker intrusion, the system uses blockchain technology for data encryption and task log traceability to ensure the non-tampering of flight instructions and prevent malicious attacks. In addition, in terms of airspace management, the system combines geofencing and dynamic airspace authorization mechanisms to ensure that drones can only fly within legal areas, and automatically trigger return or alarm when approaching sensitive areas to avoid the risk of illegal flight. Combined with intelligent obstacle avoidance algorithms, the system can also optimize the drone's flight path based on real-time environmental perception to ensure safe flight in complex environments. Through this system, the remote operation and maintenance efficiency and safety assurance capabilities of shared drones can be effectively improved, providing more stable and reliable support for large-scale shared operations.

[0101] In this embodiment, blockchain, a distributed, tamper-proof digital ledger, records the entire process of mission creation, scheduling, execution, and completion, including hash digests and signatures. Each mission record is authenticated using a digital signature algorithm (such as ECDSA (Elliptic Curve Digital Signature Algorithm)), combined with symmetric encryption (such as AES (Advanced Encryption Standard)) to protect the transmission of mission data. Each mission change on the blockchain network is packaged as a transaction and broadcast to the blockchain network. Once written, it is immutable and can be retrieved at any time, forming a complete mission traceability chain. Its core advantages include: 1) preventing the platform from tampering with mission logs or misreporting execution status; 2) enhancing user trust in the platform, making it particularly suitable for government and enterprise users; and 3) supporting multi-party collaborative drone operations (e.g., multiple governments / enterprises sharing a fleet), eliminating the need for complete reliance on a central platform for scheduling. In the drone sharing system, blockchain acts as a trusted data infrastructure, ensuring that "who initiated the mission, who executed it, and whether there were any changes midway" are all verifiable records that can be checked on the chain, providing trusted support for drone scheduling, settlement, and supervision.

[0102] Specifically, existing drone automatic landing and docking systems are easily affected by factors such as wind speed, lighting, and terrain in complex environments, resulting in low landing accuracy and even possible yaw, overturning, or crashing. In addition, traditional landing methods mostly rely on GPS signals, but in urban environments with tall buildings or in areas with signal interference, GPS positioning has errors, affecting the accuracy and safety of landing. At the same time, when performing shared tasks, drones often need to frequently land at charging stations to charge or replace batteries, and existing docking methods mostly use fixed charging piles or manual battery replacement, which are inefficient and difficult to adapt to the compatibility requirements of different types of drones. Therefore, there is an urgent need for an automatic landing and docking system with high precision, strong environmental adaptability, and compatibility with multiple models to improve the operating efficiency and reliability of drones.

[0103] In this embodiment, a UAV automatic landing and docking system based on multi-source fusion positioning and intelligent recharging is proposed. This system combines visual SLAM (Simultaneous Localization and Mapping), adaptive infrared navigation, millimeter-wave radar sensing, and wireless charging technologies to achieve high-precision landing and efficient recharging. First, for precise positioning, the system utilizes multi-source fusion technology. When GPS signals are limited or unavailable, visual SLAM (Simultaneous Localization and Mapping) combined with millimeter-wave radar enables real-time perception of terrain, landmarks, and obstacles, automatically adjusting the landing trajectory. Furthermore, the system is equipped with infrared beacons and adaptive navigation algorithms to assist UAVs in precise docking in low-light or complex environments, ensuring stable landing even at night or in inclement weather. For docking and recharging, the system utilizes modular intelligent charging and battery swapping technology, supporting wireless charging, automatic battery replacement, and multi-model compatibility. For UAVs performing short missions, efficient wireless charging technology can be used, enabling rapid charging through electromagnetic induction without mechanical contact, reducing damage caused by aging plug-in connectors. For UAVs performing longer missions, the system provides a robotic-assisted automatic battery swapping function, enabling rapid battery replacement after landing, ensuring seamless flight and improving operational efficiency. The system also features AI-powered adaptive docking, adjusting the docking port position to suit different drone models. Mechanical fine-tuning automatically adapts to the charging ports of various drones, ensuring broad compatibility. Integrating with a cloud-based scheduling platform, the system intelligently assigns the optimal landing point based on the drone's remaining battery life, mission priority, and base station availability, improving recharging efficiency and ensuring the continuous operation of the drone swarm. Upon mission completion or on standby, drones automatically return to or fly to the nearest smart storage and charging station for a short stop and recharge. These charging stations, deployed and managed by the shared drone platform operator, are located at key urban nodes, rooftops, and campus corners, forming a distributed and scalable recharging network. Charging services are provided by the system's accompanying smart charging dock or wireless charging module, typically mounted at the bottom of a storage bay or on a separate landing platform. These docking docks integrate electromagnetic induction coils, positioning and alignment devices, temperature monitoring, voltage and current control chips, and communication modules. Upon landing, the drones automatically dock with the charging coils via a magnetic self-aligning structure on their undersides, enabling contactless and rapid charging. The system simultaneously collects charging current, voltage, temperature, and other data, uploading it to the cloud in real time. A central platform manages each charging event, battery status, and charging cycles. The platform intelligently dispatches different users' drones to charging stations on demand, improving the efficiency of charging stations and enabling the shared use of equipment, energy, and resources. It is the core infrastructure supporting the "take-and-go, charge-and-stop" drone service.

[0104] In this embodiment, an adjustable positioning and docking structure is provided to achieve automatic docking and charging for different types of drones. The following methods are used to adjust and control the drone's movement position: 1) Automatic identification of drone model: When a drone lands in the docking area or identification area, the system identifies its model and size parameters through visual recognition, RFID / QR code recognition, and wireless handshake protocol. 2) Mechanical adjustment of the interface position: After identification, the system activates the built-in movable docking component for precise adjustment. The charging coil or contact electrode is mounted on an electric slide rail, which can fine-tune the docking interface position horizontally to align it with the bottom interface area of ​​different drone models. The docking module height is automatically adjusted by an electric push rod or spiral lifting mechanism to adapt to drones with different chassis heights. If the drone fails to completely align with the landing point (the deviation does not exceed the safe range), the system can also remotely fine-tune the drone's position through the flight control system, sending a slight movement command (e.g., within ±5 cm left and right / front and back) to the drone's flight control system, using four-axis thrust to slowly move to the target point. Combined with visual or laser ranging feedback, closed-loop correction control is implemented until the docking accuracy requirements (e.g., error <2 cm) are met.

[0105] This embodiment proposes a city-level shared drone system, which breaks through the limitations of traditional drone leasing and operation management and has the following advantages: it supports users to scan codes to borrow and use on demand, reducing acquisition costs, improving equipment utilization, and realizing drone sharing; it adopts a modular intelligent storage warehouse design to realize automatic access, charging, battery management, status detection and other functions to ensure that drones are available at any time; based on AI algorithms, it optimizes drone allocation, task execution and path planning, realizes intelligent scheduling of drones, and improves operational efficiency; combined with 5G / IoT communications, it realizes remote monitoring, anomaly detection, emergency obstacle avoidance, blacklist management and other functions, realizes remote operation and maintenance and safety management, and improves flight safety; it supports multiple application scenarios such as urban logistics, security inspections, agricultural spraying, emergency rescue, environmental monitoring, etc., covering a wider range of industry needs.

[0106] Example 2:

[0107] like Figure 2 As shown, the present application provides a sharing management system, the system comprising:

[0108] A shared drone storage station 1 includes at least one shared drone storage station 1 formed by splicing a plurality of drone storage compartments, each of which is used to accommodate at least one shared drone and is provided with at least one charging method for the shared drone, including battery replacement, wired charging, and wireless charging. Each shared drone storage station 1 includes three charging methods: battery replacement, wired charging, and wireless charging.

[0109] The charging management module 2 is connected to the shared drone storage station 1 and is used to respond to the first shared drone to be stored in the first shared drone storage station 1. According to the remaining power of the first shared drone and the predicted shared flight mission, the first shared drone storage warehouse of the first shared drone storage station 1 with a first charging method is selected to store the first shared drone, and the first shared drone is charged using the first charging method. The first charging method is one of battery replacement, wired charging, and wireless charging.

[0110] In one embodiment, the charging management module 2 specifically includes at least one of the following:

[0111] The sharing management server is configured to receive the remaining power of the first shared drone sent in response to the first shared drone storage station 1 to be stored in the first shared drone storage station 1, obtain the predicted shared flight mission of the first shared drone based on the historical shared flight missions and reserved shared flight missions of the first shared drone storage station 1, obtain a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement and the remaining power, and send the first charging method to the first shared drone storage station 1 so that the first shared drone storage station 1 selects a first drone storage compartment with the first charging method within the station for storing the first shared drone and charging the first shared drone using the first charging method;

[0112] The first shared drone storage station 1 is used to receive the first charging method sent by the shared management server, select the first drone storage warehouse with the first charging method in the station, and store the first shared drone and charge the first shared drone using the first charging method. The first charging method is that the shared management server receives the remaining power of the first shared drone in response to itself being stored in the first shared drone storage station 1, obtains the predicted shared flight mission of the first shared drone according to the historical shared flight missions and booked shared flight missions of the first shared drone storage station 1, and obtains the charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission according to the order of wireless charging, wired charging, battery replacement and the remaining power.

[0113] In one embodiment, the management unit of the first shared drone storage station 1 specifically includes:

[0114] A guide storage unit is used to land the first shared drone in the guide area of ​​the first shared drone storage station 1. The first shared drone storage station 1 uses a first robotic arm or a slide rail to send the first shared drone in the guide area into a selected first drone storage bin with a first charging mode and an idle one;

[0115] The charging implementation unit is used to: if the first charging method is battery replacement, the first drone storage compartment uses the second robotic arm to replace the battery for the first shared drone; if the first charging method is wired charging, the first drone storage compartment uses the third robotic arm to insert the charging connector for the first shared drone; if the first charging method is wireless charging, the first drone storage compartment uses the fourth robotic arm to align the first shared drone with the wireless charging coil.

[0116] In one embodiment, the system further comprises:

[0117] Each shared drone storage station 1 is equipped with a drone self-test unit, and each drone storage compartment is equipped with a drone self-test unit battery interface status sensor, voltage / current detection module, high-precision gyroscope calibration device, propeller torque sensor and flight control system data interface;

[0118] In response to the first shared drone being stored in the first drone storage bin, the drone self-test unit of the first shared drone storage station 1 obtains the battery charge and discharge cycle count, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone and uploads them to the shared management server;

[0119] The sharing management server maintains or modifies the predicted shared flight mission of the first shared drone based on the number of battery charge and discharge cycles, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone.

[0120] In one embodiment, the shared management server is further configured to:

[0121] In response to the prediction that the existing second drone storage bins of the second shared drone storage station do not meet / exceed the demand for storing the second shared drone, the shared management server issues an instruction to add / reduce several second drone storage bins for the second shared drone storage station.

[0122] In one embodiment, the system further comprises:

[0123] a request management module connected to the charging management module 2, configured to, in response to receiving a shared drone request, obtain a third shared drone that is compatible with the shared drone request, and formulate shared flight requirements for the third shared drone that meet the shared drone request based on the power and performance data of the third shared drone, the meteorological data of the area in which the shared drone request is executed, and the airspace management data;

[0124] The flight management module is connected to the request management module and is used to upload the shared flight mission execution data when the third shared drone is executing the shared flight mission requested by the shared drone. In response to determining based on the shared flight mission execution data that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements, the third shared drone is controlled to stop executing the shared flight mission.

[0125] In one embodiment, the request management module is specifically configured to:

[0126] In response to the shared management server receiving a shared drone request, the shared management server obtains several third shared drone storage stations within the shared drone request execution area. The several third shared drone storage stations include third shared drones of models and quantities that meet the shared drone request. Based on the power and performance data of each third shared drone, the location of each third shared storage station, and the meteorological data and airspace management data of the shared drone request execution area, the flight path requirements of each third shared drone that meets the shared drone request are planned.

[0127] In one embodiment, the flight management module is specifically configured to:

[0128] Each third shared drone is used as a blockchain light node. During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data carrying the block header to the blockchain. In response to the shared management server determining that the third shared drone's execution of the shared flight mission exceeds its shared flight requirements based on the shared flight mission execution data obtained from the blockchain, the shared management server controls the third shared drone to fly to and store the data in the fourth shared drone storage station.

[0129] In addition, the present application can also provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, it implements some steps of the shared management method described in Example 1, or implements the shared management server or the first shared drone storage station described in Example 2.

[0130] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program elements or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0131] In addition, the present application may also provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor performs some steps of the shared management method described in Example 1. The computer device may be the shared management server or the first shared drone storage station described in Example 2.

[0132] The memory is connected to the processor, the memory may be a flash memory, a read-only memory or other memory, and the processor may be a central processing unit or a single-chip microcomputer.

[0133] Embodiments 1-3 of the present application provide a sharing management method and system, which forms a shared drone storage station by splicing several drone storage compartments. Each drone storage compartment is provided with at least one charging method of battery replacement, wired charging, and wireless charging. It is possible to flexibly obtain a combination deployment of multiple charging methods for each shared drone storage station. When a shared drone needs to be stored in a shared drone storage station, the drone charging method can be determined based on the drone power level and the predicted drone flight mission. The charging method that can meet the predicted drone shared flight mission requirements is selected to charge the drone to be stored, so that when a shared flight mission is received, a shared drone that can perform the mission can be obtained in a timely manner, thereby improving the level of intelligent management of drone sharing.

[0134] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present application, and the present application is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present application, and such modifications and improvements are also considered to be within the scope of protection of the present application.

Claims

1. A sharing management method, characterized in that: The method comprises: Several drone storage compartments are spliced ​​together to form at least one shared drone storage station. Each drone storage compartment is used to accommodate at least one shared drone and is equipped with at least one charging method for the shared drone: battery replacement, wired charging, or wireless charging. Each shared drone storage station includes three charging methods: battery replacement, wired charging, and wireless charging. In response to the first shared drone being stored in the first shared drone storage station, based on the remaining power of the first shared drone and the predicted shared flight mission, the first drone storage warehouse with a first charging method of the first shared drone storage station is selected to store the first shared drone, and the first shared drone is charged using the first charging method, which is one of battery replacement, wired charging, and wireless charging.

2. The method according to claim 1, characterized in that In response to a first shared drone being stored in a first shared drone storage station, based on the remaining power of the first shared drone and the predicted shared flight mission, a first drone storage compartment with a first charging method in the first shared drone storage station is selected to store the first shared drone, and the first shared drone is charged using the first charging method, where the first charging method is one of battery replacement, wired charging, and wireless charging, specifically including: In response to the first shared drone being stored in the first shared drone storage station, the first shared drone sends its remaining power to the shared management server; The shared management server obtains a predicted shared flight mission for the first shared drone based on the historical shared flight missions and the reserved shared flight missions of the first shared drone storage station, obtains a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement, and based on the remaining power, and sends the first charging method to the first shared drone storage station; The first shared drone storage station selects a first drone storage compartment with a first charging method within the station, for storing the first shared drone and charging the first shared drone using the first charging method.

3. The method according to claim 2, characterized in that The first shared drone storage station selects a first drone storage compartment with a first charging method within the station for storing the first shared drone and charging the first shared drone using the first charging method, specifically including: The first shared drone lands in the guide area of ​​the first shared drone storage station, and the first shared drone storage station uses a first robotic arm or a slide rail to send the first shared drone in the guide area into a selected first drone storage bin that has a first charging method and is idle; If the first charging method is battery replacement, the first drone storage warehouse uses the second robotic arm to replace the battery for the first shared drone. If the first charging method is wired charging, the first drone storage warehouse uses the third robotic arm to insert the charging connector for the first shared drone. If the first charging method is wireless charging, the first drone storage warehouse uses the fourth robotic arm to align the first shared drone with the wireless charging coil.

4. The method according to claim 2, characterized in that The method further comprises: Each shared drone storage station is equipped with a drone self-test unit. Each drone storage compartment is equipped with a drone self-test unit's battery interface status sensor, voltage / current detection module, high-precision gyroscope calibration device, propeller torque sensor, and flight control system data interface. In response to the first shared drone being stored in the first drone storage bin, the drone self-test unit of the first shared drone storage station obtains the battery charge and discharge cycle count, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone and uploads them to the shared management server; The sharing management server maintains or modifies the predicted shared flight mission of the first shared drone based on the number of battery charge and discharge cycles, voltage / current detection data, gyroscope calibration data, propeller torque, and flight control system data of the first shared drone.

5. The method according to claim 2, characterized in that The method further comprises: In response to the prediction that the existing second drone storage bins of the second shared drone storage station do not meet / exceed the demand for storing the second shared drone, the shared management server issues an instruction to add / reduce several second drone storage bins for the second shared drone storage station.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: In response to receiving the shared drone request, obtaining a third shared drone that is compatible with the shared drone request, and formulating shared flight requirements for the third shared drone that meet the shared drone request based on power and performance data of the third shared drone, meteorological data of the area in which the shared drone request is to be executed, and airspace management data; During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data. In response to determining that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements based on the shared flight mission execution data, the third shared drone is controlled to stop executing the shared flight mission.

7. The method according to claim 6, characterized in that In response to receiving a shared drone request, a third shared drone that is compatible with the shared drone request is obtained, and based on the power and performance data of the third shared drone, the meteorological data and airspace management data of the area where the shared drone request is executed, shared flight requirements for the third shared drone that meet the shared drone request are formulated, specifically including: In response to the shared management server receiving a shared drone request, the shared management server obtains several third shared drone storage stations within the shared drone request execution area. The several third shared drone storage stations include third shared drones of models and quantities that meet the shared drone request. Based on the power and performance data of each third shared drone, the location of each third shared storage station, and the meteorological data and airspace management data of the shared drone request execution area, the flight path requirements of each third shared drone that meets the shared drone request are planned.

8. The method according to claim 6, characterized in that The third shared drone uploads shared flight mission execution data during execution of the shared flight mission requested by the shared drone. In response to determining, based on the shared flight mission execution data, that the third shared drone's execution of the shared flight mission exceeds the shared flight requirements, the third shared drone is controlled to stop executing the shared flight mission, specifically including: Each third shared drone is used as a blockchain light node. During the process of executing the shared flight mission requested by the shared drone, the third shared drone uploads the shared flight mission execution data carrying the block header to the blockchain. In response to the shared management server determining that the third shared drone's execution of the shared flight mission exceeds its shared flight requirements based on the shared flight mission execution data obtained from the blockchain, the shared management server controls the third shared drone to fly to and store the data in the fourth shared drone storage station.

9. A shared management system, characterized in that: The system comprises: A shared drone storage station, comprising at least one shared drone storage station formed by splicing a plurality of drone storage compartments, each drone storage compartment being used to accommodate at least one shared drone and being provided with at least one charging method for the shared drone: battery replacement, wired charging, or wireless charging. Each shared drone storage station includes three charging methods: battery replacement, wired charging, and wireless charging. The charging management module is connected to the shared drone storage station and is used to respond to the first shared drone to be stored in the first shared drone storage station. According to the remaining power of the first shared drone and the predicted shared flight mission, the module selects the first drone storage warehouse with a first charging method in the first shared drone storage station to store the first shared drone, and uses the first charging method to charge the first shared drone. The first charging method is one of battery replacement, wired charging, and wireless charging.

10. The system according to claim 9, characterized in that The charging management module specifically includes at least one of the following: A sharing management server is configured to receive the remaining power of a first shared drone sent in response to the first shared drone storage station to be stored, obtain a predicted shared flight mission for the first shared drone based on the historical shared flight missions and reserved shared flight missions of the first shared drone storage station, obtain a first charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the sequence of wireless charging, wired charging, and battery replacement and the remaining power, and send the first charging method to the first shared drone storage station so that the first shared drone storage station selects a first drone storage compartment within the station that has the first charging method for storing the first shared drone and charging the first shared drone using the first charging method; The first shared drone storage station is used to receive the first charging method sent by the shared management server, select the first drone storage warehouse with the first charging method in the station, and store the first shared drone and charge the first shared drone using the first charging method. The first charging method is that the shared management server receives the remaining power of the first shared drone in response to the first shared drone being stored in the first shared drone storage station, obtains the predicted shared flight mission of the first shared drone based on the historical shared flight missions and booked shared flight missions of the first shared drone storage station, and obtains the charging method that can fully charge the first shared drone from the current time to the predicted execution start time of the shared flight mission based on the order of wireless charging, wired charging, and battery replacement and the remaining power.

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