Unmanned aerial vehicle display system and method supporting multi-scene self-adaption

Abstract

According to the unmanned aerial vehicle display system and method supporting multi-scene self-adaption, a key transmission module monitors the network state in real time based on a QUIC protocol and dynamically adjusts a transmission strategy, and key data are transmitted preferentially to enhance the reliability of a weak network; the priority transmission module constructs a multi-dimensional scoring model through a WFQ algorithm, and dynamically allocates resources in combination with a Kafka queue to realize high-priority data preemption scheduling; the visual angle switching module utilizes WebGL to render cluster statistical information and individual states, and combines a dynamic geofence and an R tree index to realize rapid switching between global overview and detail analysis. According to the system and the method disclosed by the invention, the high throughput data processing capability, the real-time response efficiency and the multi-scene display flexibility of a large-scale unmanned aerial vehicle cluster are remarkably improved through a self-adaptive transmission strategy, intelligent resource scheduling and multi-granularity visual cooperation.

Description

Technical Field

[0001] The present invention relates to drone cluster management technology, and in particular to a drone display system and method supporting multi-scene adaptation. Background Art

[0002] Current drone management systems face significant challenges in managing the number of drones, identifying targets, and tracking them dynamically. Due to limitations in computing resources and communication bandwidth, these systems struggle to support the collaborative operations of large-scale fleets, manifesting in inadequacies in target identification accuracy and the granularity of dynamic tracking. Under existing technology frameworks, systems often fail to provide high-resolution target feature extraction and continuous motion state updates, which directly impacts the accuracy of operational support and state feedback, making it difficult to meet the requirements of complex tasks. Therefore, while enhancing the flexibility of customized data presentation for different types of drone missions, efficient computing resource allocation and intelligent management mechanisms must be addressed. Furthermore, to enhance the overall system performance, improvements to the dynamically adaptive user interface and data processing process are also needed to ensure efficient execution of critical tasks and timely response to abnormal situations, thereby better serving the personalized needs of diverse application scenarios.

[0003] 1. Different drone types have different display requirements: Different drone types and different mission scenarios have significantly different requirements for data visualization and analysis. Unfortunately, current technologies lack sufficient flexibility to support data displays tailored to these missions, particularly in providing dynamically adaptable user interfaces and data processing pipelines. These issues limit the technology's ability to meet the personalized needs of diverse application scenarios.

[0004] 2. The granularity of drone displays varies in different areas: In many mission scenarios, users not only need to obtain the real-time location information of the drone, but also need to understand its dynamic state parameters (such as velocity vector, heading angle, altitude change rate, etc.). This places higher demands on the accuracy of target detection and tracking algorithms.

[0005] 3. Limited resource allocation for large-scale drone data: Processing the high-throughput data generated by large-scale drone swarms requires efficient allocation of computing resources (such as CPU / GPU computing power and memory bandwidth) and dynamic scheduling of data processing based on task priority. This also requires the establishment of intelligent management mechanisms that prioritize task importance and real-time requirements to ensure efficient execution of critical tasks and timely response to abnormal conditions.

[0006] It should be noted that the information disclosed in the above background technology section is only used to understand the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0007] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a drone display system and method that supports multi-scene adaptation.

[0008] To achieve the above object, the present invention adopts the following technical solutions:

[0009] In a first aspect of the present invention, a drone display system supporting multi-scene adaptation is provided, comprising:

[0010] The key transmission strategy module implements an adaptive transmission mechanism based on the QUIC protocol. It dynamically adjusts the packet size and sending frequency by monitoring network status parameters in real time, and switches to a high-priority transmission mode when the network fluctuates.

[0011] The priority-based transmission module uses the weighted fair queuing (WFQ) algorithm to build a multi-dimensional priority scoring model. It combines the Kafka message queue to dynamically allocate computing resources and network bandwidth, achieving real-time push of key data and resource preemptive scheduling.

[0012] The intelligent switching module between gaze and bird's-eye view uses WebGL technology to render drone cluster statistics and individual core status data, and combines dynamic geo-fencing and R-tree indexing to achieve view switching and spatial range retrieval, realizing rapid conversion between global overview and focused analysis.

[0013] A second aspect of the present invention is a method for displaying a drone that supports multi-scene adaptation, using the drone display system that supports multi-scene adaptation. The method includes:

[0014] Monitor network status parameters in real time through the QUIC protocol, dynamically adjust transmission strategies, and switch to high-priority transmission mode when network fluctuations occur;

[0015] Dynamically allocate resources based on a multi-dimensional priority scoring model and the WFQ algorithm, and use the Kafka message queue to push key data;

[0016] Combining WebGL rendering with dynamic geo-fencing technology, a dual-view linkage display of drone cluster statistical information and individual status data is achieved, and efficient spatial range retrieval and view switching are achieved through R-tree indexing.

[0017] The present invention has the following beneficial effects:

[0018] The present invention provides a drone display system and method that supports multi-scenario adaptation. It can intelligently output data of different granularities based on complex scenarios and multi-target drones. The present invention uses an adaptive transmission mechanism based on the QUIC protocol to optimize data transmission efficiency in real time under fluctuating network conditions. It combines a multi-dimensional priority scoring model with an optimized WFQ algorithm to dynamically allocate computing resources and network bandwidth, effectively solving the problems of high-throughput data processing and real-time response to critical tasks in large-scale drone clusters. At the same time, it uses WebGL technology to achieve cluster-level statistical information rendering in a bird's-eye view and accurate display of individual states in a gaze view. Combined with the GeoJSON definition of dynamic geographic fences and efficient spatial query of R-tree indexes, the present invention significantly improves the flexibility of data display and granularity adaptive adjustment capabilities in multiple scenarios, thereby achieving comprehensive performance optimization of intelligent resource scheduling, dynamic interface adaptation, and high-precision target tracking in complex tasks.

[0019] Compared with traditional technologies, the present invention has the following significant technical advantages:

[0020] 1. Improved flexibility in displaying different types of drones: Through a dual-view linkage display method and dynamic geo-fencing technology, the present invention enables customized data display for different types of drones and mission scenarios, supports dynamically adaptable user interfaces and data processing flows, and meets personalized needs in diverse application scenarios.

[0021] 2. Adaptive adjustment of display granularity in different area areas: Utilizing a WebGL-based rendering optimization method and dynamic LOD technology, the present invention achieves adaptive adjustment of display granularity. Users can flexibly switch between global overview and individual analysis views in different area areas, while accurately presenting dynamic state parameters (such as velocity vector and heading angle), thereby improving the accuracy of target detection and tracking.

[0022] 3. Efficiency in allocating large amounts of UAV resources: Through the optimized WFQ algorithm and priority scoring model, the present invention achieves dynamic allocation of computing resources and network bandwidth, establishes an intelligent management mechanism, ensures efficient execution of key tasks and timely response to abnormal conditions, and significantly improves the system's efficiency in processing large amounts of data.

[0023] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a framework diagram of a multi-scenario adaptive drone display system according to an embodiment of the present invention.

[0025] Figure 2 This is a processing flow chart of the key transmission strategy module in an embodiment of the present invention.

[0026] Figure 3Schematic diagram of a sliding window algorithm according to an embodiment of the present invention.

[0027] Figure 4 Schematic diagram of low-latency delivery of urgent transmission according to an embodiment of the present invention.

[0028] Figure 5 This is a diagram of the QUIC protocol structure of an embodiment of the present invention.

[0029] Figure 6 4 is a processing flow chart of a priority-based transmission module according to an embodiment of the present invention.

[0030] Figure 7 Schematic diagram of the WFQ algorithm according to an embodiment of the present invention.

[0031] Figure 8 This is a schematic diagram of a Kafka message queue according to an embodiment of the present invention.

[0032] Figure 9 4 is a processing flow chart of the module for intelligently switching between the top-down and gaze perspectives according to an embodiment of the present invention.

[0033] Figure 10 Schematic diagram of R-tree index according to an embodiment of the present invention.

[0034] Figure 11 This is a structural diagram of the drone display system that supports multi-scene adaptation in the present invention.

[0035] Figure 12 This is an overall flow chart of the drone display method supporting multi-scene adaptation of the present invention. DETAILED DESCRIPTION

[0036] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope of the present invention and its application.

[0037] In response to the problems existing in the above technical background, the present invention proposes a drone display management system that supports multi-scene adaptation. It reasonably displays and allocates granular middleware resources according to user needs based on large-scale drones of different types, different mission scenarios, and different perspectives, overcomes the limitations of traditional drone management platforms in view display and resource allocation, improves the flexibility of displaying different types of drones, improves the accuracy of target detection and tracking, and improves the efficiency of resource allocation for large-scale drones.

[0038] See Figure 11, an embodiment of the present invention provides a drone display system that supports multi-scene adaptation, including a key transmission strategy module, a priority-based transmission module, and a gaze and overlooking perspective intelligent switching module. Among them, the key transmission strategy module implements an adaptive transmission mechanism based on the QUIC protocol, dynamically adjusts the data packet size and sending frequency by real-time monitoring of network status parameters, and switches to a high-priority transmission mode when the network fluctuates. The priority-based transmission module adopts the weighted fair queuing WFQ algorithm to construct a multi-dimensional priority scoring model, and combines the Kafka message queue to dynamically allocate computing resources and network bandwidth to achieve real-time push and resource preemptive scheduling of key data. The gaze and overlooking perspective intelligent switching module uses WebGL technology to render drone cluster statistical information and individual core status data, and combines dynamic geographic fences and R-tree indexes to achieve view switching and spatial range retrieval, thereby achieving rapid conversion between global overview and focused analysis.

[0039] Through adaptive transmission strategies, intelligent resource scheduling and multi-dimensional visual collaboration, the system significantly improves the high-throughput data processing capabilities, real-time response efficiency and multi-scene display flexibility of large-scale drone clusters.

[0040] In some embodiments, the key transmission strategy module includes: a network status evaluation unit, which collects round-trip time (RTT) parameters in real time and smoothes network fluctuations through a sliding window algorithm; a transmission mode switching unit, which selects a regular transmission mode or an emergency transmission mode according to the network status level, and ensures the continuity of data transmission through the multiplexing and fast retransmission mechanism of the QUIC protocol.

[0041] In some embodiments, the emergency transmission mode of the critical transmission policy module only transmits location and health status data, and ensures low-latency delivery through the high-priority channel of the QUIC protocol.

[0042] In some embodiments, the priority-based transmission module includes: a priority scoring model unit that calculates a comprehensive priority score based on task urgency, health status, and location importance; Using heap structure to implement priority queue Sequence; resource dynamic allocation unit, according to the priority score Data streams are classified and bandwidth weights are assigned, and low-latency push of high-priority data is achieved through Kafka message queues.

[0043] In some embodiments, the dynamic resource allocation unit of the priority-based transmission module is configured to trigger resource preemption according to task priority, and suspend low-priority tasks to ensure the real-time performance of critical tasks.

[0044] In some embodiments, the intelligent switching module between gaze and bird's-eye view includes: a bird's-eye view rendering unit, which uses WebGL technology to display cluster-level statistical information, including task completion rate, coverage area and energy consumption distribution; a gaze view rendering unit, which displays real-time status data of individual drones based on user selection, realizes dynamic geo-fence definition and efficient range query of R-tree index; a view switching unit, which triggers smooth transition through user operation instructions and adaptively adjusts rendering accuracy in combination with dynamic LOD technology.

[0045] In some embodiments, the bird's-eye view rendering unit uses dynamic point cloud rendering technology to dynamically adjust rendering parameters according to the number of drones and the screen display area to avoid visual overlap.

[0046] In some embodiments, the view switching unit implements smooth switching between global overview and individual analysis through the smooth transition function of WebGL, and adjusts the level of detail (LOD) rendering accuracy based on the zoom level.

[0047] In some embodiments, the dynamic geo-fence of the gaze and overlooking perspective intelligent switching module uses GeoJSON format to define polygonal or circular areas, and implements efficient spatial query through incremental index updates.

[0048] See Figure 12 An embodiment of the present invention further provides a method for displaying a drone that supports multi-scene adaptation, using the drone display system that supports multi-scene adaptation of any of the aforementioned embodiments, the method comprising:

[0049] Monitor network status parameters in real time through the QUIC protocol, dynamically adjust transmission strategies, and switch to high-priority transmission mode when network fluctuations occur;

[0050] Dynamically allocate resources based on a multi-dimensional priority scoring model and the WFQ algorithm, and use the Kafka message queue to push key data;

[0051] Combining WebGL rendering with dynamic geo-fencing technology, a dual-view linkage display of drone cluster statistical information and individual status data is achieved, and efficient spatial range retrieval and view switching are achieved through R-tree indexing.

[0052] Specific embodiments of the present invention are further described below.

[0053] The embodiment of the present invention provides a drone display system and method that supports multi-scene adaptation. The specific technical solution includes:

[0054] 1. Key transmission strategy: This system uses an adaptive transmission mechanism based on the QUIC (QuickUDP Internet Connections) protocol. By monitoring network status parameters (Round Trip Time (RTT)) in real time, it dynamically adjusts packet size and transmission frequency. In the event of network fluctuations, the system automatically switches to high-priority transmission mode.

[0055] 2. Priority-Based Information Transmission: Utilizing the WFQ (Weighted Fair Queuing) algorithm, an automated priority scheduling mechanism is implemented. This dynamically allocates processing resources based on multiple dimensions, including the drone's mission type, health status, and location importance, ensuring the immediate delivery of critical information. Efficient push notifications are delivered through Kafka message queues, ensuring real-time and consistent data. As data streams in real time, the system automatically prioritizes them using a weighted priority queue scoring model, ensuring that data from drones involved in urgent missions, equipment anomalies, or critical locations is prioritized.

[0056] 3. Intelligent Switching Between Gaze and Bird's-Eye Perspectives: An automatic switching mechanism between gaze and bird's-eye views is provided, enabling users to quickly transition between global monitoring and individual analysis. In the bird's-eye view (overview mode), WebGL technology is used to render cluster-level statistics, including key indicators such as cluster mission completion rate, average flight speed, coverage area, and energy consumption distribution, allowing users to quickly grasp the overall operational status. The gaze view focuses on core drone status data (such as real-time latitude and longitude, altitude, velocity vector, sensor readings) and mission execution progress, and a highly flexible filtering mechanism enables precise management and analysis of large-scale drone data. Using dynamic geo-fencing technology, users can define specific operating areas using GeoJSON format and, combined with R-tree geospatial indexing, implement efficient range queries, displaying only drone activity and status within that area. Fence ranges can be adjusted in real time based on mission requirements or environmental changes, ensuring data is focused on key areas.

[0057] Figure 1 This paper demonstrates the framework of a multi-scenario adaptive drone display system, based on an embodiment of the present invention. A priority-based information transmission module retrieves multi-dimensional drone priority assessment data from a server, such as mission urgency, health status, location importance, and real-time dynamic parameters. This data is then used to generate priority queues and enable real-time scheduling and transmission of critical data.

[0058] The technical solution of the present invention provides a middleware that supports the management of multi-scene adaptive drone displays. It can receive data from the server for priority sorting, policy screening, etc., and provide it to the map engine for page display in a staring or overlooking manner. The specific implementation method is as follows.

[0059] Key transmission strategy module

[0060] Figure 2 The processing flow of the key transmission strategy module of an embodiment of the present invention is shown.

[0061] QoS policy decision

[0062] 1. Real-time collection of network parameters: Collect the network's round-trip time (RTT). RTT is calculated by the time difference between sending and receiving data packets and is used to evaluate network latency.

[0063] 2. Smoothing network fluctuations: A sliding window algorithm is used to process the collected network parameter time series data. The input is the network parameter sequence, and the output is the smoothed parameter statistics (mean and variance). The sliding window technology reduces the interference of short-term fluctuations on network status assessment and ensures the stability of monitoring results. Figure 3 This is a schematic diagram of the sliding window algorithm.

[0064] Transmission mode switching method

[0065] 1. Network status assessment: Based on the collected network parameters (RTT), the network status is assessed in real time. The input is network parameter data, and the output is the network status level (good or poor), which is used to determine whether the transmission mode needs to be switched.

[0066] 2. Transmission mode selection:

[0067] Normal transmission mode: used when the network status is good, using the QUIC protocol to transmit all data (including status data, image data, and log data). The input is a complete data stream, and the output is an efficiently transmitted data packet. The QUIC protocol improves transmission efficiency through multiplexing and fast retransmission mechanisms.

[0068] Emergency transmission mode: Used when network conditions are poor, this mode transmits only critical data (such as location and health status). The input is filtered critical data, and the output is packets transmitted via QUIC's high-priority channel, ensuring low-latency delivery of critical data. Scheduled packets are transmitted via QUIC's high-priority channel, with the input being the scheduled order and the output being the completed data stream, ensuring real-time delivery of critical data. Figure 4 A schematic diagram showing low-latency delivery of urgent transmissions is shown.

[0069] 3. Mode switching implementation:

[0070] Implement mode switching logic, with the input being the network status level and the output being the transmission mode switching instruction. Through the priority channel management and data flow control functions of the QUIC protocol, ensure that data transmission is not interrupted during the mode switching process. Figure 5 Shows the QUIC protocol structure.

[0071] Priority-based transport module

[0072] Figure 6 The processing flow of the priority-based transmission module according to an embodiment of the present invention is shown.

[0073] Priority scoring model construction method

[0074] 1. Multi-dimensional indicator evaluation: Evaluate multi-dimensional indicators such as task weight, health status, and location importance. Input the task urgency score (range 0-10), health status (normal, warning, fault), and location importance (core area, general area), and output the score value of each indicator to quantify the priority of drone data.

[0075] 2. Calculate the comprehensive priority score: The score of each indicator is calculated through a weighted calculation formula to ensure that key tasks and abnormal conditions are handled first.

[0076] 3. Priority sorting: Sort the data queue according to the comprehensive priority score and use the heap structure to implement the priority queue.

[0077] Optimized WFQ algorithm

[0078] Data flow classification and weighting. Data flows are categorized into critical data (such as location and health status) and non-critical data (such as logs and images). Bandwidth weighting is dynamically assigned based on the task urgency score. The input is the data flow type and task score, and the output is the weight value of each data flow, ensuring that critical data receives higher bandwidth priority. Figure 7 Shows a schematic diagram of the WFQ algorithm.

[0079] The WFQ algorithm classifies packets based on flow characteristics. On IP networks, packets with the same source IP address, destination IP address, source port number, destination port number, protocol number, and ToS value belong to the same flow. On MPLS networks, packets with the same label and EXP field value belong to the same flow. Each flow is assigned to a queue, a process called hashing, which is automatically performed using a hashing algorithm. This method strives to divide flows with different characteristics into different queues. Each queue category can be considered a type of flow, and its packets enter the same queue in WFQ. WFQ allows a limited number of queues, which can be configured by the user. When dequeuing, WFQ allocates the egress bandwidth to each flow based on its precedence. The lower the priority value, the less bandwidth it receives. The higher the priority value, the more bandwidth it receives. This ensures fairness among services of the same priority and reflects the weighting of services of different priorities.

[0080] Kafka message queue efficient push

[0081] 1. Dynamic resource allocation: Based on the priority queue, a resource allocation plan is created to dynamically allocate computing resources (CPU, memory) and network bandwidth according to the priority score, ensuring that high-priority tasks receive more resource support.

[0082] 2. Resource preemption: After a high-priority task triggers a signal, a resource allocation adjustment instruction is issued. When a high-priority task enters, the processing of low-priority tasks is suspended to ensure the real-time performance of critical tasks.

[0083] 3. Data push: The input is filtered high-priority data, and the output is push instructions. Efficient push is achieved through the Kafka message queue to ensure low-latency transmission. Figure 8 Shows a schematic diagram of the Kafka message queue.

[0084] Intelligent switching module between gaze and overlooking perspective

[0085] Figure 9 The processing flow of the module for intelligently switching between the top-down and gaze perspectives according to an embodiment of the present invention is shown.

[0086] View switching mechanism

[0087] 1. Global overview: Get real-time data of drone clusters from the map engine. Cluster statistics are rendered using WebGL technology. The input is the drone's location coordinates (latitude and longitude) and status data (speed, mission progress). The output is a rendered visualization (such as a heat map of the mission coverage area and a drone distribution map), providing users with macro-monitoring capabilities.

[0088] 2. Focus on drone gaze view: Focus on a single drone and display its detailed information. The input is the drone ID selected by the user, and the output is the real-time status data of the drone (such as battery level, sensor readings, and real-time trajectory). Updates are pushed through the Kafka message queue to ensure real-time and consistency of data.

[0089] 3. Implement view switching: Users trigger switching by clicking on the interface or using shortcut keys. The input is the user's operation command, and the output is the view switching signal. The smooth transition function of WebGL is used to implement view switching to ensure a smooth user experience.

[0090] WebGL rendering optimization

[0091] 1. Dynamic LOD: Using dynamic LOD (level of detail) technology, the user's current zoom level is used as input and outputs rendering precision adjustment instructions. The rendering precision is adjusted according to the zoom level, reducing detail rendering at low zoom levels and increasing detail display at high zoom levels to improve rendering performance.

[0092] 2. Adaptive Point Cloud Rendering: The system takes the number of drones and the screen display area as input and outputs rendering parameter adjustments (point size, transparency). Dynamic adjustment of rendering parameters avoids visual overlap in high-density scenes, ensuring user-perceived quality. At low zoom levels, aggregated statistical information is displayed. Dynamic adjustment of displayed content based on zoom level improves rendering efficiency.

[0093] Dynamic geofencing and R-tree indexing

[0094] 1. Define dynamic geofences: Use GeoJSON format data to define dynamic geofences. Various shapes such as polygons and circles can be used. User-defined fence shapes and range coordinates are used as input, and the output is a fence data structure, providing users with flexible area management functions.

[0095] 2. Build an R-tree index: Build an R-tree geospatial index with the input of fence data and drone locations. The output is an R-tree index structure, which enables efficient range queries through spatial partitioning.

[0096] 3. Incremental index update: Input the drone position change data (coordinate offset), and output the updated R-tree index. Only local index adjustments are made to the moving drone position, avoiding global reconstruction and improving query efficiency. Figure 10 Shows an R-tree index.

[0097] A and B represent the child nodes of the two root nodes of the R-tree. They each contain multiple child nodes or MBRs of data objects. In this example, A and B are non-leaf nodes. C, D, E, and F represent the child nodes of node A. They may be leaf nodes or deeper internal nodes. If they are leaf nodes, they directly correspond to specific data objects. If it is an internal node, it continues to contain the MBR of the next layer. G, H, I, and J represent the B section. The child nodes under a node, like C, D, E, and F, are also children of node A. They may directly correspond to data objects or contain deeper MBRs. Rectangular boxes represent the MBRs of each node, enclosing the corresponding child nodes or data objects. For example, the MBR of node A encloses the MBRs of its child nodes C, D, E, and F, while the MBR of node B encloses the MBRs of its child nodes G, H, I, and J.

[0098] In summary, the present invention proposes a multi-scenario adaptive drone display system and method. Compared with the existing technology, the important features and innovative contributions of the present invention include:

[0099] 1. Constructed transmission strategy and key response modules

[0100] This invention proposes an adaptive transmission mechanism based on the QUIC protocol. By real-time monitoring of network status parameters (such as RTT, packet loss rate and available bandwidth) and dynamically adjusting the transmission strategy, combined with the multiplexing and fast retransmission characteristics of the QUIC protocol and the WFQ (weighted fair queuing) algorithm to dynamically allocate bandwidth resources, it ensures the instant transmission of key data (such as location and health status), thereby improving the data transmission efficiency and reliability in weak network environments.

[0101] 2. Constructed a priority-based information filtering module

[0102] This paper discloses an automatic scheduling mechanism based on multi-dimensional priorities formulated using the WFQ algorithm. This mechanism quantifies drone data priorities through a multi-dimensional scoring model and dynamically allocates processing resources using priority queues, optimizing resource management and critical task processing efficiency for large-scale drone clusters. It also pushes individual drone real-time data through a Kafka message queue, ensuring real-time and consistent data.

[0103] 3. Constructed an intelligent switching module for gaze and overlooking perspectives

[0104] The present invention provides a dual-view linkage and intelligent filtering mechanism. Through flexible switching between gaze and bird's-eye view and dynamic geo-fencing technology, it uses WebGL technology to render cluster-level statistical information (such as task completion rate and coverage area). Combined with GeoJSON and R-tree indexing, it realizes efficient range query of dynamic geo-fencing, thus achieving efficient display and precise management of large-scale drone data.

[0105] Compared with traditional technologies, the present invention has the following significant technical advantages:

[0106] 1. Improved flexibility in displaying different types of drones: Through a dual-view linkage display method and dynamic geo-fencing technology, the present invention enables customized data display for different types of drones and mission scenarios, supports dynamically adaptable user interfaces and data processing flows, and meets personalized needs in diverse application scenarios.

[0107] 2. Adaptive adjustment of display granularity in different areas: By using the rendering optimization method based on WebGL and dynamic LOD technology, the present invention realizes the adaptive adjustment of display granularity. Users can flexibly switch the global Overview and individual analysis views, accurately presenting the dynamic State parameters (such as velocity vector and heading angle) are collected to improve the accuracy of target detection and tracking.

[0108] 3. Efficiency in allocating large amounts of UAV resources: Through the optimized WFQ algorithm and priority scoring model, the present invention achieves dynamic allocation of computing resources and network bandwidth, establishes an intelligent management mechanism, ensures efficient execution of key tasks and timely response to abnormal conditions, and significantly improves the system's efficiency in processing large amounts of data.

[0109] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0110] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0111] An embodiment of the present invention further provides a processor, which executes a computer program and at least performs the method described above.

[0112] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory (Flash Memory), a magnetic surface memory, an optical disc or a read-only optical disc (CD-ROM); the magnetic surface memory can be a magnetic disk memory or a magnetic tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0113] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0114] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place. can also be distributed to multiple network units Yuan Shang; you can choose some of them according to actual needs or all units to achieve the purpose of the embodiment.

[0115] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0116] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0117] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0118] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0119] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0120] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0121] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. Those skilled in the art will recognize that, without departing from the scope of the present invention, several equivalent substitutions or obvious variations can be made, and the performance or use of the same should be considered to fall within the scope of protection of the present invention.

Claims

1. A drone display system that supports multi-scene adaptation, characterized in that: include: The key transmission strategy module implements an adaptive transmission mechanism based on the QUIC protocol. It dynamically adjusts the packet size and sending frequency by monitoring network status parameters in real time, and switches to a high-priority transmission mode when the network fluctuates. The priority-based transmission module uses the weighted fair queuing (WFQ) algorithm to build a multi-dimensional priority scoring model. It combines the Kafka message queue to dynamically allocate computing resources and network bandwidth, achieving real-time push of key data and resource preemptive scheduling. The intelligent switching module between gaze and bird's-eye view uses WebGL technology to render drone cluster statistics and individual core status data, and combines dynamic geo-fencing and R-tree indexing to achieve view switching and spatial range retrieval, realizing rapid conversion between global overview and focused analysis.

2. The multi-scene adaptive drone display system according to claim 1, characterized in that: The key transmission strategy module includes: The network status evaluation unit collects round-trip time (RTT) parameters in real time and smoothes network fluctuations using a sliding window algorithm. The transmission mode switching unit selects the normal transmission mode or the emergency transmission mode according to the network status level, and ensures the continuity of data transmission through the multiplexing and fast retransmission mechanism of the QUIC protocol.

3. The multi-scenario adaptive drone display system according to any one of claims 1 to 2, characterized in that: The emergency transmission mode of the key transmission strategy module only transmits location and health status data, and ensures low-latency delivery through the high-priority channel of the QUIC protocol.

4. The multi-scenario adaptive drone display system according to any one of claims 1 to 3, characterized in that: The priority-based transmission module includes: The priority scoring model unit calculates the comprehensive priority score based on task urgency, health status and location importance, and uses the heap structure to implement priority queue sorting; The resource dynamic allocation unit classifies data streams according to priority scores and allocates bandwidth weights, and achieves low-latency push of high-priority data through the Kafka message queue.

5. The multi-scene adaptive drone display system according to claim 4, characterized in that: The resource dynamic allocation unit of the priority-based transmission module is configured to trigger resource preemption according to task priority and suspend low-priority tasks to ensure the real-time performance of critical tasks.

6. The multi-scene adaptive drone display system according to any one of claims 1 to 5, characterized in that: The module for intelligently switching between gaze and overlooking viewing angles includes: The bird's-eye view rendering unit uses WebGL technology to display cluster-level statistical information, including task completion rate, coverage area, and energy consumption distribution; The gaze view rendering unit displays real-time status data of individual drones based on user selections, enabling dynamic geo-fence definition and efficient range queries using R-tree indexes; The view switching unit triggers smooth transitions through user operation instructions and adaptively adjusts rendering accuracy in combination with dynamic LOD technology.

7. The multi-scene adaptive drone display system according to claim 6, characterized in that: The bird's-eye view rendering unit adopts dynamic point cloud rendering technology and dynamically adjusts rendering parameters according to the number of drones and the screen display area to avoid visual overlap.

8. The multi-scene adaptive drone display system according to claim 6, characterized in that: The view switching unit achieves smooth switching between global overview and individual analysis through the smooth transition function of WebGL, and adjusts the level of detail (LOD) rendering accuracy based on the zoom level.

9. The multi-scene adaptive drone display system according to any one of claims 1 to 8, characterized in that: The dynamic geo-fence of the gaze and overlooking perspective intelligent switching module uses GeoJSON format to define polygonal or circular areas, and implements efficient spatial query through incremental index updates.

10. A method for displaying a drone supporting multi-scene adaptation, using the drone display system supporting multi-scene adaptation according to any one of claims 1 to 9, characterized in that: The method comprises: Monitor network status parameters in real time through the QUIC protocol, dynamically adjust transmission strategies, and switch to high-priority transmission mode when network fluctuations occur; Dynamically allocate resources based on a multi-dimensional priority scoring model and the WFQ algorithm, and use the Kafka message queue to push key data; Combining WebGL rendering with dynamic geo-fencing technology, a dual-view linkage display of drone cluster statistical information and individual status data is achieved, and efficient spatial range retrieval and view switching are achieved through R-tree indexing.

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