Intelligent route dynamic management method based on unmanned aerial vehicle

Abstract

The application discloses an intelligent route dynamic management and control method based on an unmanned aerial vehicle, relates to the field of air traffic management, and comprises the following steps: obtaining a target navigation record; obtaining a target navigation record of a target airspace; reading a predetermined unit time, dividing a target period, and obtaining a time period division result; screening a plurality of navigation routes by taking a first time period as a screening constraint, and obtaining a first navigation position set; obtaining a target restricted navigation position set, visualizing the navigability of the target airspace, and obtaining a first airspace navigable map; analyzing the first airspace navigable map in combination with a target route, obtaining a first preset navigation position; and taking the target preset route as a management and control benchmark. The application solves the technical problem that the existing unmanned aerial vehicle route management and control cannot accurately manage and control the route according to real-time flight environment, and achieves the technical effect of updating and adjusting the route in real time according to various factors in the flight process, thereby improving flight safety.

Description

Technical Field

[0001] This application relates to the field of air traffic management, and in particular to a method for intelligent dynamic control of flight routes based on unmanned aerial vehicles. Background Technology

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, UAVs are increasingly widely used, appearing in various fields from aerial photography and environmental monitoring to cargo transportation. As the number of UAVs increases, the complexity of airspace management also rises. Currently, airspace management for UAVs typically employs static route planning, where a fixed route is set before takeoff and the aircraft flies according to this route. This method is simple and easy to implement, but it cannot cope with unforeseen circumstances that may occur during flight, such as changes in weather or air traffic conditions, potentially leading to flight safety hazards.

[0003] Currently, the relevant technologies for unmanned aerial vehicle (UAV) route management suffer from the technical problem of being unable to accurately control the route based on the real-time flight environment. Summary of the Invention

[0004] This application provides an intelligent dynamic route management method for unmanned aerial vehicles (UAVs). It employs techniques such as acquiring target flight registration information and flight routes within the target airspace, dividing the target period into predetermined time units, using the time period division results as a filtering constraint to select flight routes, visualizing the navigability of the target airspace by combining it with a restricted flight position set, analyzing the navigable map of the first airspace based on the target flight route, and using the target preset flight route as a management benchmark. These techniques enable intelligent dynamic route management of UAVs, achieving the technical effect of real-time updating and adjustment of flight routes based on various factors during flight, thereby improving flight safety.

[0005] This application provides a method for intelligent flight path dynamic management based on unmanned aerial vehicles, including:

[0006] The process involves: acquiring target navigation records, which refer to the registration of target routes and target periods for a target unmanned aerial vehicle (UAV) in a target airspace; acquiring target navigation records for the target airspace, which include multiple routes for multiple UAVs; reading a predetermined unit of time and dividing the target period based on the predetermined unit of time to obtain a time period division result; filtering the multiple routes using the first time period in the time period division result as a filtering constraint to obtain a first set of navigation positions; acquiring a target restricted navigation position set for the target airspace and visualizing the navigability of the target airspace in conjunction with the first set of navigation positions to obtain a first navigable map of the airspace; analyzing the first navigable map of the airspace in conjunction with the target routes to obtain a first preset navigation position for the target UAV in the first time period; and using the target preset route formed based on the first preset navigation position as the control benchmark for the target UAV.

[0007] This application also provides an intelligent flight path dynamic management system based on unmanned aerial vehicles, including:

[0008] The system includes: a target navigation record acquisition module, which acquires target navigation records, referring to the record of target routes and target periods for target unmanned aerial vehicles (UAVs) navigating in a target airspace; a target period division module, which reads a predetermined unit of time and divides the target period based on the predetermined unit of time to obtain a time period division result; and a first flight position set acquisition module, which uses the first time period in the time period division result as a filtering constraint to select multiple flight routes. The system filters routes to obtain a first set of navigation positions; a navigable visualization module is used to acquire a set of restricted navigation positions for the target airspace and visualize the target airspace in conjunction with the first set of navigation positions to obtain a first navigable airspace map; a first preset navigation position acquisition module is used to analyze the first navigable airspace map in conjunction with the target flight path to obtain a first preset navigation position of the target UAV in the first time period; and a control module is used to use the target preset flight path formed based on the first preset navigation position as the control benchmark for the target UAV.

[0009] The proposed intelligent flight path dynamic control method based on unmanned aerial vehicles (UAVs) first obtains target flight registration, which refers to the registration of the target flight path and target period of the target UAV in the target airspace. Next, it obtains the target flight registration for the target airspace, which includes multiple flight paths for multiple UAVs. Then, it reads a predetermined unit time and divides the target period based on this unit time to obtain a time period division result. The first time period in the time period division result is then used as a filtering constraint to filter multiple flight paths, resulting in a first flight position set. Subsequently, it obtains the target restricted flight position set for the target airspace and combines it with the first flight position set to visualize the navigability of the target airspace, obtaining a navigable map of the first airspace. Then, it analyzes the navigable map of the first airspace in conjunction with the target flight path to obtain the first preset flight position of the target UAV in the first time period. Finally, it uses the target preset flight path formed based on the first preset flight position as the control benchmark for the target UAV, achieving the technical effect of real-time updating and adjustment of flight paths according to various factors during flight, thereby improving flight safety. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0011] Figure 1 A flowchart illustrating the intelligent flight path dynamic control method based on unmanned aerial vehicles provided in this application embodiment.

[0012] Figure 2 This is a schematic diagram of the structure of an intelligent flight path dynamic control system based on unmanned aerial vehicles provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: Target navigation record acquisition module 10, Target period division module 20, First navigation position set acquisition module 30, Navigable visualization module 40, First preset navigation position acquisition module 50, Control module 60. Detailed Implementation

[0014] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0015] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0017] This application provides an intelligent route dynamic management method based on unmanned aerial vehicles, such as... Figure 1 As shown, the method includes:

[0018] Step S100: Obtain target navigation registration. Target navigation registration refers to the registration of the target route and target period for the target unmanned aerial vehicle (UAV) to navigate in the target airspace. Specifically, the target UAV is identified as the UAV requiring dynamic route management. Its identification information is obtained from the flight management system, task allocation system, or UAV dispatch center. The target airspace is determined, which is the airspace range where the target UAV plans to navigate, including its geographical coordinates, altitude range, and airspace type. Based on the information of the target UAV and the target airspace, a request is sent to the flight management system, flight plan data center, or relevant aviation management agency to obtain target navigation registration data. Target navigation registration refers to the registration information of the target route and target period for the target UAV to navigate in the target airspace, including the UAV's identification information, target airspace information, target route information, and target period information. By analyzing the target flight registration data, specific information about the target UAV's target flight path and target period in the target airspace is extracted. The target flight path includes the starting point, intermediate point, and ending point, as well as the trajectory information between them. The target period refers to the time period during which the target UAV plans to fly along the target flight path, including the start time, end time, and flight frequency.

[0019] Step S200: Obtain the target navigation record for the target airspace. The target navigation record includes multiple flight routes for multiple aircraft. Specifically, access the database of the flight management system, flight plan data center, or relevant aviation management agency to query and obtain the target navigation record within the target airspace. That is, the plan record for aircraft to navigate within a specific airspace according to a predetermined route and time, including multiple flight routes for multiple aircraft. Each flight route has specific route information, flight time, flight altitude, and other parameters.

[0020] Step S300: Read the predetermined unit time and divide the target period based on the predetermined unit time to obtain the time period division result. Specifically, according to the needs of dynamic route management and the actual situation, a suitable predetermined unit time is determined, which can be minutes, hours, etc. The predetermined unit time is read from system configuration, parameter settings, or user input. Using the read predetermined unit time as the division basis, the target period is divided into several time periods, each with a length equal to the predetermined unit time, and arranged in chronological order. If the start and end times of the target period are not integer multiples of the predetermined unit time, the start and end times are adjusted or processed appropriately. The divided time periods are stored in an appropriate data structure or list format to form the time period division result, including the start time, end time, and corresponding number or identification information of each time period.

[0021] In one possible implementation, step S300 further includes step S310, where the predetermined unit time is much smaller than the target period. Specifically, a smaller unit time means a more granular division of the time period, allowing for more precise management of the target UAV's flight. Simultaneously, a smaller unit time allows for more flexible adjustments to the flight path or speed during flight to address potential unforeseen circumstances. Therefore, this implementation, by dividing the target period into multiple smaller time periods, achieves more precise monitoring and management of the target UAV's flight status, enabling timely detection and response to potential problems, thus improving the safety and flexibility of the target UAV's flight.

[0022] Step S400: Using the first time period in the time period division result as a filtering constraint, the multiple flight routes are filtered to obtain a first flight position set. Specifically, from the time period division result obtained in step S300, any time period is selected as the first time period, and this first time period is used as the filtering constraint. Based on the start and end times of the first time period, a filtering time range condition is set. The multiple flight routes obtained in step S200 are traversed, and each flight route is analyzed to check whether it overlaps with the time range in the filtering condition. If the time range of a certain flight route overlaps with the time range of the filtering condition, it is included in the filtering result. For the filtered flight routes, their corresponding flight position information is extracted, including coordinate information such as longitude, latitude, and altitude, as well as dynamic information such as flight speed and direction. The extracted flight position information is organized according to an appropriate data structure or list format to construct a first flight position set. The first flight position set contains flight position information that may conflict with the target UAV within the first time period.

[0023] Step S500: Obtain the target restricted navigation position set of the target airspace, and combine it with the first navigation position set to visualize the navigability of the target airspace, obtaining a first navigable airspace map. Specifically, obtain restricted navigation position information within the target airspace from the flight management system, airspace management department, or relevant database. These restricted navigation positions may be caused by obstacles, no-fly zones, restricted areas, etc. within the target airspace. Organize this information to form a target restricted navigation position set (a collection of restricted navigation position information within the target airspace caused by various reasons), including position coordinates, restriction type, restriction time, etc. Integrate the first navigation position set obtained in step S400 with the obtained target restricted navigation position set. During the integration process, maintain the data format and coordinate system of the two position sets consistent. Based on the integrated position set, analyze the navigable area within the target airspace, that is, exclude all positions in the restricted navigation position set and navigation positions that may conflict with the target UAV within the first time period. The remaining area is the navigable area. Using Geographic Information System (GIS) software, map-making tools, or visualization programming libraries, the analyzed navigable areas are visualized. Visualization formats can include 2D maps, 3D models, and dynamic simulations. During visualization, different colors, lines, or icons can be used to distinguish navigable areas from non-navigable locations, allowing users to intuitively understand the navigation situation within the airspace. Based on the navigable area analysis results, a first airspace navigable map is generated, which is a map file displaying the navigability within the target airspace.

[0024] In one possible implementation, a first navigable airspace map is obtained. Step S500 further includes step S510, constructing a target grid map of the target airspace. Specifically, the range and boundaries of the target airspace are determined. Based on accuracy requirements, the target airspace is divided into multiple grid cells, each representing a certain geographic space. A unique identifier is assigned to each grid cell, and its state is initialized (e.g., unoccupied). Step S520, the target restricted navigation position set and the first navigation position set form a first occupied position. Specifically, the two position sets, the target restricted navigation position set and the first navigation position set, are merged to obtain the first occupied position, which is a position that needs to be avoided during navigation. Step S530, the first occupied position is marked on the target grid map to obtain the first navigable airspace map. Specifically, each position point in the first occupied position is traversed, and the corresponding grid cell in the target grid map is found. The state of these grid cells is marked as occupied or unnavigable. Based on the marked grid map, the remaining navigable area is determined, and the first navigable airspace map is generated. This approach constructs a target raster map, dividing the target airspace into regular grid units, making map information processing and calculation more convenient and achieving a simple and effective technical result.

[0025] In one possible implementation, forming a first occupied position, step S520 further includes step S521, reading a predetermined unsafe flight distance. Specifically, the predetermined unsafe flight distance is a preset threshold distance, representing the minimum distance that the aircraft needs to maintain with other aircraft or obstacles to ensure safety during flight. The predetermined unsafe flight distance is read from system configuration or parameter settings, and is set based on factors such as the aircraft's performance and the safety requirements of the flight environment. Step S522, extracting the first flight position of the first aircraft from the first flight position set, where the first aircraft is any one of the plurality of aircraft. Specifically, extracting the current position information (first flight position) of the first aircraft from the first flight position set of all aircraft, where the first aircraft is any one of the plurality of aircraft, and the first flight position is the current flight position of the first aircraft, which can be latitude and longitude coordinates or other forms of position information. Step S523, matching the first grid corresponding to the first flight position in the target grid map. Specifically, based on the first flight position of the first aircraft, finding the corresponding grid, i.e., the first grid, in the target grid map through coordinate transformation and matching algorithms. Step S524: Construct a first neighboring grid of the first grid based on the predetermined unsafe flight distance. Specifically, around the first grid, determine the range of neighboring grids (first neighboring grids) according to the predetermined unsafe flight distance. These neighboring grids form a safety buffer zone centered on the first grid to ensure that the target UAV does not get too close to the first UAV during flight. Step S525: Add the first grid and the first neighboring grids to the first occupied position. Specifically, mark the first grid and its first neighboring grids as occupied and add them to the first occupied position list. When planning its flight path, the target UAV avoids these occupied grids, ensuring flight safety. This implementation avoids potential collisions between UAVs by defining a safety buffer zone (i.e., the first neighboring grid) for each UAV, thereby achieving the technical effect of improving flight safety.

[0026] Step S600: Analyze the first navigable airspace map in conjunction with the target flight path to obtain the first preset flight position of the target UAV within the first time period. Specifically, match key locations such as the start, end, and midpoints of the target flight path with the first navigable airspace map, check whether the key locations are within the navigable area, whether restricted flight positions are avoided, and analyze whether the target flight path conflicts with other flight paths in the first flight position set. If a conflict exists, determine the severity of the conflict, including the time and spatial range of the conflict. Under the premise of ensuring flight path safety and avoiding restricted flight positions, calculate the first preset flight position of the target UAV within the first time period based on the parameters of the target flight path and the actual situation of the first navigable airspace map. The first preset flight position is one or more specific points on the flight path used to guide the navigation of the target UAV within the first time period.

[0027] In one possible implementation, after obtaining the first preset navigation position, step S600 further includes step S610, obtaining the first historical weather record of the first preset navigation position. Specifically, based on the coordinates or identifier of the first preset navigation position, the relevant historical weather record is retrieved from the meteorological database, i.e., the first historical weather record, which contains weather data such as temperature, wind speed, wind direction, cloud cover, and precipitation probability over the past few days, weeks, or even years. Step S620, based on the first historical weather record, weather prediction is performed on the first preset navigation position under the first time period to obtain the first predicted weather. Specifically, a meteorological prediction model (such as a machine learning model, a physical model, etc.) is used to analyze the first historical weather record, and combined with other relevant factors (such as current weather conditions, atmospheric models, etc.) to predict the weather conditions of the first preset navigation position within the first time period (such as the next few hours), and the prediction result is the first predicted weather. Step S630, the predetermined navigable weather constraints are read. Specifically, the predetermined navigable weather constraints are read from the system configuration or parameter settings. These constraints refer to the weather condition thresholds or ranges within which the aircraft can safely navigate, including wind speed thresholds and precipitation probability thresholds, used to determine whether the aircraft can safely navigate under different weather conditions. Step S640: When the first predicted weather does not meet the predetermined navigable weather constraints, a first navigation adjustment command is issued. Specifically, the first predicted weather is compared with the predetermined navigable weather constraints. If certain indicators in the first predicted weather (such as wind speed and precipitation probability) exceed the predetermined navigable weather constraint range, the current weather conditions are considered unsuitable for navigation, and a first navigation adjustment command is issued, i.e., a command to adjust the flight plan. Step S650: The first preset flight position is adjusted according to the first navigation adjustment command. Specifically, after receiving the first navigation adjustment command, the first preset flight position is adjusted according to the instructions in the first navigation adjustment command, including adjusting the flight time and selecting an alternate airport. This approach obtains historical weather records and forecasts to understand the weather conditions at the first preset navigation position in the near future. If the forecasted weather does not meet the predetermined navigable weather constraints, a navigation adjustment command is issued in a timely manner, thus preventing the aircraft from navigating in adverse weather conditions and achieving the technical effect of ensuring the safety of the unmanned aerial vehicle.

[0028] In one possible implementation, after obtaining the first predicted weather, step S620 further includes step S621, performing trend analysis on the time series of the first historical temperature records in the first historical weather records to obtain the first predicted temperature. Specifically, historical temperature data is extracted from the first historical weather records, arranged in chronological order to form a historical temperature record time series, and statistical analysis is performed on this historical temperature record time series, including calculating the average, variance, trend line, etc., to identify the temperature change trend. Based on the trend analysis results, a statistical model (such as linear regression, time series prediction model, etc.) or a machine learning model is used to predict the temperature value under the first time period, i.e., the first predicted temperature. Step S622, performing trend analysis on the time series of the first historical humidity records in the first historical weather records to obtain the first predicted humidity. Specifically, historical humidity data is extracted from the first historical weather records to form a historical humidity record time series. Similarly, statistical analysis is performed on the historical humidity record time series to identify the humidity change trend, and a model is used to predict the humidity value under the first time period, i.e., the first predicted humidity. Step S623: Perform trend analysis on the time series of the first historical wind force records in the first historical weather records to obtain the first predicted wind force. Specifically, extract historical wind force data from the first historical weather records to form a time series of historical wind force records. Perform statistical analysis on the time series of historical wind force records to identify the trend of wind force changes. Similarly, use a model to predict the wind force situation under the first time period, including wind speed and wind direction, to obtain the first predicted wind force. Step S624: The first predicted temperature, the first predicted humidity, and the first predicted wind force constitute the first predicted weather. Specifically, combine the predicted temperature, predicted humidity, and predicted wind force obtained in steps S621, S622, and S623 to form a weather prediction result containing multiple meteorological elements. This prediction result is the first predicted weather, which comprehensively reflects the main characteristics of the weather conditions under the first time period. This implementation method, by performing trend analysis on key meteorological elements such as temperature, humidity, and wind force in the first historical weather records, more accurately predicts the weather conditions under the first time period, achieving the technical effect of improving the accuracy and reliability of the first predicted weather.

[0029] In one possible implementation, after composing the first predicted weather, step S620 further includes step S625, reading a predetermined weather feature weight allocation. Specifically, the predetermined weather feature weight allocation is read from a configuration file. This predetermined weight allocation is a set of pre-set weight values ​​obtained based on historical experience, expert opinions, or data analysis, used to quantify the importance of different meteorological elements in weather condition assessment. The predetermined weather feature weight allocation includes temperature weight, humidity weight, and wind force weight, etc., each weight representing the importance of the corresponding meteorological element in the overall weather condition assessment. Step S626 involves weighting the first predicted temperature, the first predicted humidity, and the first predicted wind force based on the predetermined weather feature weight allocation to obtain a first predicted weather index. This first predicted weather index is used to quantitatively represent the first predicted weather. Specifically, each predicted meteorological element (temperature, humidity, wind force) is multiplied by its corresponding weight to obtain a weighted value. All weighted values ​​are summed to obtain a comprehensive quantitative index, namely the first predicted weather index, used to quantitatively represent the first predicted weather. This implementation introduces a first forecast weather index, providing a more intuitive and quantitative way to assess the impact of weather conditions on navigation. Through weighted processing, the influence of multiple meteorological elements is comprehensively considered and weighted according to their importance, thus obtaining a more comprehensive and accurate weather condition assessment result, achieving the technical effect of improving the accuracy and reliability of weather condition assessment.

[0030] Step S700: The target preset route formed based on the first preset navigation position is used as the control benchmark for the target UAV. Specifically, based on the first preset navigation position calculated in step S600, the target preset route for the target UAV is constructed. This is the expected navigation route of the target UAV constructed based on the first preset navigation position, which is a route connecting the starting point, the first preset navigation position, and other key positions (such as intermediate points, turning points, etc.) and the endpoint. The target preset route is set as the control benchmark for the target UAV to guide its actual navigation. The target preset route is communicated to the relevant route control system and the target UAV's control system, enabling the target UAV to navigate according to the target preset route. Simultaneously, the route control system monitors and adjusts the navigation process in real time. If any abnormalities or areas requiring adjustment are detected during the navigation process, timely feedback and processing are provided. This application's embodiments employ technical means such as acquiring target navigation records and navigation routes in the target airspace, dividing the target period into predetermined unit time intervals, using the time period division results as filtering constraints to screen navigation routes, visualizing the navigability of the target airspace by combining the restricted navigation position set of the target airspace, analyzing the navigable map of the first airspace by combining the target route, and using the target preset route as a control benchmark to achieve intelligent dynamic route control of unmanned aerial vehicles. This achieves the technical effect of updating and adjusting routes in real time according to various factors during flight, thereby improving flight safety.

[0031] In the above text, refer to Figure 1 The present invention describes in detail an intelligent flight path dynamic control method based on an unmanned aerial vehicle according to an embodiment of the present invention. Next, reference will be made to... Figure 2 This invention describes an intelligent flight path dynamic control system based on unmanned aerial vehicles (UAVs) according to embodiments of the present invention.

[0032] The intelligent flight path dynamic control system based on unmanned aerial vehicles (UAVs) according to embodiments of the present invention addresses the technical problem of existing UAV flight path control systems being unable to accurately control flight paths based on real-time flight environments. It achieves the technical effect of updating and adjusting flight paths in real time based on various factors during flight, thereby improving flight safety. The intelligent flight path dynamic control system based on UAVs includes: a target navigation record acquisition module 10, a target period division module 20, a first navigation position set acquisition module 30, a navigable visualization module 40, a first preset navigation position acquisition module 50, and a control module 60.

[0033] The target navigation record acquisition module 10 is used to acquire the target navigation record, which refers to the record of the target route and target cycle of the target unmanned aerial vehicle in the target airspace. The target navigation record of the target airspace is acquired, and the target navigation record includes multiple navigation routes of multiple aircraft.

[0034] The target period division module 20 is used to read a predetermined unit time and divide the target period based on the predetermined unit time to obtain the time period division result;

[0035] The first navigation position set acquisition module 30 is used to filter the multiple navigation routes using the first time period in the time period division result as a filtering constraint to obtain the first navigation position set;

[0036] The navigable visualization module 40 is used to obtain the target restricted navigation position set of the target airspace, and combine it with the first navigation position set to perform navigable visualization of the target airspace to obtain a first airspace navigable map.

[0037] The first preset navigation position acquisition module 50 is used to analyze the first navigable airspace map in conjunction with the target route to obtain the first preset navigation position of the target unmanned aerial vehicle in the first time period.

[0038] The control module 60 is used to take the target preset route formed based on the first preset navigation position as the control benchmark for the target unmanned aerial vehicle.

[0039] The specific configuration of the target period division module 20 will be described in detail below. As mentioned above, the target period division module 20 may further include: a predetermined unit time setting unit for the predetermined unit time being much smaller than the target period.

[0040] The specific configuration of the navigable visualization module 40 will be described in detail below. As mentioned above, to obtain a first navigable airspace map, the navigable visualization module 40 may further include: a target grid map construction unit for constructing a target grid map of the target airspace; a first occupied position assembly unit for forming a first occupied position from the target restricted navigation position set and the first navigation position set; and a first navigable airspace map acquisition unit for marking the first occupied position onto the target grid map to obtain the first navigable airspace map.

[0041] The first occupied location assembly unit may further include: a non-safe flight distance reading subunit for reading a predetermined non-safe flight distance; a first flight position extraction subunit for extracting the first flight position of a first aircraft in the first flight position set, wherein the first aircraft is any one of the plurality of aircraft; a first grid matching subunit for matching the first grid corresponding to the first flight position in the target grid map; a first neighboring grid assembly subunit for assembling the first neighboring grid of the first grid in combination with the predetermined non-safe flight distance; and a first occupied location adding subunit for adding the first grid and the first neighboring grid to the first occupied location.

[0042] The specific configuration of the first preset navigation position acquisition module 50 will be described in detail below. As mentioned above, after obtaining the first preset navigation position, the first preset navigation position acquisition module 50 may further include: a first historical weather record acquisition unit for acquiring the first historical weather record of the first preset navigation position; a weather prediction unit for performing weather prediction on the first preset navigation position under the first time period based on the first historical weather record to obtain the first predicted weather; a predetermined navigable weather constraint reading unit for reading the predetermined navigable weather constraint; a first navigation adjustment command issuing unit for issuing a first navigation adjustment command when the first predicted weather does not meet the predetermined navigable weather constraint; and a navigation position adjustment unit for adjusting the first preset navigation position according to the first navigation adjustment command.

[0043] The weather forecasting unit, which obtains the first predicted weather, may further include: a first predicted temperature acquisition subunit for performing trend analysis on the time sequence of the first historical temperature records in the first historical weather records to obtain the first predicted temperature; a first predicted humidity acquisition subunit for performing trend analysis on the time sequence of the first historical humidity records in the first historical weather records to obtain the first predicted humidity; a first predicted wind force acquisition subunit for performing trend analysis on the time sequence of the first historical wind force records in the first historical weather records to obtain the first predicted wind force; and a first predicted weather composition subunit for composing the first predicted weather using the first predicted temperature, the first predicted humidity, and the first predicted wind force.

[0044] The weather forecasting unit may further include, after forming the first predicted weather, a predetermined weather feature weight allocation reading subunit for reading the predetermined weather feature weight allocation; and a weighted processing subunit for weighting the first predicted temperature, the first predicted humidity, and the first predicted wind force based on the predetermined weather feature weight allocation to obtain a first predicted weather index, wherein the first predicted weather index is used to quantitatively characterize the first predicted weather.

[0045] The intelligent flight path dynamic management system based on unmanned aerial vehicles provided in the embodiments of the present invention can execute the intelligent flight path dynamic management method based on unmanned aerial vehicles provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0046] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0047] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An unmanned aerial vehicle-based intelligent route dynamic management method, characterized in that, The method comprises: obtaining a target flight record, the target flight record being a record of a target flight path and a target period of a target UAV in a target airspace; obtaining a target flight record of the target airspace, the target flight record comprising a plurality of flight routes of a plurality of UAVs; reading a predetermined unit time, and dividing the target period based on the predetermined unit time to obtain a time period division result; screening the plurality of flight routes based on a first time period in the time period division result as a screening constraint to obtain a first flight position set; obtaining a target restricted flight position set of the target airspace, and combining the first flight position set to visually navigate the target airspace to obtain a first airspace navigable map; combining the target flight path to analyze the first airspace navigable map to obtain a first preset flight position of the target UAV in the first time period; taking a target preset flight path formed based on the first preset flight position as a control reference of the target UAV; the process of obtaining the first airspace navigable map comprises: constructing a target grid map of the target airspace; the target restricted flight position set and the first flight position set form a first occupied position; labeling the first occupied position to the target grid map to obtain the first airspace navigable map; the process of forming the first occupied position comprises: reading a predetermined unsafe flight distance; extracting a first flight position of a first UAV in the first flight position set, the first UAV being any one of the plurality of UAVs; matching a first grid corresponding to the first flight position in the target grid map; combining the predetermined unsafe flight distance to form a first adjacent grid of the first grid; adding the first grid and the first adjacent grid to the first occupied position; after obtaining the first preset flight position, the method comprises: obtaining a first historical weather record of the first preset flight position; performing weather prediction on the first preset flight position in the first time period according to the first historical weather record to obtain a first predicted weather; reading a predetermined navigable weather constraint; when the first predicted weather does not meet the predetermined navigable weather constraint, issuing a first flight adjustment instruction; adjusting the first preset flight position according to the first flight adjustment instruction; the process of obtaining the first predicted weather comprises: performing trend analysis on a first historical temperature record time sequence in the first historical weather record to obtain a first predicted temperature; performing trend analysis on a first historical humidity record time sequence in the first historical weather record to obtain a first predicted humidity; performing trend analysis on a first historical wind power record time sequence in the first historical weather record to obtain a first predicted wind power; the first predicted temperature, the first predicted humidity, and the first predicted wind power form the first predicted weather; after forming the first predicted weather, the method comprises: reading a predetermined weather feature weight distribution; weighting the first predicted temperature, the first predicted humidity and the first predicted wind force based on the predetermined weather feature weight distribution to obtain a first predicted weather index; wherein the first predicted weather index is used to quantitatively represent the first predicted weather.

2. The method of claim 1, wherein, The predetermined unit time is much smaller than the target period.

3. An intelligent route dynamic management and control system based on unmanned aerial vehicles, characterized in that, The system is used to implement the method for dynamically controlling the intelligent flight route of the unmanned aerial vehicle, and the system comprises: a target flight record acquisition module, which is used to acquire a target flight record, the target flight record being a record of a target route and a target period of a target unmanned aerial vehicle flying in a target airspace, the target flight record of the target airspace comprising a plurality of flight routes of a plurality of unmanned aerial vehicles; a target period division module, which is used to read a predetermined unit time and divide the target period based on the predetermined unit time to obtain a time period division result; a first flight position set acquisition module, which is used to screen the plurality of flight routes based on a first time period in the time period division result to obtain a first flight position set; a navigable visualization module, which is used to acquire a target restricted flight position set of the target airspace and combine the first flight position set to visually navigate the target airspace to obtain a first airspace navigable map; a first preset flight position acquisition module, which is used to analyze the first airspace navigable map in combination with the target route to obtain a first preset flight position of the target unmanned aerial vehicle in the first time period; a control module, which is used to take a target preset route formed based on the first preset flight position as a control reference of the target unmanned aerial vehicle.

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