A method and system for quick air warning command

Abstract

The present invention provides an air quick alarm command method and system, which relates to the field of public security and police management, and the method comprises: obtaining the status of a drone in real time, including the location, power, communication status and load status of the drone; constructing a finite element model of the drone according to the status of the drone, the finite element model including material properties, boundary conditions and load parameters; using the finite element model to analyze the structural response and performance of the drone under different airport conditions to obtain analysis results; optimizing the airport information according to the analysis results to obtain optimized airport information, the optimized airport information including the number, location, equipment model and runway of the required airport. The present invention can perform tasks safely and efficiently through comprehensive management of routes, intelligent early warning and management, and rapid command means.

Description

Technical Field

[0001] The present invention relates to the field of public security and police management, and in particular to an air quick police command method and system. Background Art

[0002] With its high maneuverability, rapid response speed and wide field of view, drones play an indispensable role in air emergency missions, significantly improving the efficiency and accuracy of mission execution. However, with the continuous expansion of drone fleets and the increasing variety of missions, existing command and management methods have gradually exposed some defects.

[0003] First, faced with a large number of drone swarms, how to implement efficient and orderly management to ensure that each drone can respond quickly when needed has become a prominent issue. The current management system may be deficient in resource allocation and action coordination, which increases the risk of air traffic conflicts.

[0004] Secondly, different types of missions have specific requirements for the performance, equipment configuration and flight path of drones. However, existing command systems may lack sufficient flexibility and intelligence to make rapid and accurate adjustments based on mission requirements. This may lead to reduced mission execution efficiency and even endanger the safety of drones and missions in some cases. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes an air quick warning command method and system, which can perform tasks safely and efficiently through comprehensive management of routes, intelligent early warning and management, and rapid command means.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, an air quick warning command method is provided, the method comprising:

[0008] Obtain drone status in real time, including drone location, battery level, communication status, and payload status;

[0009] According to the state of the UAV, a finite element model of the UAV is constructed, and the finite element model includes material properties, boundary conditions and load parameters;

[0010] Use finite element models to analyze the structural response and performance of the UAV under different airport conditions to obtain analysis results;

[0011] According to the analysis results, the airport information is optimized to obtain optimized airport information, which includes the number, location, equipment model and runway of the required airports;

[0012] According to the drone status, mission requirements and optimized airport information, evaluate the load information carried by the drone. The load information includes determining the type, quantity and configuration of the load that the drone needs to carry, so that the drone can carry the corresponding load when performing the mission;

[0013] According to the drone status, optimized airport information and load information, the routes of airport drones and individual drones are managed to obtain the drone routes, including creating new routes and updating existing routes;

[0014] Determine the flight plan based on the UAV’s route and conduct individual mission management to obtain flight mission information;

[0015] Provide intelligent early warning of flight mission information to obtain early warning information;

[0016] Provide command based on early warning information.

[0017] Furthermore, according to the drone status, optimized airport information and load information, the routes of the airport drone and the individual drone are managed to obtain the routes of the drone, which include creating new routes and updating existing routes, including:

[0018] Analyze the UAV flight mission according to the UAV status, optimized airport information and load information to obtain analysis results;

[0019] Based on the analysis results, determine the take-off point, flight path, landing point, flight altitude and speed of the drone to obtain a new route;

[0020] The new route is optimized to obtain an optimized new route.

[0021] Further, based on the analysis results, the take-off point, flight path, landing point, flight altitude and speed of the drone are determined to obtain a new route, including:

[0022] Based on the analysis results, the flight environment is analyzed using sensors and map data to determine the take-off point, landing point and flight path;

[0023] According to the take-off point, flight path and landing point of the drone, Calculate the final flight altitude, where is the final flight altitude, , and γ is the weight parameter, Indicates the minimum power required by the drone in hovering state; At height Performance parameters predicted by flight simulation software; is the actual flight speed of the drone; , and is the weight factor, Indicates at height The probability of a UAV colliding with a ground obstacle is measured in the following experiment; Indicates at height The following experiment measured the probability of collision between drones and aircraft; Indicates at height The following experiment measured the probability of a conflict between a drone and an aircraft; The drone is flying at a certain altitude. The hovering power required when The drone is flying at a certain altitude. The power required when

[0024] According to the take-off point, flight path and landing point of the drone, Calculate the flight speed, where is the flight speed of the drone, is the power available to the drone, is an experimental measure of the efficiency of a drone in converting power into flight speed. It is the total resistance encountered by the drone during flight.

[0025] Further, the new route is optimized to obtain an optimized new route, including:

[0026] New routes through Perform optimization to obtain an optimized new route;

[0027] in, n is the number of points on the path, and is the index, is the optimal flight path, is the first Points, Yes and Point The distance between is the distance weight; is a highly variable weight; Yes and Point The height change between is the weight of the speed change; Yes and Point The speed change between is the weight of security cost; N is the number of obstacles or no-fly zones; It is the location of obstacles or no-fly zones; It's the drone at the point With Obstacles or no-fly zone center points distance; is the radius of influence of obstacles or no-fly zones.

[0028] Furthermore, according to the route of the UAV, the flight plan is determined and individual mission management is performed to obtain flight mission information, including:

[0029] Analyze the drone's route to determine flight time, altitude, speed and heading to develop a flight plan;

[0030] According to the flight plan, take off and cruise, and perform tasks during the cruise to execute the flight plan;

[0031] According to the flight plan, tasks are assigned to individual drones, and the individual drones work together with the main drones to achieve the flight mission.

[0032] Furthermore, intelligent early warning is performed on the flight mission information to obtain early warning information, including:

[0033] Analyze flight mission information to identify potential flight risks;

[0034] Match potential flight risks with preset risk indicators to obtain matching results;

[0035] The matching results are processed to obtain early warning information.

[0036] Further, based on the early warning information, command is carried out, including:

[0037] According to the warning information, locate the warning event to obtain the warning location;

[0038] Provide command based on the warning location and deploy resources.

[0039] In the second aspect, an air quick warning command system includes:

[0040] The acquisition module is used to obtain the status of the drone in real time, including the location, power, communication status and load status of the drone; according to the status of the drone, a finite element model of the drone is constructed, and the finite element model includes material properties, boundary conditions and load parameters; the finite element model is used to analyze the structural response and performance of the drone under different airport conditions to obtain analysis results; according to the analysis results, the airport information is optimized to obtain optimized airport information, and the optimized airport information includes the number, location, equipment model and runway of the required airports; according to the status of the drone, the mission requirements and the optimized airport information, the load information carried by the drone is evaluated, and the load information includes determining the type, quantity and configuration of the load required to be carried by the drone, so that the drone can carry the corresponding load when performing the mission;

[0041] The processing module is used to manage the routes of airport drones and individual drones according to the drone status, optimized airport information and load information to obtain the drone's route, which includes creating new routes and updating existing routes; determine the flight plan according to the drone's route, and perform individual task management to obtain flight mission information; perform intelligent early warning on the flight mission information to obtain early warning information; and conduct command based on the early warning information.

[0042] According to a third aspect, a computing device includes:

[0043] one or more processors;

[0044] The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0045] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0046] The above solution of the present invention includes at least the following beneficial effects:

[0047] By comprehensively managing drone equipment, the status information of drones can be obtained in real time, which helps to detect problems and perform maintenance in a timely manner, thereby improving the efficiency and safety of drone use. By managing the number and equipment models of airports based on the status of drones, the rational allocation and efficient use of airport resources can be ensured. Through load management, the load information of drones can be mastered, which helps to optimize flight plans.

[0048] Routes can be managed based on drone status, optimized airport information, and load information. Routes can be created and edited to make flight paths more reasonable and efficient. Flight plans based on routes can be formulated and executed to improve the purposefulness and efficiency of flights. The combat effectiveness of drones can be maximized by assigning tasks to individual drones and coordinating operations with main drones. Flight risks can be monitored and analyzed in real time, potential problems can be discovered and dealt with in a timely manner, and flight safety can be improved.

[0049] Rapid command can be achieved through home page viewing and map operation, which allows quick decision-making, adjustment of flight plans and task assignments to adapt to emergencies and changing environments, and improve the overall operational efficiency of the Air Police, including response speed, task execution efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flowchart of an air quick warning command method provided by an embodiment of the present invention.

[0051] Figure 2 It is a schematic diagram of an air quick warning command method system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in a form and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0053] like Figure 1 As shown, an embodiment of the present invention provides an air quick warning command method, the method comprising the following steps:

[0054] Step 11, obtaining the drone status in real time, including the drone's location, power, communication status, and payload status;

[0055] Step 12, constructing a finite element model of the UAV according to the UAV state, the finite element model including material properties, boundary conditions and load parameters;

[0056] Step 13, using a finite element model to analyze the structural response and performance of the UAV under different airport conditions to obtain analysis results;

[0057] Step 14, optimizing the airport information according to the analysis results to obtain optimized airport information, wherein the optimized airport information includes the number, location, equipment model and runway of the required airports;

[0058] Step 15, based on the drone status, mission requirements and optimized airport information, evaluate the load information carried by the drone, the load information includes determining the type, quantity and configuration of the load required to be carried by the drone, so that the drone can carry the corresponding load when performing the mission;

[0059] Step 16, managing the routes of the airport drone and the individual drone according to the drone status, the optimized airport information and the load information to obtain the routes of the drone, including creating new routes and updating existing routes;

[0060] Step 17, determine the flight plan according to the route of the UAV, and perform individual mission management to obtain flight mission information;

[0061] Step 18, performing intelligent early warning on the flight mission information to obtain early warning information;

[0062] Step 19: Provide command based on the warning information.

[0063] In an embodiment of the present invention, by comprehensively managing the drone equipment, the status of the drone can be mastered in real time; by managing the number of airports and equipment models according to the drone status, reasonable allocation of resources can be achieved; load management according to the drone status can ensure that the drone flies with a suitable load; by managing the route by comprehensively considering the drone status, airport management and load information, route planning can be formulated; flight plans are formulated and executed according to the route, and individual task management is performed, which can make flight missions more orderly and efficient; through AI technology, intelligent warning and management of flight mission information can be carried out to timely discover potential risks and problems; home page viewing and map operations are performed according to warning processing, which can achieve rapid command.

[0064] In a preferred embodiment of the present invention, the above step 11, real-time acquisition of the drone status, including the drone's location, power, communication status, and load status, may include:

[0065] Step 111, obtaining all drone status information, the device information includes the drone's location, power, communication status, and payload status;

[0066] Step 112, performing status monitoring and data collection on the drone equipment to obtain drone data;

[0067] In an embodiment of the present invention, by registering and collecting data on all drone equipment, comprehensive monitoring and management of the drone fleet can be achieved. Real-time monitoring of drone status parameters can promptly detect abnormal situations. Based on the collected data and processing and analysis results, a more scientific and reasonable maintenance plan can be formulated. Through data analysis, the life and replacement cycle of drone components can be predicted to avoid waste and reduce operating costs.

[0068] In the specific implementation process of the present invention, the equipment model, serial number, purchase date, maintenance record, and key flight status information of each drone are obtained, and sensors and the drone's own monitoring system are used to collect various status parameters of the drone in real time, such as battery power, flight altitude, speed, direction, and body temperature. A data center is established to evaluate the performance status of the drone, such as battery life and wear of mechanical parts, by comparing historical data and standard parameters; the overall performance of the drone is comprehensively evaluated in combination with the flight status and maintenance records to obtain the current status of the drone.

[0069] In a preferred embodiment of the present invention, in the above step 12, a finite element model of the drone is constructed according to the drone state, and the finite element model includes material properties, boundary conditions and load parameters, including:

[0070] Step 121, according to the state of the UAV, a geometric model is established through CAD software, and the model includes material properties, boundary conditions and load parameters; the properties include elastic modulus, Poisson's ratio, density, and yield strength; according to the actual state of the UAV, appropriate boundary conditions are defined, for example, in the take-off state, the displacement of the bottom of the fuselage can be constrained; in the hovering state, the position of the motor can be constrained; the load includes gravity, wind resistance, and propeller thrust.

[0071] Step 122, meshing the geometric model to generate a finite element mesh to construct a finite element model of the UAV.

[0072] In the embodiments of the present invention, by building a detailed geometric model and performing finite element analysis, the performance of the drone, including its structural strength, stiffness, and vibration characteristics, can be predicted more accurately; through the analysis results, weaknesses or potential problems in the design can be identified; by ensuring that the drone meets various safety and performance standards during the design phase, the risk of failure in actual operation can be reduced. The finite element model can be easily modified and updated to accommodate design changes or new analysis requirements.

[0073] In the specific implementation process of the present invention, the current state of the drone is clarified, such as take-off, hovering, cruising or landing, and a geometric model is established through CAD software to draw the main geometric shape of the drone. It includes key components such as fuselage, wings, propellers, and motor seats. Material properties are assigned to each component, and the material properties include elastic modulus, Poisson's ratio, density, and yield strength. Boundary conditions are defined according to the actual state of the drone. For example, in the take-off state, the bottom of the fuselage may be in contact with the ground, so it is necessary to constrain the displacement of the bottom of the fuselage in the vertical direction; in the hovering state, the motor may maintain a fixed position relative to the fuselage, so the position of the motor is constrained by setting constraints or fixed points in the CAD software. According to the flight state of the drone, corresponding loads are applied, including gravity, wind resistance, and propeller pull; the complex geometric model is converted into a mesh model that can be used for finite element analysis through a meshing tool. According to the needs of the analysis and the geometric characteristics of the drone, the parameters of meshing are set, and the created geometric model is converted into a finite element mesh. After the mesh is generated, the quality of the mesh needs to be checked. Finally, the generated finite element mesh is combined with the defined material properties, boundary conditions, and load parameters to construct a finite element model of the drone.

[0074] In a preferred embodiment of the present invention, the above step 13, using a finite element model to analyze the structural response and performance of the UAV under different airport conditions to obtain analysis results, may include:

[0075] Through the finite element analysis model, according to the defined boundary conditions and load parameters, the structural response and performance of the UAV under different airport conditions are analyzed to obtain the analysis results, including displacement cloud map, stress cloud map, strain cloud map and flight trajectory map;

[0076] In the embodiments of the present invention, through the analysis results, design deficiencies can be identified and improved at an early stage to improve the structural strength and performance of the UAV; by ensuring that the UAV meets various safety and performance standards at the design stage, the risk of failure in actual operation can be reduced. The finite element model can be easily modified and updated to adapt to design changes or new analysis requirements.

[0077] In the specific implementation process of the present invention, different analysis scenarios are set according to the airport conditions through the UAV finite element model, for example, different runway lengths and widths, different surface materials, and different meteorological conditions. For each scenario, the boundary conditions and load parameters in the finite element model are adjusted. The finite element analysis software is used for operation analysis. After the analysis is completed, the software will provide a series of results, usually including: displacement cloud map: showing the displacement of the UAV at different parts; stress cloud map: showing the stress distribution in the UAV structure, especially in high stress areas; strain cloud map: providing strain information of the UAV structure to help analyze the degree of deformation of the material, as well as possible cracks or fractures. For each scenario, compare the performance differences under different scenarios.

[0078] In a preferred embodiment of the present invention, the above step 14 optimizes the airport information according to the analysis results to obtain optimized airport information, and the optimized airport information includes the number, location, equipment model and runway of the required airports, and may include:

[0079] Based on the analysis results, key factors affecting the performance and safety of drones were identified, including: runway length, surface material, and weather conditions;

[0080] Based on the analysis results, the number, location, equipment model and runway of drone airports are evaluated to obtain the evaluation results;

[0081] According to the evaluation results, the airport information is optimized to obtain optimized airport information.

[0082] In the embodiments of the present invention, by optimizing airport information, it is possible to ensure that drones take off, land and fly under safer conditions, reducing the risk of accidents. The appropriate location and number of airports and the optimized runway design help reduce the flight time and energy consumption of drones and improve operational efficiency. By reducing unnecessary physical testing and optimizing equipment selection, the research and development and operating costs of drones can be reduced. The optimized airport information enables drones to better adapt to different environments and mission requirements, broadening their scope of application.

[0083] In the specific implementation process of the present invention, the displacement cloud map, stress cloud map, strain cloud map and flight trajectory map obtained by finite element analysis are reviewed in detail to identify the key factors affecting the performance and safety of the UAV, including: runway length, surface material, and meteorological conditions. The runway length is used to evaluate the take-off, landing and taxiing performance of the UAV under different runway lengths. The surface material is used to analyze the impact of the runway surface material (such as concrete, asphalt, etc.) on the friction coefficient, rolling resistance and runway thermal effect of the UAV tire. The meteorological conditions are used to examine the impact of meteorological conditions such as wind speed, wind direction, and temperature on the performance of the UAV. For example, strong winds may cause the UAV to deviate from the predetermined trajectory, and high temperatures may affect the power system and battery performance of the UAV. According to the analysis results, the number of required airports is determined to support the safe take-off and landing and operation requirements of the UAV; the location of the existing airport is evaluated to see whether it meets the requirements of the UAV route and whether it is convenient for emergency response and maintenance. Analyze the requirements of different equipment models for airport runways, evaluate whether the length, width, and surface conditions of existing runways are suitable for various types of drones, and consider possible future upgrade or renovation needs; adjust the length, surface material, or use coatings of the runway based on the results of key factor identification to improve the take-off and landing performance and safety of drones. Optimize the layout of the airport, ensure the reasonable configuration of aprons, taxiways, runways and other areas, and update or upgrade airport equipment, such as navigation facilities, meteorological monitoring equipment, and security monitoring systems, to improve the operational efficiency and safety of the airport.

[0084] In a preferred embodiment of the present invention, the above step 15 manages the routes of the airport drone and the individual drone according to the drone status, the optimized airport information and the load information to obtain the routes of the drone, and the routes include creating new routes and updating existing routes, which may include:

[0085] Set route parameters to plan routes based on drone status, optimized airport information, and load information;

[0086] Based on the planned routes, determine the take-off and landing points and plan the flight path to create new routes;

[0087] According to the planned route, real-time monitoring of flight conditions changes to assess route adaptability;

[0088] Adjustments are made based on adaptability, route planning is adjusted, and route files are updated based on the adjusted routes to achieve updating of existing routes.

[0089] In the embodiments of the present invention, by real-time monitoring and evaluation of the adaptability of the route, potential flight risks can be discovered and avoided in a timely manner, ensuring the safe flight of the drone. The optimized route planning helps to reduce the flight time and energy consumption of the drone and improve the overall operational efficiency. The route planning can be flexibly adjusted according to real-time conditions, so that the drone can better adapt to different flight environments and mission requirements.

[0090] In the specific implementation process of the present invention, the current state of the drone, the optimized airport information and the load information are obtained, and the load information includes the weight, size and nature of the goods or equipment that the drone needs to carry. Based on the drone state, airport information and load information, set the key route parameters, including flight altitude, speed range, range limit, and safety distance, consider the performance and safety of the drone, and ensure that the parameters are set within a reasonable range. Use the route planning software or system to draw a preliminary route according to the set route parameters to ensure that the route avoids no-fly zones and dangerous areas and minimizes obstacles in flight. According to the optimized airport information, select an airport or runway suitable for drone takeoff and landing, considering the length, width, surface conditions of the runway and the docking with the route. On the basis of the preliminary route, combined with the location of the take-off and landing points, plan the flight path in detail to ensure that the flight path meets the flight performance and safety requirements of the drone. Save the planned flight path as a new route file, which should contain detailed information such as the starting point, end point, waypoints, flight altitude, speed, etc. of the route. During the flight of the drone, the weather conditions, airspace restrictions, obstacles, etc. on the route are monitored in real time through sensors and communication systems; based on the real-time monitoring data, it is evaluated whether the current route is still suitable for the flight of the drone. If the evaluation results show that the current route is no longer suitable for the flight of the drone, it needs to be adjusted in time. The adjustment may include changing the flight altitude, speed, or replanning the flight path. Save the adjusted route as a new route file to obtain an updated route file.

[0091] In a preferred embodiment of the present invention, the above step 16 includes:

[0092] Step 161, analyzing the flight mission of the drone according to the drone status, the optimized airport information and the load information to obtain an analysis result;

[0093] Step 162, based on the analysis results, determine the take-off point, flight path, landing point, flight altitude and speed of the UAV to obtain a new route;

[0094] Step 163, optimizing the new route to obtain an optimized new route;

[0095] In an embodiment of the present invention, through comprehensive analysis of the drone status, airport management and load information, a safer route can be planned to reduce the risk during the flight process. The optimized route can reduce flight time and energy consumption and improve the efficiency of the drone in performing tasks. Based on comprehensive data analysis and route planning, decision makers can make more accurate and scientific decisions; optimized routes can reduce unnecessary flight distance and time, thereby reducing the operating cost of the drone; by checking existing routes and updating existing route data, the accuracy of the navigation system can be ensured, and the route can be adjusted according to actual conditions to better adapt to different flight conditions and mission requirements.

[0096] In the specific implementation process of the present invention, according to the current state of the drone, the airport management situation and the load information, the mission to be performed by the drone is deeply analyzed, including the nature of the mission, the flight distance, the required time, and the safety risks. According to the analysis results, it is determined whether the drone is suitable for performing the mission, as well as the challenges that may be faced when performing the mission and the preparations required; based on the mission analysis results, combined with the geographic information system and route planning software, taking into account factors such as terrain, airspace restrictions, and meteorological conditions, the take-off point, flight path, and landing point are planned for the drone; according to the performance and load requirements of the drone, the appropriate flight altitude and speed are determined, and the route optimization algorithm is used to fine-tune the initially planned route;

[0097] In another specific implementation process of the present invention, based on the current status of the UAV and combined with the airport management information, the load information is analyzed, a comprehensive inspection is performed on each waypoint, flight altitude and speed of the route, the coordinates of waypoints that pose a safety hazard or are inefficient are modified, the route layout is optimized, the flight altitude or speed is adjusted, and the existing route data is updated, including waypoint coordinates, flight altitude, and speed key information, and the updated route data is synchronized to the UAV's navigation system.

[0098] In a preferred embodiment of the present invention, the above step 162 includes:

[0099] Step 1621, based on the analysis results, the flight environment is analyzed using sensors and map data to determine the take-off point, landing point and flight path;

[0100] Step 1622, according to the take-off point, flight path and landing point of the drone, Calculate the final flight altitude, where is the final flight altitude, , and γ is the weight parameter, Indicates the minimum power required by the drone in hovering state; At height Performance parameters predicted by flight simulation software; is the actual flight speed of the drone; , and is the weight factor, Indicates at height The probability of a UAV colliding with a ground obstacle is measured in the following experiment; Indicates at height The following experiment measured the probability of collision between drones and aircraft; Indicates at height The following experiment measured the probability of a conflict between a drone and an aircraft; The drone is flying at a certain altitude. The hovering power required when The drone is flying at a certain altitude. The power required when

[0101] Step 1623, according to the take-off point, flight path and landing point of the drone, Calculate the flight speed, where is the flight speed of the drone, is the power available to the drone, It is the efficiency of the drone in converting power into flight speed, measured experimentally, and the value is between 0 and 1. It is the total resistance encountered by the drone during flight.

[0102] In an embodiment of the present invention, through a detailed analysis of the flight environment, potential risk areas can be avoided and the possibility of collisions and conflicts can be reduced; by comprehensively considering multiple factors such as power consumption, flight efficiency and flight speed, the optimal flight altitude and speed can be set, thereby reducing energy consumption and improving flight efficiency. The flight parameters can be flexibly adjusted according to different flight environments and mission requirements, so that the drone can better adapt to various complex environments.

[0103] In the specific implementation process of the present invention, a variety of sensors are used to collect data on the flight environment, including terrain, obstacles, and other flying objects. Combined with map data, the flight environment is three-dimensionally modeled to identify safe take-off points, landing points, and potential flight paths. Through algorithm analysis, the optimal take-off point and landing point are selected, and a safe and efficient flight path is planned. According to the determined take-off point, flight path, and landing point, the flight altitude of the drone is preliminarily set; multiple risk factors are introduced, and the probability values ​​of these risk factors at different altitudes are obtained through experimental determination. Using a given formula, combined with weight parameters γ and static power consumption, high The performance parameters predicted by the flight simulation software and the actual flight speed variables are used to calculate the final flight altitude. The flight speed of the drone is calculated using a given formula based on the take-off point, flight path and landing point, as well as the drone's available power, overall efficiency factor and total resistance during flight.

[0104] This formula balances the drone's flight performance and power consumption, ensuring that the drone can maintain a steady speed during flight while minimizing energy consumption.

[0105] In a preferred embodiment of the present invention, in the above step 163, the new route optimization formula is:

[0106] New routes through Perform optimization to obtain an optimized new route;

[0107] in, n is the number of points on the path, and is the index, is the optimal flight path, is the first Points, Yes and Point The distance between is the distance weight; is a highly variable weight; Yes and Point The height change between is the weight of the speed change; Yes and Point The speed change between is the weight of security cost; N is the number of obstacles or no-fly zones; It is the location of obstacles or no-fly zones; It's the drone at the point With Obstacles or no-fly zone center points distance; is the radius of influence of obstacles or no-fly zones.

[0108] In an embodiment of the present invention, by introducing a weight factor of the safety cost and considering the impact of obstacles or no-fly zones, the risk of collision between the UAV and obstacles can be significantly reduced. Since the method takes multiple weight factors into account, the weights can be flexibly adjusted according to different mission requirements and flight environments to obtain optimal flight performance. By optimizing the flight path and speed changes, unnecessary flight actions and distances can be reduced, thereby reducing the energy consumption of the UAV and extending its flight time.

[0109] In a preferred embodiment of the present invention, the above step 17 includes:

[0110] Step 171, by analyzing the route of the UAV, determining the flight time, flight altitude, speed and heading, so as to formulate a flight plan;

[0111] Step 172, take off and cruise according to the flight plan, and perform tasks during the cruise to execute the flight plan;

[0112] Step 173, assign tasks to the individual UAVs according to the flight plan, and let the individual UAVs cooperate with the main UAVs to achieve the flight mission.

[0113] In the embodiments of the present invention, detailed flight plans and real-time monitoring can significantly reduce the risks during the flight process. Reasonable flight plans and task allocation can ensure that the UAV completes the task in the shortest time. The coordinated operations of the main UAV and the individual UAV can give full play to their respective advantages and improve the overall combat effectiveness. The backup flight plan and real-time monitoring mechanism can ensure that the UAV can make rapid adjustments when encountering emergencies. Through reasonable task allocation and coordination strategies, the resources of the UAV can be fully utilized to avoid waste of resources.

[0114] In the specific implementation process of the present invention, the planned UAV route is analyzed, the flight time, flight altitude, speed and heading of each flight segment are determined, and a flight plan is formulated taking into account the terrain, meteorological conditions, and airspace restrictions; according to the flight plan, before take-off, the UAV is comprehensively inspected, the take-off operation is performed according to the flight plan, and the cruise phase is entered. During the cruise, corresponding tasks are performed according to mission requirements, such as reconnaissance, photography, data transmission, and real-time monitoring of the flight status of the UAV; according to the requirements of the flight mission, specific tasks are assigned to the main UAV and the individual UAV. During the flight, the main UAV and the individual UAV cooperate in combat according to their respective tasks, such as the main UAV performs reconnaissance, while the individual UAV performs precision strikes, the execution of the tasks is monitored in real time, and the task allocation and coordination strategies are adjusted according to actual conditions.

[0115] In a preferred embodiment of the present invention, the above step 18 includes:

[0116] Step 181, analyzing the flight mission information to identify potential flight risks;

[0117] Step 182, matching the potential flight risk with the preset risk index to obtain a matching result;

[0118] Step 183, processing the matching result to obtain warning information.

[0119] In the embodiments of the present invention, by real-time monitoring and analysis of flight data, potential flight risks can be discovered in time, so that corresponding preventive measures can be taken; the introduction of the early warning mechanism can intervene before the flight risk is upgraded to an accident, effectively reducing the incidence of flight accidents, and timely early warning information can enable relevant personnel to respond quickly, improving the speed and efficiency of emergency response. Through continuous monitoring and early warning of flight risks, the flight management process can be continuously improved, and the scientificity and effectiveness of flight management can be improved. Big data analysis technology provides data support for flight decisions, making decisions more scientific and reasonable.

[0120] In the specific implementation process of the present invention, big data analysis technology is used to receive and process various data generated during the flight of the drone, and the collected data is monitored and analyzed in real time to identify potential flight risks; a comprehensive risk indicator system is established, which includes quantitative indicators and thresholds for various flight risks to evaluate the severity of flight risks, and the real-time monitoring and analysis of potential flight risks are matched with preset risk indicators. By comparing the actual data with the threshold of the risk indicator, it is determined whether the current flight risk exceeds the acceptable range; once the flight risk is monitored to exceed the preset indicator, the early warning mechanism is immediately triggered, and corresponding early warning information is generated according to the type and severity of the risk. The information should include a risk description, possible impacts, and recommended countermeasures, and the early warning information is sent to the flight control center and the drone operator in real time through radio communication or network communication.

[0121] In a preferred embodiment of the present invention, the above step 19 includes:

[0122] Step 191, locating the warning event according to the warning information to obtain the warning location;

[0123] Step 192, provide command based on the warning location and deploy resources.

[0124] In an embodiment of the present invention, a quick response can be made through the rapid positioning of warning events on the home page and the intuitive display on the map. Based on the comprehensive home page information and map analysis results, a more scientific and reasonable emergency response plan can be formulated. According to the severity and impact range of the warning information, resources can be reasonably allocated to avoid waste or shortage of resources. Through the rapid command system, the forces of all parties can be quickly assembled and coordinated to improve the overall effectiveness of emergency response. Timely warnings and rapid emergency response can effectively reduce the losses caused by disasters and protect people’s lives and property.

[0125] In the specific implementation process of the present invention, after receiving the warning information, the specific warning event is quickly located, and the detailed warning information is viewed, including the warning type, location of occurrence, warning level, and possible impact range, so as to accurately grasp the overall situation of the warning event; with the help of the integrated map function, the location of the warning event and its surrounding environment can be intuitively seen on the map, and an emergency response plan is formulated based on the home page information and the results of map analysis; according to the plan, the required emergency resources, such as rescue teams, materials and equipment, are deployed to ensure that the resources can arrive at the location of the warning event in time, coordinate the forces of all parties, conduct joint actions, and monitor and command the on-site situation in real time through real-time communication and command systems to ensure the accuracy and timeliness of emergency response.

[0126] In an embodiment of the present invention, a quick response can be made through the rapid positioning of warning events on the home page and the intuitive display on the map. Based on the comprehensive home page information and map analysis results, a more scientific and reasonable emergency response plan can be formulated. According to the severity and impact range of the warning information, resources can be reasonably allocated to avoid waste or shortage of resources. Through the rapid command system, the forces of all parties can be quickly assembled and coordinated to improve the overall effectiveness of emergency response. Timely warnings and rapid emergency response can effectively reduce the losses caused by disasters and protect people’s lives and property.

[0127] like Figure 2 As shown, an embodiment of the present invention further provides an air quick warning command method system 20, comprising:

[0128] The acquisition module 21 is used to comprehensively manage the UAV equipment to obtain the UAV status; manage the number and equipment model of the airports where the UAV takes off and lands according to the UAV status to achieve airport management; and manage the load according to the UAV status to obtain the load information;

[0129] The processing module 22 is used to manage the routes of the airport drone and the individual drone according to the drone status, optimized airport information and load information to obtain the route of the drone, which includes creating a new route and updating an existing route; determine the flight plan according to the drone route, and perform individual task management to obtain flight mission information; perform intelligent warning on the flight mission information to obtain warning information; and conduct command based on the warning information.

[0130] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0131] The embodiment of the present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0132] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An air quick warning command method, characterized in that: The method comprises: Obtain drone status in real time, including drone location, battery level, communication status, and payload status; According to the state of the UAV, a finite element model of the UAV is constructed, and the finite element model includes material properties, boundary conditions and load parameters; Use finite element models to analyze the structural response and performance of the UAV under different airport conditions to obtain analysis results; According to the analysis results, the airport information is optimized to obtain optimized airport information, which includes the number, location, equipment model and runway of the required airports; According to the drone status, mission requirements and optimized airport information, evaluate the load information carried by the drone. The load information includes determining the type, quantity and configuration of the load that the drone needs to carry, so that the drone can carry the corresponding load when performing the mission; According to the drone status, optimized airport information and load information, the routes of airport drones and individual drones are managed to obtain the drone routes, including creating new routes and updating existing routes; Determine the flight plan based on the UAV’s route and conduct individual mission management to obtain flight mission information; Provide intelligent early warning of flight mission information to obtain early warning information; Provide command based on early warning information; According to the drone status, optimized airport information and load information, the routes of airport drones and individual drones are managed to obtain the drone routes. The routes include creating new routes and updating existing routes, including: Analyze the UAV flight mission according to the UAV status, optimized airport information and load information to obtain analysis results; Based on the analysis results, determine the take-off point, flight path, landing point, flight altitude and speed of the drone to obtain a new route; Optimize the new route to obtain an optimized new route; according to the analysis results, determine the take-off point, flight path, landing point, flight altitude and speed of the drone to obtain a new route, including: Based on the analysis results, the flight environment is analyzed using sensors and map data to determine the take-off point, landing point and flight path; According to the take-off point, flight path and landing point of the drone, Calculate the final flight altitude, where is the final flight altitude, , and γ is the weight parameter, Indicates the minimum power required by the drone in hovering state; At height Performance parameters predicted by flight simulation software; is the actual flight speed of the drone; , and is the weight factor, Indicates at height The probability of a UAV colliding with a ground obstacle is measured in the following experiment; Indicates at height The following experiment measured the probability of collision between a drone and an aircraft; Indicates at height The following experiment measured the probability of a conflict between a drone and an aircraft; The drone is flying at a certain altitude. The hovering power required when The drone is flying at a certain altitude. The power required when According to the take-off point, flight path and landing point of the drone, Calculate the flight speed, where is the flight speed of the drone, is the power available to the drone, is an experimental measure of the efficiency of a drone in converting power into flight speed. It is the total resistance encountered by the drone during flight.

2. The air quick warning command method according to claim 1, characterized in that: Optimize the new route to obtain an optimized new route, including: New routes through Optimize to obtain an optimized new route; in, n is the number of points on the path, i and j is the index, is the optimal flight path, is the first i Points, Yes and Point The distance between is the distance weight; is a highly variable weight; Yes and Point The height change between is the weight of the speed change; Yes and Point The speed change between is the weight of security cost; N is the number of obstacles or no-fly zones; It is j the location of obstacles or no-fly zones; It's the drone at the point With j Obstacles or no-fly zone center points distance; is the radius of influence of obstacles or no-fly zones.

3. The air quick warning command method according to claim 2, characterized in that: According to the UAV route, determine the flight plan and conduct individual mission management to obtain flight mission information, including: Analyze the drone's route to determine flight time, altitude, speed and heading to develop a flight plan; According to the flight plan, take off and cruise, and perform tasks during the cruise to execute the flight plan; According to the flight plan, tasks are assigned to individual drones, and the individual drones work together with the main drones to achieve the flight mission.

4. The air quick warning command method according to claim 3, characterized in that: Provide intelligent early warning of flight mission information to obtain early warning information, including: Analyze flight mission information to identify potential flight risks; Match potential flight risks with preset risk indicators to obtain matching results; The matching results are processed to obtain early warning information.

5. The air quick warning command method according to claim 4, characterized in that: Conduct command based on early warning information, including: According to the warning information, locate the warning event to obtain the warning location; Provide command based on the warning location and deploy resources.

6. An air quick warning command system, characterized in that: Applied to the method according to any one of claims 1 to 5, comprising: The acquisition module is used to obtain the status of the drone in real time, including the location, power, communication status and load status of the drone; according to the status of the drone, a finite element model of the drone is constructed, and the finite element model includes material properties, boundary conditions and load parameters; the finite element model is used to analyze the structural response and performance of the drone under different airport conditions to obtain analysis results; according to the analysis results, the airport information is optimized to obtain optimized airport information, and the optimized airport information includes the number, location, equipment model and runway of the required airports; according to the status of the drone, the mission requirements and the optimized airport information, the load information carried by the drone is evaluated, and the load information includes determining the type, quantity and configuration of the load required to be carried by the drone, so that the drone can carry the corresponding load when performing the mission; The processing module is used to manage the routes of airport drones and individual drones according to the drone status, optimized airport information and load information to obtain the drone's route, which includes creating new routes and updating existing routes; determine the flight plan according to the drone's route, and perform individual task management to obtain flight mission information; perform intelligent early warning on the flight mission information to obtain early warning information; and conduct command based on the early warning information.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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