Method, system and electronic device for sharing of drone flight resources

Abstract

Embodiments of the present application disclose a method and system for sharing of unmanned aerial vehicle flight resources and an electronic device. The method comprises receiving an interaction request and identifying a request type; obtaining a social relationship graph corresponding to a master terminal; if the request type is a takeover request, performing permission verification on the takeover request based on the social relationship graph, the takeover request carrying a user control instruction and being sent by a non-master terminal; in response to the permission verification passing, performing security constraint projection processing on the user control instruction to obtain a safe control instruction; controlling the unmanned aerial vehicle to perform a flight operation based on the safe control instruction to obtain takeover flight data and synchronizing the takeover flight data to a digital avatar bound to the unmanned aerial vehicle, the unmanned aerial vehicle being bound to the master terminal and the digital avatar being associated with historical flight data corresponding to the unmanned aerial vehicle; and if the request type is a sharing request, performing data sharing based on flight data associated with the digital avatar, the method supporting flexible cooperation control and data sharing among multiple parties.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) communication technology, and in particular to a method, system, and electronic device for sharing UAV flight resources. Background Technology

[0002] The core focus of existing unmanned aerial vehicle (UAV) systems lies in basic functions such as flight control execution, image transmission, and automatic obstacle avoidance. The flight data they generate is typically stored locally in discrete log files or specific formats (such as KML / GPX). This file-based data management method strongly links flight records to specific hardware devices. Once the equipment is replaced or the data is migrated, it is easy to lose or interrupt historical flight data. Moreover, when sharing data with other devices, only locally stored raw data can be transmitted one-to-one, making it difficult for the data recipient to effectively filter and reuse the massive amount of raw point cloud data.

[0003] Furthermore, the existing underlying communication protocols and control logic for remote control of drones are preset for a single operator and do not support the access of third-party control flows while maintaining the priority of the master operator. This makes it difficult to achieve secure and controllable command interaction and permission transfer at the physical link level in scenarios that require multi-person collaboration. Summary of the Invention

[0004] One objective of this application is to provide a method, system, and electronic device for sharing unmanned aerial vehicle (UAV) flight resources, in order to solve the technical problem that related technologies do not support multi-party collaborative control and data sharing.

[0005] In a first aspect, embodiments of this application provide a method for sharing unmanned aerial vehicle (UAV) flight resources, comprising: receiving an interaction request and identifying the request type of the interaction request; obtaining a social relationship graph corresponding to the master control terminal; in response to the request type being a takeover request, performing permission verification on the takeover request based on the social relationship graph, wherein the takeover request carries user control instructions and is sent by a non-master control terminal; in response to the permission verification passing, performing security constraint projection processing on the user control instructions to obtain security control instructions; controlling the UAV to perform flight operations based on the security control instructions to obtain takeover flight data, and synchronizing the takeover flight data to a digital clone bound to the UAV, wherein the UAV is also bound to the master control terminal, and the digital clone is associated with historical flight data corresponding to the UAV; and in response to the request type being a sharing request, performing data sharing based on the flight data associated with the digital clone.

[0006] Optionally, the step of verifying the takeover request based on the social relationship graph includes: performing a preliminary verification on the drone based on the social relationship graph; in response to the passing of the preliminary verification, generating a request random number corresponding to the takeover request and pushing a takeover request carrying the request random number to the master control terminal; acquiring sensor time-series data from the master control terminal; calculating the motion energy characteristics and motion morphology characteristics of the master control terminal based on the sensor time-series data, and performing a matching verification with a preset authorized motion template; in response to the passing of both the motion energy characteristics and motion morphology characteristics, determining that the permission verification has passed.

[0007] Optionally, the step of performing security constraint projection processing on the user control instruction in response to the successful permission verification to obtain a security control instruction includes: in response to the successful permission verification, obtaining the user control instruction from the non-master terminal and the original control instruction from the master terminal; obtaining an instruction arbitration model, the instruction arbitration model including a dynamic takeover weight, a security constraint projection operator, and a security boundary set, the dynamic takeover weight representing the weighting ratio of the user control instruction; using the instruction arbitration model, based on the dynamic takeover weight, performing weighted fusion of the user control instruction and the original control instruction, and projecting the fused user control instruction into the security boundary set based on the security constraint projection operator to obtain the security control instruction.

[0008] Optionally, the sharing request includes a flight path sharing request, and the flight path sharing request is sent by the master control terminal. The data sharing based on the flight data associated with the digital avatar includes: in response to the flight path sharing request from the master control terminal, converting the flight data requested to be shared in the flight path sharing request into a target format of original waypoint sequence and flight path associated metadata; performing a publishability verification on the original waypoint sequence in combination with preset publishing constraints; in response to the publishability verification passing, generating a shared flight path template, the shared flight path template including the original waypoint sequence, flight path associated metadata, geographic anchors, and discoverable range; publishing the shared flight path template to the information sharing community based on the social relationship graph; and in response to a drone of a non-master control terminal in the information sharing community entering the discoverable range corresponding to the geographic anchor, pushing the corresponding shared flight path template to the non-master control terminal.

[0009] Optionally, the method further includes: in response to the non-master terminal receiving the shared flight path template and triggering a follow-fly command, obtaining preset follow-fly constraints for the UAV corresponding to the non-master terminal; based on the preset follow-fly constraints, resampling the original waypoint sequence to generate candidate adaptation routes; obtaining a comprehensive cost evaluation model, wherein the comprehensive cost evaluation model performs weighted calculations based on the shape difference between the original waypoint sequence and the candidate adaptation routes, the energy consumption estimate of the candidate adaptation routes, safety risks, and motion smoothness as evaluation dimensions, and outputs the comprehensive cost value of the candidate adaptation routes; performing follow-fly constraint verification on the candidate adaptation routes, and in response to the follow-fly constraint verification passing, determining the candidate adaptation routes that minimize the comprehensive cost value as the target follow-fly routes, and the UAV of the non-master terminal performing flight operations based on the target follow-fly routes to complete the follow-fly task.

[0010] Optionally, it further includes: in response to the drone completing the follow-flying task on a non-master terminal, mapping the flight path data obtained from the follow-flying task to a unified geospatial grid cell to obtain gridded follow-flying trajectory data; in response to the follow-flying sharing command from the non-master terminal, publishing the follow-flying trajectory to the corresponding information sharing community and associating it with the corresponding shared flight path template; in response to the number of follow-flying trajectories of the shared flight path template exceeding a preset first threshold, calculating the heat value of each spatial grid cell associated with the shared flight path template based on a preset thermal evaluation index; in response to the completion of the update of the thermal values ​​of the entire spatial grid, aggregating all spatial grids to generate a thermal trajectory corridor; and publishing the thermal trajectory corridor to the corresponding information sharing community.

[0011] Optionally, the sharing request includes a borrowing request, and the data sharing based on the flight data associated with the digital avatar includes: responding to a borrowing request from a non-master terminal, the borrowing request including the requested view granularity, access time range, and access space range; verifying whether the information sharing community of the non-master terminal meets the preset borrowing authorization conditions; responding to the borrowing authorization verification passing, using the view granularity, access time range, and space range as the scope of a read-only token, and generating a cloud-signed read-only token; responding to the read-only token passing verification, intercepting and de-identifying the flight data according to the scope of the scope, and pushing the de-identified flight data to the non-master terminal.

[0012] Optionally, the sharing request includes a borrowing request, which converts the flight data associated with the digital avatar into shared flight information and performs data sharing based on the flight data associated with the digital avatar. This includes: responding to a borrowing request from a non-master terminal, the borrowing request including the requested view granularity, access time range, and access space range; verifying whether the non-master terminal or the information sharing community meets preset borrowing authorization conditions, verifying whether the view granularity meets read-only and anonymization constraints, and verifying whether the access time range and space range meet preset restrictions, wherein the preset borrowing authorization conditions are set based on the social relationship graph; responding to the successful borrowing authorization verification, using the view granularity, access time range, and space range as the scope of a read-only token, and generating a cloud-signed read-only token; responding to the successful verification of the read-only token, intercepting and anonymizing the flight data according to the scope, and pushing the anonymized flight data to the non-master terminal.

[0013] Optionally, the drone continuously synchronizes real-time status data to the corresponding digital clone. The method further includes: acquiring real-time status data of the drones corresponding to the main control terminal and the designated terminal based on the digital clones of the main control terminal and the designated terminal, respectively; calculating the predicted minimum distance, the time difference of arrival at the location of the predicted minimum distance, and the altitude difference based on the real-time status data of the drones corresponding to the main control terminal and the designated terminal; acquiring a multi-dimensional spatiotemporal risk assessment model, the multi-dimensional spatiotemporal risk assessment model including a risk feature mapping function; inputting the predicted minimum distance, the time difference of arrival, and the altitude difference into the multi-dimensional spatiotemporal risk assessment model, so that the multi-dimensional spatiotemporal risk assessment model calculates risk feature values ​​based on the risk feature mapping function, and calculates a collision risk score based on the risk feature values; and generating a collision risk warning and pushing it to the main control terminal in response to the collision risk score exceeding a preset second threshold.

[0014] Optionally, the flight data uploaded to the digital avatar includes drone trajectory sampling points, time data, spatial data, drone status data, and media metadata corresponding to the sampling points. The method further includes: in response to the digital avatar receiving the flight data, serializing the sampling points, the time data, spatial data, and drone status data corresponding to the sampling points to obtain a trajectory sampling sequence; segmenting the trajectory sampling sequence according to a preset time window constraint or spatial length constraint to generate several trajectory segments; calculating a hash integrity certificate corresponding to the trajectory segment based on the trajectory segment and the media metadata corresponding to the trajectory segment, and binding and storing the hash integrity certificate, the trajectory segment, and the media metadata to obtain stored flight data; in response to the operation of reading the stored flight data from the digital avatar, verifying whether the stored flight data is corrupted based on the hash integrity certificate; and in response to determining that the stored flight data is corrupted, pushing a data corruption warning to the main control terminal.

[0015] In a second aspect, embodiments of this application provide a drone flight resource sharing system for executing the drone flight resource sharing method described above, comprising: a storage unit for storing the digital clone; an access control unit for receiving interaction requests and identifying the request type of the interaction requests; obtaining a social relationship graph corresponding to the master control terminal; in response to the request type being a takeover request, performing access verification on the takeover request based on the social relationship graph; and an execution unit for performing security constraint projection processing on the user control instructions in response to the access verification passing, obtaining security control instructions; controlling the drone to perform flight operations based on the security control instructions, obtaining takeover flight data, and synchronizing the takeover flight data to the digital clone bound to the drone.

[0016] In a third aspect, embodiments of this application provide an electronic device, a microcontroller, configured to output an enable signal; as described above in the drone flight resource sharing system, the access control unit in the drone flight resource sharing system is electrically connected to the microcontroller.

[0017] The embodiments of this application can achieve the following technical effects: In the drone flight resource sharing method of this application embodiment, a single physical flight is transformed into a digital asset that can reside for a long time and be continuously updated through digital avatars, effectively solving the problem of flight records being difficult to accumulate; by identifying the type of interaction request and combining it with the social relationship graph for permission verification, precise interaction control can be achieved based on social relationships, providing a security foundation for social collaboration without the need for a pre-set whitelist; in the takeover scenario, security constraint projection processing is performed on user control commands to ensure flight safety during the takeover collaboration process, prevent unauthorized or dangerous operations, and achieve flexible collaborative control among multiple parties; synchronizing takeover flight data to the digital avatar ensures the integrity and continuity of the digital avatar's timeline, helps to provide reliable data basis, and can persist flight resources, thereby broadening the dimensions of social sharing. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A schematic diagram of the overall structure of a drone flight resource sharing system provided in this application embodiment; Figure 2 A schematic diagram of the overall structure of a drone flight resource sharing system provided for another embodiment of this application; Figure 3 A schematic diagram of the overall process of a method for sharing unmanned aerial vehicle (UAV) flight resources provided in another embodiment of this application; Figure 4 This is a schematic diagram of the structure of a computer device provided in another embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, all of which are within the protection scope of this application. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. Moreover, the terms "first," "second," and "third" used in this application do not limit the data or execution order, but only distinguish identical or similar items with essentially the same function and effect.

[0022] The core focus of existing unmanned aerial vehicle (UAV) systems lies in basic functions such as flight control execution, image transmission, and automatic obstacle avoidance. The flight data they generate is typically stored locally in discrete log files or specific formats (such as KML / GPX). This file-based data management method strongly links flight records to specific hardware devices. Once the equipment is replaced or the data is migrated, it is easy to lose or interrupt historical flight data. Moreover, when sharing data with other devices, only locally stored raw data can be transmitted one-to-one, making it difficult for the data recipient to effectively filter and reuse the massive amount of raw point cloud data.

[0023] Furthermore, the existing underlying communication protocols and control logic for remote control of drones are preset for a single operator and do not support the access of third-party control flows while maintaining the priority of the master operator. This makes it difficult to achieve secure and controllable command interaction and permission transfer at the physical link level in scenarios that require multi-person collaboration.

[0024] In view of this, please refer to Figure 1 This application provides a drone flight resource sharing system 100, comprising: Storage unit 200 is used to store the digital clone.

[0025] A digital clone refers to a digital mapping object of a drone in the cloud. It is a virtual object created and persistently stored in the cloud, associated with historical flight data generated by the drone's historical flight operations (such as trajectory, attitude, speed, timestamp, keyframe image metadata, flight events, etc.), and can subsequently add flight data (such as takeover flight data, flight data generated based on control commands from the main control terminal, etc.).

[0026] As an example, a connection is established between the digital avatar, the drone, and the master terminal (such as the master user's mobile device) through the digital avatar identifier Avatar-ID, the drone identifier Drone-ID, and the owner identifier Owner-ID. For example, when a friend requests to take over the drone through a non-master terminal, system 100 first finds the corresponding digital avatar based on the Avatar-ID in the request; then it reads the Owner-ID from the digital avatar to locate the master user and query their social relationship graph; subsequently, the bound Drone-ID can be used to accurately route security control commands to the corresponding drone for execution.

[0027] Understandably, storage unit 200 provides a data carrier that can exist long-term without physical devices, ensuring that flight records are not lost due to device replacement or power outages, and providing a data foundation for subsequent social authorization and resource sharing. It realizes the transformation of flight data from discrete files to structured digital assets, enabling physical flight experience to be accumulated, traced, and managed.

[0028] As an example, the storage unit 200 may include a digital clone object module 210, a timeline ledger module 220, a media metadata module 230, a distributed object storage module 240, and a spatiotemporal index module 250. The digital clone object module 210 manages and maintains data clones; the distributed object storage module 240 persistently stores flight data, which is divided into several trajectory segments based on a time window or spatial length, and these trajectory segments can also be bound to hash integrity credentials; the spatiotemporal index module 250 provides a service for spatiotemporally indexing flight data based on hashes and R-trees, so that subsequent units can read the required content; the timeline event ledger module records events occurring in the system 100 based on the timeline, such as borrowing, takeover, and authorization events of non-master terminals, to achieve full-link traceability and auditing of flight data; and the media metadata module 230 manages the image resource index associated with the flight trajectory, which can be borrowed or shared.

[0029] The permission control unit 300 is used to receive interaction requests and identify the request type of the interaction request; obtain the social relationship graph corresponding to the master terminal; and, in response to the request type being a takeover request, perform permission verification on the takeover request based on the social relationship graph.

[0030] Interaction requests refer to operation requests initiated by non-master terminals (such as a friend's mobile device), including takeover requests (such as briefly controlling the drone gimbal or briefly controlling the drone's flight) and sharing requests (such as requests to read flight data).

[0031] The social relationship graph is a data structure centered on the master terminal user, describing the social relationships between the master terminal user and other users (potential non-master terminal users) and groups. Social relationship types include friends, group members, public followers, blacklists, and interest communities joined. It is the foundation for all permission determinations based on social relationships (such as whether to take over, browse, or share). It can be automatically built and maintained through users' social behaviors within the application (adding friends, joining groups).

[0032] As an example, the request type can be identified by parsing specific fields or path parameters in the request.

[0033] As an example, please refer to Figure 2 In practical applications, the permission control unit 300 can be divided into a user and social layer 310 and a permission and control session layer 320. The user and social layer 310 includes an application used by the master user, which is the direct interface for the master user to interact with other users and other units. It can be used to receive and display interaction requests, and then send the interaction requests to the permission and control session layer 320. When the interaction request type is a sharing request, the user and social layer 310 can be used to send shared flight data to designated friends or communities, and can also display shared flight data to the master user.

[0034] The permission and control session layer 320 may include a policy and token module 321, a social relationship graph module 322, a takeover confirmation module 323, and an instruction proxy gateway module 324. Specifically, the social relationship graph module 322 can query the social relationship graph, and the policy and token module 321 can determine whether the interaction request has passed the pre-permission verification. The takeover confirmation module 323 can also add a function to allow the master user to confirm whether to authorize. If the pre-permission verification is passed and the master user authorizes, a short-term token is generated and then sent to the instruction proxy gateway module 324. The instruction proxy gateway module 324 verifies the short-term token and sends the user control instruction to the execution unit 100 after the token verification is successful.

[0035] The execution unit 100 is used to perform security constraint projection processing on the user control command in response to the successful permission verification, to obtain a security control command; based on the security control command, control the drone to perform flight operations, obtain takeover flight data, and synchronize the takeover flight data to the digital clone bound to the drone.

[0036] Safety constraint projection processing refers to mapping user control commands from non-master terminals (friends) to a preset set of safety constraints to ensure that flight operations resulting from friend takeover are confined within safety boundaries.

[0037] Safety control commands refer to control commands that have been projected and are confined within safety boundaries, ensuring that situations such as collisions with walls or stalling will not occur, thus preventing the occurrence of situations that exceed safety boundaries.

[0038] Takeover flight data refers to data such as flight trajectory, attitude, and operation logs generated during periods when the data is not under the control of the master terminal.

[0039] As an example, the execution unit 100 may include a flight control safety agent module 410, a UAV flight control module 420, an edge synchronization cache module 430, and an airborne image and gimbal module 440. The flight control safety agent module 410 is used to perform safety constraint projection processing, obtain safety control commands, and send them to the UAV flight control module 420. The UAV flight control module 420 is used to drive the UAV to perform flight operations based on the safety control commands. The edge synchronization cache module 430 is used to acquire UAV status data such as position, altitude, speed, battery level, gimbal angle, GPS, IMU, and mission status based on sensors and low-latency telemetry, and supports breakpoint resume. The airborne image and gimbal module 440 is used to adjust the gimbal attitude according to the received safety control commands, and is used to acquire image streams, extract key frames and their corresponding shooting parameters and spatial position information, and generate media metadata for storage unit 200 to record.

[0040] Understandably, the execution unit 100 not only meets the needs of social collaborative control of drones, but also ensures flight safety during takeover through the constraint means of safety constraint projection.

[0041] The sharing unit 500 is used to convert the flight data stored in the digital clone into shared flight information in response to the request type being a sharing request.

[0042] The access control unit 300 is also used for data sharing based on the flight data associated with the digital clone.

[0043] Sharing flight information refers to converting flight data associated with digital avatars into information formats suitable for dissemination on social networks, such as route stickers and anonymized data.

[0044] As an example, the sharing unit 500 may include a compliance and privacy module 510, a route sharing module 520, a shared trajectory generation module 530, a heat map corridor module 540, and a social collision warning module 550. The compliance and privacy module 510 is used to perform publishability verification on the data to be shared and to perform anonymization processing on the shared data that passes the publishability verification. The route sharing module 520 is used to convert the anonymized flight data to be shared into the target format and to perform resampling processing on the shared trajectory template when the master terminal decides to follow the flight based on the shared trajectory template (route sticker) published by the non-master terminal. The shared trajectory generation module 530... Block 530 is used to convert flight data in target format into shared flight track templates. Heat track corridor module 540 is used to aggregate heat values ​​of flight data based on shared flight track templates and provide a visualized heat track corridor to show the heat distribution and user preferences of shared flight tracks, providing route recommendation basis and flight safety reference for information sharing communities. Social collision warning module 550 is used to obtain real-time status data of the UAV and other UAVs associated with social networks based on the spatiotemporal index module 250 of storage unit 200, based on the location of the UAV, and to determine whether to push social collision warnings to the master terminal and non-master terminals based on the obtained data.

[0045] Understandably, the Shared Unit 500 transforms individual flight experiences into reusable resources for the community, making it easier for other users to refer to, follow, or challenge.

[0046] As an example, the storage unit 200 not only stores historical flight data corresponding to the drone's historical flight operations, but also receives takeover flight data returned by the execution unit 100, continuously enriching the content of the digital clone. It is understood that flight data generated by controlling the drone to perform flight operations through the main control terminal will also be synchronized to the digital clone. In addition, the storage unit 200 also provides a data source for the access control unit 300 and the sharing unit 500.

[0047] The permission control unit 300 connects external requests and internal execution. It is used to read social relationship information in the social relationship graph to determine whether permission verification is passed, and send execution permission to the execution unit 100 or data extraction instructions to the sharing unit 500 based on the determination result. It can also write events such as sending execution permission to the storage unit 200.

[0048] After obtaining permission from the authorization control unit 300, the execution unit 100 processes the execution instructions and controls the drone, while feeding back the data generated during the execution process to the storage unit 200.

[0049] According to the instructions of the access control unit 300, the sharing unit 500 extracts data from the storage unit 200 and converts it into shared flight information, which is then published to non-master terminals or communities, thereby completing the transformation from personal private data to social public resources. The shared flight information can also be sent to the storage unit 200.

[0050] This application embodiment provides a data foundation through storage unit 200, enabling persistent flight resources; through permission control unit 300, a flexible and secure authorization mechanism is established using social relationship graphs; through execution unit 100, collaborative sharing of UAV control rights is achieved under the premise of ensuring security, based on security constraint projection technology; through sharing unit 500, flight data is transformed into social assets, which can effectively solve the problem that existing technologies cannot securely share control resources and data resources based on social relationships, thereby improving the social value, collaborative efficiency, and security of UAV use.

[0051] Please see Figure 3 This application also provides a method for sharing unmanned aerial vehicle (UAV) flight resources, including: S1: Receive an interaction request and identify the request type of the interaction request.

[0052] S2: Obtain the social relationship graph corresponding to the main control terminal.

[0053] S3: In response to the request type being a takeover request, based on the social relationship graph, perform permission verification on the takeover request. The takeover request carries user control instructions and is sent by a non-master terminal.

[0054] A takeover request refers to a request initiated by a non-master terminal (i.e., a friend terminal) to gain partial or complete control of the drone for a period of time. This request includes the requester's identity, the target's digital avatar identifier, and the desired scope of control.

[0055] User control commands are instructions that the user expects the drone to perform, either implicitly or explicitly included in the takeover request (such as changing the gimbal angle or moving the drone's position).

[0056] Permission verification refers to the process by which the system verifies, based on preset rules, whether a non-controlling terminal initiating a takeover request is qualified to take over. The purpose is to ensure that only authorized friends can initiate a takeover under permitted conditions. Verification is based on social relationship graphs (e.g., determining if the user is a friend) and can also incorporate multi-dimensional evaluation criteria such as drone status (determining whether it is safe to take over) and user reputation (whether the user is on a blacklist).

[0057] Non-master terminals refer to user terminals other than the master terminal, usually belonging to the master terminal's friends or members of the same community.

[0058] It is understandable that, for drone A, the master control terminal is terminal A, and the non-master control terminals are terminals B, C, D, etc. For drone B, the master control terminal is terminal B, and terminal A is a non-master control terminal. That is, the master control terminal can change as the binding relationship between the master user and the terminal changes. This application does not specify a particular terminal as the master control terminal.

[0059] S4: In response to the successful permission verification, the user control instruction is subjected to security constraint projection processing to obtain a security control instruction.

[0060] S5: Based on the safety control command, control the drone to perform flight operations, obtain takeover flight data, and synchronize the takeover flight data to the digital clone bound to the drone. The drone is also bound to the main control terminal, and the digital clone is associated with the drone's corresponding historical flight data.

[0061] Takeover flight data refers to the flight data generated by the drone during the period when a friend takes over the flight, including the trajectory, attitude, and records of the control commands executed during the takeover period.

[0062] Binding refers to the association established between the drone and the main control terminal, or between the digital clone and the drone.

[0063] The master control terminal refers to a mobile device (such as a mobile phone or tablet) that is bound to the drone and has the highest control authority over the drone. It can be a mobile device bound to the master control user (such as the drone owner or main operator).

[0064] Historical flight data refers to data generated by a drone during one or more past flights, including trajectory sampling points (latitude, longitude, altitude, heading, speed, timestamp), flight status (battery level, GPS signal quality), flight events (takeoff, landing, obstacle avoidance), and corresponding image metadata. This data is synchronized to the digital avatar and can be considered historical flight data in subsequent operations.

[0065] S6: In response to the request type being a sharing request, data sharing is performed based on the flight data associated with the digital clone.

[0066] This application's embodiments transform a single physical flight into a digital asset that can reside long-term and be continuously updated through digital avatars, effectively solving the problem of flight records being difficult to accumulate. By identifying interaction request types and combining them with social relationship graphs for permission verification, precise interactive control based on social relationships can be achieved, providing a secure foundation for social collaboration. In takeover scenarios, user control commands are subjected to security constraint projection processing, ensuring flight safety during the takeover collaboration process and preventing unauthorized or dangerous operations. Synchronizing takeover flight data to the digital avatar ensures the integrity and continuity of the digital avatar's timeline, helping to provide reliable data support and enabling the persistence of flight resources, thereby broadening the dimensions of social sharing.

[0067] In one possible implementation, step S3, based on the social relationship graph, performs permission verification on the takeover request, including: S31: Perform preliminary verification on the drone based on the social relationship graph.

[0068] Pre-validation refers to validation performed before the main authority determination (i.e., confirmation by the controller), which can quickly filter out requests that obviously do not meet the conditions.

[0069] The pre-verification includes basic risk verification based on criteria such as application frequency (whether it is too frequent), friend relationships, compliance of flight area, drone battery level, GPS quality, obstacle avoidance status, and the drone's current mission type.

[0070] S32: In response to the passing of the pre-verification, generate a request random number corresponding to the takeover request, and push a takeover request carrying the request random number to the master control terminal.

[0071] The requested random number is an unpredictable string of values ​​that is uniquely associated with the current takeover request. This prevents replay attacks and serves as the sole credential for binding subsequent gesture confirmations to the takeover request. S33: Obtain sensor timing data from the main control terminal.

[0072] Sensor timing data refers to a continuous sequence of data collected in chronological order by the built-in sensors (such as accelerometers and gyroscopes) of the main control terminal (usually a mobile phone) over a period of time. The data sequence includes data such as triaxial acceleration and triaxial angular velocity.

[0073] S34: Based on the sensor timing data, calculate the motion energy characteristics and motion morphology characteristics of the main control terminal, and perform matching verification with the preset authorized motion template. In response to the fact that both the motion energy characteristics and motion morphology characteristics pass the matching verification, determine that the permission verification is passed.

[0074] Action energy characteristics and action morphology characteristics refer to the indicators used to quantify the intensity of dynamic force and the value of morphological characteristics of the terminal when the main control user makes a confirmation action (such as shaking the phone).

[0075] As an example, the confirmation gesture features (motion pattern features) are constructed using the mobile phone's accelerometer and gyroscope sequences, including:

[0076] In the formula, This represents the sequence of gesture sensors collected by the user's mobile phone. Indicates triaxial acceleration, Indicates the angular velocity of the three axes. Indicates the start time of the gesture. Indicates the length of the gesture capture window.

[0077] The system can calculate the shaking energy. (Motion energy characteristics), including:

[0078] In the formula, Represents the three-axis acceleration vector. Represents the gravitational acceleration vector. This represents the angular velocity weighting coefficient.

[0079] like And the dynamic time warping distance satisfies If the device signature, timestamp, and takeover request random number match (a preset authorization action template), then the gesture confirmation is considered valid, including:

[0080] In the formula, This indicates the shaking energy threshold. Indicates the dynamic time-warped distance. This indicates a preset or learned confirmation gesture template. The threshold representing the dynamic time warping distance. Indicates an indicator function, This represents the device signature verification function. This gesture indicates confirmation of the result.

[0081] In this embodiment, pre-verification filters out unnecessary interruptions and risky requests. The combination of request random numbers and gesture confirmation prevents the abuse of intercepted authorization tokens. Dual verification of action energy and form ensures that authorized actions must be consciously performed by a real human controller and conform to the agreed-upon form, effectively preventing accidental touches, misauthorization, or even false confirmations simulated by malicious software.

[0082] In one possible implementation, in response to the successful permission verification, step S4 performs security constraint projection processing on the user control instruction to obtain a security control instruction, including: S41: In response to the successful authorization verification, obtain the user control instructions from the non-master terminal and the original control instructions from the master terminal. The original control instructions include user control instructions preset by the master terminal or user control instructions that are actively intervened. S42: Obtain the instruction arbitration model, which includes dynamic takeover weights, security constraint projection operators, and a security boundary set. The dynamic takeover weights represent the weighting ratio of the user control instructions. S43: Using the instruction arbitration model, based on the dynamic takeover weight, the user control instruction and the original control instruction are weighted and fused, and the fused user control instruction is projected into the security boundary set based on the security constraint projection operator to obtain the security control instruction; Specifically, when the original control command is a user control command that the main control terminal actively intervenes in, the dynamic takeover weight approaches zero; when the original control command is a preset user control command, the dynamic takeover weight is determined according to the takeover range of the user control command.

[0083] As an example, let the user control command be... The original control command was The flight control safety boundary set is The instruction arbitration model includes:

[0084] In the formula, Indicates safety control commands. This represents a mapping function that projects instructions onto a set of security constraints; it also represents a security constraint projection operator. To implement dynamic takeover weighting, measures will be taken when the master terminal actively intervenes, the takeover risk increases, or the session expires. Approaching 0; when the session is valid and conflict-free, Values ​​can be selected based on the scope of control. Safety constraint set. This can include constraints related to speed, acceleration, altitude, distance, geofencing, and gimbal angle.

[0085] In the formula, and These represent the current speed and the maximum allowed speed, respectively. and These represent the current acceleration and the maximum permissible acceleration, respectively. , and These represent the current altitude, minimum altitude, and maximum altitude, respectively. Indicates the current geographical location. Geofencing indicating permitted flight zones , and These represent the gimbal angle, the minimum gimbal angle, and the maximum gimbal angle, respectively.

[0086] As an example, the scope of the user control commands can be divided into three types: First, only the virtual view is controlled, meaning that the friend can only view the cropped or replay view of the real-time video stream without affecting the drone; Second, only the gimbal and shooting parameters are controlled, meaning that the friend can adjust the gimbal angle, focal length, exposure, or start / stop recording; Third, limited flight control control is controlled, meaning that the friend can send movement, hovering, or detour commands within a limited altitude, speed, distance, geofence, and time window.

[0087] As an example, the user can use the application in the social layer 310 to display the identity of the requester, the scope of the takeover, the expected duration and the risk level of the takeover request to the master user. If the master user allows the takeover request, the request can be approved by shaking the phone, biometrics or equivalent gesture confirmation.

[0088] As an example, the flight control safety agent module 410 can authenticate, limit, throttle, and arbitrate each control command from a friend. For instance, if the master user and a friend input commands simultaneously, the system can override the friend's command based on the master user's priority. If low battery, loss of connection, approach to a no-fly zone, obstacle avoidance alarm, or takeover timeout occurs, the flight control safety agent immediately downgrades to hovering, returning to home, or takeover by the master user. This combined mechanism makes brief takeovers both socially collaborative and maintains flight control safety boundaries.

[0089] In this embodiment, a command arbitration model based on dynamic takeover weights and safety constraint projection is introduced to achieve fine-grained safety control in takeover scenarios. The dynamic weight mechanism grants the controller the ability to "reclaim control at any time," which is the core of collaborative safety, ensuring that the physical operator has the final decision-making power in any emergency. The safety constraint projection operator constructs an insurmountable physical safety fence, strictly limiting both accidental operation by a friend and intervention commands from the controller to the drone's own safe flight envelope, preventing the risk of loss of control or crash due to collaboration. This mechanism of "authorization but intervention allowed, sharing but guaranteed safety" provides a solid technical guarantee for social flight. In one possible implementation, the sharing request includes a flight path sharing request, and the flight path sharing request is sent by the master terminal. Step S6, which involves data sharing based on the flight data associated with the digital clone, includes: S601: In response to the track sharing request from the master terminal, the flight data requested to be shared by the track sharing request is converted into the original waypoint sequence and track-related metadata in the target format.

[0090] The target format raw waypoint sequence refers to the standardized point sequence format used internally by the system to describe flight routes. Each waypoint typically includes longitude, latitude, altitude, heading, speed, expected arrival time, and possible gimbal movements, used to uniformly process flight data from different sources.

[0091] Track-related metadata refers to descriptive or constraining information related to the flight path, such as images taken by the gimbal, the time the flight path was created, the original drone model, the weather at the time of recording, the difficulty level, recommended shooting locations, and author information.

[0092] S602: Combine the preset publishing constraints to perform a publishability verification on the original waypoint sequence, wherein the preset publishing constraints include preset UAV capability constraints, preset rule constraints and / or environmental constraints.

[0093] Unmanned aerial vehicle (UAV) capability constraints refer to the physical and performance limitations of UAVs, such as maximum flight time, maximum speed, maximum tilt angle, and maximum turning radius, used to assess whether a flight path is too difficult for certain UAVs.

[0094] Rule constraints refer to hard restrictions set by laws, regulations, or platform policies, such as no-fly zones, maximum flight altitudes, and whether airspace permits are required.

[0095] Environmental constraints refer to the geographical and meteorological characteristics of the area where the flight route is located, such as topographic relief, building density, current wind direction and speed, visibility, etc., which are used to determine the feasibility and risks of the flight.

[0096] If the original waypoint sequence satisfies all the constraints in the preset publishing constraints, then the publishing verification is deemed successful.

[0097] S603: In response to the successful publication verification, a shared track template is generated. The shared track template includes the original waypoint sequence, track-related metadata, geographic anchors, and discoverable range. The geographic anchors include spatial reference points selected by the master terminal, and the discoverable range is set based on the geographic anchors.

[0098] Geographic anchors refer to one or more key points on the Earth's surface where a flight path is located, used for positioning on a map and for nearby searches.

[0099] The discoverable area refers to a circular or polygonal region with a set radius centered on a geographic anchor point.

[0100] S604: Based on the social relationship graph, publish the shared trajectory template to the information sharing community.

[0101] Publishing to an information-sharing community means storing the shared track template in the community's public space or registering its index information in the community's search service. The community can be public (visible to all users) or private (visible to group members).

[0102] S605: In response to a drone that is a non-master terminal within the information sharing community entering the discoverable range, push the corresponding shared flight path template to the non-master terminal.

[0103] As an example, the shared track template can be in the form of a route sticker.

[0104] In this embodiment, publishability verification ensures that shared flight routes are safe and legal, reducing the overall risk of community flights from the source. Combined with geographic anchors and discoverability mechanisms, it transforms traditional file sharing, which requires active searching, into a location-based, proactively discovered social experience. When users arrive at relevant locations, they can automatically discover and obtain similar routes, which facilitates knowledge sharing, technical exchange, and creative interaction within the community.

[0105] In one possible implementation, it further includes: S611: In response to the non-master terminal receiving the shared flight path template and triggering a follow-fly command, obtaining the preset follow-fly constraints of the UAV corresponding to the non-master terminal.

[0106] S612: Based on the preset follow-fly constraints, the original waypoint sequence is resampled to generate candidate adaptation routes.

[0107] S613: Obtain the comprehensive cost assessment model, which calculates the comprehensive cost of the candidate adaptation route by weighting the shape difference between the original waypoint sequence and the candidate adaptation route, the energy consumption estimate of the candidate adaptation route, the safety risk, and the smoothness of the action as assessment dimensions, and outputs the comprehensive cost of the candidate adaptation route.

[0108] The comprehensive cost assessment model refers to a mathematical model used to quantitatively evaluate the merits of candidate adaptation routes.

[0109] S614: Perform a flight constraint verification on the candidate adaptation route. In response to the successful flight constraint verification, determine the candidate adaptation route that minimizes the comprehensive cost value as the target flight route. The UAV of the non-master terminal performs flight operations based on the target flight route to complete the flight mission.

[0110] As an example, suppose the original waypoint sequence is The flight-following constraints include the current drone capability constraints. Legal constraints and environmental constraints The generated candidate adaptation routes ,include:

[0111]

[0112] In the formula, Indicates the first One waypoint, This represents the number of waypoints in the original waypoint sequence. This represents the resampling function.

[0113] The comprehensive cost assessment model can be expressed as:

[0114] In the formula, This represents the objective function to be optimized. These represent the weight values ​​of each item. This indicates the shape difference; This represents the energy consumption estimate; This represents the aforementioned security risk, and also the normalized risk value; It represents the smoothness of motion corresponding to speed, heading, and gimbal, and also represents the cost of smoothness.

[0115] The regulatory constraints include:

[0116]

[0117] Current drone capability constraints include:

[0118] Environmental constraints include:

[0119] In the formula, These represent the minimum and maximum permitted heights, respectively. Indicates the candidate adaptation route in arc length The height of the location Indicates the curvature of the candidate adaptation route. Indicates the maximum permissible curvature. Indicates the speed of the candidate adaptation route. Indicates the maximum permissible speed. Indicates the power safety factor. Indicates the remaining battery power. Indicates assembly in the no-fly zone. This represents the empty set.

[0120] In this embodiment, based on preset follow-fly constraints, resampling processing, and a comprehensive cost evaluation model, the original waypoint sequence can be adaptively converted into a flight route that is suitable for the follow-flying drone and the follow-flying scenario, ensuring follow-flying safety under the condition of shared route.

[0121] In one possible implementation, it further includes: S621: In response to the drone, which is not a master terminal, completing the following flight task, mapping the trajectory data obtained from performing the following flight task to a unified geospatial grid cell to obtain gridded following flight trajectory data.

[0122] Gridded flight trajectory data refers to a spatial grid sequence or set formed by mapping each point in the original waypoint sequence to a unified geospatial grid unit based on its latitude and longitude coordinates.

[0123] A unified geospatial grid unit can be a unified geogrid unit or an H3 unit. H3 stands for Hexagonal Hierarchical Geospatial Index System, which is used to divide the Earth's surface into uniformly sized hexagonal grids.

[0124] S622: In response to the flight sharing command from a non-master terminal, the flight trajectory is published to the corresponding information sharing community and associated with the corresponding shared flight trajectory template.

[0125] The "follow-fly sharing" command refers to the user's action of actively choosing to share the flight trajectory to a social media group after completing a follow-fly, or it can be set to be automatically shared by default.

[0126] S623: In response to the number of following trajectories of the shared flight path template exceeding a preset first threshold, calculate the thermal value of each spatial grid cell associated with the shared flight path template based on a preset thermal evaluation index.

[0127] S624: In response to the completion of the global spatial grid thermal value update, aggregate all spatial grids to generate thermal trajectory corridors.

[0128] The completion of the global spatial grid thermal value update means that the thermal values ​​of all spatial grid cells within the geographical area covered by the shared track template have been calculated or updated.

[0129] S625: Publish the thermal trajectory corridor to the corresponding information sharing community.

[0130] Thermal trajectory corridors can be created by stitching together spatial grids according to geographical adjacency and visualized as a colored corridor shape, with shades of color (such as red, orange, and yellow) representing the intensity of thermal activity.

[0131] As an example, based on preset thermal evaluation indicators, the thermal values ​​of each spatial grid cell associated with the shared track template are calculated, including:

[0132] In the formula, Represents a grid or H3 cell The heat value, This refers to users who perform flight operations based on routes corresponding to shared flight path templates. Indicates the thermal aggregation weight. , , 、 Representing users respectively No. The corresponding metrics for each operation include route completion, keyframe quality or video stability, community interaction metrics, and risk event metrics. Represents the distance decay kernel function. Represents a grid or H3 cell To users No. Distance of the secondary trajectory Indicates user No. The track corresponding to this operation.

[0133] Among them, the community interaction index represents the popularity value corresponding to interactive behaviors such as likes and favorites. The higher the popularity value, the more popular the route is. The risk event index is used to punish flight records with risks. The popularity value can also be used to generate challenge rankings and official recommended routes.

[0134] As an example, the system parses and standardizes the flight path before publishing the route sticker. For KML / GPX files, the system converts latitude, longitude, altitude, timestamp, heading, and extended tags into a unified waypoint model (the original waypoint sequence and track-related metadata in the target format), and performs a publishability verification based on current regulatory data, no-fly zones, altitude restrictions, terrain, weather, battery level, and drone performance. If the route poses a risk, the system suggests lowering the altitude, cropping, waiting for weather conditions, or publishing only as a viewing route. If publishable, the system generates a route sticker and registers the geographic anchor point and discoverable range in the spatiotemporal index module 250. When a user subsequently enters this geographic area, the app can discover nearby route stickers based on the current location. After the user selects one-click follow-fly, the system resamples and adapts the flight path according to the current drone capabilities, including waypoint spacing, maximum speed, maximum acceleration, minimum turning radius, gimbal motion smoothness, and battery budget. The adapted flight path can also undergo a secondary review through the flight control safety agent module 410. After the second review is passed, the corresponding flight operation is executed. The system uploads the flight record, completion rate, trajectory deviation and keyframe metadata, and updates the heat map trajectory corridor. It can be applied to scenarios such as drone racing forums, film and television shooting collaborations and official aerial photography routes for scenic spots.

[0135] In this application embodiment, individual, scattered follower flight behaviors are transformed into intuitive and valuable community public assets (heat trajectory corridors) through spatial aggregation and value assessment. The existence of heat corridors provides highly valuable flight guidelines for subsequent followers (e.g., which areas to focus on shooting in), while also incentivizing users to create higher-quality and safer follower flight records to contribute heat, greatly enriching the shared flight map and enhancing the community's activity and content value.

[0136] As one possible implementation, the sharing request includes a borrowing request, and step S6, which involves sharing data based on the flight data associated with the digital avatar, includes: S631: In response to a borrowing request from a non-master terminal, the borrowing request includes the requested view granularity, access time range, and access space range.

[0137] View granularity refers to the level of detail users expect to see in flight data.

[0138] S632: Verify whether the information sharing community of the non-master terminal meets the preset borrowing authorization conditions.

[0139] As an example, the preset borrowing authorization conditions include: preset read-only and de-identification constraints, preset restrictions corresponding to the access time range and space range.

[0140] S633: In response to the successful verification of the borrowing authorization, the view granularity, the access time range, and the spatial range are used as the scope of the read-only token, and a read-only token signed by the cloud is generated.

[0141] A read-only token is a security credential generated by a cloud server using a private key to digitally sign a document. It ensures that the token's origin is trustworthy and its content has not been tampered with.

[0142] S634: In response to the read-only token being verified, the flight data is intercepted and desensitized according to the scope of action, and the desensitized flight data is pushed to the non-master terminal.

[0143] As an example, for users Request to view digital clone In the time window and spatial range The verification of the trajectory view within the device includes:

[0144] In the formula, Indicates user Digital clone View access permission results Indicates view granularity. This indicates a request to verify the social relationship between the user and the owner of the clone, specifically a verification based on a social relationship graph. Indicates the owner of the digital clone (master terminal / user). It indicates view extent constraints, and also indicates preset read-only and desensitized constraints; and These represent the preset time limits and spatial constraints corresponding to the access time range, respectively. This indicates the conditions for blacklisting or blocking risks. This indicates logical NOT.

[0145] Read-only token include:

[0146] In the formula, This refers to the cloud signature function. Indicates the scope of the token's effect. Indicates the token's expiration time. This represents a one-time random number, used to prevent replay attacks; This represents the authorization policy hash, used to bind tokens to the policy version.

[0147] As an example, the read-only token is signed by the cloud policy service, and its validity can be verified by the flight record storage service and the command agent gateway.

[0148] This application's embodiments, based on the finely defined scope of the token, ensure that users are prevented from overstepping their authority, and provide privacy and performance guarantees through interception and desensitization processing, ensuring that sharing flight experiences on social networks can balance security and convenience.

[0149] In one possible implementation, the drone continuously synchronizes real-time status data to the corresponding digital clone. The method further includes: S71: Based on the digital clones of the main control terminal and the designated terminal, the real-time status data of the drones corresponding to the main control terminal and the designated terminal are obtained respectively; wherein, the designated terminal is a terminal belonging to the social relationship graph and is within a preset spatial retrieval range centered on the real-time location of the drone corresponding to the main control terminal.

[0150] Real-time status data refers to the dynamic information reported by the UAV at the current moment through the telemetry link, including position (latitude and longitude), altitude, speed, heading angle, flight mode, GPS signal quality, etc.

[0151] As an example, the master control terminal identifies which designated terminals are near the spatiotemporal index module 250.

[0152] S72: Based on the real-time status data of the UAV corresponding to the main control terminal and the designated terminal, calculate the predicted minimum distance, the time difference of arrival at the location of the predicted minimum distance, and the altitude difference.

[0153] S73: Obtain a multidimensional spatiotemporal risk assessment model, which includes a risk feature mapping function, as well as social visibility weights and trajectory prediction uncertainty weights.

[0154] S74: Input the predicted minimum distance, time arrival difference, and height difference into the multidimensional spatiotemporal risk assessment model so that the multidimensional spatiotemporal risk assessment model can calculate the risk feature value based on the risk feature mapping function, and calculate the collision risk score based on the risk feature value.

[0155] S75: In response to the collision risk score exceeding the preset second threshold, a collision risk warning is generated and pushed to the main control terminal.

[0156] As an example, the native drone Drones with Friends In the prediction time window The location within is predicted as and The predicted minimum spacing includes:

[0157] In the formula, This indicates the preset prediction time window. These represent the times of the two drones. The predicted location (obtained based on the first / second predicted trajectory). This represents the minimum distance within the prediction time window. This represents the Euclidean distance norm.

[0158] The time difference of arrival includes:

[0159] In the formula, This indicates the time difference between the arrival of the two aircraft at their closest point. They represent and The moment when the closest point is reached.

[0160] The multidimensional spatiotemporal risk assessment model includes:

[0161] In the formula, Indicates the collision risk score. This represents the Sigmoid function. This indicates the weight of each risk item. Indicates the horizontal or three-dimensional safety distance threshold. Indicates the safety time difference threshold. Indicates the current or predicted altitude of the two drones. Indicates the safety height difference threshold. Indicates social visibility weight, This indicates uncertainty in trajectory prediction. This indicates the warning trigger threshold (preset second threshold).

[0162] In this embodiment, the social collision warning does not replace airborne obstacle avoidance. Instead, it uses a social relationship graph, leveraging friend relationships, group visibility, and a spatiotemporal index module 250 to provide advance warnings of potential trajectory intersections, spatial proximity, or photographic interference from nearby friends' drones. The system only loads friend trajectories that meet the visibility policy and allows for privacy-granular control over precise locations. Based on short-term trajectory prediction and multi-dimensional spatiotemporal risk assessment, it can detect potential conflict risks earlier and more intelligently than simple distance warnings. Furthermore, based on social visibility weights and prediction uncertainty weights, the sensitivity can be dynamically adjusted according to user relationships and data quality, achieving a balance between effective warnings and avoiding false alarms, significantly improving overall safety and user experience in social flight environments.

[0163] In one possible implementation, the flight data uploaded to the digital avatar includes the drone's flight path sampling points, the time data corresponding to the sampling points, spatial data, drone status data, and media metadata.

[0164] Media metadata refers to information about the content captured by the camera / gimbal that is associated with a trackpoint or time period.

[0165] The method further includes: S81: In response to the digital clone receiving the flight data, the sampling point, the time data, spatial data and UAV status data corresponding to the sampling point are serialized to obtain a trajectory sampling sequence.

[0166] S82: The trajectory sampling sequence is segmented according to a preset time window constraint or spatial length constraint to generate several trajectory segments.

[0167] S83: Based on the trajectory segment and the media metadata corresponding to the trajectory segment, calculate the hash integrity certificate corresponding to the trajectory segment, and bind and store the hash integrity certificate, the trajectory segment and the media metadata to obtain the stored flight data.

[0168] A hash integrity credential is a digest value calculated using a cryptographic hash algorithm to verify data integrity and prevent tampering. For a trajectory segment, the hash value of the entire segment can be calculated. Furthermore, hash chains or Merkle trees can be used to make the verification of each point more refined.

[0169] S84: In response to the operation of reading the stored flight data from the digital clone, verify whether the stored flight data is corrupted based on the hash integrity certificate.

[0170] S85: In response to the determination that the stored flight data is corrupted, a data corruption warning is pushed to the main control terminal.

[0171] This application embodiment organizes massive, continuous flight data into discrete data that is easy to manage and verify based on serialization and segmentation processing. Moreover, based on the hash integrity certificate mechanism, it can promptly detect data corruption caused by problems in the transmission / storage process or by malicious tampering, thus ensuring data reliability.

[0172] As an example, suppose that during a single flight, the trajectory sampling sequence in the flight data corresponding to the UAV is... ,include:

[0173] In the formula, Indicates the first One trajectory sampling point, Indicates longitude, latitude, and altitude. Indicates the heading angle. , These represent speed and timestamp, respectively.

[0174] Each sampling point Each corresponds to a specific moment in the physical world's state of the drone. The digital clone object tracks the trajectory according to time windows. or spatial length Segmentation:

[0175] In the formula, Indicates the first Each trajectory segment, Indicates a segmented time window. This indicates the upper limit of the segment length. Indicates the length of the segmented trajectory. Indicates the first The hash value corresponding to each sampling point Represents a hash function. This represents the serialization result of the sampling points. Represents metadata associated with the sampling point (keyframe index, sensor status, etc.). Indicates the first Merkle root of each segment.

[0176] As an example, during drone flight, the drone's 3D trajectory, timestamps, attitude, speed, gimbal angle, keyframe image metadata, flight status, and safety status are collected by the drone itself, a mobile app, or an edge synchronization module, and uploaded to the cloud-based digital avatar object service. Secondly, the cloud uses Avatar-ID, Drone-ID, and Owner-ID as core identifiers to store trajectory segments, keyframe metadata, and flight events in a distributed object storage system by time period, and establishes geographic and temporal indexes in the spatiotemporal index service. Thirdly, the social relationship graph and policy token service generate read-only cloud access tokens, route loading tokens, or short-term takeover tokens based on friend relationships, groups, blacklists, geofences, time windows, privacy levels, and takeover scope.

[0177] In cloud-based browsing scenarios, a friend's app can only access the authorized time window and granular trajectory history view based on a read-only token, and cannot modify the digital clone's content. In short-term takeover scenarios, the friend's app submits a takeover request. After receiving the request, the original operator's (master user's) app confirms it by shaking the phone, using biometrics, or equivalent physical confirmation actions. The confirmation service verifies the mobile terminal's IMU gesture characteristics, device signature, and timestamp, and then generates a short-term takeover session token. Based on this, the command proxy gateway allows the friend to control the gimbal, virtual view, or limited flight control within a limited range. The drone's flight control security agent performs authentication, amplitude limiting, throttling, conflict arbitration, and security degradation on each command, ensuring that the original operator can regain control at any time.

[0178] In route sharing scenarios, users can publish their historical trajectories, KML, or GPX routes as route stickers visible to public communities or groups. Route stickers include geographic anchors, waypoint sequences, gimbal actions, airworthiness conditions, no-fly zone verification results, validity period, author information, and popularity metrics. Subsequent users passing through that geographic area can discover the route sticker through their apps, and the system resamples and adapts the route for safety based on the current drone model, endurance, turning radius, altitude restrictions, and weather conditions, enabling users to "fly the same route as friends with one click." After multiple users complete a route challenge, the system aggregates completion rates, trajectory deviations, and keyframe metadata into a heatmap trajectory corridor, forming a new type of UGC flight content.

[0179] As an example, in a specific implementation scenario, assume user A (corresponding to the main control terminal) owns drone D1 and is conducting aerial photography in a scenic area. User A controls drone D1 to take off, and the drone flight control module 420 collects real-time status data such as D1's 3D trajectory, timestamp, gimbal angle, and attitude. This data is uploaded to the cloud in real time. After receiving the data, the cloud-based digital avatar residing layer (storage unit 200) stores the data in distributed object storage according to time segments using the identifier corresponding to D1, and updates the digital avatar object.

[0180] At the same time, the spatiotemporal indexing service uses the received location information to build a real-time geographic index in the database, marking D1 as currently located in the northern slope area of ​​the scenic area.

[0181] User B sees User A's digital clone moving in real-time on the social map and wants to assist in adjusting the gimbal's composition via the app. User B initiates a gimbal takeover request at the user-social layer 310. The permission control unit 300 receives the request, checks the social relationship graph, confirms that B is A's friend and not on the blacklist, thus satisfying the pre-verification permission. The system then displays a confirmation pop-up in User A's app. User A shakes their phone; this action is captured and transmitted to the gesture confirmation service. After the service verifies the shake feature match, authorization is granted.

[0182] User B swipes their finger on the app, attempting to quickly rotate the gimbal downwards by 90 degrees. This command is sent to execution unit 100. The flight control safety agent determines that this command would cause the gimbal angle to exceed the drone's capability limit and corrects B's command to the maximum pitch angle within a safe range (e.g., 60 degrees downwards), generating a safety control command. The safety control command is then sent to drone D1 for execution, and the gimbal rotates smoothly. Simultaneously, the flight data generated by B's operation (takeover time, operator ID, corrected actions) is synchronized back to storage unit 200 in real time, ensuring a complete record of the collaborative process for subsequent traceability.

[0183] After the flight, User A felt the aerial photography route was worth sharing and clicked "Publish Route" on the App. The sharing unit 500 extracted the historical flight data corresponding to D1 and generated a route sticker containing geographic anchor points, waypoint sequences, and gimbal movements.

[0184] User C brought drone D2 to the area. His app, using its own location and spatiotemporal index module 250, discovered nearby route stickers. User C clicked "Follow Flight," and the sharing unit 500 read the drone D2's model parameters (such as maximum speed and turning radius). It found that D2 could not complete some sharp turns on the original route. The system automatically resampled and safely adapted the route, changing sharp turns into smooth curves and generating a modified flight path.

[0185] At this moment, user C is preparing to take off and follow, while drone D1 has just flown back to the area. D1 and D2 enter the preset radius corresponding to the collision warning. D1 and D2 obtain each other's real-time status data based on the spatiotemporal index service in their respective storage units 200, and perform collision prediction based on their own real-time status data. When D2 first takes off, the collision risk score does not exceed the preset threshold. After D2 flies for a period of time, the collision risk score exceeds the preset threshold, and both user C and user A receive a collision warning. If drone D1 is taken over by user B at this time, user B will also receive a collision warning.

[0186] That evening, 50 tourists in the scenic area followed the route stickers posted by user A. The shared unit 500 called the data from the storage unit 200 to obtain these 50 flight paths, projected these paths onto the spatial grid, and calculated the pass frequency and number of likes for each grid. After the grid heat value update operation for the entire scenic area was completed, the heat map on the App was refreshed, displaying heat path corridors with color to show the heat value, intuitively showing the most popular aerial photography perspectives.

[0187] Please see Figure 4This application also provides a computer device 10, which includes a processor 11 and a memory 12. The memory 12 is connected to the processor 11, for example, via a bus.

[0188] Processor 11 is configured to support the computer device 10 in performing the corresponding functions in the methods described in the above method embodiments. Processor 11 may be a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof. The aforementioned hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The aforementioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0189] Memory 12 is used to store program code, etc. Memory 12 may include volatile memory (VM), such as random access memory (RAM); memory 12 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid-state drive (SSD); memory 12 may also include combinations of the above types of memory.

[0190] The memory 12 is used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the UAV flight resource sharing method in the embodiments of this application. The processor executes various functional applications and data processing of the UAV flight resource sharing method by running the non-volatile software programs, instructions, and modules stored in the memory, thereby realizing the functions of the UAV flight resource sharing method provided in the above method embodiments.

[0191] The memory 12 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function. The data storage area may store data created based on the use of the 3D simulation circuit display device. In some embodiments, the memory may include memory remotely located relative to the processor, which can be connected to the 3D simulation circuit display device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0192] The one or more modules are stored in the memory. When executed by the one or more processors, they perform the UAV flight resource sharing method in any of the above method embodiments. For example, they perform the method steps described in the above method embodiments to realize the functions of the modules described in the above device embodiments.

[0193] This application also provides an electronic device, including: The microcontroller is configured to output an enable signal; In the aforementioned drone flight resource sharing system, the access control unit in the drone flight resource sharing system is electrically connected to the microcontroller.

[0194] This application also provides a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the method as described in the foregoing embodiments.

[0195] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0196] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.

Claims

1. A method for sharing unmanned aerial vehicle (UAV) flight resources, characterized in that, include: Receive interaction requests and identify the request type of the interaction request; Obtain the social relationship graph corresponding to the main control terminal; In response to the request type being a takeover request, based on the social relationship graph, the takeover request is subject to permission verification. The takeover request carries user control instructions and is sent by a non-master terminal. In response to successful permission verification, the user control instruction is subjected to security constraint projection processing to obtain a security control instruction. Based on the safety control command, the drone is controlled to perform flight operations, obtain takeover flight data, and synchronize the takeover flight data to the digital clone bound to the drone. The drone is also bound to the main control terminal, and the digital clone is associated with the drone's historical flight data. In response to the request type being a sharing request, data sharing is performed based on the flight data associated with the digital clone.

2. The method for sharing UAV flight resources according to claim 1, characterized in that, The permission verification of the takeover request based on the social relationship graph includes: The drone is pre-verified based on the social relationship graph. In response to the passing of the pre-verification, a request random number corresponding to the takeover request is generated, and a takeover request carrying the request random number is pushed to the master control terminal; Acquire sensor timing data from the main control terminal; Based on the sensor timing data, the motion energy characteristics and motion morphology characteristics of the main control terminal are calculated and matched with the preset authorized motion template. If both the motion energy characteristics and motion morphology characteristics pass the matching verification, the permission verification is deemed successful.

3. The method for sharing UAV flight resources according to claim 1, characterized in that, In response to successful permission verification, the user control instruction undergoes security constraint projection processing to obtain a security control instruction, including: In response to successful permission verification, obtain user control commands from non-master terminals and original control commands from master terminals; Obtain an instruction arbitration model, which includes dynamic takeover weights, a security constraint projection operator, and a security boundary set. The dynamic takeover weights represent the weighting ratio of the user control instructions. Using the instruction arbitration model and based on the dynamic takeover weight, the user control instruction and the original control instruction are weighted and fused. Based on the security constraint projection operator, the fused user control instruction is projected onto the security boundary set to obtain the security control instruction.

4. The method for sharing UAV flight resources according to claim 1, characterized in that, The sharing request includes a flight path sharing request, and the flight path sharing request is sent by the main control terminal. The data sharing based on the flight data associated with the digital clone includes: In response to the track sharing request from the master terminal, the flight data requested to be shared in the track sharing request is converted into the original waypoint sequence and track-related metadata in the target format; Based on preset publishing constraints, the original waypoint sequence is verified to be publishable. Upon successful publication verification, a shared track template is generated, which includes the original waypoint sequence, track-related metadata, geographic anchors, and discoverable range. Based on the aforementioned social relationship graph, the shared flight path template is published to the information sharing community; In response to a drone that is a non-master terminal within the information sharing community entering the discoverable range corresponding to a geographic anchor point, the corresponding shared flight path template is pushed to the non-master terminal.

5. The method for sharing UAV flight resources according to claim 4, characterized in that, Also includes: In response to the non-master terminal receiving the shared flight path template and triggering a follow-fly command, the preset follow-fly constraints of the UAV corresponding to the non-master terminal are obtained; Based on the preset flight constraints, the original waypoint sequence is resampled to generate candidate adaptation routes; A comprehensive cost assessment model is obtained. The comprehensive cost assessment model performs weighted calculations based on the shape difference between the original waypoint sequence and the candidate adapted route, the energy consumption estimate of the candidate adapted route, safety risks, and motion smoothness as assessment dimensions, and outputs the comprehensive cost value of the candidate adapted route. The candidate adaptation routes are subjected to follow-fly constraint verification. In response to the pass of the follow-fly constraint verification, the candidate adaptation route that satisfies the optimization condition of minimizing the comprehensive cost value is determined as the target follow-fly route. The UAV of the non-master terminal performs flight operations based on the target follow-fly route to complete the follow-fly task.

6. The method for sharing UAV flight resources according to claim 5, characterized in that, Also includes: In response to the drone completing the following flight task, the flight trajectory data obtained from the following flight task is mapped to a unified geospatial grid cell to obtain gridded following flight trajectory data. In response to a follow-fly sharing command from a non-master terminal, the follow-fly trajectory is published to the corresponding information sharing community and associated with the corresponding shared flight track template; In response to the number of following trajectories of the shared flight path template exceeding a preset first threshold, the thermal value of each spatial grid cell associated with the shared flight path template is calculated based on a preset thermal evaluation index. In response to the completion of the global spatial grid thermal value update, all spatial grids are aggregated to generate thermal trajectory corridors; The thermal trajectory corridor is published to the corresponding information sharing community.

7. The method for sharing UAV flight resources according to claim 1, characterized in that, The sharing request includes a borrowing request, and the data sharing based on the flight data associated with the digital avatar includes: In response to a borrowing request from a non-master terminal, the borrowing request includes the requested view granularity, access time range, and access space range; Verify whether the information sharing community of the non-master terminal meets the preset borrowing authorization conditions; In response to the successful verification of the borrowing authorization, the view granularity, the access time range, and the spatial range are used as the scope of the read-only token, and a read-only token signed by the cloud is generated. In response to the read-only token being verified, the flight data is intercepted and de-identified according to the scope of the action, and the de-identified flight data is pushed to the non-master terminal.

8. The method for sharing UAV flight resources according to any one of claims 1 to 7, characterized in that, The method further includes: The drone continuously synchronizes real-time status data to the corresponding digital clone. Based on the digital clones of the main control terminal and the designated terminal, real-time status data of the drones corresponding to the main control terminal and the designated terminal are obtained respectively; Based on the real-time status data of the UAV corresponding to the main control terminal and the designated terminal, the predicted minimum distance, the time difference of arrival to the location of the predicted minimum distance, and the height difference are calculated. A multidimensional spatiotemporal risk assessment model is obtained, wherein the multidimensional spatiotemporal risk assessment model includes a risk feature mapping function; The predicted minimum distance, time arrival difference, and height difference are input into the multidimensional spatiotemporal risk assessment model so that the multidimensional spatiotemporal risk assessment model can calculate the risk feature value based on the risk feature mapping function, and calculate the collision risk score based on the risk feature value. In response to the collision risk score exceeding a preset second threshold, a collision risk warning is generated and pushed to the main control terminal.

9. The method for sharing UAV flight resources according to any one of claims 1 to 7, characterized in that, The flight data uploaded to the digital avatar includes the drone's flight path sampling points, the corresponding time data, spatial data, drone status data, and media metadata. The method also includes: In response to the digital clone receiving the flight data, the sampling points, the corresponding time data, spatial data and UAV status data are serialized to obtain a trajectory sampling sequence; The trajectory sampling sequence is segmented according to a preset time window constraint or spatial length constraint to generate several trajectory segments; Based on the trajectory segments and the corresponding media metadata, a hash integrity certificate corresponding to the trajectory segment is calculated, and the hash integrity certificate, the trajectory segments, and the media metadata are bound and stored to obtain the stored flight data; In response to the operation of reading the stored flight data from the digital clone, the stored flight data is checked for corruption based on the hash integrity certificate; In response to the determination that the stored flight data is corrupted, a data corruption warning is pushed to the main control terminal.

10. A system for sharing unmanned aerial vehicle (UAV) flight resources, characterized in that, include: Storage unit for storing the digital clone; The access control unit is used to receive interaction requests and identify the request type of the interaction request; Obtain the social relationship graph corresponding to the main control terminal; In response to the request type being a takeover request, permission verification is performed on the takeover request based on the social relationship graph; An execution unit is configured to perform security constraint projection processing on the user control instruction in response to the successful permission verification, thereby obtaining a security control instruction. Based on the security control command, the drone is controlled to perform flight operations, obtain takeover flight data, and synchronize the takeover flight data to the digital clone bound to the drone. A sharing unit is used to share data based on the flight data associated with the digital clone in response to a request of type sharing.

11. An electronic device, characterized in that, include: The microcontroller is configured to output an enable signal; The drone flight resource sharing system as described in claim 10, wherein the access control unit in the drone flight resource sharing system is electrically connected to the microcontroller.

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