Cluster unmanned aerial vehicle instruction execution-oriented batch state management method

Abstract

The invention discloses a batch state management method and device for cluster unmanned aerial vehicle instruction execution, a medium and equipment, and relates to the field of unmanned aerial vehicle command and control. The method comprises the following steps: receiving and analyzing a network data packet in real time through UDP multicast, and obtaining return information containing an unmanned aerial vehicle ID, an instruction return type and a timestamp; a sliding window algorithm is adopted to identify instruction return batches, the same instructions in the same time window are classified into one batch, and a new batch is created if a new instruction or overtime occurs; a state mapping table is maintained based on an RC / TC / Complete three-level state model, and batch completion is judged through an instant and timing mechanism; and counting the proportions of various states and incrementally updating the completion degree index, and presenting the states through gridding, batch counting and historical batch three-layer display layers. According to the method, accurate batch management and state tracking of multi-machine instructions are realized, and the instruction execution monitoring efficiency and the operation convenience of the cluster unmanned aerial vehicles are improved.

Description

Technical Field

[0001] This application relates to the technical field of command and control of unmanned aerial vehicles (UAVs), and in particular to a batch-based state management method, apparatus, medium, and device for the execution of commands by swarm UAVs. Background Technology

[0002] Current technologies for managing the execution status of drone commands have significant limitations. Existing technologies, such as single-drone status tracking systems, can only handle the command status of a single drone and cannot effectively manage scenarios where multiple drones execute commands simultaneously. While timestamp-based command management technologies can record the temporal sequence of commands, they lack a batch concept based on time windows, making it impossible to accurately distinguish command sets from different time periods. Furthermore, existing technologies generally employ a single event trigger mechanism. When some drones fail to report their status in a timely manner, the system cannot promptly identify the batch completion status, resulting in insufficient real-time performance and reliability. In terms of visualization, existing technologies mostly use simple status lists or charts, lacking an intuitive multi-layered visualization mechanism, making it difficult for operators to quickly understand the overall status of the drone swarm and identify anomalies. Summary of the Invention

[0003] The main objective of this application is to provide a batch-based state management method, apparatus, medium, and device for swarm drone command execution, aiming to achieve automatic batch identification and management based on time windows.

[0004] To achieve the above objectives, this application provides a batch-based state management method for swarm UAV command execution, comprising: The network data packets are received and parsed in real time via UDP multicast to obtain the drone report information. The drone report information includes the drone ID, command report type and current timestamp. The command report type includes remote control command report and telemetry command report. The sliding window algorithm is used to automatically identify the batch of instruction reports in the drone report information. Specifically, the same instruction report type received by different drones within the same time window is identified as the same batch, and a new batch is created when a new instruction report type is detected or the time window of the sliding window exceeds the threshold. Based on a predefined three-level state model, a state mapping table is maintained for each UAV. The batch maintenance of the UAV's state mapping table is identified through an instant judgment mechanism and a timed mechanism. The three-level state includes RC state, TC state, and Complete state. RC state indicates that the remote control command has been received but the telemetry command has not been received. TC state indicates that the telemetry command has been received. Complete state indicates that the remote control command and the telemetry command are consistent. The system generates batch completion indicators by statistically analyzing the percentage of each status in the status mapping table in real time. It also updates the percentage of each status using an incremental update mechanism and presents the drone command execution status through a gridded status display layer, a batch statistics layer, and a historical batch display layer.

[0005] Optionally, the step of determining the same command report type received by different drones within the same time window as the same batch, and creating a new batch when a new command report type is detected or the time window of the sliding window exceeds a threshold, includes: Maintain a batch list based on the same instruction report name; If a new report of the same instruction arrives within the time window of the sliding window, it belongs to that batch; otherwise, a new batch is created. Create a new batch when different instruction reports arrive.

[0006] Optionally, the three-level state model includes: When a remote control report is received, a new FeedbackItem is created and its status is set to RC (Responding Control). When a telemetry report is received, a FeedbackItem with the same name that is not yet completed within the last 1000 milliseconds is searched. If it exists and its status is RC, it is upgraded to Complete; otherwise, a new FeedbackItem is created and its status is set to TC (Tracking Control). The system implements a state deduplication mechanism to ignore duplicate state updates and avoid state rollback or confusion. The upgrade process of the three-level state model is from RC state to TC state, and from TC state to Complete state.

[0007] Optionally, the lifecycle of the instruction report batch in the drone report information includes a creation phase, a collection phase, a completion phase, a processing phase, and a destruction phase: During the creation phase, this batch is initialized based on drone report information; During the collection phase, receive and process instruction reports, and update the drone status list; During the completion phase, batches that have met the completion conditions are marked as completed. During the processing phase, a visual update is performed after the batch is completed; During the destruction phase, batch information is archived and memory resources are released.

[0008] Optionally, the real-time statistical status mapping table includes the following: the percentage of each status type and the generation of a batch completion index. Real-time collection of multi-dimensional statistical data; The multi-dimensional statistical data includes batch ID, instruction report type, total number of drones, number of drones in Complete status, number of drones in TC status, number of drones in RC status, and a list of drone IDs corresponding to each status. The percentage of each status is determined based on multi-dimensional statistical data, and a batch completion rate indicator is generated.

[0009] Optionally, the gridded status display layer is used to display the command report status of each UAV in real time; The batch statistics layer is used to calculate the batch Complete status rate, TC status rate, and RC status rate in real time to quantitatively evaluate the batch execution performance. The historical batch display layer is used to display the completion status of the most recent batch in reverse chronological order, forming a status evolution trajectory.

[0010] Furthermore, to achieve the above objectives, this application also provides a batch-based state management device for swarm drone command execution, comprising: The data parsing module is used to receive and parse network data packets in real time via UDP multicast to obtain drone report information. The drone report information includes drone ID, command report type and current timestamp. Command report types include remote control command report and telemetry command report. The batch identification module is used to automatically identify the batch of instruction reports in the drone report information using a sliding window algorithm. Specifically, the same instruction report type received by different drones within the same time window is identified as the same batch, and a new batch is created when a new instruction report type is detected or the time window of the sliding window exceeds the threshold. The batch status determination module is used to maintain a state mapping table for each UAV based on a predefined three-level state model, and to identify whether the batch in the UAV's state mapping table is completed through an instant judgment mechanism and a timed mechanism. The three-level states include RC state, TC state, and Complete state. RC state indicates that a remote control command report has been received but a telemetry command report has not been received. TC state indicates that a telemetry command report has been received. Complete state indicates that the remote control command and telemetry report are consistent. The status display module is used to statistically analyze the proportion of various statuses in the status mapping table in real time and generate batch completion indicators. It adopts an incremental update mechanism to update the proportion of various statuses and generate batch completion indicators. The module presents the execution status of UAV commands through a gridded status display layer, a batch statistics layer, and a historical batch display layer.

[0011] To achieve the above objectives, this application also provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the batch state management method for instruction execution of swarmed UAVs provided in the above embodiments.

[0012] To achieve the above objectives, this application also provides an electronic device, which includes: at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the batch state management method for instruction execution of swarmed UAVs provided in any of the foregoing embodiments.

[0013] This application proposes a batch-based state management method, apparatus, medium, and device for swarm drone command execution. The method receives and parses network data packets in real time via UDP multicast to obtain drone report information. The drone report information includes the drone ID, command report type, and current timestamp. Command report types include remote control command reports and telemetry command reports. A sliding window algorithm is used to automatically identify command report batches in the drone report information. Within the same time window, drones receiving the same command report type are identified as belonging to the same batch. A new batch is created when a new command report type is detected or the sliding window's time window exceeds a threshold. Based on a predefined three-level state model, a state mapping table is maintained for each drone. An instant judgment mechanism and a timing mechanism are used to identify whether a batch in the drone's state mapping table is complete. The three-level state includes RC state, TC state, and Complete state. RC state indicates that a remote control command report has been received but no telemetry command report has been received; TC state indicates that a telemetry command report has been received; and Complete state indicates that a batch has been completed. The system ensures consistency between remote control commands and telemetry reports. It provides real-time statistics on the percentage of each state in the state mapping table and generates batch completion indicators. An incremental update mechanism is used to update the percentage of each state and generate batch completion indicators. The system presents the UAV command execution status through a gridded state display layer, a batch statistics layer, and a historical batch display layer. This multi-layered visualization mechanism based on a state closed loop enables real-time monitoring of UAV command execution status, significantly improving the efficiency of command personnel in understanding the execution status of UAV swarms. A sliding window algorithm and lifecycle management mechanism are used to automatically identify, create, collect, complete, and destroy command batches, effectively solving the challenge of managing concurrent commands from multiple UAVs. A three-level state model and strict state upgrade rules ensure accurate identification of command execution status, avoiding state confusion or misjudgment. Color coding and a state mapping mechanism form an intuitive state identification system, enabling users to quickly understand the UAV command execution status and reducing cognitive load. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating an embodiment of the batch-based state management method for command execution of swarm drones according to this application. Figure 2 This application provides a batch state management system as an embodiment of a batch state management method for swarm drone command execution.

[0015] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0017] Reference Figure 1 and Figure 2 The first embodiment of this application provides a batch-based state management method for instruction execution of swarmed unmanned aerial vehicles (UAVs), the method may include: S101. Receive and parse network data packets in real time via UDP multicast to obtain drone report information. The drone report information includes drone ID, command report type and current timestamp. The command report type includes remote control command report and telemetry command report. In one embodiment of this application, network data packets are received and parsed in real time via UDP multicast. Specifically, the system continuously listens to a specified UDP multicast address, filters and parses the received network data packets, and extracts key UAV report information. The UAV ID is used to uniquely identify each UAV participating in the cluster operation, ensuring that subsequent status tracking and management can accurately correspond to specific devices. The classification of command report types (remote control command report, telemetry command report) clarifies the attributes of the report information, providing a basis for subsequent status judgment. A remote control command report indicates that the UAV command and control system has issued a control command, while a telemetry command report indicates that the UAV has actually executed the command.

[0018] S102. The sliding window algorithm is used to automatically identify the batch of instruction reports in the drone report information. Specifically, the same instruction report type received by different drones within the same time window is determined to be the same batch, and a new batch is created when a new instruction report type or the time window of the sliding window exceeds the threshold. The sliding window algorithm is used to automatically identify instruction report batches, and its batch identification logic is as follows: The system maintains a dedicated batch list for the same instruction report name. When a new instruction report arrives, it checks the time difference starting from the most recently created batch. If the time interval between the new report and the latest batch is within 1000 milliseconds, it is assigned to that batch. If it exceeds 1000 milliseconds, or if a new instruction report type (different from the current batch instruction report type) is detected, a new batch is automatically created.

[0019] In one embodiment of this application, the process of determining the same instruction report type received by different UAVs within the same time window as the same batch, and creating a new batch when a new instruction report type is detected or the time window of the sliding window exceeds a threshold, may include the following: maintaining a batch list based on the same instruction report name; when a new instruction report of the same type arrives within the time window of the sliding window, it belongs to that batch; otherwise, a new batch is created; when different instruction reports arrive, a new batch is created.

[0020] Specifically, the system also implements a timestamp-based deduplication mechanism for instruction reports, stipulating that instruction reports with the same name received within 500ms are considered the same report. This effectively filters redundant reports caused by duplicate instruction transmissions or backup data links, preventing data corruption in subsequent processing. Simultaneously, the system establishes a standardized data structure for each type of instruction report, including fields such as instruction report identifier, receiving timestamp, instruction parameters, and priority. This ensures that instruction reports of different types and from different sources can be processed according to a unified standard in subsequent batch identification and status judgment stages.

[0021] In one embodiment of this application, the lifecycle of the instruction report batch in the drone report information includes a creation stage, a collection stage, a completion stage, a processing stage, and a destruction stage: During the creation phase, this batch is initialized based on drone report information; During the collection phase, receive and process instruction reports, and update the drone status list; During the completion phase, batches that have met the completion conditions are marked as completed. During the processing phase, a visual update is performed after the batch is completed; During the destruction phase, batch information is archived and memory resources are released.

[0022] Specifically, the lifecycle of each instruction report batch fully covers five stages: creation, collection, completion, processing, and destruction. In the creation stage, the system initializes the batch based on the parsed drone report information, including assigning a unique batch ID and recording basic information such as instruction type. In the collection stage, the system continuously receives and processes the instruction reports corresponding to the batch, updating the status list of drones participating in the batch in real time. Once the batch completion conditions are met, it enters the completion stage and is marked as complete. The processing stage executes the business logic after the batch is completed, primarily triggering an update to the visual interface, allowing operators to promptly obtain the batch completion status. The destruction stage archives and stores all information for the batch and releases occupied memory resources to ensure system efficiency. Simultaneously, the system maintains an active batch list, supporting the management of up to three concurrent batches. When this limit is exceeded, the oldest completed batch is automatically cleaned up.

[0023] Through the technical means in S102, automated identification, full lifecycle management, and concurrent control of instruction reporting batches are achieved. The sliding window algorithm and clear batch attribution rules ensure the accuracy of batch division, avoiding confusion between instructions from different time periods and of different types. The complete lifecycle management mechanism makes the entire process from batch creation to destruction orderly and controllable, ensuring the traceability of batch information and optimizing system resource consumption through concurrent quantity limits and old batch cleanup mechanisms. This effectively solves the problem of chaotic management of concurrent instructions from multiple drones in existing technologies, significantly improving the batch management capabilities of clustered drone instruction execution. Clearly, step S102 enables efficient reception, accurate parsing, and standardized organization of drone report information, eliminating redundant and invalid data while ensuring data format consistency. This lays a solid foundation for the smooth operation of subsequent batch identification, status management, and other processes, significantly improving the accuracy and efficiency of data processing.

[0024] S103. Based on a predefined three-level state model, maintain a state mapping table for each UAV, and identify whether the batch in the UAV's state mapping table maintenance is completed through an instant judgment mechanism and a timing mechanism. The three-level state includes RC state, TC state, and Complete state. RC state indicates that a remote control command report has been received but a telemetry command report has not been received. TC state indicates that a telemetry command report has been received. Complete state indicates that the remote control command and telemetry report are consistent. In one embodiment of this application, the three-level state model includes: when a remote control report command is received, a new FeedbackItem is created and its state is set to RC (Remote Controlled) state; when a telemetry report is received, a FeedbackItem with the same name that is incomplete within the last 1000 milliseconds is searched. If it exists and its state is RC, it is upgraded to the Complete state; otherwise, a new FeedbackItem is created and its state is set to TC (Tracking Controlled) state. The system implements a state deduplication mechanism to ignore duplicate state updates and avoid state rollback or confusion; wherein, the upgrade process of the three-level state model is from RC state to TC state, and from TC state to Complete state.

[0025] The two mechanisms for batch completion determination are as follows: The immediate determination mechanism triggers batch completion immediately when all drones in the batch have reached the Complete state; the timed mechanism determines batch completion regardless of whether all drones in the batch have reached the Complete state, as long as the batch duration exceeds a preset threshold of 500ms. Batch completion is determined by satisfying either of these mechanisms, triggering subsequent callback processing.

[0026] The three-level status definitions and upgrade rules are as follows: The three-level status includes RC status, TC status and Complete status. RC status corresponds to red, which means that a remote control command report has been received but a telemetry command report has not been received; TC status corresponds to yellow, which means that a telemetry command report has been received; Complete status corresponds to green, which means that the remote control command and telemetry report are consistent, and the command sending and execution form a closed loop.

[0027] Through S103, this application establishes a precise and orderly state management and batch completion judgment system. Clear state definitions and one-way upgrade rules ensure accurate identification of command execution status, avoiding the ambiguity and confusion in state judgment in existing technologies. The state deduplication mechanism further guarantees the reliability of state data. The dual batch completion judgment mechanism balances the integrity and timeliness of batch execution, ensuring timely confirmation of batch completion when all UAVs have completed the command, while preventing batches from remaining incomplete for extended periods due to individual UAVs failing to provide feedback. This significantly improves the accuracy and real-time performance of the system's batch completion judgment.

[0028] S104. Real-time statistics of the percentage of each type of status in the status mapping table and generation of batch completion indicators. The incremental update mechanism is used to update the percentage of each type of status and generate batch completion indicators. The execution status of UAV commands is presented through a gridded status display layer, a batch statistics layer and a historical batch display layer.

[0029] In one embodiment of this application, the process of generating a batch completion index by calculating the proportion of various states in the real-time statistical state mapping table may include the following: Real-time collection of multi-dimensional statistical data; The multi-dimensional statistical data includes batch ID, instruction report type, total number of drones, number of drones in Complete status, number of drones in TC status, number of drones in RC status, and a list of drone IDs corresponding to each status. The percentage of each status is determined based on multi-dimensional statistical data, and a batch completion rate indicator is generated.

[0030] Specifically, based on this multi-dimensional data, the system calculates the proportion of each state in real time, thereby generating batch completion indicators. Simultaneously, the system employs an incremental update mechanism to update statistical information; that is, relevant statistical data is only updated when the drone's state changes, rather than a full update, effectively reducing the system's data processing pressure and improving statistical efficiency. Furthermore, the system maintains statistical information for the three most recent batches, arranged in reverse chronological order of batch completion time, ensuring that the latest batch statistics are displayed first.

[0031] In one embodiment of this application, the gridded status display layer is used to display the command report status of each drone in real time; The batch statistics layer is used to calculate the batch Complete status rate, TC status rate, and RC status rate in real time to quantitatively evaluate the batch execution performance. The historical batch display layer is used to display the completion status of the most recent batch in reverse chronological order, forming a status evolution trajectory.

[0032] Specifically, the gridded status display layer is used to display the command report status of each drone in real time, supporting parallel monitoring of multiple drones. Operators can intuitively see the specific status of a single drone. The batch statistics layer quantifies the batch execution effect by calculating key indicators such as the Complete status rate, TC status rate, and RC status rate in real time, allowing operators to quickly grasp the overall execution status of the batch. The historical batch display layer displays the completion status of the most recent batch in reverse chronological order, forming a status evolution trajectory, which facilitates operators to trace historical execution. All three display layers combine color coding and status mapping mechanisms, with red corresponding to RC status, yellow to TC status, and green to Complete status, forming an intuitive status identification system.

[0033] The S104 architecture enables accurate and efficient generation of batch statistical data and intuitive visualization of command execution status. Multi-dimensional statistical data and an incremental update mechanism ensure the comprehensiveness and real-time nature of the statistical results, providing operators with detailed batch execution data support. Meanwhile, the three-layer visualization architecture and color-coding mechanism make complex status information clear and easy to understand. Operators can quickly grasp the status of each drone, the completion status of each batch, and the execution trajectory of historical batches without having to consult large amounts of raw data. This significantly reduces cognitive load, improves operational efficiency, and solves the problems of unintuitive visualization and difficulty in quickly identifying anomalies in existing technologies.

[0034] In another embodiment of this application, the implementation process of the present invention is described in detail using the scenario of simultaneously monitoring a cluster of 20 drones as an example.

[0035] Implementation Scenario: Assuming it is necessary to simultaneously monitor the command execution status of 20 drones (numbered UAV001-UAV020), the system will operate according to the following steps: Step 1: The system receives and parses network data packets in real time via UDP multicast. In a scenario monitoring 20 drones, the system continuously receives command report data from each drone, including the drone ID, command report type, current timestamp, and command report content. A 500ms timestamp deduplication mechanism is implemented to ensure that for each drone, command reports with the same name received within 500ms are considered the same report. For example, if UAV001 receives eight command reports with the content "fly directly" within 500ms, the system only retains the first report and ignores subsequent duplicate reports. Duplicate reports are usually caused by multiple transmissions of commands or the existence of backup data links.

[0036] Step Two: The system automatically identifies command reporting batches based on time window parameters. In a scenario monitoring 20 UAVs, when the operator sends a "takeoff" command to UAV001-UAV015, the system will receive a "direct flight" command report. Within 500ms of receiving the first "direct flight" report, all "direct flight" reports are considered to be in the same batch. If a different command report is received within 500ms, such as UAV011 receiving a "left turn" command, a new batch is created. Two batches can be managed simultaneously for their entire lifecycle. If a command report is received after 500ms, a new batch is created. Each batch undergoes full lifecycle management through five stages: creation, collection, completion, processing, and destruction.

[0037] Step 3: The system defines a three-level state model: RC state (red) indicates that the command has been sent but no telemetry confirmation has been received; TC state (yellow) indicates that a telemetry report has been received; and Complete state (green) indicates that the command has been sent and executed. In a scenario with 20 drones, when the system receives a remote control report of "direct flight" from UAV001-UAV015, it sets the state of these drones to RC state. Subsequently, when it receives another telemetry report of "direct flight" from UAV001-UAV005, the system searches for a FeedbackItem with the same name that has not been completed within the last 500 milliseconds and upgrades the state of these drones from RC to Complete state. When the system only receives a telemetry report of "right turn" from UAV016-UAV020, it sets UAV016-UAV020 to TC state.

[0038] State escalation follows strict one-way rules, prohibiting state downgrades or cross-level escalations.

[0039] Step Four: The system uses two mechanisms to identify batch completion. In a scenario monitoring 20 drones, if all drones in the first batch (UAV001-UAV015) reach the Complete state within 500ms after the command is sent, the system triggers batch completion via an immediate judgment mechanism. For the second batch (UAV016-UAV020), if four drones reach the Complete state and one drone remains in the RC state 600ms after the command is sent, but the 500ms timing threshold has been exceeded, the system determines batch completion via a timing mechanism. Batch completion is determined when either condition is met, and the system automatically executes the batch completion callback mechanism.

[0040] Step 5: Batch Statistics and Status Aggregation The system implements multi-dimensional batch statistics. For a monitoring scenario of 20 drones, the statistics for the first batch are: Batch ID - Batch001, Command Type - Straight Flight, Total Number of Selected Drones - 15, Number of Drones in Complete State - 15, Number of Drones in TC State - 0, Number of Drones in RC State - 0. The statistics for the second batch are: Batch ID - Batch002, Command Type - Hover, Total Number of Drones - 5, Number of Drones in Complete State - 4, Number of Drones in TC State - 0, Number of Drones in RC State - 1. The system calculates the percentage of each state in real time, generates batch completion indicators, and maintains the statistics for the three most recent batches, sorted in reverse chronological order by batch completion time.

[0041] Step 6: Multi-layered visual status display The system uses a three-layer architecture to intuitively present the command execution status of 20 drones. The first layer, a gridded status display layer, shows the command reporting status of each drone in real time. The first row of the grid, UAV001-UAV015, is displayed in green (Complete status), the second row, UAV016-UAV019, is also displayed in green (Complete status), and UAV020 is displayed in red (RC status). The second layer, the batch statistics layer, shows that batch 1: 15 / 15 is completed (100%), and batch 2: 4 / 5 is completed (80%). The third layer, the historical batch display layer, shows the completion status of the three most recent batches in reverse chronological order, forming a status evolution trajectory.

[0042] Based on the above embodiments, this application also provides a batch-based state management device for swarm drone command execution, comprising: The data parsing module is used to receive and parse network data packets in real time via UDP multicast to obtain drone report information. The drone report information includes drone ID, command report type and current timestamp. Command report types include remote control command report and telemetry command report. The batch identification module is used to automatically identify the batch of instruction reports in the drone report information using a sliding window algorithm. Specifically, the same instruction report type received by different drones within the same time window is identified as the same batch, and a new batch is created when a new instruction report type is detected or the time window of the sliding window exceeds the threshold. The batch status determination module is used to maintain a state mapping table for each UAV based on a predefined three-level state model, and to identify whether the batch in the UAV's state mapping table is completed through an instant judgment mechanism and a timed mechanism. The three-level states include RC state, TC state, and Complete state. RC state indicates that a remote control command report has been received but a telemetry command report has not been received. TC state indicates that a telemetry command report has been received. Complete state indicates that the remote control command and telemetry report are consistent. The status display module is used to statistically analyze the proportion of various statuses in the status mapping table in real time and generate batch completion indicators. It adopts an incremental update mechanism to update the proportion of various statuses and generate batch completion indicators. The module presents the execution status of UAV commands through a gridded status display layer, a batch statistics layer, and a historical batch display layer.

[0043] Based on the above embodiments, this application also provides a computer-readable storage medium including instructions that, when run on a computer, cause the computer to execute the batch state management method for instruction execution of swarmed UAVs provided in the above embodiments.

[0044] Based on the above embodiments, this application also provides an electronic device, the electronic device comprising: at least one processor, a memory, and an input / output unit; wherein, the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the batch state management method for instruction execution of swarm UAVs provided in any of the foregoing embodiments.

[0045] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A batch-based state management method for instruction execution in swarmed unmanned aerial vehicles (UAVs), characterized in that, include: The network data packets are received and parsed in real time via UDP multicast to obtain the drone report information. The drone report information includes the drone ID, command report type and current timestamp. The command report type includes remote control command report and telemetry command report. The sliding window algorithm is used to automatically identify the batch of instruction reports in the drone report information. Specifically, the same instruction report type received by different drones within the same time window is identified as the same batch, and a new batch is created when a new instruction report type is detected or the time window of the sliding window exceeds the threshold. Based on a predefined three-level state model, a state mapping table is maintained for each UAV. The batch maintenance of the UAV's state mapping table is identified through an instant judgment mechanism and a timed mechanism. The three-level state includes RC state, TC state, and Complete state. RC state indicates that the remote control command has been received but the telemetry command has not been received. TC state indicates that the telemetry command has been received. Complete state indicates that the remote control command and the telemetry command are consistent. The system generates batch completion indicators by statistically analyzing the percentage of each status in the status mapping table in real time. It also updates the percentage of each status using an incremental update mechanism and presents the drone command execution status through a gridded status display layer, a batch statistics layer, and a historical batch display layer.

2. The batch-based state management method for swarm UAV command execution as described in claim 1, characterized in that, The step of identifying the same command report type received by different drones within the same time window as the same batch, and creating a new batch when a new command report type is detected or the time window of the sliding window exceeds a threshold, includes: Maintain a batch list based on the same instruction report name; If a new report of the same instruction arrives within the time window of the sliding window, it belongs to that batch; otherwise, a new batch is created. Create a new batch when different instruction reports arrive.

3. The batch-based state management method for swarm UAV command execution as described in claim 1, characterized in that, The three-level state model includes: When a remote control report is received, a new FeedbackItem is created and its status is set to RC (Responding Control). When a telemetry report is received, a FeedbackItem with the same name that is not yet completed within the last 1000 milliseconds is searched. If it exists and its status is RC, it is upgraded to Complete; otherwise, a new FeedbackItem is created and its status is set to TC (Tracking Control). The system implements a state deduplication mechanism to ignore duplicate state updates and avoid state rollback or confusion. The upgrade process of the three-level state model is from RC state to TC state, and from TC state to Complete state.

4. The batch-based state management method for swarm UAV command execution as described in claim 1, characterized in that, The lifecycle of the instruction report batch in the drone report information includes the creation stage, collection stage, completion stage, processing stage, and destruction stage: During the creation phase, this batch is initialized based on drone report information; During the collection phase, receive and process instruction reports, and update the drone status list; During the completion phase, batches that have met the completion conditions are marked as completed. During the processing phase, a visual update is performed after the batch is completed; During the destruction phase, batch information is archived and memory resources are released.

5. The batch-based state management method for swarm UAV command execution as described in claim 1, characterized in that, The real-time statistical status mapping table generates batch completion indicators based on the proportion of various statuses, including: Real-time collection of multi-dimensional statistical data; The multi-dimensional statistical data includes batch ID, instruction report type, total number of drones, number of drones in Complete status, number of drones in TC status, number of drones in RC status, and a list of drone IDs corresponding to each status. The percentage of each status is determined based on multi-dimensional statistical data, and a batch completion rate indicator is generated.

6. The batch-based state management method for swarm UAV command execution as described in claim 1, characterized in that, The gridded status display layer is used to display the command report status of each drone in real time; The batch statistics layer is used to calculate the batch Complete status rate, TC status rate, and RC status rate in real time to quantitatively evaluate the batch execution performance. The historical batch display layer is used to display the completion status of the most recent batch in reverse chronological order, forming a status evolution trajectory.

7. A batch-based status management device for instruction execution of swarmed unmanned aerial vehicles (UAVs), characterized in that, include: The data parsing module is used to receive and parse network data packets in real time via UDP multicast to obtain drone report information. The drone report information includes drone ID, command report type and current timestamp. Command report types include remote control command report and telemetry command report. The batch identification module is used to automatically identify the batch of instruction reports in the drone report information using a sliding window algorithm. Specifically, the same instruction report type received by different drones within the same time window is identified as the same batch, and a new batch is created when a new instruction report type is detected or the time window of the sliding window exceeds the threshold. The batch status determination module is used to maintain a state mapping table for each UAV based on a predefined three-level state model, and to identify whether the batch in the UAV's state mapping table is completed through an instant judgment mechanism and a timed mechanism. The three-level states include RC state, TC state, and Complete state. RC state indicates that a remote control command report has been received but a telemetry command report has not been received. TC state indicates that a telemetry command report has been received. Complete state indicates that the remote control command and telemetry report are consistent. The status display module is used to statistically analyze the proportion of various statuses in the status mapping table in real time and generate batch completion indicators. It adopts an incremental update mechanism to update the proportion of various statuses and generate batch completion indicators. The module presents the execution status of UAV commands through a gridded status display layer, a batch statistics layer, and a historical batch display layer.

8. A computer-readable storage medium, characterized in that, It includes instructions that, when run on a computer, cause the computer to perform the batch state management method for instruction execution of swarmed UAVs as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, The electronic device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the batch state management method for instruction execution of swarmed UAVs according to any one of claims 1 to 6.

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