Police unmanned aerial vehicle management and control system for handling emergencies

Abstract

The invention relates to the field of unmanned aerial vehicle control, and discloses a police unmanned aerial vehicle management and control system for handling emergencies, which comprises a sensing module, a distribution module, a planning module, a control module, a tracking module and a feedback module. The sensing module is used for collecting environment data in real time and generating a three-dimensional environment model; the distribution module optimizes an unmanned aerial vehicle task distribution scheme; the planning module dynamically plans the optimal flight path of the unmanned aerial vehicle according to the task allocation result and the three-dimensional environment model; the control module dynamically adjusts the attitude and flight height of the unmanned aerial vehicle; the tracking module tracks the target position in real time and predicts the motion trail of the target; and the feedback module transmits the execution condition of the unmanned aerial vehicle, the target tracking result and the field real-time picture back to the command center. Through cooperative work of all the modules, rapid response and efficient disposal of emergencies are achieved, the system is particularly suitable for police service tasks in complex and dynamic environments, and the system has the advantages of being high in response efficiency, high in environmental adaptability and high in resource utilization rate.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and specifically to a police UAV management and control system for handling emergencies. Background Art

[0002] In recent years, with the rapid development of UAV technology, its application in the police field has gradually become a research hotspot. Police UAVs, with their flexibility and real-time response capabilities, have been widely used in tasks such as on-site monitoring of emergencies, target tracking, and area alert. However, in practical applications, due to the high dynamics, complexity, and variability of emergencies, existing police UAV management and control systems still face many technical bottlenecks when dealing with complex scenarios.

[0003] Existing technologies usually rely on single UAVs or simple multi-UAV cooperation models, with insufficient optimization capabilities for task allocation, path planning, and target tracking. In multi-target, multi-UAV task scenarios, due to the lack of an effective dynamic allocation mechanism, existing systems often struggle to adjust UAV resource allocation according to task priorities and real-time environments, resulting in low response efficiency. In addition, existing path planning methods mostly use preset paths or static planning, making it difficult to adjust in real time in a dynamic environment, causing UAVs to easily deviate from the target or increase flight risks in scenarios with dense obstacles or changing wind speeds.

[0004] In terms of target tracking, most existing systems use simple positioning and tracking algorithms, making it difficult to accurately predict moving targets, especially in cases where the target's movement trajectory is complex or there are multi-target interferences, prone to tracking errors or even target loss. In addition, in terms of data feedback and command linkage, existing systems usually only support simple one-way data transmission and cannot provide efficient real-time command support, restricting the flexible application of UAVs in emergencies.

[0005] In summary, existing technologies have technical deficiencies in police UAV management and control for handling emergencies, including low response efficiency, poor environmental adaptability, insufficient target tracking capabilities, and weak command linkage. There is an urgent need for a systematic solution that can comprehensively address the above problems. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a police UAV management and control system for handling emergencies, which solves the problems of low response efficiency, unreasonable task allocation, insufficient path planning capabilities, and inaccurate target tracking and data feedback of existing police UAVs in complex dynamic environments.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A police UAV management and control system for handling emergencies, comprising:

[0008] A perception module, which is used to collect environmental data in real time. The environmental data includes obstacle distribution, wind speed, target dynamic position, etc., and generates a three-dimensional environmental model;

[0009] An allocation module, which is used to optimize the UAV task allocation scheme based on the environmental data of the perception module and the priority of the police situation task;

[0010] A planning module, which is used to plan the optimal flight path of the UAV according to the task allocation result of the allocation module and the three-dimensional environmental model. The path planning avoids obstacles and minimizes the flight time;

[0011] A control module, which is used to dynamically adjust the attitude and flight altitude of the UAV according to the flight path of the planning module;

[0012] A tracking module, which is used to track the target position in real time and predict the trajectory of the dynamic target;

[0013] A feedback module, which is used to transmit back to the command center the task execution situation of the UAV, the target tracking result and the environmental data.

[0014] Preferably, the perception module includes a lidar, an ultrasonic sensor and a wind speed sensor. The lidar is used to collect obstacle distribution point cloud data, the ultrasonic sensor is used to sense close-range obstacles, and the wind speed sensor is used to generate a wind speed distribution model.

[0015] Preferably, the allocation module realizes task allocation optimization based on an improved Hungarian algorithm. The optimization goal of the task allocation is to minimize the comprehensive cost of the UAV to execute the task. The comprehensive cost is composed of the distance from the UAV to the target, the task priority, and the influence weights of the wind speed and obstacles.

[0016] Preferably, the planning module realizes dynamic planning of the flight path based on the particle swarm optimization algorithm. The optimization goals of the flight path are:

[0017] Minimize the UAV flight time;

[0018] Avoid obstacles on the flight path;

[0019] Reduce the interference of the wind speed on the UAV flight.

[0020] Preferably, the particle swarm optimization algorithm realizes path planning through the following steps:

[0021] Initialize path nodes based on the three-dimensional environmental model;

[0022] According to the current position of the UAV, the target position and the obstacle distribution, iteratively update the speed and position of the path nodes;

[0023] The optimal path is selected by comparing the historical optimal position of the path nodes with the global optimal position.

[0024] Preferably, the control module dynamically adjusts the attitude of the UAV, including the pitch angle, yaw angle and flight altitude, based on adaptive control theory, wherein the attitude state vector of the UAV is calculated in real time, a Lyapunov function is constructed, and the control input of the UAV is adjusted to ensure the stability of the flight attitude.

[0025] Preferably, the tracking module uses an extended Kalman filter algorithm to predict the target trajectory, and the extended Kalman filter algorithm includes the following steps:

[0026] Predict the target's next position based on the target's motion state and the drone's flight state;

[0027] Correct the predicted target position based on the target observation data collected by the sensor;

[0028] Update the target state and its covariance matrix.

[0029] Preferably, the feedback module includes a real-time video feedback unit and a remote shouting unit, the video feedback unit is used to transmit the on-site image to the command center, and the remote shouting unit is used to convey instructions or warning information to the target area through the drone.

[0030] Preferably, the system re-triggers the allocation module and the planning module based on the real-time perceived environmental change data to update the task allocation results and the flight path.

[0031] The present invention also provides a police drone control method for handling emergencies, comprising the following steps:

[0032] Collect environmental data and generate a three-dimensional environmental model;

[0033] Optimize the drone task allocation plan based on environmental data and police task priorities;

[0034] Dynamically plan the optimal flight path of the UAV based on the task allocation results and the three-dimensional environment model;

[0035] According to the planned path, dynamically adjust the attitude and flight altitude of the drone to ensure that the drone flies according to the planned path;

[0036] Track the target position in real time and predict the target's trajectory;

[0037] The drone execution status, target tracking results and real-time images are transmitted back to the command center through the feedback module.

[0038] The present invention provides a police drone control system for emergency response.

[0039] Beneficial effects:

[0040] 1. Through the real-time data collection of the sensing module and the dynamic optimization of the distribution module in the present invention, the police task in an emergency can be quickly allocated and processed. The planning module generates the optimal flight path, and the control module ensures the stable flight of the UAV. The overall process is compact and efficient, greatly shortening the task response time and improving the system's response ability in emergencies.

[0041] 2. Through the three-dimensional environment model generated by the sensing module in the present invention, the system can sense and dynamically update complex conditions such as obstacles and wind speed in the environment in real time. The planning module and the control module adjust the path and attitude according to the environmental data to ensure that the UAV can adapt to different scenarios and dynamic changes, enhancing the robustness and flexibility of the system in complex environments.

[0042] 3. The tracking module in the present invention adopts the extended Kalman filter algorithm to perform real-time tracking and trajectory prediction on dynamic targets, ensuring the high precision and continuity of the target position data. Whether the target is stationary or moving, it can achieve accurate locking and continuous monitoring of the target, providing reliable support for task execution.

[0043] 4. Through the real-time data feedback function of the feedback module in the present invention, the command center can obtain the flight status of the UAV, the target tracking result, and the on-site video information at any time, providing a comprehensive decision-making basis for the commander. In addition, the remote voice function can directly convey instructions or warning information to the target area, improving the effectiveness of command and intervention.

[0044] 5. The distribution module in the present invention rationally allocates UAV resources through a dynamic optimization algorithm, and can maximize the use of system resources in complex scenarios with multiple tasks and multiple UAVs. The path planning module further optimizes the flight path, reducing unnecessary flight distance and energy consumption, thereby significantly improving the resource utilization efficiency and task completion rate.

[0045] 6. The present invention supports dynamically updating task allocation and flight path according to real-time environmental changes. Through the continuous monitoring of the sensing module and the closed-loop response of the feedback module, the system can quickly adjust the task plan when the target position changes or the environment suddenly changes, maintaining the continuity and accuracy of task execution. Brief description of the drawings

[0046] Figure 1 is a schematic diagram of the system architecture of the present invention;

[0047] Figure 2 is a schematic diagram of the method flow of the present invention. Detailed implementation manners

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] Please refer to the attached Figure 1 , the present invention provides a police unmanned aerial vehicle (UAV) control system for handling emergencies, which realizes the rapid response, precise execution, and real-time feedback of UAVs in emergencies. This system can adapt to the dynamic changes in complex environments, optimize the allocation of UAV resources, and improve the handling efficiency. The system includes a sensing module, an allocation module, a planning module, a control module, a tracking module, and a feedback module. The following will detail each module of the system of the present invention.

[0050] Sensing module

[0051] The sensing module is one of the core components of the police UAV control system of the present invention, mainly responsible for real-time collecting environmental data, providing data support and decision-making basis for the system's allocation, planning, control, and tracking modules. Through the collaborative work of multiple sensors, this module obtains data such as the distribution of obstacles, wind speed dynamics, and target positions at the emergency scene and its surrounding areas, and finally generates a three-dimensional environmental model.

[0052] As a technical implementation, this module aims to solve the problem of insufficient perception of dynamic environmental data by UAVs in complex environments. By real-time constructing a high-precision three-dimensional environmental model, it provides basic support for path planning and task allocation.

[0053] It should be noted that the sensing module not only supports obstacle recognition, but also can dynamically monitor the wind speed changes in the environment and collect position data of moving targets. These functions are realized through the integration of different sensors and algorithms, and the specific technical implementation is as follows.

[0054] In this embodiment, the sensing module mainly consists of a light detection and ranging (LIDAR), an ultrasonic sensor, and a wind speed sensor, which collaborate to collect environmental information. Exemplarily, the data collection process is as follows:

[0055] The LIDAR emits laser pulses and receives the returned signals to calculate the spatial positions of obstacles and form point cloud data. As an option, the sensing range of the LIDAR can be adjusted according to the specific flight altitude of the UAV to ensure adaptability to different terrains and building environments.

[0056] Specifically, the point cloud data generated by the LIDAR can be described as:

[0057]

[0058] Wherein:

[0059] (x i , y i , z i ) represents the three-dimensional coordinates of each point;

[0060] d i represents the distance between the point measured by the lidar and the drone;

[0061] R represents the sensing range of the lidar.

[0062] It can be understood that when the environmental complexity is high, such as when there are high-density buildings or multi-layer obstacles, the resolution of the point cloud data has a greater impact on the accuracy of subsequent path planning. Therefore, the present invention preferably uses a high-precision lidar device to improve the reliability of the data.

[0063] In some embodiments, the ultrasonic sensor is mainly used to sense the short-distance obstacles around the drone, supplementing the deficiency of the lidar for environmental data within a short distance. As an option, the ultrasonic sensor can be installed at the bottom and sides of the drone to cover the sensing blind spots in different directions.

[0064] Specifically, the distance detection formula of the ultrasonic sensor is:

[0065]

[0066] Wherein:

[0067] v is the propagation speed of ultrasonic waves in the air;

[0068] t is the time from the emission to the reception of the ultrasonic wave.

[0069] It should be noted that the accuracy of the ultrasonic sensor is greatly affected by environmental noise. Therefore, in this embodiment, a signal filtering algorithm is used to reduce noise interference, thereby improving the stability of distance detection.

[0070] In this embodiment, the wind speed sensor is used to measure the magnitude and direction of the wind speed in the environment. Specifically, the vector representation of the wind speed is:

[0071] V wind =(v x , v y , v z )

[0072] Wherein:

[0073] v x , v y , v z are respectively the components of the wind speed on the three-dimensional coordinate axes.

[0074] As an implementation, the wind speed data is dynamically integrated into the three-dimensional environment model to provide real-time reference for the flight control and path planning of the UAV. It can be understood that the wind speed has a direct impact on the flight stability of the UAV. Therefore, the present invention preferably uses a high-sensitivity wind speed sensor.

[0075] It should be specifically noted that the perception module in the present invention generates a unified three-dimensional environment model by fusing the data collected by the above sensors. Specifically, the process of model integration includes:

[0076] Data preprocessing: Denoising and complementing the point cloud data, ultrasonic distance data, and wind speed data;

[0077] Data coordinate unification: Mapping the data of different sensors to the global coordinate system of the UAV;

[0078] Model generation: Dividing the environment based on the three-dimensional grid method, and each grid cell records the state information of obstacles, wind speed, and dynamic targets.

[0079] As a possible implementation, the grid accuracy of the three-dimensional environment model can be adjusted according to the actual scene requirements. For example, in a high-complexity scene, the grid resolution can be increased to more finely describe the environmental characteristics.

[0080] Exemplarily, the mathematical representation of the three-dimensional grid method is:

[0081]

[0082] Where:

[0083] G i,j,k represents the state of the three-dimensional grid cell;

[0084] G i,j,k = 1 indicates that there is an obstacle or abnormal wind speed in the cell, and G i,j,k = 0 indicates that the cell is empty.

[0085] It should be noted that the three-dimensional environment model not only supports the static description of obstacles but also can be dynamically updated. For example, when the target moves in the model, its position is updated in real time through the feedback of the tracking module.

[0086] As an extended solution, the perception module can further integrate visual sensors to achieve image recognition of the environment. For example, capturing feature markers in the environment through a camera to assist in judging the category and nature of obstacles. This extended function can provide richer reference information for subsequent allocation and planning.

[0087] The perception module of the present invention provides precise environmental data support for the mission execution of police drones through the collaborative work of multiple sensors and efficient data fusion processing, and can operate efficiently and stably in dynamic and complex emergencies. These features lay a solid foundation for the implementation of the subsequent allocation module and planning module.

[0088] Allocation module

[0089] The allocation module is one of the core components of the police drone control system of the present invention, mainly used for optimizing the allocation of drone mission resources when receiving multiple police mission tasks. By comprehensively analyzing the environmental data provided by the perception module, the priority of police mission tasks, and the real-time position of the drone, this module generates an optimal mission allocation plan. The implementation of the allocation module can ensure the accuracy of mission allocation, thus maximizing the utilization of drone resources and improving the efficiency of handling emergencies.

[0090] It should be noted that the allocation module uses an optimization algorithm to achieve dynamic adjustment of the mission allocation plan. In the present invention, the allocation module is particularly suitable for handling complex scenarios where multiple drones respond to multiple sudden police emergencies simultaneously, and its optimization goals include minimizing the mission response time and minimizing the mission execution cost.

[0091] In this embodiment, the allocation module realizes task optimization allocation based on an improved Hungarian algorithm. The Hungarian algorithm is a classic method for efficiently solving the task allocation problem, which can find the allocation plan with the minimum total cost between tasks and resources. The present invention improves this algorithm to enable it to dynamically adapt to environmental changes and the requirements of multi-task priorities.

[0092] Specifically, the allocation module first constructs a cost matrix C for task allocation, which is defined as follows:

[0093] C ij = α·d ij + β·E ij + γ·P j

[0094] Where:

[0095] C ij represents the comprehensive cost of drone i executing task j;

[0096] d ij represents the Euclidean distance between drone i and task point j, defined as:

[0097]

[0098] Where (x i , y i , z i ) and (x j, y j , z j are the three-dimensional coordinates of the UAV and the mission point respectively;

[0099] E ij represents the environmental impact factor, including wind speed, obstacle density, etc., which is calculated from the data provided by the sensing module;

[0100] P j represents the priority of the mission. The higher the priority of the mission, the greater its weight;

[0101] α, β, γ are weight coefficients used to balance the influence of the above factors.

[0102] It can be understood that the above cost matrix is dynamically updated. As the environmental data provided by the sensing module changes, the allocation module can adjust the task allocation scheme in real time.

[0103] In a possible implementation, the allocation module first preprocesses the cost matrix to ensure that each task can obtain at least one UAV response. Exemplarily, for an invalid task allocation path (such as a path blocked by an obstacle and unable to be completed), its corresponding cost value can be set to infinity to avoid this path being selected.

[0104] It should be noted that the improved Hungarian algorithm realizes task allocation through the following steps:

[0105] Initial allocation: Allocate the lowest-cost path between each UAV and the mission point to it;

[0106] Row and column adjustment: Adjust the values in the matrix to reduce the allocation conflicts between tasks;

[0107] Optimization iteration: Recalculate the allocation scheme according to the remaining tasks and unallocated UAVs until all tasks are completed.

[0108] In some embodiments, to improve the response speed of allocation, the allocation module can also use parallel computing technology to accelerate the Hungarian algorithm. Specifically, the grouping of mission points can be processed in parallel, and global optimization and integration are performed after each group is allocated.

[0109] As an option, to cope with the situation where the task priority changes dynamically in emergencies, the allocation module also supports dynamic adjustment of the priority factor. For example, when the urgency of a certain task suddenly rises, the system will automatically increase its priority weight P j , so as to preferentially allocate resources.

[0110] It should be noted in particular that the allocation module is not only applicable to the allocation scenarios of multiple UAVs for multiple tasks, but can also flexibly handle special cases where a single UAV responds to multiple police situations. In such cases, the system will generate an execution queue in sequence according to the priority of the tasks to ensure that high-priority tasks are responded to first.

[0111] In a possible extended implementation, the allocation module can also combine machine learning techniques to further optimize the effect of task allocation. For example, by training a prediction model with historical police situation data to estimate the environmental complexity of the task points, the environmental impact factor E ij can be assigned more accurately. This extended implementation method is particularly suitable for dealing with areas where the distribution of police situations is relatively regular, such as specific business districts or scenic spots.

[0112] It can be understood that the design of the allocation module aims to form efficient cooperation with other modules. For example, through the linkage with the planning module, the allocation module can further optimize the allocation plan according to the flight path cost feedback by the planning module; through the interaction with the tracking module, the allocation module can adjust the task allocation result according to the dynamic position of the target.

[0113] In summary, the allocation module of the present invention realizes the efficiency and flexibility of UAV task allocation in emergencies through the improved Hungarian algorithm, dynamic weight adjustment, and deep integration of environmental data.

[0114] Planning module

[0115] The planning module is an important part of the police UAV control system of the present invention. Its core function is to generate the optimal flight path for the UAV according to the task results provided by the allocation module and the three-dimensional environmental model generated by the perception module. The path generation of the planning module needs to meet multiple optimization objectives, including avoiding obstacles, reducing the influence of wind speed, shortening the flight time, etc., to ensure that the UAV can execute tasks efficiently and stably.

[0116] It should be noted that the design of the planning module is particularly suitable for complex dynamic environments and can dynamically adjust the path to cope with real-time changes. Through the path generation method based on the particle swarm optimization (PSO) algorithm, the planning module can effectively balance the safety and execution efficiency of the path.

[0117] In this embodiment, the planning module realizes the three-dimensional path planning of the UAV based on the particle swarm optimization algorithm. The particle swarm optimization algorithm is a commonly used global optimization method that can find the optimal solution to the problem through the iterative update of particles. In this embodiment, the path nodes are represented as the positions of particles, and the optimal path of the UAV is generated through the search process of the particle swarm algorithm.

[0118] Specifically, the optimization objective function of path planning can be expressed as:

[0119] f(X)=α·T(X)+β·O(X)+γ·D(X)

[0120] in:

[0121] T(X): Total flight time of the path, defined as:

[0122]

[0123] where ||X i+1 -X i || represents the distance between adjacent path nodes, v drone Indicates the flight speed of the drone.

[0124] O(X): The risk of obstacles on the path, calculated as:

[0125]

[0126] Where W wind (X i ) is the path point X i The wind speed at the location It represents the density of obstacles at the path point, and η is the adjustment coefficient.

[0127] D(X): The distance between the end point of the path and the target point, defined as:

[0128] D(X)=||X N -X target ||

[0129] Where X N is the last node of the path, X target is the coordinate of the target point.

[0130] As an option, the weight coefficients α, β, and γ of the optimization objective function can be adjusted according to the task requirements to give priority to satisfying specific constraints. For example, in an environment with high wind speed, the value of β can be increased to pay more attention to avoiding the influence of wind speed.

[0131] In one possible implementation, the search process of the particle swarm optimization algorithm includes the following steps:

[0132] Initialize the particle swarm: initialize the position of each particle to a random path from the current coordinates of the drone to the target point, and initialize the velocity to a random vector.

[0133] Particle update rules: The position and velocity of each particle are updated according to the following formula:

[0134] V k+1 =w·V k +c1·r1·(P k -Xk ) + c2·r2·(G k - X k )

[0135] X k+1 = X k + V k+1

[0136] Where:

[0137] V k and X k are the velocity and position of particle k, respectively;

[0138] P k represents the historical best position of particle k;

[0139] G k represents the global best position;

[0140] w, c1, c2 are inertia coefficients and learning factors, used to balance global search and local search;

[0141] r1, r2 are random numbers, used to introduce the randomness of the search.

[0142] Convergence judgment: The algorithm iteratively executes until one of the following convergence conditions is met:

[0143] The path cost values of all particles tend to be stable;

[0144] The maximum number of iterations is reached.

[0145] It should be noted that the particle swarm optimization algorithm evaluates the feasibility of the particle path in each iteration. For example, for the path nodes with obstacle crossing, they will be corrected through the obstacle distribution data provided by the sensing module to ensure the safety of the path.

[0146] In some embodiments, to improve the real-time performance of path planning, the planning module can accelerate the particle swarm optimization algorithm by combining parallel computing technology. Specifically, the particle swarm can be grouped for parallel evaluation, and the global optimal solution of each group of particles is updated to the global optimization result after integration. This method is particularly suitable for the fast response requirements of path planning in a highly dynamic environment.

[0147] In an extended scheme, the planning module can also introduce a multi-objective optimization mechanism to balance the resource allocation of path planning among multiple tasks. For example, when multiple unmanned aerial vehicles need to complete different tasks simultaneously, the system can preferentially generate better paths for high-priority tasks while appropriately weighing the optimization of low-priority task paths.

[0148] It is understandable that the implementation of the planning module requires close cooperation with the perception module and the allocation module. The environmental data provided by the perception module is the basis for path planning, while the task allocation result of the allocation module directly determines the target position of path planning. The optimal path generated by the planning module is transmitted to the control module to ensure that the UAV flies precisely along the planned path.

[0149] The planning module of the present invention realizes the efficiency and dynamic adaptability of path generation through a three-dimensional path planning method based on the particle swarm optimization algorithm.

[0150] Control module

[0151] The control module is a key functional unit in the UAV management and control system of the present invention. Its main function is to dynamically adjust the flight attitude and altitude of the UAV according to the path generated by the planning module, ensuring that the UAV flies stably along the optimal path and effectively completes tasks. This module realizes real-time response through adaptive control technology and can maintain the stability and path following performance of the UAV in complex and dynamic environments.

[0152] It should be noted that the control module plays a connecting role in the whole system. Its input includes the environmental data provided by the perception module and the path information output by the planning module, while its output directly affects the execution effects of the tracking module and the feedback module. By adjusting the attitude and flight parameters in real time, the control module ensures the flight safety of the UAV and the accuracy of task execution.

[0153] In this embodiment, the control module realizes the real-time adjustment of the flight attitude based on the adaptive control theory. The flight attitude of the UAV includes the pitch angle, yaw angle, and roll angle, and the dynamic adjustment of these parameters is regulated by the control input. The implementation steps and principles of the control module are as follows:

[0154] First, the flight state of the UAV is described by the following state equation:

[0155]

[0156] Where:

[0157] x(t) represents the state vector of the UAV, including position, velocity, and attitude angle;

[0158] u(t) is the control input vector, including thrust, direction adjustment amount, etc.;

[0159] d(t) is the external disturbance, such as the influence of wind speed;

[0160] A and B are system parameter matrices, which respectively describe the dynamic model of the UAV and the influence of the control input on the state.

[0161] Specifically, the control module analyzes the flight attitude stability by constructing the Lyapunov function V(x) to ensure the smoothness of the flight process and the rapidity of the control response. The Lyapunov function is defined as:

[0162]

[0163] Where:

[0164] P is a symmetric positive definite matrix;

[0165] x T represents the transpose of the state vector.

[0166] As an option, to ensure the stability of the system, the control module designs the control input u(t) such that That is:

[0167]

[0168] By solving the above conditions, the calculation formula of the control input u(t) can be expressed as:

[0169] u(t) = -Kx(t)

[0170] Where K is the feedback gain matrix, and its value is obtained by solving the Lyapunov equation.

[0171] In a possible implementation, to further improve the robustness of the system, the control module dynamically adjusts the feedback gain K to adapt to different environmental disturbances. For example, when the sensing module detects an increase in wind speed, the control module can appropriately increase the gain value to enhance the ability to suppress external disturbances.

[0172] In some embodiments, the control module can also combine the path following control algorithm to achieve precise tracking of the planned path. The goal of path following control is to minimize the deviation between the current position of the UAV and the path reference point, and the specific control law is:

[0173] u p (t) = K p ·e(t)

[0174] Where:

[0175] u p (t) is the path following control input;

[0176] K p is the path following gain;

[0177] e(t) is the error vector between the current position and the reference point.

[0178] It should be noted that the path following control and the attitude adjustment control are executed in parallel, and their synergistic effect ensures that the UAV can not only maintain stable attitude but also fly efficiently along the planned path.

[0179] As an option, the execution process of the control module can introduce a predictive control mechanism to cope with potential changes in the environment in advance. For example, by predicting the wind speed data provided by the perception module, the control module can adjust the flight parameters in advance before the wind speed changes, thereby further improving flight stability.

[0180] In an extended scheme, the control module can also incorporate self-learning algorithms, such as an adaptive control method based on neural networks. By online learning the disturbance characteristics in the environment and the dynamic response of the UAV, the control module can gradually optimize the control input to adapt to more complex environmental conditions. This extended scheme is particularly suitable for high-dynamic scenarios with frequent emergencies, such as the scene of mass incidents in urban dense areas.

[0181] It can be understood that the performance of the control module has a direct impact on the task execution effect of the entire system. Through close cooperation with the planning module and the perception module, the control module ensures that the UAV can quickly respond to environmental changes and complete the execution of the planned path with high precision.

[0182] Tracking module

[0183] The tracking module is an important part of the UAV control system for police affairs in the present invention. Its main function is to real-time track dynamic targets and predict the movement trajectories of the targets, providing accurate target positions and movement information for the control module and the feedback module. The design of the tracking module is particularly suitable for high-dynamic target scenarios in complex environments, such as the flow of people or the movement of suspicious vehicles during emergencies. Through advanced prediction and correction technologies, the tracking module can maintain high-efficiency tracking capabilities in the case of variable target behaviors.

[0184] It should be noted that the tracking module highly coordinates with the perception module and the control module. The perception module provides real-time observation data of the target, and the control module relies on the predicted trajectory output by the tracking module to adjust the flight path and attitude of the UAV to ensure continuous tracking of the target.

[0185] In this embodiment, the tracking module realizes real-time tracking and trajectory prediction of dynamic targets based on the Extended Kalman Filter (EKF) algorithm. EKF is a commonly used method for state estimation of nonlinear systems, which can efficiently predict and correct the position of targets in a noisy environment.

[0186] Specifically, the state model of target movement can be described as:

[0187] x k= Fx k-1 + Gu k-1 + w k

[0188] Where:

[0189] x k represents the state vector of the target at time k, including position, velocity, and acceleration;

[0190] F is the state transition matrix, describing the variation law of the target state over time;

[0191] G is the control input matrix, describing the influence of the control quantity on the state;

[0192] u k-1 is the control input;

[0193] w k is the process noise, following a Gaussian distribution with zero mean.

[0194] The observation model of the target can be described as:

[0195] z k = Hx k + v k

[0196] Where:

[0197] z k is the observation value, including the real-time position of the target;

[0198] H is the observation matrix, describing the relationship between the state vector and the observation value;

[0199] v k is the measurement noise, following a Gaussian distribution with zero mean.

[0200] It should be noted that the EKF algorithm is divided into two stages: prediction and correction. The specific implementation is as follows:

[0201] In the prediction stage:

[0202]

[0203] Where:

[0204] is the predicted state;

[0205] is the predicted covariance matrix;

[0206] Q is the process noise covariance matrix.

[0207] In the correction stage:

[0208]

[0209] Wherein:

[0210] K k is the Kalman gain, which is used to balance the error between prediction and observation;

[0211] is the updated state estimate;

[0212] R is the observation noise covariance matrix.

[0213] In a possible implementation, the tracking module can optimize the state transition matrix F by combining the behavior model of the dynamic target. For example, for a target moving at a constant speed, the state transition matrix can be expressed as:

[0214]

[0215] where Δt is the time step.

[0216] For a target moving at a non-constant speed, an acceleration term can be introduced and the state vector can be extended to adapt to a more complex motion model.

[0217] In some embodiments, the tracking module can also optimize the observation matrix H by combining a machine learning model. For example, by analyzing the motion pattern of the target through deep learning methods, the parameters of H can be dynamically adjusted to improve the accuracy of the observation model.

[0218] As an option, to cope with the complex scenario of multi-target tracking, the tracking module can extend its function through a multi-target tracking algorithm (Multiple Target Tracking, MTT). The specific implementation can adopt the Joint Probabilistic Data Association (JPDA) method to associate the observations of multiple targets with the state predictions to ensure the tracking accuracy among multiple targets.

[0219] It should be noted that the output of the tracking module includes the predicted position of the target and the confidence of the current state, and this information can be used for the control module to adjust the flight attitude of the UAV and for the feedback module to generate more accurate command decision information.

[0220] In an extended scheme, the tracking module can also combine visual perception technology to capture the target image in real time through the camera on the UAV, and identify and locate the target by combining the target feature point matching algorithm. This extended scheme is particularly suitable for the task of identifying the identity of dynamic targets, such as locking a specific target in a crowd.

[0221] It is understandable that the performance of the tracking module directly affects the system's response efficiency to the target and the accuracy of task completion. Through high coordination with the perception module and the control module, the tracking module can provide stable and efficient target tracking services for the entire system.

[0222] Feedback module

[0223] The feedback module is an important part of the police drone control system of the present invention. Its main function is to transmit the drone's mission execution status, target tracking results and environmental data to the command center in real time, providing data support for command decision-making and dynamic task adjustment. Through the feedback module, the system can achieve comprehensive monitoring of the drone's mission execution status and transmit real-time information to the target area.

[0224] It should be noted that the feedback module not only assumes the responsibility of data transmission, but also directly intervenes in the target area through audio and video transmission and remote shouting functions, which is especially suitable for rapid information communication in emergency handling. This module works in conjunction with the control module and the tracking module, and its output directly affects the decision-making efficiency of the command center.

[0225] In this embodiment, the feedback module mainly includes a real-time data transmission unit, a video return unit and a remote shouting unit. The specific implementation of each unit is as follows:

[0226] In the real-time data transmission unit, the feedback module uses wireless communication technology to transmit the status data and environmental perception data of the drone back to the command center. For example, the data sent back includes the current coordinates, flight speed, attitude angle and real-time position of the target of the drone.

[0227] As an option, to ensure the stability of data transmission, this embodiment adopts a method of dynamically adjusting bandwidth allocation to give priority to the transmission of high-priority data according to the importance and real-time requirements of the data. For example, target location and video stream data are given a higher transmission priority, while non-real-time data (such as task completion records) can be delayed for transmission.

[0228] It should be noted that this dynamic bandwidth allocation mechanism can give priority to the transmission of key data when network resources are limited, thereby improving the overall response efficiency of the system.

[0229] In the video transmission unit, this embodiment uses a high-definition camera to capture live images and uses an efficient compression algorithm to transmit the video stream back to the command center in real time. As a possible implementation method, the resolution and frame rate of the video transmission can be dynamically adjusted according to the network conditions to ensure the clarity of the picture while reducing the data transmission delay.

[0230] In some embodiments, in order to enhance the transmission reliability of video data, the video return unit also integrates forward error correction (FEC) technology, which can automatically correct errors that occur during the transmission process to a certain extent by embedding redundant information in the data stream.

[0231] In the remote shouting unit, this embodiment uses the speaker carried by the drone to conduct audio intervention on the target area, and can transmit the command of the command center or make warning calls in real time. Specifically, the remote shouting unit includes a speech synthesis module and an audio amplification module, wherein the speech synthesis module converts text instructions into audio signals.

[0232] As an option, the speech synthesis module uses speech synthesis technology based on deep learning, which can achieve flexible output in multiple languages ​​and tones. For example, the tone and speed of the synthesized speech can be adjusted according to the needs of different scenarios, such as selecting high-speed and high-volume speech output during emergency evacuation.

[0233] It should be noted that in order to avoid audio interference in the target area, the remote shouting unit can also dynamically adjust the output power of the speaker according to the ambient noise level.

[0234] In one possible implementation, the feedback module works in conjunction with the tracking module to dynamically adjust the directional output of the speaker according to the real-time position of the target, ensuring that the content of the shouting can be accurately delivered to the target area.

[0235] In some embodiments, in order to enhance the functional expansibility of the feedback module, the system can integrate voice recognition and text analysis technology. For example, the voice command of the command center can be converted into text through the voice recognition module and directly transmitted to the drone's shouting unit, realizing efficient transmission of voice commands.

[0236] Understandably, the core value of the feedback module lies in its comprehensive presentation of the UAV mission status and its ability to directly intervene in the target area.

[0237] In a preferred embodiment of the present invention, in order to adapt to the dynamic environmental changes during the emergency handling process, the system can re-trigger the allocation module and planning module according to the real-time perceived environmental data to dynamically update the task allocation results and flight paths. Through this mechanism, the system can effectively respond to the task adjustment requirements caused by changes in target position, environmental interference (such as sudden obstacles or wind speed changes), etc., thereby ensuring the efficient operation of the drone.

[0238] Specifically, the perception module continuously collects environmental data through a variety of sensors (such as lidar, wind speed sensor, etc.), and triggers an event update signal when a significant change in the environmental data is detected.

[0239] For example:

[0240] The target position deviates;

[0241] Suddenly a new obstacle appears, affecting the feasibility of the original path;

[0242] The wind speed direction or intensity changes, affecting the flight stability of the UAV.

[0243] These triggering conditions can be achieved through a threshold judgment mechanism. Exemplarily, the offset threshold of the target position can be defined as:

[0244]

[0245] When Δd > d threshold the system determines that the target position change reaches the adjustment threshold and triggers dynamic update.

[0246] After the trigger signal arrives at the allocation module, the system will recalculate the task allocation scheme. The allocation module updates the task cost matrix C based on the improved Hungarian algorithm, combined with real-time environmental data ij .

[0247] The dynamic adjustment of the allocation module ensures that the UAV can give priority to responding to the target with the most significant change or the most urgent task. For example, when the target priority changes, the allocation module will appropriately increase the weight of γ and preferentially allocate resources to high-priority tasks.

[0248] After the allocation result is updated, the planning module will regenerate the optimal flight path of the UAV according to the new task allocation result and environmental data. The planning module adopts the particle swarm optimization algorithm and quickly iterates under dynamic constraint conditions to ensure the real-time performance and safety of path planning.

[0249] Specifically, when the sensing module detects an update of the obstacle position, the planning module will dynamically adjust the feasibility of the path nodes. For example, when a certain path node X i is determined to be impassable, the particle swarm algorithm will automatically exclude this node and recalculate the global optimal path.

[0250] As an option, the path update of the planning module can adopt a distributed computing method to improve the response speed of the algorithm in complex scenarios. For a multi-UAV scenario, each UAV can independently plan its path and communicate with other UAVs at the same time to avoid path conflicts.

[0251] It should be noted that the dynamic adjustment mechanism is the result of the collaborative work of multiple modules. The perception module is responsible for providing trigger signals for environmental changes, and the allocation module and the planning module respectively complete the dynamic updates of task allocation and path planning. Through this modular collaborative approach, the system can quickly adapt to dynamic changes in complex scenarios, ensuring the continuity and efficiency of tasks.

[0252] The dynamic adjustment mechanism of this embodiment is particularly applicable to the following scenarios:

[0253] Sudden changes in the target location, such as large-scale migrations of crowds at the event site;

[0254] Drastic changes in environmental conditions, such as a sharp change in wind speed caused by severe weather;

[0255] The emergence of new obstacles at the scene, such as vehicle blockages or building collapses.

[0256] This dynamic adjustment mechanism significantly improves the adaptability of the system in complex environments, enabling the UAV to efficiently and precisely execute tasks in dynamically changing scenarios, while ensuring the optimality of resource allocation and the real-time nature of path planning.

[0257] Generally speaking, through the collaborative work of each module, the present invention realizes rapid response, precise handling, and real-time feedback for emergencies. The perception module collects environmental data in real time and generates a three-dimensional environmental model. The allocation module realizes UAV task allocation based on a dynamic optimization algorithm. The planning module generates a flight path that avoids obstacles and is time-optimal. The control module dynamically adjusts the UAV attitude to ensure flight stability. The tracking module realizes precise tracking of dynamic targets through an extended Kalman filter. The feedback module transmits the UAV task execution status and on-site audio and video data back to the command center. The present invention is particularly applicable to complex and dynamic environments and can efficiently and flexibly complete police tasks.

[0258] Please refer to the appendix Figure 2 , correspondingly, the present invention provides a method for controlling a police UAV to handle emergencies, which completes efficient response, task execution, and command support for emergencies through multi-module collaboration. The following describes the specific implementation manners of the method of the present invention in combination with the system working process.

[0259] S1: Collect environmental data and generate a three-dimensional environmental model

[0260] This method first collects real-time environmental data of the emergency scene through the perception module, including obstacle distribution, wind speed dynamics, target location, etc. The perception module integrates the collected data through devices such as lidar, ultrasonic sensors, and wind speed sensors to generate a three-dimensional environmental model.

[0261] During the acquisition process, the sensing module can dynamically adapt to environmental changes and update the positions of obstacles and targets in real time. The generated three-dimensional environmental model provides basic data support for subsequent modules.

[0262] S2: Optimize the UAV mission assignment plan

[0263] Based on the environmental data generated by the sensing module and the priorities of police tasks, the assignment module uses a dynamic optimization algorithm to calculate the mission assignment plan. The assignment module comprehensively analyzes the resource allocation between multiple UAVs and multiple task points and gives priority to responding to high-priority tasks.

[0264] Through the optimized calculation of the assignment module, the UAV can quickly adjust the assignment plan according to real-time task requirements, laying a foundation for the efficient handling of emergencies.

[0265] S3: Dynamically plan the optimal flight path of the UAV

[0266] Based on the mission assignment results output by the assignment module and the three-dimensional environmental model provided by the sensing module, the planning module generates an optimal flight path for each UAV to avoid obstacles and reduce wind speed interference.

[0267] The planning module ensures that the UAV reaches the target position in the shortest time and the safest path through dynamic path planning methods. During the planning process, the system can update the path in real time according to changes in environmental data to adapt to complex and dynamic environmental scenarios.

[0268] S4: Dynamically adjust the attitude and flight altitude of the UAV

[0269] According to the planned path, the control module dynamically adjusts the flight attitude and altitude of the UAV to ensure that the UAV accurately executes the path flight task. The control module real-time monitors the flight state of the UAV and maintains flight stability by adjusting the pitch angle, yaw angle, and thrust parameters.

[0270] The control module can also adjust the attitude during flight according to changes in environmental conditions to avoid flight deviations caused by wind speed interference or obstacles during flight.

[0271] S5: Real-time track the target position and predict the target movement trajectory

[0272] The tracking module uses a multi-sensor fusion method to real-time track the target position and predicts its future trajectory according to the movement law of the target. The prediction data of the tracking module provides key support for UAV path adjustment.

[0273] When the target position changes significantly, the tracking module can quickly correct the target positioning information to ensure that the system always maintains a high-precision tracking ability for the target.

[0274] S6: Transmit the execution status and feedback data back to the command center

[0275] The feedback module transmits the task execution status of the UAV, the target tracking results, and the real-time on-site images back to the command center, providing comprehensive decision-making support for the commander. The transmitted data includes the flight status of the UAV, target positioning information, and video images.

[0276] In addition, the feedback module also supports the function of shouting and intervening in the target area, transmitting instructions or warning messages to the target area through the speaker carried by the UAV, further improving the command efficiency.

[0277] Through the above steps, the method of the present invention can achieve the full-process control of emergencies. Each step is interconnected to form a closed-loop real-time response mechanism, thereby ensuring the efficient operation and task completion of the system in a complex dynamic environment.

[0278] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A police drone control system for emergency response, characterized in that: include: A perception module is used to collect environmental data in real time, including obstacle distribution, wind speed, target dynamic position, etc., and generate a three-dimensional environmental model; An allocation module, used for optimizing the UAV task allocation scheme based on the environmental data and the priority of the warning task of the perception module; A planning module, used to plan an optimal flight path for the UAV according to the task allocation result of the allocation module and the three-dimensional environment model, wherein the path planning avoids obstacles and minimizes flight time; A control module, used to dynamically adjust the attitude and flight altitude of the UAV according to the flight path of the planning module; Tracking module, used to track the target position in real time and predict the trajectory of dynamic targets; The feedback module is used to transmit the UAV's mission execution status, target tracking results and environmental data to the command center.

2. The police drone control system for emergency response according to claim 1 is characterized in that: The perception module includes a laser radar, an ultrasonic sensor and a wind speed sensor. The laser radar is used to collect obstacle distribution point cloud data, the ultrasonic sensor is used to perceive close-range obstacles, and the wind speed sensor is used to generate a wind speed distribution model.

3. The police drone control system for emergency response according to claim 1 is characterized in that: The allocation module realizes task allocation optimization based on the improved Hungarian algorithm. The optimization goal of the task allocation is to minimize the comprehensive cost of the UAV to perform the task. The comprehensive cost is composed of the distance from the UAV to the target, the task priority, and the influence weights of wind speed and obstacles.

4. The police drone control system for emergency response according to claim 1 is characterized in that: The planning module implements dynamic planning of the flight path based on the particle swarm optimization algorithm. The optimization goal of the flight path is: Minimize drone flight time; Avoid obstacles in the flight path; Reduce the interference of wind speed on drone flight.

5. The police drone control system for emergency response according to claim 4 is characterized in that: The particle swarm optimization algorithm implements path planning through the following steps: Initialize path nodes based on the three-dimensional environment model; Iteratively update the speed and position of the path nodes according to the current position of the UAV, the target position and the obstacle distribution; The optimal path is selected by comparing the historical optimal position of the path nodes with the global optimal position.

6. The police drone control system for emergency response according to claim 1 is characterized in that: The control module dynamically adjusts the attitude of the UAV, including the pitch angle, yaw angle and flight altitude, based on adaptive control theory. The attitude state vector of the UAV is calculated in real time, a Lyapunov function is constructed, and the control input of the UAV is adjusted to ensure the stability of the flight attitude.

7. The police drone control system for emergency response according to claim 1 is characterized in that: The tracking module uses an extended Kalman filter algorithm to predict the target trajectory, and the extended Kalman filter algorithm includes the following steps: Predict the target's next position based on the target's motion state and the drone's flight state; Correct the predicted target position based on the target observation data collected by the sensor; Update the target state and its covariance matrix.

8. The police drone control system for emergency response according to claim 1 is characterized in that: The feedback module includes a real-time video feedback unit and a remote shouting unit. The video feedback unit is used to transmit the on-site image to the command center, and the remote shouting unit is used to convey instructions or warning information to the target area through the drone.

9. The police drone control system for emergency response according to claim 1 is characterized in that: The system re-triggers the allocation module and the planning module according to the real-time perceived environmental change data to update the task allocation results and the flight path.

10. A police drone control method for emergency response, based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect environmental data and generate a three-dimensional environmental model; Optimize the drone task allocation plan based on environmental data and police task priorities; Dynamically plan the optimal flight path of the UAV based on the task allocation results and the three-dimensional environment model; According to the planned path, dynamically adjust the attitude and flight altitude of the drone to ensure that the drone flies according to the planned path; Track the target position in real time and predict the target's trajectory; The drone execution status, target tracking results and real-time images are transmitted back to the command center through the feedback module.

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