Unmanned aerial vehicle cluster instruction control and return monitoring method and system based on dynamic configuration

Abstract

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle cluster instruction control and return monitoring method and system based on dynamic configuration, and the method comprises the steps: analyzing an external configuration file, and dynamically generating a control interface of an unmanned aerial vehicle instruction and logic associated with the control interface; wherein the external configuration file defines metadata, a parameter structure and a verification rule of the instruction in a declarative mode, and the metadata comprises classification and attributes; in response to the operation of the user, sending an instruction to an unmanned aerial vehicle cluster based on the control interface of the unmanned aerial vehicle instruction; in a preset time window, collecting state feedback data of the unmanned aerial vehicle cluster for the instruction, and calculating a cluster-level instruction execution efficiency index based on the state feedback data; and outputting the efficiency index in a visual mode to assist in decision making. The method can solve the technical problems that an instruction system is rigid, and command decisions are reduced to excessively depend on subjective experience of operators and manual statistics.

Description

Technical Field

[0001] The embodiments of this application relate to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method and system for UAV swarm command control and reporting monitoring based on dynamic configuration. Background Technology

[0002] With the rapid development of drone technology, drone swarms are increasingly being used in disaster relief, agricultural plant protection, and other fields. Traditional drone swarm control systems typically use hard-coded control commands, but this has significant limitations.

[0003] Currently, most UAV swarm control systems employ a hard-coded approach to implement control command sets, meaning that command definitions, user interface layouts, and control logic are all embedded in the software source code (such as C++ header files or UI files). This architecture means that any modification to the command system (such as adding, deleting, or adjusting commands) requires developers to modify the code, recompile, test, and deploy the entire software, a process that can take days to weeks and cannot adapt to rapidly changing requirements in mission environments. While some solutions exist that define UI layouts using configuration files, these solutions only address button arrangement and text display, failing to deeply define command semantics, parameter structures, and underlying control logic, resulting in poor system scalability.

[0004] Furthermore, traditional systems lack the ability to quantitatively assess cluster-level performance. When controlling multiple drones to execute the same command, the system can only provide status feedback at the individual drone level (e.g., "Drone A command sent successfully"). Commanders cannot quickly and intuitively grasp the overall execution effect of the command at the cluster level (e.g., how many out of 10 drones successfully executed the route update command). Decision-making relies on the operator's subjective experience and manual statistics, resulting in slow response times and a high risk of errors, making it difficult to meet real-time command requirements.

[0005] Therefore, how to solve the rigidity of the command system and reduce the over-reliance on the subjective experience of operators and manual statistics in command and decision-making are urgent technical problems that need to be solved. Summary of the Invention

[0006] In view of this, embodiments of this application propose a method and system for command control and reporting monitoring of UAV swarms based on dynamic configuration. This solves the technical problems of rigid command systems and reducing the over-reliance on operators' subjective experience and manual statistics in command decisions. It realizes a UAV control system with dynamic configuration of command system and provides swarm-level quantitative performance feedback, thereby improving flexibility, scalability and scientific decision-making.

[0007] To achieve the above objectives, embodiments of this application propose a method for command control and reporting monitoring of a drone swarm based on dynamic configuration, the method comprising the following steps: By parsing external configuration files, the control interface for drone commands and the logic associated with the control interface are dynamically generated. The external configuration file defines the metadata, parameter structure, and verification rules of the commands in a declarative manner. The metadata includes categories and attributes. In response to user actions, the control interface based on drone commands sends instructions to the drone cluster. Within a preset time window, collect status feedback data of the drone cluster to commands, and calculate cluster-level command execution performance indicators based on the status feedback data; Output performance indicators in a visual manner to assist decision-making.

[0008] To achieve the above objectives, embodiments of this application also propose a dynamically configured unmanned aerial vehicle (UAV) swarm command control and reporting monitoring system, the system comprising: The configuration parsing and interface generation module is used to dynamically generate the control interface for UAV commands and the logic associated with the control interface by parsing external configuration files. The external configuration file defines the metadata, parameter structure and verification rules of the commands in a declarative manner. The metadata includes categories and attributes. The command sending module is used to respond to user operations and send commands to the drone cluster based on the drone command control interface; The cluster performance statistics module is used to collect the status feedback data of the UAV cluster to commands within a preset time window, and to calculate the cluster-level command execution performance index based on the status feedback data. The visualization module is used to output performance indicators in a visual way to assist decision-making.

[0009] To achieve the above objectives, embodiments of this application also propose an electronic device, including a processor and a memory, wherein the memory stores instructions executable by the processor, and the processor is configured to execute the instructions such that the electronic device can implement a dynamically configured UAV swarm command control and reporting monitoring method as described above.

[0010] To achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program that, when executed by a processor, enables a method for controlling and monitoring the command and feedback of a drone swarm based on dynamic configuration as described above.

[0011] This application proposes a method for controlling and monitoring the command and feedback of a drone swarm based on dynamic configuration. By parsing an external configuration file, it dynamically generates a control interface for drone commands and the logic associated with that interface. Then, in response to user operations, it sends commands to the drone swarm based on the control interface. Next, within a preset time window, it collects status feedback data from the drone swarm regarding the commands and calculates swarm-level command execution performance metrics based on this data. Finally, it outputs the performance metrics in a visual manner to aid decision-making. Because the external configuration file defines the metadata, parameter structure, and verification rules of the commands in a declarative manner, the metadata data... This method incorporates classification and attributes, thus separating instruction definitions from program code and describing them using a declarative, structured configuration file. This achieves physical separation between the instruction set and software code, and allows for functional definition through configuration file modification, improving the flexibility of configured UAV swarm instructions. Furthermore, by calculating instruction execution performance indicators and outputting these indicators in a visual manner, commanders can be provided with objective and quantifiable metrics, avoiding tedious manual calculations. Based on this, this method enables UAV control systems with dynamic instruction set configuration and swarm-level quantitative performance feedback, enhancing flexibility, scalability, and scientific decision-making.

[0012] Optionally, the logic associated with the control interface includes: categorized navigation elements and interactive components; the control interface and the logic associated with the control interface that dynamically generate UAV commands by parsing external configuration files include: parsing external configuration files to obtain command categorization structures and dynamically creating categorized navigation elements; determining the type of command and generating interactive components differentiated based on the type of command.

[0013] Optionally, the type of instruction is determined, and interactive components are generated differently based on the type of instruction, including: if the instruction is a switch instruction type, a button component that is directly triggered is generated; if the instruction is a composite instruction type, a dialog box containing parameter input controls is dynamically created; wherein, the parameter input control type is dynamically determined based on the parameter data type defined in the external configuration file.

[0014] Optionally, if the instruction is a composite instruction type, a dialog box containing parameter input controls is dynamically created, including: if the instruction is a composite instruction type, dynamically instantiating and configuring the corresponding input validation logic based on the parameter data type; wherein, the input validation logic includes: numerical range, enumerated options, mandatory field checks, and automatic association with map point selection function for special parameters; special parameters include latitude and longitude parameters.

[0015] Optionally, the external configuration file is in XML format, and its structure is defined by XML Schema; wherein, the XML Schema definition includes command categories, command types, parameter types, and option lists.

[0016] Optionally, the instruction execution performance indicator is the instruction closure rate. Within a preset time window, status feedback data of the UAV cluster to instructions is collected, and a cluster-level instruction execution performance indicator is calculated based on the status feedback data. This includes: monitoring and receiving status feedback data of the UAV cluster within the preset time window; performing correlation and aggregation analysis on status feedback data of the same instruction sent to multiple UAVs to obtain the number of UAVs in the UAV cluster that have completed instruction closure; and obtaining the instruction closure rate based on the number of UAVs that have completed instruction closure and the total number of UAVs in the UAV cluster. Instruction closed-loop rate Calculated using the following formula: .

[0017] in, This indicates the number of drones that successfully responded to the command. This indicates the total number of target drones.

[0018] Optionally, the method provided in the embodiments of this application further includes: triggering alarms of different levels based on the instruction closure rate using a set threshold; the step of outputting performance indicators in a visual manner to assist decision-making includes: if the instruction closure rate is greater than or equal to a first threshold, then outputting it with a green visual icon; if the instruction closure rate is greater than or equal to a second threshold and less than the first threshold, then outputting it with a yellow visual icon; if the instruction closure rate is less than the second threshold, then outputting it with a red visual icon. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies of this application will be briefly introduced below. Obviously, the following drawings 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. The drawings described herein are only used to explain this application and are not intended to limit this application.

[0020] Figure 1 This is a flowchart of a method for controlling and monitoring the command and feedback of a drone swarm based on dynamic configuration, provided in one embodiment of this application; Figure 2 This is a schematic diagram of an external configuration file provided in one embodiment of this application; Figure 3This is a flowchart of an interface generation method provided in one embodiment of this application; Figure 4 This is a flowchart illustrating the statistics of cluster instruction loop closure rate provided in one embodiment of this application; Figure 5 This is a schematic diagram of a cluster performance statistics interface provided in one embodiment of this application; Figure 6 This is a system architecture and workflow diagram provided in one embodiment of this application; Figure 7 This is a schematic diagram of the structure of a dynamically configured unmanned aerial vehicle (UAV) swarm command control and reporting monitoring system provided in another embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. Those skilled in the art will understand that many technical details have been presented in the embodiments of this application to facilitate better understanding. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments. The division of the following embodiments is for ease of description and should not constitute any limitation on the specific implementation of this application. The following embodiments can be combined with and referenced by each other without contradiction.

[0022] With the rapid development of drone technology, drone swarms are increasingly being used in disaster relief, agricultural plant protection, and other fields. Traditional drone swarm control systems typically use hard-coded control commands, but this has significant limitations.

[0023] Currently, most UAV swarm control systems employ a hard-coded approach to implement control command sets, meaning that command definitions, user interface layouts, and control logic are all embedded in the software source code (such as C++ header files or UI files). This architecture means that any modification to the command system (such as adding, deleting, or adjusting commands) requires developers to modify the code, recompile, test, and deploy the entire software, a process that can take days to weeks and cannot adapt to rapidly changing requirements in mission environments. While some solutions exist that define UI layouts using configuration files, these solutions only address button arrangement and text display, failing to deeply define command semantics, parameter structures, and underlying control logic, resulting in poor system scalability.

[0024] Furthermore, traditional systems lack the ability to quantitatively assess cluster-level performance. When controlling multiple drones to execute the same command, the system can only provide status feedback at the individual drone level (e.g., "Drone A command sent successfully"). Commanders cannot quickly and intuitively grasp the overall execution effect of the command at the cluster level (e.g., how many out of 10 drones successfully executed the route update command). Decision-making relies on the operator's subjective experience and manual statistics, resulting in slow response times and a high risk of errors, making it difficult to meet real-time command requirements.

[0025] Therefore, how to solve the rigidity of the command system and reduce the over-reliance on the subjective experience of operators and manual statistics in command and decision-making are urgent technical problems that need to be solved.

[0026] In view of this, embodiments of this application propose a method and system for command control and reporting monitoring of UAV swarms based on dynamic configuration. This solves the technical problems of rigid command systems and reducing the over-reliance on operators' subjective experience and manual statistics in command decisions. It realizes a UAV control system with dynamic configuration of command system and provides swarm-level quantitative performance feedback, thereby improving flexibility, scalability and scientific decision-making.

[0027] One embodiment of this application proposes a method for command control and reporting monitoring of a drone swarm based on dynamic configuration, applied to an electronic device, wherein the electronic device can be a terminal or a server. This embodiment and the following embodiments will use a server as an example for illustration. The implementation details of the method for command control and reporting monitoring of a drone swarm based on dynamic configuration proposed in this embodiment will be described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0028] The specific process of the UAV swarm command control and report monitoring method based on dynamic configuration proposed in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: By parsing the external configuration file, dynamically generate the control interface for the drone commands and the logic associated with the control interface.

[0029] The external configuration file defines the metadata, parameter structure, and validation rules of the instructions in a declarative manner. The metadata includes categories and attributes.

[0030] In one possible embodiment, the external configuration file is in XML format, and its structure is defined by an XML Schema; wherein the XML Schema definition includes instruction categories, instruction types, parameter types, and option lists.

[0031] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of an external configuration file provided for an embodiment of this application.

[0032] The system relies on a predefined configuration file conforming to a specific XML schema. This file declaratively describes the metadata of the entire UAV command system. Its core structure includes: Command button set: As the root element, it contains multiple <category> nodes.

[0033] Category: Each category has attributes such as "name" and "icon path", which are used to generate navigation menus on the interface.

[0034] Directives: Defined under categories, these are the core elements. Each directive defines its key information through attributes, including: Identifier: The unique ID of the instruction.

[0035] Name: The text displayed on the interface.

[0036] Type: For example, "Switch command" (no parameters required, one-click trigger) or "Compound command" (requires input of multiple parameters).

[0037] Instruction code: The underlying communication protocol code corresponding to this instruction.

[0038] Parameters: For "compound instructions", there will be multiple <parameter> child nodes. Each parameter defines: Types include "numeric", "integer", "Boolean", "enumeration", "text", etc.

[0039] Minimum / Maximum: Range constraints on numerical parameters.

[0040] Default value: The default value of the parameter.

[0041] Options: For the "enumeration" type, define all its optional values ​​(value-display text pairs).

[0042] Understandably, by having each category have attributes such as "name" and "icon path" and generating a navigation menu on the interface, the fixed category logic in traditional systems can be replaced.

[0043] In one possible embodiment, the logic associated with the control interface includes: category navigation elements and interactive components; step 101 includes: parsing the external configuration file to obtain the instruction category structure and dynamically creating category navigation elements; determining the type of instruction and generating interactive components differentiated based on the type of instruction.

[0044] For example, such as Figure 3 As shown, Figure 3This is a flowchart illustrating an interface generation method proposed in an embodiment of this application. After loading and parsing the XML configuration file, a structured instruction tree model, i.e., an instruction classification structure, can be constructed in memory. The instruction tree model reflects the hierarchical relationship and attributes of the categories, instructions, and parameters defined in the configuration file. Then, by traversing the category nodes in the instruction tree model, corresponding category buttons or menu items are dynamically created in the navigation area of ​​the software's main interface. Users can switch between different instruction sets by clicking these buttons. When a user selects a category, all instructions under that category can be traversed; interactive components can be generated differently based on the type of instruction. See the following embodiments for details.

[0045] In one possible embodiment, the type of instruction is determined, and interactive components are generated differently based on the type of instruction, including: if the instruction is a switch instruction type, a button component that is directly triggered is generated; if the instruction is a composite instruction type, a dialog box containing parameter input controls is dynamically created.

[0046] The parameter input control type is dynamically determined based on the parameter data type defined in the external configuration file.

[0047] For example, if the instruction is a switch instruction type, a UI button control can be dynamically created, its display text set to the instruction's "name," and its click event callback function bound to the instruction's "identifier" and "instruction code." This process does not require writing any instruction-specific UI code and generates a directly triggered button component.

[0048] For a detailed description of the instruction as a compound instruction type, please refer to the following example.

[0049] In one possible embodiment, if the instruction is a composite instruction type, a dialog box containing parameter input controls is dynamically created, including: if the instruction is a composite instruction type, dynamically instantiating and configuring corresponding input validation logic based on the parameter data type.

[0050] The input validation logic includes: numerical range, enumeration options, mandatory field checks, and automatic association of special parameters with the map point selection function; special parameters include latitude and longitude parameters.

[0051] For example, if the instruction is a compound instruction type, a button can be created whose click event triggers a dynamic dialog assembly process: 1. Create a blank dialog box: The system instantiates an empty dialog box window.

[0052] 2. Dynamically create parameter input controls: The engine reads all parameter definitions of the instruction and dynamically instantiates and configures the corresponding input components based on the "type" attribute of each parameter. Type="Number" or "Integer": Creates a number input box and automatically sets its minimum, maximum, unit, and default value.

[0053] Type="enumeration": Creates a drop-down selection box and populates it with the list of <options> defined in the XML.

[0054] Type="Boolean": Creates a checkbox.

[0055] 3. Attaching validation logic: The system automatically attaches input validation logic (such as range check and required field check) to each input control according to the parameter definition to ensure the validity of the input data.

[0056] 4. Bind Confirmation Event: Arrange all dynamically created input controls in sequence within the dialog box. Finally, bind the "Confirm" button in the dialog box to the command sending logic.

[0057] Understandably, the above process allows for the generation of a fully functional command interface without requiring the writing of any specific UI code. New commands can then be generated by adding the corresponding configuration nodes to the XML configuration file.

[0058] Step 102: In response to the user's operation, send instructions to the drone cluster based on the drone command control interface; For example, after triggering a command through a dynamically generated interface, the system can respond to the user's operation on the control interface and send commands to the drone cluster based on the drone command control interface. For instance, in response to the user's operation, after sending commands to N selected drones, a statistical batch is created for each command, recording the command identifier, target drone list, and time window; simultaneously, drone feedback and statistical batches are associated within a preset time window (e.g., 10 seconds), and performance calculation is automatically triggered after the timeout.

[0059] For example, after a user clicks the command button generated by the above steps, a command can be sent to the drone swarm. For on / off commands, predefined command codes can be used directly. For compound commands, user-inputted or selected parameter values ​​can be collected from various input controls (input boxes, drop-down menus, etc.) in a dynamically generated dialog box. Then, according to the command's communication protocol format, the command code and the collected parameter values ​​are assembled into a command data message that can be parsed by the drones. Finally, the assembled command message is sent to one or more drones currently selected by the user (i.e., the drone swarm) through the communication module. At this point, the user's operation is translated into actual control signals.

[0060] Step 103: Within a preset time window, collect status feedback data of the UAV cluster to the commands, and calculate cluster-level command execution performance indicators based on the status feedback data.

[0061] For example, the preset time window is the effective period for waiting for and collecting drone status feedback. The preset time window can be a manually set time window or a time window pre-configured by the device; this embodiment does not impose specific limitations on this. For example, the preset time window can be 10 seconds (s).

[0062] In one possible embodiment, the instruction execution performance indicator is the instruction loop closure rate; step 103 includes: monitoring and receiving status feedback data of the drone cluster within a preset time window; performing correlation and aggregation analysis on the status feedback data of the same instruction sent to multiple drones to obtain the number of drones in the drone cluster that have completed instruction loop closure; and obtaining the instruction loop closure rate based on the number of drones that have completed instruction loop closure and the total number of drones in the drone cluster. Instruction closed-loop rate Calculated using the following formula: .

[0063] in, This indicates the number of drones that successfully responded to the command. This indicates the total number of target drones.

[0064] For example, such as Figure 4 As shown, Figure 4 This is a flowchart illustrating a cluster command closure rate statistics method provided in an embodiment of this application. When a command is sent to a cluster containing N drones, a statistical batch object can be created. This object records key information, including: command identifier, target drone list (number N), and batch creation timestamp. A timer is then started for this batch, with a preset time window T (e.g., 10 seconds). Within the time window T, the communication link is continuously monitored to receive status feedback messages from the drones. For each received feedback message: it is determined whether the command identifier in the feedback message matches an active statistical batch, and it is confirmed whether the drone sending the feedback is in the target list of that batch. The feedback message is parsed to determine whether it indicates that the command has been successfully received and executed by the drone (e.g., the feedback status is "success" or "complete"). If it is determined to be successful, the unique identifier of the drone is recorded in the "successfully responded list" of the corresponding statistical batch, ultimately collecting M drones that successfully responded to the command.

[0065] Statistical calculations for this batch are triggered when one of the following two conditions occurs: Case 1: If the timer times out and the preset time window T ends, the instruction closure rate will be forcibly calculated and the statistics interface will be updated.

[0066] Scenario 2: If feedback has been received from all N drones in the target list before the end of time window T, then calculate the quality closure rate and update the statistics interface.

[0067] Step 104: Output the performance indicators in a visual manner to assist decision-making.

[0068] For example, a single line of information can be displayed for each active or completed batch of instructions in the cluster instruction statistics monitoring window (e.g., a separate visualization panel). Figure 5 As shown, Figure 5 This is a schematic diagram of a cluster performance statistics interface provided for an embodiment of this application; the cluster performance statistics interface includes command name, cluster progress, loop closure rate, etc.

[0069] In one possible embodiment, the method provided in this application further includes: triggering alarms of different levels based on the instruction loop closure rate using a set threshold; step 104 includes: if the instruction loop closure rate is greater than or equal to a first threshold, then outputting a green visual icon; if the instruction loop closure rate is greater than or equal to a second threshold and less than the first threshold, then outputting a yellow visual icon; if the instruction loop closure rate is less than the second threshold, then outputting a red visual icon.

[0070] For example, the second threshold is less than the first threshold. For instance, the first threshold could be 90%, and the second threshold could be 70%. Figure 5 As shown, when the instruction is target tracking, the instruction closure rate is 100%. At this time, it can be output as a green visual icon to indicate that the status is good and no intervention is required. This indicates that the instruction has been successfully executed by the vast majority of units in the cluster, and the commander can consider that the instruction task is progressing smoothly.

[0071] When the instruction is a formation change, the instruction closure rate is 80%. At this time, a yellow visual icon can be output to indicate that the status is normal, so as to remind the commander that there is a certain delay in the execution of the instruction or that some units are encountering problems. Attention should be paid, but immediate intervention may not be necessary.

[0072] When the command is for area reconnaissance, the command closure rate is 50%. At this time, a red visual icon can be output to indicate that the status is crossed and immediate intervention is required. This indicates that the command is blocked at the cluster level. The commander needs to find out the cause immediately (such as communication link interruption, individual UAV failure, inapplicable command parameters, etc.) and decide whether to resend the command or adjust the mission plan.

[0073] like Figure 6 As shown, Figure 6This application provides a system architecture and workflow diagram for embodiments of the present application; the system architecture includes: an XLM configuration file module, a configuration parsing engine, a human-machine interface, a cluster statistician, a command sending module, and a UAV status feedback module.

[0074] The XML configuration file module contains the system's external driver source and declarative instruction library. Independent of the system program code, it defines the metadata of all executable instructions for the drone swarm using structured markup language (XML). The XML code snippet shown in the diagram illustrates the configuration structure, including definitions of category types, elements, and their attributes. Specifically, this may include instruction identifiers, names, parameter types, validation rules, etc. This achieves a physical separation between instruction logic and software code.

[0075] The configuration parsing engine can receive and parse XML configuration files. It reads all metadata declared in the XML configuration file, such as instructions, parameters, and interface layout, and builds a structured, programmable instruction tree model in system memory.

[0076] The human-computer interaction interface can receive the instruction tree model from the configuration parsing engine and dynamically generate a graphical user interface based on it. At the same time, it can receive and visualize the calculation results from the cluster statistician, such as graphically displaying performance indicators such as instruction closure rate.

[0077] The command sending module can respond to user actions on the dynamically generated interface (such as clicking a button or entering a confirmation dialog). Based on the command definition corresponding to the action and the parameters input by the user, the command is assembled into a low-level communication protocol message that the drone can recognize, and then sent to the selected drone swarm.

[0078] The UAV status feedback module can intuitively display the discrete execution status (such as success, failure, execution) of multiple UAVs (such as UAV1, UAV2, ...) for commands. This raw data is the input for the cluster statistician to perform aggregation calculations.

[0079] The cluster statistics tool allows for the creation of statistical batches, collection of feedback data, calculation of loop closure rates, and triggering of alarms. Specifically, a statistical batch is created for each command sent, recording the command identifier, the list of target drones, and a preset statistical time window. Within the time window, status feedback from the drones is continuously received. After the window ends, cluster-level metrics such as the "command loop closure rate" are calculated based on the number of successfully responding drones and the total number of targets.

[0080] It is understood that the embodiments of this application propose a dynamic control system architecture based on an external model, specifically by extracting the definition of instructions from the program code and describing them using a declarative, structured configuration file (such as an XML configuration file). The system parses this file at runtime and dynamically constructs a complete control interface, including navigation categories, buttons, dialog boxes, parameter input validation, and its underlying logic. Compared to existing technologies, the embodiments of this application achieve the automatic generation of interactive software functions from instruction metadata, transforming the UAV control system from fixed-function software into a platform whose functions can be defined by modifying configuration files.

[0081] It is also understood that the embodiments of this application propose a quantified statistical method for command effectiveness in clusters. Specifically, for each command sent to the cluster, the feedback status of each UAV is tracked and aggregated within a configurable time window; then, a quantitative indicator reflecting the overall execution performance, namely the command closure rate, is calculated. Compared with the prior art, the embodiments of this application obtain decision-making effectiveness indicators through specific statistical algorithms to evaluate cluster performance.

[0082] This application proposes a method for controlling and monitoring drone swarm commands based on dynamic configuration. It dynamically generates a control interface for drone commands and the logic associated with that interface by parsing an external configuration file. Then, in response to user operations on the control interface, commands are sent to the drone swarm based on the drone command control interface. Next, within a preset time window, status feedback data of the drone swarm to the commands is collected, and swarm-level command execution performance indicators are calculated based on the status feedback data. Finally, the performance indicators are output in a visual manner to assist decision-making. Because the external configuration file defines the metadata, parameter structure, and verification rules of the commands in a declarative manner, the metadata... Based on classification and attributes, the definition of instructions can be extracted from the program code and described using a declarative, structured configuration file. This achieves physical separation between the instruction system and the software code, and allows for functional definition by modifying the configuration file, thereby improving the flexibility of configured UAV swarm instructions. Furthermore, by calculating instruction execution performance indicators and outputting these indicators in a visual manner, objective and quantifiable metrics can be provided to commanders, avoiding tedious manual calculations. Based on this, this method enables UAV control systems with dynamic instruction system configuration and swarm-level quantitative performance feedback, thereby improving flexibility, scalability, and scientific decision-making.

[0083] In summary, the embodiments of this application can achieve the following technical effects: 1. Improve command extension efficiency: Reduce the development-deployment cycle required to add a new command from the traditional "days" to the "minutes" level. It only requires editing the XML configuration file to take effect, improving efficiency by more than 95% and significantly reducing reliance on developers.

[0084] 2. Enhance swarm situational awareness: The command closure rate is provided as a quantitative indicator in the UAV control system, enabling commanders to instantly grasp the overall execution status of commands in the swarm, providing unprecedented data support for UAV decision-making.

[0085] 3. Significantly reduced costs: Since the maintenance of the instruction set does not require frequent software development cycles, the long-term maintenance and upgrade costs of the system are reduced dramatically.

[0086] 4. Enhance system reliability and consistency: By using XML Schema to define the strong type and range of command parameters, the failure of command sending due to incorrect parameter input is reduced from the source, thus improving the reliability of operation.

[0087] The steps described above are for clarity only. In implementation, they can be combined into one step, or some steps can be broken down into multiple steps, as long as they involve the same logical relationship, they are all within the scope of protection of this application. Adding insignificant modifications or introducing insignificant designs to the algorithm or process, without changing the core design of the algorithm and process, are also within the scope of protection of this application.

[0088] Another embodiment of this application proposes a dynamically configured UAV swarm command control and reporting monitoring system. The details of this dynamically configured UAV swarm command control and reporting monitoring system are described below. The following content is for ease of understanding and is not essential for implementing this example. Figure 7 This is a schematic diagram of the structure of a dynamically configured UAV swarm command control and reporting monitoring system proposed in this embodiment, including: The configuration parsing and interface generation module 210 is used to dynamically generate the control interface of the UAV command and the logic associated with the control interface by parsing the external configuration file; wherein, the external configuration file defines the metadata, parameter structure and verification rules of the command in a declarative manner, and the metadata includes categories and attributes; The instruction sending module 220 is used to respond to user operations and send instructions to the drone cluster based on the drone instruction control interface; The cluster performance statistics module 230 is used to collect the status feedback data of the UAV cluster to the command within a preset time window, and calculate the cluster-level command execution performance index based on the status feedback data. The visualization module 240 is used to output performance indicators in a visual manner to assist decision-making.

[0089] It is not difficult to see that this embodiment is a system embodiment corresponding to the above method embodiments, and this embodiment can be implemented in conjunction with the above method embodiments. The relevant technical details and technical effects mentioned in the above method embodiments are still valid in this embodiment, and will not be repeated here to reduce repetition. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above method embodiments.

[0090] It is worth mentioning that all modules and units involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units do not exist in this embodiment.

[0091] Another embodiment of this application provides an electronic device, such as Figure 8 As shown, it includes a processor 31 and a memory 32. The memory 32 stores instructions that the processor 31 can execute. When the processor 31 is configured to execute the instructions, the electronic device can realize a method for controlling and monitoring the drone swarm command based on dynamic configuration as described in the above method embodiment.

[0092] The memory and processor are connected via a bus, which includes any number of interconnecting buses and bridges, connecting various circuits of one or more processors and the memory. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0093] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0094] Another embodiment of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, can implement a dynamically configured UAV swarm command control and reporting monitoring method as described in the above method embodiments.

[0095] That is, those skilled in the art will understand that all or part of the steps in the above method embodiments can be implemented by a program instructing related hardware. The program is stored in a storage medium and includes several instructions to cause a device (such as a microcontroller, chip, etc.) or processor to execute all or part of the steps of the method described in the method embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0096] Those skilled in the art will understand that the above embodiments are specific implementations of this application, and in practical applications, various changes can be made in form and detail without departing from the spirit and scope of this application. For those skilled in the art, several improvements and modifications can be made without departing from the principles of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for command control and reporting monitoring of unmanned aerial vehicle (UAV) swarms based on dynamic configuration, characterized in that, The method includes: By parsing external configuration files, the control interface for drone commands and the logic associated with the control interface are dynamically generated. The external configuration file defines the metadata, parameter structure, and verification rules of the commands in a declarative manner. The metadata includes categories and attributes. In response to user actions, the control interface based on drone commands sends instructions to the drone cluster. Within a preset time window, collect status feedback data of the drone cluster to commands, and calculate cluster-level command execution performance indicators based on the status feedback data; Output performance indicators in a visual manner to assist decision-making.

2. The method according to claim 1, characterized in that, The logic associated with the control interface includes: category navigation elements and interactive components; The control interface for dynamically generating UAV commands by parsing external configuration files and the logic associated with the control interface include: The external configuration file is parsed to obtain an instruction tree model, and categorized navigation elements are dynamically created; the instruction tree model reflects the external configuration file. Determine the type of instruction and generate interactive components differently based on the type of instruction.

3. The method according to claim 2, characterized in that, The determination of the instruction type and the generation of differentiated interactive components based on the instruction type include: If the instruction is a switch instruction type, a button component that is directly triggered will be generated; If the instruction is a composite instruction type, a dialog box containing parameter input controls is dynamically created; the parameter input control type is dynamically determined based on the parameter data type defined in the external configuration file.

4. The method according to claim 3, characterized in that, If the instruction is a compound instruction type, a dialog box containing parameter input controls is dynamically created, including: If the instruction is a composite instruction type, the corresponding input validation logic is dynamically instantiated and configured based on the parameter data type. The input validation logic includes: numerical range, enumeration options, mandatory field checks, and automatic association of special parameters with the map point selection function. Special parameters include latitude and longitude parameters.

5. The method according to claim 4, characterized in that, The external configuration file is in XML format, and its structure is defined by XML Schema; the XML Schema definition includes command categories, command types, parameter types, and option lists.

6. The method according to claim 1, characterized in that, The performance metric for instruction execution is the instruction loop closure rate; Within a preset time window, collect status feedback data of the drone swarm to commands, and calculate swarm-level command execution performance indicators based on the status feedback data, including: Within a preset time window, monitor and receive status feedback data from the drone cluster; By correlating and aggregating the status feedback data of the same command sent to multiple drones, the number of drones in the drone cluster that have completed the command loop can be obtained. The command closure rate is obtained based on the number of drones that have completed command closure and the total number of drones in the drone swarm. Instruction closed-loop rate Calculated using the following formula: 。 in, This indicates the number of drones that successfully responded to the command. This indicates the total number of target drones.

7. The method according to claim 6, characterized in that, The method further includes: Based on the instruction loop closure rate, different levels of alarms are triggered by setting a threshold. The method of visualizing performance indicators to aid decision-making includes: If the instruction closure rate is greater than or equal to the first threshold, it will be output as a green visual icon; If the instruction closure rate is greater than or equal to the second threshold and less than the first threshold, it will be output as a yellow visual icon. If the instruction closure rate is less than the second threshold, it will be output as a red visual icon.

8. A dynamically configured unmanned aerial vehicle (UAV) swarm command control and reporting monitoring system, characterized in that, The system includes: The configuration parsing and interface generation module is used to dynamically generate the control interface for UAV commands and the logic associated with the control interface by parsing external configuration files. The external configuration file defines the metadata, parameter structure and verification rules of the commands in a declarative manner. The metadata includes categories and attributes. The command sending module is used to respond to user operations and send commands to the drone cluster based on the drone command control interface; The cluster performance statistics module is used to collect the status feedback data of the UAV cluster to commands within a preset time window, and to calculate the cluster-level command execution performance index based on the status feedback data. The visualization module is used to output performance indicators in a visual way to assist decision-making.

9. An electronic device, characterized in that, include: The processor and memory, wherein the memory stores instructions that the processor can execute, and the processor is configured to, when executing the instructions, enable the electronic device to implement a dynamically configured UAV swarm command control and reporting monitoring method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can implement a method for controlling and monitoring the command of a drone swarm based on dynamic configuration as described in any one of claims 1 to 7.

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