Unmanned aerial vehicle scheduling method and device based on C4ISR framework and distributed control

Abstract

The invention belongs to the field of unmanned aerial vehicle cooperative control, and relates to a C4ISR framework and distributed control unmanned aerial vehicle scheduling method and device, and the method comprises the steps: constructing a risk potential field graph which carries out the unified cognition of an environment, and carrying out the pre-distribution of the communication and calculation resources of a cluster; establishing a kinematic model, and describing the motion law of the kinematic model under physical conditions; planning a macroscopic waypoint sequence for the whole cluster; under the condition that each unmanned aerial vehicle only communicates with a neighbor, distributed cluster consistency control is carried out, and a control instruction is generated; when a ground station communication link is interrupted, distributed task point consensus is carried out; and the control instruction is converted into actual physical motion of the unmanned aerial vehicle, and the flight state is fed back to the C4ISR system. The global situation awareness and active avoidance capability is enhanced, a dynamic variable cluster cooperation mechanism is provided, the robustness and autonomy of the system are enhanced, and the contradiction between communication dependence and task guidance is solved.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology for unmanned aerial vehicles (UAVs), and more particularly to a method and apparatus for scheduling UAVs based on the C4ISR framework and distributed control. Background Technology

[0002] Unmanned aerial vehicle (UAV) swarm warfare has demonstrated enormous potential in the military field due to its high cost-effectiveness and systemic advantages. However, existing UAV swarm scheduling technologies have the following limitations: Limitations in perception capabilities: In traditional distributed control methods, UAVs rely solely on local neighbor information for decision-making, lacking global situational awareness capabilities. When faced with sudden risks, they can only react passively and cannot proactively predict and avoid them.

[0003] Inflexible adjustment mechanisms: The coupling strength between drones within a cluster is usually determined solely by physical distance, failing to dynamically adjust based on risk information and security levels. This leads to potential exposure due to excessive communication in high-risk areas, while insufficient coordination can negatively impact efficiency in secure areas.

[0004] Insufficient robustness: Centralized planning is highly dependent on ground station communication, and the mission will fail once the link is interrupted; while pure distributed control, although without a central node, lacks high-level mission guidance and is difficult to complete complex global target tasks.

[0005] As the central hub of modern warfare, the C4ISR system possesses powerful global information fusion and situational awareness capabilities, and its open architecture makes it possible to integrate new technologies. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a scheduling method for unmanned aerial vehicles (UAVs) based on the C4ISR framework and distributed control, employing the following technical solution, including the following steps: By fusing multi-source data, a risk potential field map is constructed to achieve a unified understanding of the environment, and based on the risk potential field map, the communication and computing resources of the cluster are pre-allocated. Establish a kinematic model for each drone in the cluster to describe its motion under physical conditions; Based on the aforementioned risk potential field diagram, a macroscopic waypoint sequence is planned for the entire cluster from the starting point to the end point, which can effectively avoid known and predicted risks and meets the physical performance constraints of the UAV. Each drone can perform distributed cluster consistency control under mission guidance by communicating only with its neighbors, and generate control commands. When the ground station communication link is interrupted, distributed task point consensus is performed; The control commands are translated into the actual physical motion of the UAV, and the flight status is fed back to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

[0007] Preferably, the step of constructing a risk potential field map for unified understanding of the environment through multi-source data fusion, and pre-allocating the cluster's communication and computing resources based on the risk potential field map specifically includes: Multi-source data fusion and environmental modeling are performed to construct a risk potential field diagram for a unified understanding of the environment; Calculate the resource requirement coefficient for the node; Based on real-time risk and the node resource demand coefficient, a dynamic communication strategy is generated, and dynamic adaptive communication weight allocation is performed.

[0008] Preferably, the step of establishing a kinematic model for each drone in the cluster to describe its motion under physical conditions specifically includes: Perform six-degree-of-freedom rigid body dynamics modeling.

[0009] Preferably, the step of planning a macroscopic waypoint sequence for the entire cluster based on the risk potential field diagram, which effectively avoids known and predicted risks and meets the physical performance constraints of the UAV, specifically includes: Based on the aforementioned risk potential field diagram, the state space is discretized, and a cost function is designed. Perform a heuristic search to generate discrete waypoints; The discrete waypoint sequence is smoothed to generate a continuous, flyable smooth trajectory. The discrete waypoint sequence and the corresponding expected arrival time window are periodically broadcast to all UAVs in the cluster.

[0010] Preferably, the step of enabling each drone to perform task-guided distributed cluster consistency control and generate control commands by communicating only with its neighbors specifically includes: Treating the macroscopic task point as a gravitational source, a global task-guided potential energy term is added to the traditional potential field function based solely on neighbors; Construct a dynamic coupling strength model that is simultaneously affected by the distance between UAVs and the regional threat level; Develop an improved distributed collaborative control protocol.

[0011] Preferably, when the ground station communication link is interrupted, the steps for performing distributed task point consensus specifically include: Perform interruption detection and local information caching; Perform distributed average consensus algorithm iteration; Each drone uses the consensus value as the current valid task point and inputs it into the control protocol to continue guiding the swarm's movement.

[0012] Preferably, the step of converting the control commands into the actual physical motion of the UAV and feeding back the flight status to the C4ISR system in real time to form a closed loop of continuous learning and self-improvement specifically includes: The control commands are issued and physically executed. Real-time feedback on the flight status of the drone; Based on the feedback information, the prediction model is updated and the planning parameters are optimized.

[0013] To address the aforementioned technical problems, this invention also provides a C4ISR-based distributed control UAV scheduling device, employing the following technical solution, including: The module is used to construct a risk potential field map for unified understanding of the environment through multi-source data fusion, and to pre-allocate the cluster's communication and computing resources based on the risk potential field map; The description module is used to build a kinematic model for each drone in the cluster, describing its motion under physical conditions; The planning module is used to plan a macroscopic waypoint sequence for the entire cluster based on the risk potential field diagram, from the starting point to the ending point, which can effectively avoid known and predicted risks and meet the physical performance constraints of the UAV. The control module is used to enable each drone to perform distributed cluster consistency control under mission guidance while communicating only with its neighbors, and to generate control commands. The consensus module is used to achieve distributed consensus among task points when the communication link between local ground stations is interrupted. The optimization module is used to convert the control commands into the actual physical motion of the UAV and feed back the flight status to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

[0014] To address the aforementioned technical problems, the present invention also provides a computer device that employs the technical solution described below, comprising a memory and a processor. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the above-described C4ISR-based distributed control UAV scheduling method.

[0015] To address the aforementioned technical problems, the present invention also provides a computer-readable storage medium, which employs the technical solution described below. The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the above-described C4ISR-based distributed control UAV scheduling method.

[0016] Compared with the prior art, the present invention has the following main advantages: (1) By fusing multi-source data from the C4ISR system, a unified risk potential field map is constructed, and global situational awareness is introduced into the distributed cluster. UAVs can predict risk evolution based on potential field information, realize the leap from passive response to active avoidance, and greatly improve the survival probability.

[0017] (2) Based on the risk potential field, communication and computing resources are pre-allocated so that the coupling strength can be dynamically adjusted according to the risk information security level. High-risk areas actively reduce the frequency of interaction to reduce exposure risks, while safe areas enhance collaboration to improve task efficiency, thus achieving a flexible balance between concealment and collaboration.

[0018] (3) Adopting a hybrid architecture of macro-path guidance and distributed consensus control: Under normal circumstances, the global mission direction is guaranteed by macro-path. When the ground station communication is interrupted, the cluster can switch to the distributed mission point consensus mechanism to autonomously maintain the collaborative completion of the goal, effectively solving the contradiction between communication dependence and mission guidance, and significantly improving the system reliability in complex environments. Attached Figure Description

[0019] To more clearly illustrate the solutions in this invention, the accompanying drawings used in the description of the embodiments of this invention will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an embodiment of the C4ISR framework and distributed control UAV scheduling method of the present invention; Figure 2 This is an exemplary system architecture diagram in which the C4ISR-based distributed control UAV scheduling method of the present invention can be applied; Figure 3 This is a schematic diagram of the node communication method used in the C4ISR-based distributed control UAV scheduling method of the present invention; Figure 4 This is a schematic diagram of an embodiment of the C4ISR framework and distributed control UAV scheduling device of the present invention; Figure 5 This is a schematic diagram of another embodiment of the C4ISR-based distributed control UAV scheduling device of the present invention; Figure 6 This is a schematic diagram of the structure of an embodiment of the computer device of the present invention. Detailed Implementation

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the specification is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings are used to distinguish different objects and not to describe a particular order.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0024] It should be noted that the UAV scheduling method based on the C4ISR framework and distributed control provided in the embodiments of the present invention is generally executed by a server / terminal device, and correspondingly, the UAV scheduling device based on the C4ISR framework and distributed control is generally set in the server / terminal device.

[0025] It should be understood that the number of terminal devices, networks, and servers is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be used.

[0026] Example 1 Please refer to Figure 1 The flowchart illustrates an embodiment of the UAV scheduling method based on the C4ISR framework and distributed control according to the present invention. The UAV scheduling method based on the C4ISR framework and distributed control includes the following steps: Step S1: By fusing multi-source data, a risk potential field map is constructed to achieve a unified understanding of the environment, and based on the risk potential field map, the communication and computing resources of the cluster are pre-allocated.

[0027] In this embodiment, electronic devices (e.g., servers / terminal devices) running on the C4ISR framework and distributed control drone scheduling method can receive drone scheduling requests based on the C4ISR framework and distributed control method via wired or wireless connections. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future-developed wireless connection methods.

[0028] In this embodiment, step S1 may specifically include the following steps: S11 involves multi-source data fusion and environmental modeling to construct a risk potential field diagram for a unified understanding of the environment.

[0029] The ground command center accesses various sensors in real time, including early warning radar, electronic intelligence systems, and electro-optical / infrared reconnaissance equipment, via data links. It performs spatiotemporal alignment and coordinate system transformation on the raw data to eliminate perception errors. Data fusion algorithms (such as Kalman filtering and Bayesian inference) are used to comprehensively process multi-source information, identifying and locating various static and dynamic risk sources (such as enemy radar, air defense fire units, no-fly zones, and terrain obstacles).

[0030] Based on the fused risk source information, a quantitative, four-dimensional dynamic risk potential field diagram is generated. The definition is as follows: .in: : Represents the position and time of any point in space. : The position vector of a point in space. : No. One source of risk The position vector at any given time. : No. The weighting coefficient of each risk source is a scalar quantity that is preset according to the lethality of the risk or dynamically adjusted by C4ISR intelligence. For example, the weight of air defense missile sites with a high probability of kill is much higher than that of radars that only play a detection role. Risk decay function, used to describe the degree of risk as a function of distance. The physical property of increasing and decreasing. This invention adopts the Gaussian form: ,in This determines the scope of the risk source's impact.

[0031] This formula transforms discrete, unstructured risk source information into a continuous, differentiable scalar field function. The function value at any point in the spatial domain represents the overall risk level at that point at that moment. This provides a mathematical basis for subsequent macro-path planning that can be directly used for cost calculation, achieving a leap from knowing that there is risk to quantifying the degree of risk.

[0032] S12, calculate the resource requirement coefficient of the node.

[0033] After acquiring the environmental situation, the system needs to assess the computing and communication resources required for each UAV to complete its mission under the current situation. This invention designs a resource requirement coefficient that comprehensively considers the mission, environment, and communication status.

[0034] No. A drone in Resource demand coefficient at any time Defined as: .in: Drones exist The position vector at any given time. : The final target point location vector. : The preset maximum task distance, used for normalization. Drones The risk value at the current location is provided by the risk potential field diagram in step S11. Drones The current number of neighbors. : Maximum number of effective communication neighbors, used for normalization. Weighting coefficients can be dynamically adjusted based on the task type. For example, for breakthrough missions, the weighting coefficients are increased. Reconnaissance mission improved . It is a dimensionless comprehensive indicator that quantifies the urgent resource needs of each drone. The first metric is mission urgency: the farther the drone is from the target, the more power and navigation resources are needed. The second metric is risk exposure: the higher the risk, the more computing resources are needed for evasion algorithms and encrypted communication. The third metric is communication load: the more neighbors a drone has, the more bandwidth resources are needed. The system... The system dynamically reserves bandwidth and computing threads for each drone, enabling on-demand resource allocation.

[0035] S13 generates a dynamic communication strategy based on real-time risk and node resource demand coefficients, and performs dynamic adaptive communication weight allocation.

[0036] Remain silent in high-risk areas to reduce exposure risk, and be active in safe areas to ensure collaboration.

[0037] Define drones With drones Communication links between Allocation weights : .in: Drones and The dynamic coupling strength between them represents the physical coordination requirements between them. Drones and The overall risk level of the area connecting the two machines is calculated in real time by the C4ISR system based on the location of the two machines and the distribution of risk sources. : Adjustment coefficient, which controls the degree to which risk diminishes the communication weight.

[0038] This weighting design reflects the principle of prioritizing safety while also considering coordination. (Regarding the risk in the area between the two machines...) When the weight increases, Automatic reduction means the system will reduce the communication time slots and power allocated to this link, or even temporarily interrupt communication, thereby reducing the probability of being detected by enemy electronic reconnaissance systems. At the same time, high physical coordination requirements ( Large (e.g., wingman-wingman) links can maintain a relatively high weight even under risk. Communication systems are based on... Dynamically adjust the communication topology to achieve adaptive optimization of communication behavior based on risk.

[0039] The purpose of step S1 is to construct a unified understanding of the environment (risk potential field diagram) through multi-source data fusion, and based on this understanding, to pre-allocate the cluster's communication and computing resources, providing a data foundation and resource guarantee for intelligent scheduling. Step S1 is executed by the ground center of the C4ISR system.

[0040] Step S2: Establish a kinematic model for each drone in the cluster to describe its motion under physical conditions.

[0041] In this embodiment, step S2 may specifically include the following steps: S21, perform six-degree-of-freedom rigid body dynamics modeling.

[0042] A six-degree-of-freedom rigid body model is used to describe the motion of the UAV. This model includes 12 state variables, such as position, velocity, attitude, and angular velocity, which comprehensively reflects the spatial motion state of the UAV.

[0043] Define drones Determine the state vector and its dynamic differential equation. The state vector includes: Position vector ; velocity vector (In body coordinate system); Attitude angle vector (Roll, Pitch, Yaw); Attitude angular velocity vector The dynamic equations can be simplified to the following form: , , , .in: : The rotation matrix from the body coordinate system to the ground coordinate system. Drones The quality. The component of the resultant force (including thrust, aerodynamic force, and gravity) acting on the UAV in the body coordinate system. This is the force input that the subsequent control protocol needs to calculate. : The transformation matrix that converts angular velocity into Euler angular rate. : The rotational inertia matrix of the UAV. : The component of the resultant torque acting on the UAV in the body coordinate system. This is the torque input that the subsequent control protocol needs to calculate. Symbol: Represents the cross product operation of vectors.

[0044] This set of differential equations precisely describes the relationship between the rate of change of the UAV's state over time and the applied external forces / torques. In step S4, the control protocol calculates the required... and Then, by solving the differential equation (or its discrete form), the UAV can simulate the state at the next moment and drive the control surfaces or motors accordingly to achieve closed-loop control.

[0045] The purpose of step S2 is to establish an accurate mathematical model for each drone in the cluster, describing its motion under the influence of forces and torques. This is crucial to ensuring that the subsequently generated control commands are physically feasible (i.e., the drones can fly), and is the cornerstone for achieving a unity between theoretical optimization and physical feasibility.

[0046] Step S3: Based on the risk potential field diagram, plan a macroscopic waypoint sequence from the starting point to the end point for the entire cluster, which can effectively avoid known and predicted risks and meet the physical performance constraints of the UAV.

[0047] In this embodiment, step S3 may specifically include the following steps: S31, based on the risk potential field diagram, discretizes the state space and designs the cost function.

[0048] The continuous task space is divided into a three-dimensional grid with a certain resolution (e.g., 0.5 km), where each grid node represents a discrete state. The core of the planning problem is to define a state starting from a node. Transfer to adjacent nodes The cost.

[0049] Design a cost function that incorporates multiple factors. : .in: Physical feasibility cost. Measured from... arrive Whether the transfer is in line with the drone's maneuverability, such as turning radius and maximum climb rate. If the transfer is not feasible, the cost is set to infinity. Speed ​​cost. Assess arrival status. Whether the expected speed is within the reasonable cruising speed range. High cost. Assessment status. Is the height appropriate (e.g., too low and it's easy to crash into a mountain, too high and it's easy to be detected by long-range radar)? Probabilistic cost. Assessing the state based on historical data and the current risk potential. The probability of being detected or hit by the enemy. : Adaptively adjustable weighting coefficients, which can be flexibly configured according to mission type (such as low-altitude penetration, high-altitude reconnaissance).

[0050] This cost function quantifies the abstract concept of the optimal path into a computable numerical value. It not only considers the path length but also incorporates the physical limitations of the drone and environmental safety factors, ensuring that the searched path is both flyable and safe.

[0051] S32 performs a heuristic search to generate discrete waypoints.

[0052] Using A Algorithms and their variants employ heuristic search. The core of the algorithm is the evaluation function. .in, From the starting point to the current state The actual cumulative cost is determined by the cost function in step 3.1. The sum is obtained by accumulation. Heuristic functions are used to estimate the probability of success from the current state. The minimum remaining cost to reach the target point.

[0053] The heuristic function designed in this invention as follows: .in: :state The corresponding position vector. : Target point position vector. : The Euclidean distance from the current location to the target point. : Estimated average flight speed of the drone in the current environment. Target type adjustment factor. For moving targets, It will be greater than 1, to penalize the search direction for lagging behind the target movement. It provides an optimistic estimate of the remaining time, guiding the search direction to prioritize the target point and greatly improving search efficiency.

[0054] Through A The algorithm searches in a 3D raster graph and ultimately outputs a cumulative cost. The shortest path is a series of discrete macroscopic waypoints. .

[0055] S33 smooths the discrete waypoint sequence to generate a continuous, flyable smooth trajectory, and periodically broadcasts the discrete waypoint sequence and the corresponding expected arrival time window to all UAVs in the cluster.

[0056] The generated discrete waypoint sequence is smoothed (e.g., by B-spline curve fitting) to eliminate unnecessary inflections, resulting in a continuous, flyable, smooth trajectory. Then, the ground station transmits the waypoint sequence and its corresponding expected arrival time window via an anti-interference data link. It periodically broadcasts to all drones within the cluster. Each drone locally stores its currently valid task point. .

[0057] Step S3 aims to provide a macroscopic waypoint sequence that effectively avoids known and predicted risks while adhering to the physical performance constraints of the UAV. This is equivalent to providing the swarm with a marching route map. This step is executed by the ground-based C4ISR center.

[0058] Step S4: Each drone performs distributed cluster consistency control under mission guidance, generating control commands, while communicating only with its neighbors.

[0059] In this embodiment, step S4 may specifically include the following steps: S41 treats the macroscopic task point as a gravitational source and adds a global task-guided potential energy term to the traditional potential field function based only on neighbors.

[0060] Define the task-guided potential energy function It is in quadratic form: .in: : Guiding coefficient, which controls the intensity of the guiding force. Drones The current location. : The effective macroscopic task point at the current moment.

[0061] This potential energy generates a large positive value when the drone is far from the mission point; its negative gradient is the mission guidance force. This is a linear restoring force pointing towards the task point. This force is then compared to the collision avoidance potential field in the traditional artificial potential field method. ( For drones and The relative position vectors between them are merged to form a fused potential field term.

[0062] To balance the sometimes conflicting goals of completing the task and maintaining formation, an adaptive task-guided weight is introduced. : .in, It is an internal consistency error within the cluster. This is the current risk. When the risk is high, Increased size, emphasizing rapid passage (sacrificing formation); when internal chaos occurs, Reduce the risk and prioritize restoring formation stability.

[0063] S42, construct a dynamic coupling strength model that is simultaneously affected by the distance between UAVs and the regional threat level.

[0064] The dynamic coupling strength model uses the basic distance relationship between the two drones as the base and the real-time regional threat level provided by the C4ISR system as the exponential adjustment factor, so that the cooperative coupling strength between drones can adapt to the degree of environmental threat.

[0065] drones with neighbors dynamic coupling strength Defined as: .in: The basic coupling strength is usually determined by the distance between the two machines; the closer the distance, the stronger the coupling. The larger. Risk attenuation coefficient: controls the degree to which risk suppresses the coupling strength. Drones provided by C4ISR and Normalized risk level (range 0~1) for the region between.

[0066] This formula achieves adaptive risk avoidance. It considers the risk level of the area between the two machines. When high, the exponential factor The rapid decrease leads to a decrease in dynamic coupling strength. This means that in high-risk areas, the soft constraints between drones weaken, and they tend to disperse, reduce unnecessary communication, and avoid close proximity to decrease the probability of being captured in one fell swoop. When the risk level decreases, The cluster resumes close collaboration after recovery. Simultaneously, a lower limit on coupling strength is set. This ensures basic network connectivity.

[0067] S43, construct an improved distributed collaborative control protocol.

[0068] drones Control inputs (including force) and torque It is generated by the following formula: ; ; in: Drones The set of neighbors. :Neighbor Speed ​​and position. The gradient of the traditional collision avoidance potential field generates a repulsive force to prevent collisions. The task-guided potential gradient with adaptive weights generates an attraction pointing towards the task point. The compensation term derived from the dynamic model in step S2 is used to counteract the dynamic coupling and nonlinear characteristics of the UAV itself, making the control input purer.

[0069] This protocol integrates local collaboration (the first two items), safe obstacle avoidance (the first part of the third item), global mission (the second part of the third item), and physical feasibility (the fourth item). Each drone only needs to obtain the state of its neighbors and its current mission point to calculate control commands that satisfy local consistency, obey global guidance, and adapt to the environment.

[0070] Step S4 aims to ensure that each drone, while communicating only with its neighbors, maintains internal cluster stability (consistent speed, collision avoidance), is guided towards the target by macro-level task points, and dynamically adjusts its coordination strength with neighbors based on environmental risks. This step is deployed on the onboard computer of each drone.

[0071] Step S5: When the ground station communication link is interrupted, perform distributed task point consensus.

[0072] In this embodiment, step S5 may specifically include the following steps: S51 performs interrupt detection and local information caching.

[0073] Each drone continuously monitors the communication link quality with the ground station. If no valid mission point update is received within a preset period, communication is considered interrupted. Simultaneously, the drone's local cache always stores the last valid mission point received from the ground station. and its timestamp.

[0074] S52 performs distributed average consensus algorithm iteration.

[0075] The system automatically switches to distributed consensus mode. Each drone uses its locally cached task points as its initial estimate. Then, the distributed average consensus algorithm is executed iteratively through wireless communication with neighbors.

[0076] In each iteration At that time, drones Update your task point estimate: .in: Drones In the The estimated value of the task point in the next iteration. The convergence step size is a value smaller than 1. Small positive numbers ensure algorithm convergence. Drones The set of neighbors at the current moment.

[0077] This iterative formula is essentially a low-pass filter that updates the estimate for each drone in the direction of the average of the estimates from all its neighbors. ( After several iterations (to the size of the cluster), the estimated value for the entire cluster will converge to a common consensus value. This is the average of the initial values ​​of all drones. Since the initial values ​​are the same, the consensus value is the point itself, thus ensuring the consistency of the cluster's objectives.

[0078] S53, each drone uses the consensus value as the current valid task point and inputs it into the control protocol to continue guiding the swarm movement.

[0079] After convergence, each drone will share the consensus value. As the current valid task point, it is input into the control protocol of step S4 to continue guiding the cluster movement. At the same time, the module continuously monitors the communication link. Once it detects that the interference has weakened and communication has been restored, it will re-receive the global task point issued by the ground station and switch back to normal mode through a smooth transition (such as weighted averaging) to eliminate command jumps.

[0080] The purpose of step S5 is to provide a robust guarantee mechanism. When the ground station communication link is interrupted due to electronic interference or physical obstruction, the cluster can automatically activate this mechanism to reach a consensus on subsequent mission points through interaction between UAVs, ensuring that the mission is not interrupted until communication is restored.

[0081] Step S6 converts the control commands into the actual physical motion of the UAV and feeds back the flight status to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

[0082] In this embodiment, step S6 may specifically include the following steps: S61 issues control commands and performs them physically.

[0083] The onboard computer calculates the desired force and torque in step S4. , Through analog-to-digital conversion and servo control algorithms, the commands are converted into specific control surface deflection angles and motor speeds, and then sent to actuators such as servos and motors via the onboard bus to drive the UAV to change its flight state.

[0084] The S62 provides real-time feedback on the flight status of the drone.

[0085] Onboard sensors such as IMU, GPS, and atmospheric data computers collect data on the UAV's actual position, speed, attitude, angular velocity, and remaining battery and fuel levels at high frequencies (e.g., 100Hz). This data is then transmitted in real-time via a data link to the dynamic environment perception module of the ground-based C4ISR system.

[0086] S63 updates the prediction model and optimizes the planning parameters based on feedback information.

[0087] The ground-based C4ISR system utilizes received real-world flight data to perform multi-faceted closed-loop optimization: Update situational awareness: If a discrepancy is found between the perceived risk and the actual flight status (e.g., the number of times a drone is illuminated by fire control radar) in a certain area, then dynamically adjust the risk potential field parameters for that area (e.g., ...). ).

[0088] Correcting the prediction model: For example, if the moving speed or trajectory of a mobile air defense unit is found to be inconsistent with prior predictions, its motion model is updated online for subsequent macro-track planning.

[0089] Optimize planning parameters: Compare the deviation between the planned trajectory and the actual flight trajectory, and analyze the weights of the cost function. Is the configuration unreasonable, or is it a heuristic function? If the estimate is inaccurate, the relevant parameters will be adjusted accordingly to make the next plan more accurate.

[0090] The role of step S6 is as follows: This step acts as the actuator and sensor of the closed-loop system. It transforms the control commands generated in step S4 into the actual physical motion of the UAV and feeds back the flight status to the C4ISR system in real time for updating the environmental model and optimizing subsequent planning, forming a closed loop of continuous learning and self-improvement.

[0091] Figure 2 This is an exemplary system architecture diagram showing how the C4ISR-based distributed control UAV scheduling method of the present invention can be applied. (See diagram below.) Figure 2As shown, this architecture comprises four core nodes: early warning radar, ground command center, swarm leader aircraft, and swarm drones, as well as two supporting links: reconnaissance data and electronic intelligence, forming an integrated system of perception, decision-making, collaboration, and execution.

[0092] The early warning radar is responsible for wide-area scanning and automatic identification, transmitting raw air situation data to the ground command center via communication. The reconnaissance data link simultaneously acquires and processes intelligence, while electronic intelligence provides intelligence services and planning. These two systems, along with radar information, are integrated to construct a complete situational awareness for the command center. Based on the fused data, the ground command center conducts situational analysis, plans macro-level paths and mission objectives, and issues commands to the cluster leader.

[0093] As the airborne formation's hub, the swarm leader receives macro-level commands, decomposes the tasks to each UAV through consistency adjustments, and continuously guides formation coordination. Based on mission consensus, the swarm UAVs rely on communication and computing to achieve precise guidance and dynamic adaptation of weapon payloads, while simultaneously transmitting their own status and detection data back, forming a closed loop.

[0094] The entire architecture achieves rapid closure from early warning detection to fire strike through layered communication and distributed computing: early warning and intelligence drive command and decision-making, the lead aircraft maintains formation cohesion, and the drones cooperate autonomously, thereby efficiently completing swarm combat missions in complex electromagnetic environments.

[0095] Figure 3 This is a schematic diagram illustrating the node communication method used in the C4ISR-based distributed control UAV scheduling method of this invention. Figure 3 As shown, a hierarchical heterogeneous communication network with narrowband and broadband is adopted, and information is distributed and coordinated between nodes according to task requirements and link characteristics.

[0096] The broadband network connects the ground command center, early warning radar, early warning aircraft, and electronic intelligence and reconnaissance data nodes, primarily carrying high-bandwidth, high-real-time services. Early warning radar and early warning aircraft transmit high-resolution air situation data generated from wide-area scanning and automatic identification to the ground command center in real time via broadband links; reconnaissance data nodes also transmit fused information after intelligence processing and planning instructions formulated by electronic intelligence nodes to the command center via the broadband network. The broadband network ensures the low latency and high throughput required for situational integration and macro-level decision-making.

[0097] Narrowband networks are specifically designed for networking and communication between the swarm leader and the swarm drones. Narrowband communication offers strong anti-jamming capabilities and a low probability of interception, making it suitable for drone swarm control. The swarm leader breaks down the macro-level path planning issued by the ground command center into specific tasks, and sends consistency adjustment commands, target allocations, and formation maintenance parameters to the drone swarm via narrowband links in broadcast or multicast mode. Each drone then uses the narrowband uplink channel to transmit status information and payload data back, forming a closed loop within the swarm.

[0098] As the gateway node between the two types of networks, the cluster leader performs protocol conversion and information relay between the broadband and narrowband networks. It receives command and intelligence support from the rear while maintaining reliable control of the cluster at the front line. Through this communication architecture of broadband backbone and narrowband endpoints, the system achieves efficient unification of long-range perception and short-range collaboration in complex electromagnetic environments.

[0099] The beneficial effects of implementing this embodiment are: (1) By fusing multi-source data from the C4ISR system, a unified risk potential field map is constructed, and global situational awareness is introduced into the distributed cluster. UAVs can predict risk evolution based on potential field information, realize the leap from passive response to active avoidance, and greatly improve the survival probability.

[0100] (2) Based on the risk potential field, communication and computing resources are pre-allocated so that the coupling strength can be dynamically adjusted according to the risk information security level. High-risk areas actively reduce the frequency of interaction to reduce exposure risks, while safe areas enhance collaboration to improve task efficiency, thus achieving a flexible balance between concealment and collaboration.

[0101] (3) Adopting a hybrid architecture of macro-path guidance and distributed consensus control: Under normal circumstances, the global mission direction is guaranteed by macro-path. When the ground station communication is interrupted, the cluster can switch to the distributed mission point consensus mechanism to autonomously maintain the collaborative completion of the goal, effectively solving the contradiction between communication dependence and mission guidance, and significantly improving the system reliability in complex environments.

[0102] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0104] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0105] Example 2 Further reference Figure 4 As a response to the above Figure 1 The present invention provides an embodiment of a C4ISR-based distributed control UAV scheduling device, which is similar to the implementation of the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0106] like Figure 4 As shown, the C4ISR-based distributed control UAV scheduling device 70 described in this embodiment includes: a construction module 71, a description module 72, a planning module 73, a control module 74, a consensus module 75, and an optimization module 76. Wherein: The construction module 71 is used to construct a risk potential field map for unified understanding of the environment through multi-source data fusion, and to pre-allocate the cluster's communication and computing resources based on the risk potential field map; Description module 72 is used to build a kinematic model for each drone in the cluster and describe its motion under physical conditions; The planning module 73 is used to plan a macroscopic waypoint sequence for the entire cluster based on the risk potential field map, from the starting point to the ending point, which can effectively avoid known and predicted risks and meet the physical performance constraints of the UAV. Control module 74 is used to enable each drone to perform task-guided distributed cluster consistency control of the drone while communicating only with its neighbors, and to generate control commands. Consensus module 75 is used to perform distributed task point consensus when the local ground station communication link is interrupted; The optimization module 76 is used to convert the control commands into the actual physical motion of the UAV and to feed back the flight status to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

[0107] The beneficial effects of implementing this embodiment are: enhanced global situational awareness and proactive avoidance capabilities, a dynamically variable cluster collaboration mechanism, enhanced system robustness and autonomy, effective resolution of the contradiction between communication dependence and task guidance, and significant improvement of system reliability in complex environments.

[0108] Example 3 Further reference Figure 5 As a response to the above Figure 1 The present invention provides another embodiment of a C4ISR-based distributed control UAV scheduling device, which is similar to the method shown. Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0109] like Figure 5 As shown in the figure, the UAV scheduling device based on the C4ISR framework and distributed control described in this embodiment is based on dynamic environmental perception and resource computing, guided by macro-level trajectory planning, centered on an improved distributed cooperative control protocol, and guaranteed by distributed task point consensus. It constructs a highly robust and adaptive UAV swarm cooperative scheduling system that covers the entire process from environmental perception, task planning, swarm control to execution feedback, solving the problems of low perception capability, inflexible adjustment, and poor robustness in existing technologies. The device relies on a ground-based high-performance computing server, large-capacity storage devices, high-speed data link communication equipment, and an UAV-borne embedded computer in its hardware. In terms of software, it includes a ground control center subsystem, an airborne cooperative control subsystem, and a data link subsystem.

[0110] The dynamic environment perception module is responsible for fusing and processing multi-source sensor data on the environment, assessing risk distribution and node resource requirements in real time, and providing decision-making basis for subsequent planning and control. This module is deployed on a high-performance server in the ground command center and connects to various sensors such as early warning radar, electronic intelligence systems, and optoelectronic reconnaissance equipment through a dedicated data interface.

[0111] The main functions of the dynamic environment perception module include: receiving and parsing raw data from different sensors to generate a unified information security situation dataset; assessing the resource demand coefficient of each UAV based on its current location, mission distance, risk level, and number of neighbors, and dynamically adjusting the computing task allocation and communication bandwidth reservation strategy accordingly to achieve risk adaptive optimization of the communication topology.

[0112] The macro-path planning module is responsible for planning the globally optimal macro-waypoint sequence for the UAV swarm based on the results of dynamic environment perception, so as to achieve macro-guidance under the guidance of mission objectives.

[0113] The main functions of the macro-track planning module include: dividing the continuous mission airspace into a three-dimensional grid at a certain resolution, with each grid node representing a discrete state, providing a basis for subsequent searches; for each state transition, comprehensively considering physical feasibility, speed rationality, altitude suitability, and prior probabilities, calculating a comprehensive cost function to ensure that the generated trajectory conforms to the UAV's maneuverability while avoiding known risks; and employing A... The algorithm framework uses the comprehensive cost as the movement cost and the estimated minimum time cost to reach the target as the heuristic function. It searches for the path with the minimum cumulative cost in the discrete state space to generate a macroscopic waypoint sequence. The generated waypoint sequence is smoothed to eliminate unnecessary inflections and generate a flyable continuous trajectory. This trajectory is periodically broadcast to each UAV in the cluster via a data link, and the expected arrival time window is also issued to allow the UAVs to adjust their speed autonomously.

[0114] The dynamics modeling module is deployed on the ground control computer and is responsible for establishing and updating the six-degree-of-freedom dynamics model of the UAV in real time, providing an accurate controlled object model and state variables for the cooperative control protocol.

[0115] The main functions of the dynamic modeling module include: loading inherent parameters such as mass, moment of inertia, and aerodynamic coefficients corresponding to the UAV model from local memory; calculating and updating state variables such as position, velocity, attitude, and attitude angular velocity in real time based on sensor input information such as acceleration and angular velocity, with the control cycle as the step size; and calculating the transformation matrix in the dynamic equation in real time to provide necessary dynamic compensation terms for the control protocol.

[0116] The mission guidance module is deployed on the cluster leader machine. It is responsible for receiving the waypoint sequence output by the macro-path planning module, calculating the mission guidance potential gradient, and generating adaptive guidance weights.

[0117] The main functions of the mission guidance module include: maintaining a macroscopic waypoint sequence, determining the current valid mission point based on the current timestamp, and supporting advance issuance and dynamic updates; calculating the mission guidance force, which will be used as a potential field gradient in the airborne control protocol to guide the UAV to move towards the mission point; generating adaptive weight coefficients based on the internal consistency error fed back by the C4ISR system and the current regional risk level, which are used to dynamically balance mission advancement and internal stability; and packaging the current mission point and adaptive weights and periodically broadcasting them to all UAVs in the cluster via data link.

[0118] The neighbor coupling strength calculation module is deployed on the onboard embedded computer of each UAV. It is responsible for calculating the dynamic coupling strength with each neighbor in real time based on the risk prediction information provided by the C4ISR system, so as to realize the risk adaptive adjustment of the communication topology.

[0119] The main functions of the neighbor coupling strength calculation module include: receiving risk prediction information from the C4ISR system via the data link, which reflects the normalized risk level of the area between the two machines; calculating the coupling strength with each neighbor based on the current distance and risk level, and adjusting it according to a preset lower limit constraint to ensure that coupling is reduced in high-risk areas to reduce exposure risks and coupling is restored in safe areas to ensure collaborative efficiency; dynamically updating the neighbor set according to the communication radius and coupling strength threshold, eliminating links with excessively low coupling strength to ensure that communication resources are focused on effective collaboration; and using the calculated coupling strength as input to the control protocol for subsequent consistency adjustments.

[0120] The distributed consensus module is deployed on the onboard embedded computer of each drone. It is responsible for maintaining the consistency of mission objectives through neighbor interaction when ground station communication is interrupted, thus ensuring mission continuity.

[0121] The main functions of the distributed consensus module include: monitoring the data link status with the ground station; if no valid task point update is received for several consecutive cycles, it determines that the communication is interrupted and automatically activates the consensus mode; saving the last received valid task point and its timestamp as the initial estimate; interacting with neighboring UAVs and iteratively updating the local task point estimate according to the distributed average consensus algorithm until the estimates of all UAVs converge to the consensus value; after convergence, inputting the consensus value as the current task point into the distributed collaborative control module to replace the original ground station task point until communication is restored, the ground station task point is resynchronized, and the consensus mode is exited.

[0122] The distributed control module is deployed on the onboard embedded computer of each UAV. It is responsible for running the improved distributed cooperative control protocol and generating specific force and torque control commands. The main functions of the distributed control module include: obtaining its own state from the local dynamics modeling module and obtaining the states and dynamic coupling strengths of its neighbors from the communication module; calculating the collision avoidance potential energy gradient with each neighbor and superimposing the task guidance potential energy gradient to form a fused potential field term, ensuring that the UAV moves towards the task point while maintaining a safe distance; calculating the force and torque inputs according to the improved control protocol formula, integrating the velocity consistency term, position consistency term, fused potential field control term, and dynamic compensation term; and sending the generated force and torque commands to the flight control actuators (servos, motors, etc.) via the onboard bus to drive the UAV to move according to the desired state.

[0123] The pre-built strategy library serves as the knowledge support for the dynamic environment perception module and the macro-course planning module. It stores rule sets derived from domain expert knowledge and historical mission experience, primarily stored in high-speed solid-state storage at the ground center for real-time retrieval by relevant modules. Rules within the library exist in the form of "condition-action" pairs. For example, it adjusts risk source weights based on detected risk types, sets planning weights and dynamic coupling strength attenuation coefficients based on mission types, and temporarily adjusts mission guidance weights based on internal consistency errors. The strategy library supports online updates and expansions, enabling the system to continuously accumulate experience and adapt to new environmental and mission requirements.

[0124] The data repository is responsible for storing, backing up, and managing all intermediate and final data generated during system operation. It mainly consists of a large-capacity disk array at the ground center and the UAV's local flash memory.

[0125] The main functions of the data repository include: storing waypoint sequences output by the macro-track planning module, raw sensor data from the dynamic environment perception module, risk potential field maps, and resource allocation logs; recording the control command history of the distributed collaborative control module, the iteration process of the distributed consensus module, and flight status data of each UAV; supporting rapid data retrieval and post-mission backtracking analysis to provide data support for system performance evaluation and parameter optimization; and ensuring the security of critical data through a redundant backup mechanism to prevent data loss due to hardware failure.

[0126] Example 4 To address the aforementioned technical problems, embodiments of the present invention also provide a computer device. Please refer to [link / reference needed]. Figure 6 , Figure 6 This is a basic structural block diagram of the computer device in this embodiment.

[0127] The aforementioned computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected via a system bus. It should be noted that only the computer device 8 with components 81, 82, and 83 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0128] The aforementioned computer devices can be desktop computers, laptops, handheld computers, and cloud servers, among other computing devices. These devices can facilitate human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0129] The aforementioned memory 81 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the aforementioned memory 81 may be an internal storage unit of the aforementioned computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the aforementioned memory 81 may also be an external storage device of the aforementioned computer device 8, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 8. Of course, the aforementioned memory 81 may also include both the internal storage unit and its external storage device of the aforementioned computer device 8. In this embodiment, the aforementioned memory 81 is typically used to store the operating system and various application software installed on the aforementioned computer device 8, such as computer-readable instructions based on the C4ISR framework and distributed control UAV scheduling method. In addition, the aforementioned memory 81 can also be used to temporarily store various types of data that have been output or will be output.

[0130] In some embodiments, the processor 82 may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 82 is typically used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to execute computer-readable instructions stored in the memory 81 or to process data, for example, to execute the computer-readable instructions based on the C4ISR framework and the distributed control UAV scheduling method.

[0131] The network interface 83 may include a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the computer device 8 and other electronic devices.

[0132] The beneficial effects of implementing this embodiment are: enhanced global situational awareness and proactive avoidance capabilities, a dynamically variable cluster collaboration mechanism, enhanced system robustness and autonomy, effective resolution of the contradiction between communication dependence and task guidance, and significant improvement of system reliability in complex environments.

[0133] Example 5 The present invention also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the C4ISR framework-based distributed control UAV scheduling method described above.

[0134] The beneficial effects of implementing this embodiment are: enhanced global situational awareness and proactive avoidance capabilities, a dynamically variable cluster collaboration mechanism, enhanced system robustness and autonomy, effective resolution of the contradiction between communication dependence and task guidance, and significant improvement of system reliability in complex environments.

[0135] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0136] Obviously, the embodiments described above are merely some embodiments of the present invention, not all embodiments. The accompanying drawings show preferred embodiments of the present invention, but do not limit the patent scope of the present invention. The present invention can be implemented in many different forms; rather, these embodiments are provided to provide a more thorough and complete understanding of the disclosure of the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the patent protection scope of this invention.

Claims

1. A scheduling method for unmanned aerial vehicles (UAVs) based on the C4ISR framework and distributed control, characterized in that, Includes the following steps: By fusing multi-source data, a risk potential field map is constructed to achieve a unified understanding of the environment, and based on the risk potential field map, the communication and computing resources of the cluster are pre-allocated. Establish a kinematic model for each drone in the cluster to describe its motion under physical conditions; Based on the aforementioned risk potential field diagram, a macroscopic waypoint sequence is planned for the entire cluster from the starting point to the end point, which can effectively avoid known and predicted risks and meets the physical performance constraints of the UAV. Each drone can perform distributed cluster consistency control under mission guidance by communicating only with its neighbors, and generate control commands. When the ground station communication link is interrupted, distributed task point consensus is performed; The control commands are translated into the actual physical motion of the UAV, and the flight status is fed back to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

2. The UAV scheduling method based on the C4ISR framework and distributed control as described in claim 1, characterized in that, The steps of constructing a risk potential field map for unified environmental understanding through multi-source data fusion, and pre-allocating cluster communication and computing resources based on the risk potential field map, specifically include: Multi-source data fusion and environmental modeling are performed to construct a risk potential field diagram for a unified understanding of the environment; Calculate the resource requirement coefficient for the node; Based on real-time risk and the node resource demand coefficient, a dynamic communication strategy is generated, and dynamic adaptive communication weight allocation is performed.

3. The UAV scheduling method based on the C4ISR framework and distributed control as described in claim 1, characterized in that, The steps of establishing a kinematic model for each drone in the cluster to describe its motion under physical conditions specifically include: Perform six-degree-of-freedom rigid body dynamics modeling.

4. The UAV scheduling method based on the C4ISR framework and distributed control as described in claim 1, characterized in that, The steps of planning a macroscopic waypoint sequence for the entire cluster based on the risk potential field diagram, which effectively avoids known and predicted risks and meets the physical performance constraints of the UAV, specifically include: Based on the aforementioned risk potential field diagram, the state space is discretized, and a cost function is designed. Perform a heuristic search to generate discrete waypoints; The discrete waypoint sequence is smoothed to generate a continuous, flyable smooth trajectory. The discrete waypoint sequence and the corresponding expected arrival time window are periodically broadcast to all UAVs in the cluster.

5. The UAV scheduling method based on the C4ISR framework and distributed control as described in claim 1, characterized in that, The steps for enabling each drone to perform task-guided distributed cluster consistency control and generate control commands by communicating only with its neighbors specifically include: Treating the macroscopic task point as a gravitational source, a global task-guided potential energy term is added to the traditional potential field function based solely on neighbors; Construct a dynamic coupling strength model that is simultaneously affected by the distance between UAVs and the regional threat level; Develop an improved distributed collaborative control protocol.

6. The UAV scheduling method based on the C4ISR framework and distributed control as described in claim 1, characterized in that, When the ground station communication link is interrupted, the steps for achieving distributed task point consensus specifically include: Perform interruption detection and local information caching; Perform distributed average consensus algorithm iteration; Each drone uses the consensus value as the current valid task point and inputs it into the control protocol to continue guiding the swarm's movement.

7. The UAV scheduling method based on the C4ISR framework and distributed control according to any one of claims 1 to 6, characterized in that, The steps of converting the control commands into the actual physical motion of the UAV and feeding back the flight status to the C4ISR system in real time to form a closed loop of continuous learning and self-improvement specifically include: The control commands are issued and physically executed. Real-time feedback on the flight status of the drone; Based on the feedback information, the prediction model is updated and the planning parameters are optimized.

8. A scheduling device for unmanned aerial vehicles (UAVs) based on the C4ISR framework and distributed control, characterized in that, include: The module is used to construct a risk potential field map for unified understanding of the environment through multi-source data fusion, and to pre-allocate the cluster's communication and computing resources based on the risk potential field map; The description module is used to build a kinematic model for each drone in the cluster, describing its motion under physical conditions; The planning module is used to plan a macroscopic waypoint sequence for the entire cluster based on the risk potential field diagram, from the starting point to the ending point, which can effectively avoid known and predicted risks and meet the physical performance constraints of the UAV. The control module is used to enable each drone to perform distributed cluster consistency control under mission guidance while communicating only with its neighbors, and to generate control commands. The consensus module is used to achieve distributed consensus among task points when the communication link between local ground stations is interrupted. The optimization module is used to convert the control commands into the actual physical motion of the UAV and feed back the flight status to the C4ISR system in real time, forming a closed loop of continuous learning and self-improvement.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the C4ISR-based distributed control UAV scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the C4ISR-based distributed control UAV scheduling method as described in any one of claims 1 to 7.

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