Methods, systems, and media for managing unmanned aerial vehicles based on big data identification

Abstract

This application provides a method, system, and medium for managing unmanned aerial vehicles (UAVs) based on big data identification. The method includes: acquiring UAV feature identification information and flight data information; extracting feature identification data and acquiring traceability information, safety data, and pre-stored flight datasets; acquiring authorization level data; displaying the authorization level data according to warning threshold levels; generating flight feature maps and predicting proposed flight trajectories based on the pre-stored flight dataset; combining the authorization level data and pre-stored flight data to obtain a flight permission correlation coefficient; comparing the coefficient with a preset authorization threshold to determine whether the UAV is authorized to fly and taking warnings or interference measures. This method uses big data identification technology to evaluate the UAV's feature information and flight data for flight permission assessment, achieving authorization judgment based on UAV monitoring information data and authorization parameters, thus improving the accuracy of UAV airspace safety management.

Description

Technical Field

[0001] This application relates to the fields of big data security and unmanned aerial vehicle management technology, specifically to unmanned aerial vehicle management methods, systems and media based on big data identification. Background Technology

[0002] As unmanned aerial vehicle (UAV) technology matures and its applications become more frequent and widespread, coupled with lower manufacturing costs and barriers to entry, the market for consumer-grade micro-drones and civilian UAVs has exploded. However, the country currently lacks a comprehensive and systematic regulatory framework and standards for UAVs, and has not yet established an authoritative standard management system. With the rapid development of the UAV market, the demand for urban UAV airspace control systems is becoming increasingly urgent.

[0003] To protect areas and limit airspace, existing cities designate no-fly zones or monitored zones over urban areas, prohibiting unmanned aerial vehicles (UAVs) other than those with authorized registration from entering. While there are intelligent methods and systems for the supervision and identification of UAVs, there is an over-reliance on traditional systems or administrator-style management. These conventional systems and methods lack systematicity, mobility, flexibility, and intelligence, which inhibits the beneficial use and rapid development of UAVs, reduces their contribution to urban informatization and digitalization, and hinders the scientific, rational, and convenient use of UAVs due to excessive human interference in the identification and management of UAVs, thus affecting the sensitivity and accuracy of the judgment.

[0004] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, and medium for managing unmanned aerial vehicles (UAVs) based on big data identification, which can improve the accuracy of identifying and managing the flight safety authorization status of UAVs based on the collected UAV information data.

[0006] This application also provides a method for managing unmanned aerial vehicles based on big data identification, including the following steps:

[0007] Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the border area or have been declared;

[0008] Based on the feature identification information, extract the feature identification data of the unmanned aerial vehicle and obtain traceability information and safety data; based on the flight data information, extract the pre-stored flight dataset of the unmanned aerial vehicle.

[0009] The authorization level data of the unmanned aerial vehicle is obtained based on the feature identification data, traceability information and safety data, and the authorization level data is compared and displayed according to a preset warning threshold level.

[0010] The flight feature map of the unmanned aerial vehicle is generated based on the pre-stored flight dataset, and the simulated flight trajectory data of the unmanned aerial vehicle is predicted based on the flight trajectory prediction model.

[0011] Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight quasi-relevance coefficient of the unmanned aerial vehicle.

[0012] The flight permission correlation coefficient is compared with a preset authorization threshold. The result of the threshold comparison determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

[0013] Optionally, in the unmanned aerial vehicle management method based on big data identification described in the embodiments of this application, the step of monitoring and acquiring the feature identification information and flight data information of unmanned aerial vehicles in endangered areas or those that have been declared includes:

[0014] Based on the transmission signals of monitored or reported unmanned aerial vehicles (UAVs) in the critical area, obtain the characteristic identification information of the UAVs, including type information, purpose information, ownership and registration information, and special certification information.

[0015] Based on the ownership registration information and special certification information, the identity information of the unmanned aerial vehicle is identified to obtain the operational purpose information and air traffic control declaration information of the unmanned aerial vehicle;

[0016] Flight data information is integrated based on the aforementioned mission objective information, air traffic control declaration information, and special certification information.

[0017] Optionally, in the unmanned aerial vehicle management method based on big data identification described in this application embodiment, the step of extracting the feature identification data of the unmanned aerial vehicle and obtaining traceability information and safety data based on the feature identification information, and extracting the pre-stored flight dataset of the unmanned aerial vehicle based on the flight data information, includes:

[0018] Based on the feature identification information, the corresponding feature identification data is obtained by querying the preset aircraft identification database, including aircraft type data, operational purpose data, and control affiliation data;

[0019] Based on the control attribution data, the traceability information of the unmanned aerial vehicle is obtained;

[0020] Based on the traceability information, combined with the operational purpose information and air traffic control declaration information, the safety data of the unmanned aerial vehicle is extracted from the aircraft identification database.

[0021] The pre-stored flight dataset of the unmanned aerial vehicle is extracted based on the flight data information, including flight destination data, airspace warning information data, mission instruction data, and special operation data.

[0022] Optionally, in the unmanned aerial vehicle management method based on big data identification described in this application embodiment, the step of obtaining the authorization level data of the unmanned aerial vehicle according to the feature identification data, traceability information, and safety data, and comparing and displaying the authorization level data according to a preset warning threshold level, includes:

[0023] Based on the aircraft type data, operational purpose data, and control attribution data, risk values ​​are clustered using corresponding risk parameters to obtain risk information value K1.

[0024] The security identification value K2 is obtained by weighting the traceability information and security data.

[0025] Based on the risk information value K1 and the safety identification value K2, the authorization level data Y = (K1 + K2) / K2 of the unmanned aerial vehicle is calculated.

[0026] The warning level of the unmanned aerial vehicle is obtained by comparing the authorized level data Y with the preset warning threshold, and then displayed according to the warning level.

[0027] Optionally, in the unmanned aerial vehicle management method based on big data recognition described in the embodiments of this application, the step of generating the flight feature map of the unmanned aerial vehicle according to the pre-stored flight dataset and predicting the proposed flight trajectory data of the unmanned aerial vehicle according to the flight trajectory prediction model includes:

[0028] A flight feature map is generated based on the flight destination data, airspace warning information data, mission instruction data, and special operation data of the unmanned aerial vehicle.

[0029] The flight mission feature information is extracted from the flight feature map and input into the preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the proposed flight trajectory data of the unmanned aerial vehicle, including route data, airspace polygon data, and asymptotic data.

[0030] Optionally, in the unmanned aerial vehicle management method based on big data identification described in this application embodiment, the step of obtaining the unmanned aerial vehicle's flight clearance correlation coefficient by judging the authorization correlation based on the proposed flight trajectory data combined with the authorization level data and the data in the pre-stored flight dataset includes:

[0031] Based on the flight route data, airspace multilateral data, and asymptotic data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data, and special operation data, the authorization correlation is determined to obtain the flight clearance correlation coefficient.

[0032] The formula for calculating the flight apprehension correlation coefficient is as follows:

[0033]

[0034] Where P is the flight clearance correlation coefficient, s0 is the flight route data, t0 is the airspace multilateral data, h0 is the asymptote data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, and l is the special operation data. For the safety factor of unmanned aerial vehicle certification, ε k Credit index for owners of unmanned aerial vehicles.

[0035] Optionally, in the unmanned aerial vehicle (UAV) management method based on big data identification described in this application embodiment, the step of comparing the flight permission correlation coefficient with a preset authorization threshold, determining whether the UAV is authorized to fly based on the threshold comparison result, and issuing a warning or interfering with the UAV if it is determined to be an abnormal authorization, includes:

[0036] A preset authorization threshold is obtained by querying the feature identification information of the unmanned aerial vehicle;

[0037] The threshold is compared with the pre-set authorization threshold based on the flight approval correlation coefficient;

[0038] If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized.

[0039] Signal jamming or command warnings are issued to unauthorized unmanned aerial vehicles.

[0040] Secondly, embodiments of this application provide an unmanned aerial vehicle (UAV) management system based on big data recognition. The system includes a memory and a processor. The memory includes a program for an UAV management method based on big data recognition. When the program for the UAV management method based on big data recognition is executed by the processor, it implements the following steps:

[0041] Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the border area or have been declared;

[0042] Based on the feature identification information, extract the feature identification data of the unmanned aerial vehicle and obtain traceability information and safety data; based on the flight data information, extract the pre-stored flight dataset of the unmanned aerial vehicle.

[0043] The authorization level data of the unmanned aerial vehicle is obtained based on the feature identification data, traceability information and safety data, and the authorization level data is compared and displayed according to a preset warning threshold level.

[0044] The flight feature map of the unmanned aerial vehicle is generated based on the pre-stored flight dataset, and the simulated flight trajectory data of the unmanned aerial vehicle is predicted based on the flight trajectory prediction model.

[0045] Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight quasi-relevance coefficient of the unmanned aerial vehicle.

[0046] The flight permission correlation coefficient is compared with a preset authorization threshold. The result of the threshold comparison determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

[0047] Optionally, in the unmanned aerial vehicle management system based on big data identification described in the embodiments of this application, the monitoring and acquisition of the feature identification information and flight data information of unmanned aerial vehicles in the endangered area or those that have been reported includes:

[0048] Based on the transmission signals of monitored or reported unmanned aerial vehicles (UAVs) in the critical area, obtain the characteristic identification information of the UAVs, including type information, purpose information, ownership and registration information, and special certification information.

[0049] Based on the ownership registration information and special certification information, the identity information of the unmanned aerial vehicle is identified to obtain the operational purpose information and air traffic control declaration information of the unmanned aerial vehicle;

[0050] Flight data information is integrated based on the aforementioned mission objective information, air traffic control declaration information, and special certification information.

[0051] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a program for an unmanned aerial vehicle (UAV) management method based on big data identification. When the program for the UAV management method based on big data identification is executed by a processor, it implements the steps of the UAV management method based on big data identification as described in any of the preceding claims.

[0052] As can be seen from the above, the unmanned aerial vehicle (UAV) management method, system, and medium based on big data identification provided in this application obtains the feature identification information and flight data information of the UAV, extracts the feature identification data, obtains traceability information and safety data, as well as pre-stored flight datasets and authorization level data. Based on the authorization level data, it displays the data according to warning threshold levels. It generates flight feature maps based on the pre-stored flight dataset and predicts the proposed flight trajectory data. Then, it combines the authorization level data with the pre-stored flight data to determine the flight permission correlation coefficient. Based on the coefficient and a preset authorization threshold, it determines whether the UAV is authorized to fly and takes warnings or interference measures. Thus, based on big data identification technology, it performs authorization flight permission assessment on the UAV's feature information and flight data, realizing authorization judgment technology based on UAV monitoring information data to obtain authorization parameters, thereby improving the accuracy of UAV airspace safety management.

[0053] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart of an unmanned aerial vehicle management method based on big data identification provided in an embodiment of this application;

[0056] Figure 2 A flowchart illustrating the process of acquiring unmanned aerial vehicle (UAV) feature identification information and flight data information using a big data recognition-based UAV management method provided in this application embodiment;

[0057] Figure 3 A flowchart illustrating the process of acquiring feature identification data, traceability information, safety data, and pre-stored flight datasets in the big data-based unmanned aerial vehicle management method provided in this application embodiment;

[0058] Figure 4 This is a schematic diagram of a structure of an unmanned aerial vehicle management system based on big data recognition provided in an embodiment of this application. Detailed Implementation

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0060] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0061] Please refer to Figure 1 , Figure 1 This is a flowchart of an unmanned aerial vehicle (UAV) management method based on big data identification, as described in some embodiments of this application. This UAV management method based on big data identification is used in terminal devices, such as computers and mobile phones. The UAV management method based on big data identification includes the following steps:

[0062] S101. Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the vicinity or have been declared;

[0063] S102. Extract the feature identification data of the unmanned aerial vehicle based on the feature identification information and obtain traceability information and safety data; extract the pre-stored flight dataset of the unmanned aerial vehicle based on the flight data information.

[0064] S103. Obtain the authorization level data of the unmanned aerial vehicle based on the feature identification data, traceability information and safety data, and display the authorization level data according to a preset warning threshold level.

[0065] S104. Generate the flight feature map of the unmanned aerial vehicle based on the pre-stored flight dataset, and predict the proposed flight trajectory data of the unmanned aerial vehicle based on the flight trajectory prediction model.

[0066] S105. Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight quasi-correlation coefficient of the unmanned aerial vehicle.

[0067] S106. Compare the flight permission correlation coefficient with the preset authorization threshold, and determine whether the unmanned aircraft is authorized to fly based on the threshold comparison result. If it is determined to be an abnormal authorization, issue a warning or interfere with the unmanned aircraft.

[0068] It should be noted that, in order to monitor unmanned aerial vehicles (UAVs) approaching a preset airspace or UAVs with temporary communication declarations, and to determine whether airspace flight authorization, warnings, interference, or even forced landings are permissible, the system extracts UAV feature identification data by acquiring UAV feature identification information, obtains traceability information and safety data, and extracts pre-stored flight datasets based on flight data. Based on the feature identification data, traceability information, and safety data, the system obtains the UAV's authorization level data, and compares this data with preset warning threshold levels. This allows the system or regulatory personnel to clearly understand the UAV's authorization level, and then, based on the pre-stored flight datasets, generate... The system generates flight characteristic maps of unmanned aerial vehicles (UAVs) and predicts their proposed flight trajectories using a flight trajectory prediction model. Finally, it assesses the authorization relevance of the proposed flight trajectory data with authorization level data and pre-stored flight datasets to obtain the UAV's flight permission relevance coefficient. The flight permission relevance coefficient is then compared with a preset authorization threshold to determine whether the UAV is authorized to fly. If an abnormal authorization is detected, the UAV is warned or interfered with. By acquiring and processing the identifiers and information data of the UAV to be judged, the system performs authorization determination, thereby achieving intelligent big data-driven airspace authorization management for UAVs and ensuring airspace safety management.

[0069] Please refer to Figure 2 , Figure 2 This is a flowchart illustrating the acquisition of unmanned aerial vehicle (UAV) feature identification information and flight data information in some embodiments of the unmanned aerial vehicle (UAV) management method based on big data identification in this application. According to embodiments of the present invention, the monitoring and acquisition of feature identification information and flight data information of UAVs in endangered areas or those that have been reported specifically involves:

[0070] S201. Obtain the characteristic identification information of unmanned aerial vehicles (UAVs) based on the transmission signals of monitored UAVs or reported UAVs, including type information, purpose information, ownership and registration information, and special certification information.

[0071] S202. Based on the ownership registration information and special certification information, perform unmanned aerial vehicle (UAV) identity information identification to obtain the operational purpose information and air traffic control declaration information of the UAV;

[0072] S203. Integrate flight data information based on the aforementioned mission information, air traffic control declaration information, and special certification information.

[0073] It should be noted that for unmanned aerial vehicles (UAVs) monitored in airspace, the identification of the UAVs to be evaluated must be carried out through identification and aircraft data acquisition. The characteristic identification information of the UAVs must be obtained based on the transmitted signals of the monitored UAVs, including type information, purpose information, registration information, and special certification information. This can reflect the category of the UAV (e.g., large, small, or micro), purpose (e.g., military, civilian, commercial), and the registration status of the company, group, or individual to which the UAV belongs. It can also include information on UAVs with special functions such as special-type UAVs, emergency rescue UAVs, and air defense UAVs. The identification information of the UAVs is obtained through registration and special certification information to acquire operational purpose information and air traffic control declaration information, such as the issuing party of the flight command, flight segment, flight time and duration, takeoff weight, and details of the payload. Flight data information is then integrated based on the operational purpose information, air traffic control declaration information, and special certification information.

[0074] Please refer to Figure 3 , Figure 3 This is a flowchart illustrating the acquisition of feature identification data, traceability information, safety data, and pre-stored flight datasets in some embodiments of the unmanned aerial vehicle (UAV) management method based on big data identification in this application. According to embodiments of the present invention, the step of extracting feature identification data of the UAV based on the feature identification information and obtaining traceability information and safety data, and extracting the pre-stored flight dataset of the UAV based on the flight data information, specifically involves:

[0075] S301. Based on the feature identification information, query the preset aircraft identification database to obtain the corresponding feature identification data, including aircraft type data, operational purpose data, and control affiliation data;

[0076] S302. Obtain the traceability information of the unmanned aerial vehicle based on the control attribution data;

[0077] S303. Based on the traceability information, the operation purpose information, and the air traffic control declaration information, extract the safety data of the unmanned aerial vehicle from the aircraft identification database.

[0078] S304. Extract the pre-stored flight dataset of the unmanned aerial vehicle based on the flight data information, including flight destination data, airspace warning information data, mission instruction data, and special operation data.

[0079] It should be noted that, based on the characteristic identification information of the unmanned aerial vehicle (UAV), the corresponding characteristic identification data is obtained by querying the preset aircraft identification database. This data reflects the UAV type, function, purpose of use, flight command issuer, UAV ownership unit, and flight operator. Based on the control ownership data, the traceability information of the UAV is obtained, reflecting the data source of the owner or operator. Based on the traceability information, combined with the operation purpose information and air traffic control declaration information, the safety data of the UAV is extracted from the aircraft identification database. In other words, the safety data of the UAV can be identified by querying the obtained UAV source information, flight operation information, command function information, and air traffic control declaration data. At the same time, the pre-stored flight dataset of the UAV is extracted based on the flight data information, including flight destination data, airspace warning information data, mission command data, and special operation data. That is, the pre-stored flight data such as the UAV's flight purpose, flight range, airspace route, airspace warnings, airspace markings and command guidance, special operations, and special missions are obtained.

[0080] According to an embodiment of the present invention, the step of obtaining the authorization level data of the unmanned aerial vehicle based on the feature identification data, traceability information, and safety data, and then displaying the authorization level data in comparison with a preset warning threshold level, specifically includes:

[0081] Based on the aircraft type data, operational purpose data, and control attribution data, risk values ​​are clustered using corresponding risk parameters to obtain risk information value K1.

[0082] The security identification value K2 is obtained by weighting the traceability information and security data.

[0083] Based on the risk information value K1 and the safety identification value K2, the authorization level data Y = (K1 + K2) / K2 of the unmanned aerial vehicle is calculated.

[0084] The warning level of the unmanned aerial vehicle is obtained by comparing the authorized level data Y with the preset warning threshold, and then displayed according to the warning level.

[0085] It should be noted that, to obtain the authorization level of an unmanned aerial vehicle (UAV), the authorization level data is calculated based on risk information values ​​and safety identification values. This data is then compared with a preset warning threshold. The warning level corresponding to the UAV's warning threshold is determined based on the comparison result. Warning levels are divided into four levels, from level one to level four, with level one having a threshold range of (0.75, 1], level two (0.5, 0.75], level three (0.25, 0.5], and level four (0, 0.25). For example, if the comparison threshold for UAV A is 0.7, then UAV A's warning level is level two. The warning level is displayed based on the obtained warning level, which is determined by combining the aircraft type data A1, operational purpose data A2, and control attribution data A3 with the corresponding risk parameters. The formula for calculating the risk information value by performing risk value clustering is as follows: The formula for calculating the safety identification value, which is obtained by weighting the traceability source information τ0 and the safety level data S, is K2 = τ0 × S.

[0086] According to an embodiment of the present invention, the step of generating a flight feature map of the unmanned aerial vehicle based on the pre-stored flight dataset and predicting the proposed flight trajectory data of the unmanned aerial vehicle based on the flight trajectory prediction model specifically includes:

[0087] A flight feature map is generated based on the flight destination data, airspace warning information data, mission instruction data, and special operation data of the unmanned aerial vehicle.

[0088] The flight mission feature information is extracted from the flight feature map and input into the preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the proposed flight trajectory data of the unmanned aerial vehicle, including route data, airspace polygon data, and asymptotic data.

[0089] It should be noted that a flight feature map of the unmanned aerial vehicle (UAV) is generated by extracting flight destination data, airspace warning information data, mission instruction data, and special operation data from the pre-stored flight dataset. This flight feature map reflects flight mission information such as flight purpose, airspace warnings, air traffic control markings, mission details, and instruction lists during commanded flight operations. The flight mission feature information extracted from the flight feature map is input into a pre-trained preset flight trajectory prediction model to pre-set the flight trajectory and obtain the proposed flight trajectory data. In order to obtain accurate data on the pre-flight trajectories of various types of UAVs performing various missions, a preset flight trajectory prediction model is established. The preset flight trajectory prediction model is trained on a large amount of flight mission sample data from historical flight mission archives of various types of UAVs. The larger the amount of data, the more accurate the result. In this scheme, the preset flight trajectory prediction model is trained by inputting the flight mission feature information from historical sample data and actual flight trajectory data into the model to obtain the output value. When the output value meets the preset requirements, the training stops, and the pre-trained preset flight trajectory prediction model is obtained.

[0090] According to an embodiment of the present invention, the step of obtaining the flight clearance correlation coefficient of the unmanned aerial vehicle by performing authorization correlation judgment based on the proposed flight trajectory data, authorization level data, and data from the pre-stored flight dataset is specifically as follows:

[0091] Based on the flight route data, airspace multilateral data, and asymptotic data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data, and special operation data, the authorization correlation is determined to obtain the flight clearance correlation coefficient.

[0092] The formula for calculating the flight apprehension correlation coefficient is as follows:

[0093]

[0094] Where P is the flight clearance correlation coefficient, s0 is the flight route data, t0 is the airspace multilateral data, h0 is the asymptote data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, and l is the special operation data. For the safety factor of unmanned aerial vehicle certification, ε k Credit index for unmanned aerial vehicle owners (UAVs) and ε k (Obtained by querying the aircraft identification database based on the characteristic identification information of the unmanned aerial vehicle).

[0095] It should be noted that, in order to assess the authorized flight status of an unmanned aerial vehicle (UAV), the UAV's flight qualification correlation coefficient is obtained by processing the UAV's simulated flight trajectory data, authorization level data, and pre-stored flight dataset data. This coefficient can reflect the authorized flight status of the UAV.

[0096] According to an embodiment of the present invention, the step of comparing the flight permission correlation coefficient with a preset authorization threshold, determining whether the unmanned aerial vehicle (UAV) is authorized to fly based on the threshold comparison result, and issuing a warning or interfering with the UAV if the authorization is deemed abnormal, specifically involves:

[0097] A preset authorization threshold is obtained by querying the feature identification information of the unmanned aerial vehicle;

[0098] The threshold is compared with the pre-set authorization threshold based on the flight approval correlation coefficient;

[0099] If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized.

[0100] Signal jamming or command warnings are issued to unauthorized unmanned aerial vehicles.

[0101] It should be noted that, to determine whether an unmanned aerial vehicle (UAV) is authorized to fly, a preset authorization threshold is obtained by querying the UAV's characteristic identification information in the aircraft identification database based on the UAV's attribute information. The UAV's flight authorization correlation coefficient is then compared with the preset authorization threshold. If the flight authorization correlation coefficient is greater than the preset authorization threshold, the UAV is determined to have normal authorization, i.e., it has obtained airspace authorization. If the flight authorization correlation coefficient is not greater than the preset authorization threshold, the UAV is determined to have abnormal authorization, i.e., the UAV cannot obtain airspace authorization, and signal interference or command warnings need to be applied to the UAV to achieve intelligent identification and release or warning processing of the UAV.

[0102] like Figure 4 As shown, the present invention also discloses an unmanned aerial vehicle management system based on big data recognition, including a memory 41 and a processor 42. The memory includes a program for an unmanned aerial vehicle management method based on big data recognition. When the processor executes the program for the unmanned aerial vehicle management method based on big data recognition, it performs the following steps:

[0103] Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the border area or have been declared;

[0104] Based on the feature identification information, extract the feature identification data of the unmanned aerial vehicle and obtain traceability information and safety data; based on the flight data information, extract the pre-stored flight dataset of the unmanned aerial vehicle.

[0105] The authorization level data of the unmanned aerial vehicle is obtained based on the feature identification data, traceability information and safety data, and the authorization level data is compared and displayed according to a preset warning threshold level.

[0106] The flight feature map of the unmanned aerial vehicle is generated based on the pre-stored flight dataset, and the simulated flight trajectory data of the unmanned aerial vehicle is predicted based on the flight trajectory prediction model.

[0107] Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight quasi-relevance coefficient of the unmanned aerial vehicle.

[0108] The flight permission correlation coefficient is compared with a preset authorization threshold. The result of the threshold comparison determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

[0109] It should be noted that, in order to monitor unmanned aerial vehicles (UAVs) approaching a preset airspace or UAVs with temporary communication declarations, and to determine whether airspace flight authorization, warnings, interference, or even forced landings are permissible, the system extracts UAV feature identification data by acquiring UAV feature identification information, obtains traceability information and safety data, and extracts pre-stored flight datasets based on flight data. Based on the feature identification data, traceability information, and safety data, the system obtains the UAV's authorization level data, and compares this data with preset warning threshold levels. This allows the system or regulatory personnel to clearly understand the UAV's authorization level, and then, based on the pre-stored flight datasets, generate... The system generates flight characteristic maps of unmanned aerial vehicles (UAVs) and predicts their proposed flight trajectories using a flight trajectory prediction model. Finally, it assesses the authorization relevance of the proposed flight trajectory data with authorization level data and pre-stored flight datasets to obtain the UAV's flight permission relevance coefficient. The flight permission relevance coefficient is then compared with a preset authorization threshold to determine whether the UAV is authorized to fly. If an abnormal authorization is detected, the UAV is warned or interfered with. By acquiring and processing the identifiers and information data of the UAV to be judged, the system performs authorization determination, thereby achieving intelligent big data-driven airspace authorization management for UAVs and ensuring airspace safety management.

[0110] According to an embodiment of the present invention, the monitoring and acquisition of the characteristic identification information and flight data information of the endangered or declared unmanned aerial vehicle specifically includes:

[0111] Based on the transmission signals of monitored or reported unmanned aerial vehicles (UAVs) in the critical area, obtain the characteristic identification information of the UAVs, including type information, purpose information, ownership and registration information, and special certification information.

[0112] Based on the ownership registration information and special certification information, the identity information of the unmanned aerial vehicle is identified to obtain the operational purpose information and air traffic control declaration information of the unmanned aerial vehicle;

[0113] Flight data information is integrated based on the aforementioned mission objective information, air traffic control declaration information, and special certification information.

[0114] It should be noted that for unmanned aerial vehicles (UAVs) monitored in airspace, the identification of the UAVs to be evaluated must be carried out through identification and aircraft data acquisition. The characteristic identification information of the UAVs must be obtained based on the transmitted signals of the monitored UAVs, including type information, purpose information, registration information, and special certification information. This can reflect the category of the UAV (e.g., large, small, or micro), purpose (e.g., military, civilian, commercial), and the registration status of the company, group, or individual to which the UAV belongs. It can also include information on UAVs with special functions such as special-type UAVs, emergency rescue UAVs, and air defense UAVs. The identification information of the UAVs is obtained through registration and special certification information to acquire operational purpose information and air traffic control declaration information, such as the issuing party of the flight command, flight segment, flight time and duration, takeoff weight, and details of the payload. Flight data information is then integrated based on the operational purpose information, air traffic control declaration information, and special certification information.

[0115] According to an embodiment of the present invention, the step of extracting the feature identification data of the unmanned aerial vehicle and obtaining traceability information and safety data based on the feature identification information, and extracting the pre-stored flight dataset of the unmanned aerial vehicle based on the flight data information, specifically includes:

[0116] Based on the feature identification information, the corresponding feature identification data is obtained by querying the preset aircraft identification database, including aircraft type data, operational purpose data, and control affiliation data;

[0117] Based on the control attribution data, the traceability information of the unmanned aerial vehicle is obtained;

[0118] Based on the traceability information, combined with the operational purpose information and air traffic control declaration information, the safety data of the unmanned aerial vehicle is extracted from the aircraft identification database.

[0119] The pre-stored flight dataset of the unmanned aerial vehicle is extracted based on the flight data information, including flight destination data, airspace warning information data, mission instruction data, and special operation data.

[0120] It should be noted that, based on the characteristic identification information of the unmanned aerial vehicle (UAV), the corresponding characteristic identification data is obtained by querying the preset aircraft identification database. This data reflects the UAV type, function, purpose of use, flight command issuer, UAV ownership unit, and flight operator. Based on the control ownership data, the traceability information of the UAV is obtained, reflecting the data source of the owner or operator. Based on the traceability information, combined with the operation purpose information and air traffic control declaration information, the safety data of the UAV is extracted from the aircraft identification database. In other words, the safety data of the UAV can be identified by querying the obtained UAV source information, flight operation information, command function information, and air traffic control declaration data. At the same time, the pre-stored flight dataset of the UAV is extracted based on the flight data information, including flight destination data, airspace warning information data, mission command data, and special operation data. That is, the pre-stored flight data such as the UAV's flight purpose, flight range, airspace route, airspace warnings, airspace markings and command guidance, special operations, and special missions are obtained.

[0121] According to an embodiment of the present invention, the step of obtaining the authorization level data of the unmanned aerial vehicle based on the feature identification data, traceability information, and safety data, and then displaying the authorization level data in comparison with a preset warning threshold level, specifically includes:

[0122] Based on the aircraft type data, operational purpose data, and control attribution data, risk values ​​are clustered using corresponding risk parameters to obtain risk information value K1.

[0123] The security identification value K2 is obtained by weighting the traceability information and security data.

[0124] Based on the risk information value K1 and the safety identification value K2, the authorization level data Y = (K1 + K2) / K2 of the unmanned aerial vehicle is calculated.

[0125] The warning level of the unmanned aerial vehicle is obtained by comparing the authorized level data Y with the preset warning threshold, and then displayed according to the warning level.

[0126] It should be noted that, to obtain the authorization level of an unmanned aerial vehicle (UAV), the authorization level data is calculated based on risk information values ​​and safety identification values. This data is then compared with a preset warning threshold. The warning level corresponding to the UAV's warning threshold is determined based on the comparison result. Warning levels are divided into four levels, from level one to level four, with level one having a threshold range of (0.75, 1], level two (0.5, 0.75], level three (0.25, 0.5], and level four (0, 0.25). For example, if the comparison threshold for UAV A is 0.7, then UAV A's warning level is level two. The warning level is displayed based on the obtained warning level, which is determined by combining the aircraft type data A1, operational purpose data A2, and control attribution data A3 with the corresponding risk parameters. The formula for calculating the risk information value by performing risk value clustering is as follows: The formula for calculating the safety identification value, which is obtained by weighting the traceability source information τ0 and the safety level data S, is K2 = τ0 × S.

[0127] According to an embodiment of the present invention, the step of generating a flight feature map of the unmanned aerial vehicle based on the pre-stored flight dataset and predicting the proposed flight trajectory data of the unmanned aerial vehicle based on the flight trajectory prediction model specifically includes:

[0128] A flight feature map is generated based on the flight destination data, airspace warning information data, mission instruction data, and special operation data of the unmanned aerial vehicle.

[0129] The flight mission feature information is extracted from the flight feature map and input into the preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the proposed flight trajectory data of the unmanned aerial vehicle, including route data, airspace polygon data, and asymptotic data.

[0130] It should be noted that a flight feature map of the unmanned aerial vehicle (UAV) is generated by extracting flight destination data, airspace warning information data, mission instruction data, and special operation data from the pre-stored flight dataset. This flight feature map reflects flight mission information such as flight purpose, airspace warnings, air traffic control markings, mission details, and instruction lists during commanded flight operations. The flight mission feature information extracted from the flight feature map is input into a pre-trained preset flight trajectory prediction model to pre-set the flight trajectory and obtain the proposed flight trajectory data. In order to obtain accurate data on the pre-flight trajectories of various types of UAVs performing various missions, a preset flight trajectory prediction model is established. The preset flight trajectory prediction model is trained on a large amount of flight mission sample data from historical flight mission archives of various types of UAVs. The larger the amount of data, the more accurate the result. In this scheme, the preset flight trajectory prediction model is trained by inputting the flight mission feature information from historical sample data and actual flight trajectory data into the model to obtain the output value. When the output value meets the preset requirements, the training stops, and the pre-trained preset flight trajectory prediction model is obtained.

[0131] According to an embodiment of the present invention, the step of obtaining the flight clearance correlation coefficient of the unmanned aerial vehicle by performing authorization correlation judgment based on the proposed flight trajectory data, authorization level data, and data from the pre-stored flight dataset is specifically as follows:

[0132] Based on the flight route data, airspace multilateral data, and asymptotic data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data, and special operation data, the authorization correlation is determined to obtain the flight clearance correlation coefficient.

[0133] The formula for calculating the flight apprehension correlation coefficient is as follows:

[0134]

[0135] Where P is the flight clearance correlation coefficient, s0 is the flight route data, t0 is the airspace multilateral data, h0 is the asymptote data, Y is the authorization level data, d is the flight destination data, c is the airspace warning information data, w is the mission instruction data, and l is the special operation data. For the safety factor of unmanned aerial vehicle certification, ε k Credit index for unmanned aerial vehicle owners (UAVs) and ε k (Obtained by querying the aircraft identification database based on the characteristic identification information of the unmanned aerial vehicle).

[0136] It should be noted that, in order to assess the authorized flight status of an unmanned aerial vehicle (UAV), the UAV's flight qualification correlation coefficient is obtained by processing the UAV's simulated flight trajectory data, authorization level data, and pre-stored flight dataset data. This coefficient can reflect the authorized flight status of the UAV.

[0137] According to an embodiment of the present invention, the step of comparing the flight permission correlation coefficient with a preset authorization threshold, determining whether the unmanned aerial vehicle (UAV) is authorized to fly based on the threshold comparison result, and issuing a warning or interfering with the UAV if the authorization is deemed abnormal, specifically involves:

[0138] A preset authorization threshold is obtained by querying the feature identification information of the unmanned aerial vehicle;

[0139] The threshold is compared with the pre-set authorization threshold based on the flight approval correlation coefficient;

[0140] If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized.

[0141] Signal jamming or command warnings are issued to unauthorized unmanned aerial vehicles.

[0142] It should be noted that, to determine whether an unmanned aerial vehicle (UAV) is authorized to fly, a preset authorization threshold is obtained by querying the UAV's characteristic identification information in the aircraft identification database based on the UAV's attribute information. The UAV's flight authorization correlation coefficient is then compared with the preset authorization threshold. If the flight authorization correlation coefficient is greater than the preset authorization threshold, the UAV is determined to have normal authorization, i.e., it has obtained airspace authorization. If the flight authorization correlation coefficient is not greater than the preset authorization threshold, the UAV is determined to have abnormal authorization, i.e., the UAV cannot obtain airspace authorization, and signal interference or command warnings need to be applied to the UAV to achieve intelligent identification and release or warning processing of the UAV.

[0143] A third aspect of the present invention provides a readable storage medium including a program for an unmanned aerial vehicle (UAV) management method based on big data identification, wherein when the program is executed by a processor, it implements the steps of the unmanned aerial vehicle management method based on big data identification as described in any of the preceding claims.

[0144] This invention discloses a method, system, and medium for managing unmanned aerial vehicles (UAVs) based on big data identification. It extracts feature identification data from UAV feature identification information and flight data, obtains traceability information and safety data, and acquires pre-stored flight datasets and authorization level data. Based on the authorization level data, it displays warning threshold levels. It generates flight feature maps based on the pre-stored flight dataset and predicts proposed flight trajectories. Then, it combines the authorization level data with the pre-stored flight data to obtain a flight permission correlation coefficient. The coefficient is compared with a preset authorization threshold to determine whether the UAV is authorized to fly and to issue warnings or intervene. Thus, based on big data identification technology, it evaluates the authorization permission of UAV feature information and flight data, realizing a technology for evaluating and obtaining authorization parameters based on UAV monitoring information data for authorization judgment, thereby improving the accuracy of UAV airspace safety management.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0146] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0148] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for managing unmanned aerial vehicles based on big data recognition, characterized in that, Includes the following steps: Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the border area or have been declared; Based on the feature identification information, extract the feature identification data of the unmanned aerial vehicle and obtain traceability information and safety data; based on the flight data information, extract the pre-stored flight dataset of the unmanned aerial vehicle. The authorization level data of the unmanned aerial vehicle is obtained based on the feature identification data, traceability information, and safety data. The authorization level data is then compared and displayed according to a preset warning threshold level. Specifically, this includes: clustering risk values ​​based on aircraft type data, operational purpose data, and control attribution data, combined with corresponding risk parameters, to obtain risk information values. A security identification value is obtained by weighting the traceability information and security data. Based on the risk information value and security identification value The authorization level data of the unmanned aerial vehicle was calculated and obtained. The system compares the authorized level data Y with a preset warning threshold to obtain the warning level corresponding to the warning threshold of the unmanned aerial vehicle, and displays the warning level accordingly. The flight feature map of the unmanned aerial vehicle (UAV) is generated based on the pre-stored flight dataset, and the proposed flight trajectory data of the UAV is predicted based on the flight trajectory prediction model. Specifically, this includes: generating the flight feature map based on the UAV's flight destination data, airspace warning information data, mission instruction data, and special operation data; extracting flight mission feature information from the flight feature map and inputting it into the preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the proposed flight trajectory data of the UAV, including route data, airspace polygon data, and asymptotic data. Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight clearance correlation coefficient of the unmanned aerial vehicle. The flight permission correlation coefficient is compared with a preset authorization threshold. The result of the threshold comparison determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

2. The unmanned aerial vehicle management method based on big data identification according to claim 1, characterized in that, The monitoring acquires the characteristic identification information and flight data information of unmanned aerial vehicles (UAVs) in the endangered area or those that have been reported, including: Based on the transmission signals of monitored or reported unmanned aerial vehicles (UAVs) in the critical area, obtain the characteristic identification information of the UAVs, including type information, purpose information, ownership and registration information, and special certification information. Based on the ownership registration information and special certification information, the identity information of the unmanned aerial vehicle is identified to obtain the operational purpose information and air traffic control declaration information of the unmanned aerial vehicle; Flight data information is integrated based on the aforementioned mission objective information, air traffic control declaration information, and special certification information.

3. The unmanned aerial vehicle management method based on big data identification according to claim 2, characterized in that, The step of extracting the feature identification data of the unmanned aerial vehicle based on the feature identification information and obtaining traceability information and safety data, and extracting the pre-stored flight dataset of the unmanned aerial vehicle based on the flight data information, includes: Based on the feature identification information, the corresponding feature identification data is obtained by querying the preset aircraft identification database, including aircraft type data, operational purpose data, and control affiliation data; Based on the control attribution data, the traceability information of the unmanned aerial vehicle is obtained; Based on the traceability information, combined with the operational purpose information and air traffic control declaration information, the safety data of the unmanned aerial vehicle is extracted from the aircraft identification database. The pre-stored flight dataset of the unmanned aerial vehicle is extracted based on the flight data information, including flight destination data, airspace warning information data, mission instruction data, and special operation data.

4. The unmanned aerial vehicle management method based on big data recognition according to claim 3, characterized in that, The step of determining the authorization relevance coefficient of the unmanned aerial vehicle by combining the proposed flight trajectory data with the authorization level data and the data in the pre-stored flight dataset includes: Based on the flight route data, airspace multilateral data, and asymptotic data of the unmanned aerial vehicle, combined with the authorization level data Y and the flight destination data, airspace warning information data, mission instruction data, and special operation data, the authorization correlation is determined to obtain the flight clearance correlation coefficient. The formula for calculating the flight apprehension correlation coefficient is as follows: ； Where P is the flight precision correlation coefficient. For route data, For spatial multilateral data, The data represents asymptotic lines, Y represents authorization level data, d represents flight destination data, c represents airspace warning information data, w represents mission instruction data, and l represents special operation data. For the safety factor of unmanned aerial vehicle certification, Credit index for owners of unmanned aerial vehicles.

5. The unmanned aerial vehicle management method based on big data identification according to claim 4, characterized in that, The process involves comparing the flight permission correlation coefficient with a preset authorization threshold, determining whether the unmanned aerial vehicle (UAV) is authorized to fly based on the threshold comparison result, and issuing warnings or interfering with the UAV if the authorization is deemed abnormal. This includes: A preset authorization threshold is obtained by querying the feature identification information of the unmanned aerial vehicle; The threshold is compared with the pre-set authorization threshold based on the flight approval correlation coefficient; If the flight permission correlation coefficient is greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be normally authorized; if the flight permission correlation coefficient is not greater than the preset authorization threshold, the unmanned aerial vehicle is determined to be abnormally authorized. Signal jamming or command warnings are issued to unauthorized unmanned aerial vehicles.

6. An unmanned aerial vehicle management system based on big data recognition, characterized in that: The system includes a memory and a processor. The memory contains a program for an unmanned aerial vehicle (UAV) management method based on big data recognition. When the processor executes the program for the UAV management method based on big data recognition, it performs the following steps: Monitor and acquire the characteristic identification information and flight data information of unmanned aerial vehicles that are in the border area or have been declared; Based on the feature identification information, extract the feature identification data of the unmanned aerial vehicle and obtain traceability information and safety data; based on the flight data information, extract the pre-stored flight dataset of the unmanned aerial vehicle. The authorization level data of the unmanned aerial vehicle is obtained based on the feature identification data, traceability information, and safety data. The authorization level data is then compared and displayed according to a preset warning threshold level. Specifically, this includes: clustering risk values ​​based on aircraft type data, operational purpose data, and control attribution data, combined with corresponding risk parameters, to obtain risk information values. A security identification value is obtained by weighting the traceability information and security data. Based on the risk information value and security identification value The authorization level data of the unmanned aerial vehicle was calculated and obtained. The system compares the authorized level data Y with a preset warning threshold to obtain the warning level corresponding to the warning threshold of the unmanned aerial vehicle, and displays the warning level accordingly. The flight feature map of the unmanned aerial vehicle (UAV) is generated based on the pre-stored flight dataset, and the proposed flight trajectory data of the UAV is predicted based on the flight trajectory prediction model. Specifically, this includes: generating the flight feature map based on the UAV's flight destination data, airspace warning information data, mission instruction data, and special operation data; extracting flight mission feature information from the flight feature map and inputting it into the preset flight trajectory prediction model to preset the flight trajectory, thereby obtaining the proposed flight trajectory data of the UAV, including route data, airspace polygon data, and asymptotic data. Based on the proposed flight trajectory data, combined with the authorization level data and the data in the pre-stored flight dataset, the authorization correlation is determined to obtain the flight clearance correlation coefficient of the unmanned aerial vehicle. The flight permission correlation coefficient is compared with a preset authorization threshold. The result of the threshold comparison determines whether the unmanned aerial vehicle is authorized to fly. If it is determined to be an abnormal authorization, the unmanned aerial vehicle is warned or interfered with.

7. The unmanned aerial vehicle management system based on big data recognition according to claim 6, characterized in that, The monitoring acquires the characteristic identification information and flight data information of unmanned aerial vehicles (UAVs) in the endangered area or those that have been reported, including: Based on the transmission signals of monitored or reported unmanned aerial vehicles (UAVs) in the critical area, obtain the characteristic identification information of the UAVs, including type information, purpose information, ownership and registration information, and special certification information. Based on the ownership registration information and special certification information, the identity information of the unmanned aerial vehicle is identified to obtain the operational purpose information and air traffic control declaration information of the unmanned aerial vehicle; Flight data information is integrated based on the aforementioned mission objective information, air traffic control declaration information, and special certification information.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for an unmanned aerial vehicle (UAV) management method based on big data identification. When the program is executed by a processor, it implements the steps of the unmanned aerial vehicle management method based on big data identification as described in any one of claims 1 to 5.

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