Control methods and related devices based on unmanned aerial vehicles

Abstract

This application discloses a control method and related apparatus based on unmanned aerial vehicles (UAVs), applied to a control platform in an UAV control system. The method includes: sending a first request message to the target UAV when it is detected entering a preset airspace; detecting whether a first response message is received within a preset time period; if so, verifying the target UAV's identity information through a preset UAV database to obtain a first verification result; collecting multi-dimensional data when the first verification result includes successful verification; determining the target's flight state based on the first response message and the multi-dimensional data; determining a target warning level based on the multi-dimensional data when the target's flight state includes unauthorized flight; determining a target processing scheme corresponding to the target warning level; and processing the target UAV according to the target processing scheme to remove it from unauthorized flight. Using this embodiment improves the accuracy of UAV flight state recognition.

Description

Technical Field

[0001] This application relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a control method and related apparatus based on UAVs. Background Technology

[0002] With the rapid development of drone technology, drones are being used more and more widely. However, along with the surge in drone applications, safety risks are also becoming increasingly prominent. According to research institutions, incidents of "black flights" (flying drones without permission or in violation of regulations) have been on the rise in recent years, causing serious safety hazards.

[0003] Currently, drone monitoring is generally done through radar detection. However, radar has insufficient detection accuracy for small drones and is prone to blind spots, resulting in low accuracy in identifying drone flight status and making it difficult to meet the needs of precise monitoring.

[0004] Therefore, improving the accuracy of drone flight status recognition has become an urgent problem to be solved. Summary of the Invention

[0005] This application provides a control method and related apparatus based on unmanned aerial vehicles (UAVs), which improves the accuracy of UAV flight status recognition.

[0006] In a first aspect, embodiments of this application provide a control method based on an unmanned aerial vehicle (UAV), applied to a control platform in an UAV control system. The UAV control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The method includes:

[0007] When a target drone is detected to have entered a preset airspace, a first request message is sent to the target drone;

[0008] Detect whether a first response message from the target drone in response to the first request message is received within a preset time period; the first response message includes the target drone's identity information.

[0009] If so, the identity information of the target drone is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful, verification failed;

[0010] When the first verification result includes successful verification, multi-dimensional data of the target UAV is collected through the data acquisition center.

[0011] Based on the first response information and the multi-dimensional data, the target flight state of the target UAV is determined; the target flight state includes one of the following: normal state, unauthorized flight state;

[0012] When the target flight state includes the unauthorized flight state, the target warning level corresponding to the target drone is determined based on the multi-dimensional data; the target processing scheme corresponding to the target warning level is determined; and the target drone is processed according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

[0013] Secondly, embodiments of this application provide a control device based on an unmanned aerial vehicle (UAV), applied to a control platform in an UAV control system. The UAV control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The device includes: a monitoring unit, a status recognition unit, and an early warning processing unit, wherein:

[0014] The monitoring unit is configured to send a first request message to the target drone when it detects that the target drone has entered a preset airspace; detect whether it receives a first response message from the target drone in response to the first request message within a preset time period; the first response message includes the target drone's identity information; if so, the target drone's identity information is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful or verification failed; when the first verification result includes verification successful, multi-dimensional data of the target drone is collected through the data acquisition center.

[0015] The state recognition unit is used to determine the target flight state of the target UAV based on the first response information and the multi-dimensional data; the target flight state includes one of the following: normal state and unauthorized flight state;

[0016] When the target flight state includes the unauthorized flight state, the early warning processing unit determines the target early warning level corresponding to the target drone based on the multi-dimensional data; determines the target processing scheme corresponding to the target early warning level; and processes the target drone according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

[0017] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing the steps in the first aspect of embodiments of this application.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program for electronic data interchange, wherein the computer program causes a computer to perform some or all of the steps described in the first aspect of embodiments of this application.

[0019] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0020] Implementing this application will have the following beneficial effects:

[0021] As can be seen, the UAV-based control method described in this application verifies the identity information in the first response information by pre-setting a UAV database. This can filter out UAVs with non-compliant identities and ensure that the subsequent analysis objects are "registered UAVs with compliant identities," reducing misjudgments caused by false subject information. After successful identity verification, multi-dimensional data is collected through the data acquisition center, breaking through the limitations of traditional single radar data. The actual flight status of the UAV is analyzed from multiple dimensions, thereby improving the accuracy of UAV flight status identification. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0023] Figure 1 This is a schematic diagram of the structure of an unmanned aerial vehicle (UAV) control system provided in an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of the structure of a data acquisition center provided in an embodiment of this application;

[0025] Figure 3 This is an application scenario diagram of an unmanned aerial vehicle (UAV) control system provided in an embodiment of this application;

[0026] Figure 4 This is a flowchart illustrating a control method based on an unmanned aerial vehicle (UAV) provided in an embodiment of this application.

[0027] Figure 5 This is a schematic diagram of multi-dimensional data provided in an embodiment of this application;

[0028] Figure 6 This is a flowchart illustrating a method for determining the flight status of a target according to an embodiment of this application;

[0029] Figure 7 This is a functional unit block diagram of a control device based on an unmanned aerial vehicle (UAV) provided in an embodiment of this application;

[0030] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0032] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0033] It should be understood that the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document indicates that the preceding and following related objects are in an "or" relationship. In the embodiments of this application, "multiple" refers to two or more.

[0034] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0035] In this application, the term "connection" refers to various connection methods, such as direct connection or indirect connection, to achieve communication between devices. This application does not impose any limitations on this.

[0036] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0037] The electronic devices described in this application embodiment may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablet computers, PDAs, laptops, video matrices, monitoring platforms, mobile internet devices (MIDs), or wearable devices, etc. The above are merely examples and not exhaustive, and include but are not limited to the above devices.

[0038] Of course, the aforementioned electronic devices can also be servers, such as cloud servers.

[0039] The following describes the relevant content, concepts, meanings, technical issues, technical solutions, and beneficial effects involved in the embodiments of this application.

[0040] First, let me explain some of the technical terms or phrases used in this application:

[0041] Unauthorized drone flights: This refers to the flight of drones without a flight permit, without completing the required airspace registration, or in violation of relevant regulations. Typical scenarios include: entering no-fly zones, exceeding legal flight altitude or speed limits, using unregistered equipment, and falsifying flight registration information.

[0042] Drone broadcast signals refer to the radio signals actively transmitted by a drone during flight, primarily used for identification, status feedback, and communication. The signal content typically includes the drone's unique identifier, registrant information (name, organization), equipment attributes (model, weight class), and real-time flight status (location, altitude, battery level). Drone broadcast signals are a crucial data source for the control platform to verify the drone's identity and compliance.

[0043] Received Signal Strength Indication (RSSI) is a quantitative indicator that measures the strength of the radio communication signal between a drone and its remote controller, control platform, or monitoring equipment. The unit is usually dBm (decibels per milliwatt). Its value ranges from -30dBm (extremely strong, short-range communication) to -120dBm (extremely weak, near disconnection). A value closer to 0 indicates a stronger signal, and a more negative value indicates a weaker signal. This indicator reflects the stability of drone communication and is also one of the important bases for identifying unauthorized drone flights. For example, unauthorized drones may deliberately reduce signal strength or frequently interrupt signals to evade monitoring.

[0044] ADS-B protocol: It is an air-to-air and air-to-ground surveillance protocol widely used in the aviation field (including drones). Its core is to enable aircraft (or drones) to actively broadcast their own key information to surrounding equipment through satellite navigation and data link technology, so as to achieve accurate status monitoring and identification.

[0045] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a drone control system provided in an embodiment of this application. It can be seen that the drone control system (hereinafter referred to as the system) may include a control platform and a data acquisition center, etc., which are not limited here. The data acquisition center is responsible for collecting multi-dimensional data of the drone and transmitting it to the control platform. The control platform is used to analyze the multi-dimensional data and determine the flight status of the drone (normal or illegal flight). If the flight status is illegal flight, a corresponding processing plan is generated to enable the drone to leave the illegal flight state.

[0046] Optional, please refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of a data acquisition center provided in an embodiment of this application; it can be seen that the data acquisition center may include: a broadcast monitoring unit, a radar unit, a spectrum detection unit, etc., which are not limited here; wherein:

[0047] The broadcast monitoring unit is used to capture broadcast signals actively sent by the drone and extract core identity and status information, such as the drone's unique device identifier, registrant information, and device attributes (model, weight class), while also acquiring its real-time flight status (e.g., reported flight path, battery level). This information is transmitted to the control platform to provide basic data for subsequent identity compliance verification and is a key data source for identifying "forged identities" and "unregistered devices".

[0048] The radar unit is used to achieve airspace positioning and dynamic tracking of UAVs, and to collect motion dimension data, such as accurately monitoring the UAV's three-dimensional position (latitude, longitude, altitude), flight speed, trajectory and heading changes, covering the wide-area detection needs of the preset airspace; it makes up for the limitations of single signal acquisition, provides spatial behavior data support for determining whether the UAV has entered a no-fly zone or deviated from the reported route, and solves the long-distance positioning problem of small UAVs.

[0049] The spectrum detection unit is used to collect spectrum characteristic data of drone communication and analyze signal compliance. Specifically, it is used to: monitor the communication frequency band used by the drone (whether it is an authorized frequency band), the type of communication protocol (standard protocol or proprietary encryption protocol), RSSI and fluctuation characteristics, frequency hopping mode, etc.; by identifying abnormal characteristics such as "illegal frequency band use" and "deliberate signal concealment", it provides signal dimension basis for judging illegal flights and helps to distinguish between legal flights and illegal flights that evade supervision.

[0050] Please see Figure 3 , Figure 3 This is an application scenario diagram of an unmanned aerial vehicle (UAV) control system provided in an embodiment of this application; Figure 3 It includes equipment such as target drones, broadcast monitoring units, radar units, and spectrum detection units, which are not limited here. Among them:

[0051] Target drones: These are the objects that are monitored, identified, and controlled. Their flight status, identity information, and signal characteristics are the core data sources for system analysis.

[0052] Radar Unit: Using radar detection technology, it performs airspace positioning and dynamic tracking of target UAVs, collects their motion data, and provides spatial behavior data to determine whether the UAV has entered a no-fly zone or deviated from its reported flight path.

[0053] Spectrum detection unit: Collects communication spectrum characteristic data of the target UAV.

[0054] Broadcast monitoring unit: captures broadcast signals actively sent by the target drone, providing basic information for the control platform to verify the drone's identity and compliance.

[0055] These units together constitute the data acquisition center, providing multi-dimensional data support for the subsequent control platform's identity verification, flight status determination, early warning classification, and handling decisions, thereby enabling accurate identification and control of the drone's flight status.

[0056] Please see Figure 4 , Figure 4This is a flowchart illustrating a control method based on an unmanned aerial vehicle (UAV) according to an embodiment of this application. The method is applied to a control platform within a UAV control system. The UAV control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The method includes, but is not limited to, the following steps:

[0057] S401. When the target drone is detected to have entered the preset airspace, a first request message is sent to the target drone.

[0058] In this embodiment of the application, the preset airspace is a pre-defined area that needs to be monitored.

[0059] In a specific embodiment, the data acquisition center includes a radar unit that can continuously scan a preset airspace. When a target drone is detected (identified as a drone through echo characteristics, rather than a bird, balloon, or other object), its location information is obtained. Then, the control platform can compare this location information with the geographical boundaries of the preset airspace (e.g., latitude and longitude range, altitude range) to determine whether the target drone has entered the preset airspace. For example, assuming the preset airspace is a "normal flight zone (longitude A~B, latitude C~D, altitude 0~100 meters)," if the radar data shows that the coordinates of the target drone fall within this range, it is determined that "the target drone has entered the preset airspace." Conversely, if the coordinates of the target drone do not fall within this range, it is determined that "the target drone has not entered the preset airspace."

[0060] In some embodiments, the data acquisition center may also include a broadcast monitoring unit and a spectrum detection unit. By combining the signal characteristics of the spectrum detection unit (e.g., detecting the communication signal of the target UAV) and the preliminary signal of the broadcast monitoring unit (e.g., capturing the broadcast signal of the target UAV), the target UAV can be further confirmed to be within a preset airspace, thus avoiding misjudgment by the radar unit.

[0061] When a target drone is detected entering a preset airspace, the control platform can generate a first request message according to the drone's standard protocol (e.g., ADS-B). For example, the first request message could be "Please return the drone's identity information, the registration number for this flight, the approved airspace, and the flight route." Then, the control platform can detect the target drone's communication frequency band using a spectrum detection unit. For example, if the target drone and its remote controller are detected communicating in the 5.8GHz band, the control platform can then call the corresponding frequency band's radio frequency transmission module (e.g., a 5.8GHz radio frequency module) to send the first request message to the target drone's airspace at compliant power (to avoid interfering with other devices), as detailed below:

[0062] Set the transmission power: Adjust the power to ensure signal coverage based on the distance between the target drone and the control platform, as well as environmental obstructions (e.g., use low power for close range and appropriately increase the transmission power for long range).

[0063] Set the sending frequency: If there is no response on the first send, you can repeat the sending 2 to 3 times within a certain period of time (such as 30 seconds) to avoid single transmission failure.

[0064] S402. Detect whether a first response message from the target drone in response to the first request message is received within a preset time period; the first response message includes the target drone's identity information.

[0065] In this embodiment of the application, the preset duration can be preset in advance or defaulted, for example, 60 seconds.

[0066] In a specific embodiment, the control platform can call the radio frequency receiving module that is consistent with the "first request information transmission frequency band" (for example, if the 5.8GHz frequency band is used for transmission, then the 5.8GHz receiving module is called), and configure its operating parameters:

[0067] Frequency band locking: Fix the operating frequency band of the radio frequency receiving module to the communication frequency band of the target drone to ensure that only the response signal of the target drone is monitored;

[0068] Signal bandwidth and modulation method: Match the communication protocol characteristics of the target UAV (e.g., bandwidth 20MHz, modulation method OFDM) to ensure that the signal can be correctly parsed.

[0069] Within a preset time period after sending the first request information, the system continuously monitors radio signals within the aforementioned communication frequency band, filters noise (e.g., environmental electromagnetic interference, signals from other devices), amplifies, filters, demodulates, and processes the captured signals to extract valid data frames. Then, the valid data frame can be parsed to determine whether it is a valid response from the target UAV to the first request information (i.e., the first response information), as detailed below:

[0070] Protocol matching verification: Check whether the protocol format of the response information (the above valid data frames) is consistent with the communication protocol of the UAV (e.g., frame header, command type field). For example, if the first request information requires the target UAV to return identity information, the response information must include fields such as "response type = authentication" and "target request ID = request identifier sent by the control platform".

[0071] Content integrity verification: Confirm whether the response information contains the core content requested in the first request (e.g., unique device identifier, registrant information, filing number, etc.). If key fields are missing from a valid data frame, the response is deemed invalid.

[0072] Identity verification: Compare the "device unique identifier" in the response information with the target drone identifier identified by the radar unit (e.g., the drone ID in the radar echo) to ensure that the response comes from the target drone (avoiding false responses from other drones).

[0073] If a response that conforms to the protocol format, is complete, and is associated with the identity is parsed within the preset time period, it is determined that "the first response information has been received"; if no valid response is parsed after the preset time period ends, or only incomplete or mismatched signals are received, it is determined that "the first response information has not been received".

[0074] S403. If so, the identity information of the target drone is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful or verification failed.

[0075] In this embodiment of the application, the preset drone database can be preset in advance or defaulted to. The preset drone database is a database containing the identity identifiers of all registered legal drones, which is used to verify the legality of drones.

[0076] In a specific embodiment, if the first response information is received within a preset time period, the unique device identifier can be extracted from the target drone's identity information and matched with the identifier in the preset drone database. If the unique device identifier does not exist in the preset drone database, the first verification result is determined to be a verification failure; if the unique device identifier exists in the preset drone database, the first verification result is determined to be a verification success.

[0077] If no response is received within the preset time, it means that the target drone is not responding. At this time, the control platform can determine that the target drone is flying illegally, or it can resend a new request message to the target drone. If the target drone still does not respond, it is determined that the target drone is flying illegally. If the target drone responds, step S403 is executed.

[0078] S404. When the first verification result includes successful verification, multi-dimensional data of the target UAV is collected through the data acquisition center.

[0079] Optionally, the data acquisition center includes: a broadcast monitoring unit, a radar unit, and a spectrum detection unit; please refer to [link / reference]. Figure 5 , Figure 5This is a schematic diagram of multi-dimensional data provided in an embodiment of this application. As can be seen, the multi-dimensional data includes: target motion data of the target drone, target drone broadcast signal corresponding to the target drone, and signal characteristic data of the target drone; step S404, collecting the multi-dimensional data of the target drone through the data acquisition center, may include the following steps:

[0080] S41. Listen to the drone broadcast signals in the preset airspace through the broadcast monitoring unit to obtain a drone broadcast signals; a is a natural number.

[0081] S42. When the broadcast signals of the a drones do not include the broadcast signal of the target drone, it is determined that the target flight state includes the unauthorized flight state;

[0082] S43. When the broadcast signal of drone a includes the broadcast signal of the target drone, determine the identity information of the broadcasting drone corresponding to the broadcast signal of the target drone; if the identity information of the broadcasting drone is inconsistent with the identity information of the target drone, determine that the target flight state includes the illegal flight state; if the identity information of the broadcasting drone is consistent with the identity information of the target drone, collect the target motion data through the radar unit; collect the signal feature data through the spectrum detection unit.

[0083] In this embodiment, the broadcast monitoring unit, radar unit, and spectrum detection unit can all be deployed at high locations (e.g., rooftops, signal towers) or at key nodes at the edge of the pre-defined airspace to ensure signal coverage without blind spots.

[0084] In a specific embodiment, a broadcast monitoring unit listens to drone broadcast signals within a preset airspace to obtain *a* drone broadcast signals. Specifically, the broadcast monitoring unit can employ a multi-band RF receiving module (covering mainstream drone broadcast frequency bands, such as 2.4GHz, 5.8GHz, 900MHz, and 1090MHz commonly used in the ADS-B protocol), paired with a high-gain directional antenna (for the preset airspace direction) or an omnidirectional antenna (for wide-area coverage), and configured with a signal amplification and filtering module (to reduce environmental noise interference). The broadcast monitoring unit polls and scans all detectable frequency bands according to priority, for example, in the order of 5.8GHz, 2.4GHz, and 1090MHz. Then, the dwell time for signal-dense frequency bands (e.g., frequency bands where drone signals are detected multiple times) can be extended, for example, from 1 second / frequency. The frequency band is increased to 3 seconds per band to improve acquisition efficiency, thus obtaining multiple monitoring signals. Then, a drone broadcast signals can be extracted from these multiple monitoring signals. Specifically, a preliminary screening can be performed based on a preset signal strength, retaining only signals with a signal strength greater than the preset signal strength (e.g., -110dBm) and excluding extremely weak noise. Then, all remaining monitoring signals are amplified, filtered (to remove interference from other devices in the same frequency band), and demodulated (converting the radio frequency signal into a digital signal frame) to obtain a parsable raw data frame. The content of the raw data frame is further analyzed. If it contains drone-specific fields (e.g., unique device identifier, registrant identifier, location information, etc.), it is confirmed as a "valid drone broadcast signal"; if it is only meaningless data (such as interference signals), the signal is excluded. In this way, a drone broadcast signals can be obtained.

[0085] Next, we can analyze whether the broadcast signals of the aforementioned 'a' drones contain the broadcast signal of the target drone. Specifically, we can parse the broadcast signals of these 'a' drones to obtain 'a' unique device identifiers. Then, we can extract the unique device identifier of the target drone from the radar unit detection data (for example, the device ID resolved by radar echo). We can then compare the 'a' unique device identifiers with the unique device identifier of the target drone in turn. If there is an identifier among the 'a' unique device identifiers that is completely identical to the unique device identifier of the target drone, we can determine that the broadcast signals of the 'a' drones contain the broadcast signal of the target drone. Otherwise, we can determine that the broadcast signals of the 'a' drones do not contain the broadcast signal of the target drone.

[0086] When the target drone's broadcast signal is not included in the broadcast signal of drone a, the target's flight status can be determined as unauthorized flight.

[0087] When a drone broadcast signal includes the target drone broadcast signal, the target drone broadcast signal can be parsed to obtain the broadcast drone's identity information. The broadcast drone's identity information is then compared with the target drone's identity information. If they do not match, the target's flight status is determined to be unauthorized flight. If they match, target motion data is collected through a radar unit. Specifically, the radar unit can include at least one of the following: pulse radar, Doppler radar, phased array radar, etc., without limitation. First, the radar unit's operating parameters are set, for example, the scanning range matches a preset airspace, and the scanning refresh rate is set to 10Hz to ensure the capture of high-speed moving targets. The RCS (radar cross section) threshold is set to be less than or equal to 0.1 square meters (suitable for small drones). (Human-machine interface); The radar unit continuously emits electromagnetic waves to scan the airspace and receives the echo signals reflected by the target (i.e., the target UAV). By analyzing the echo intensity and phase changes, it identifies the moving target and obtains the target motion data. Then, it can also collect signal characteristic data through a spectrum detection unit. Specifically, the spectrum detection unit can be a spectrum analyzer, configured with corresponding signal sampling parameters, such as a sampling rate of 20MHz (adapting to the UAV's communication bandwidth), and a demodulation method matching common modulation types (e.g., OFDM, FSK, QPSK). By scanning the UAV's commonly used frequency bands through the spectrum detection unit, radio signals are captured in real time. By analyzing the power spectral density, environmental noise is filtered out, and only signals that conform to the UAV's communication bandwidth are retained, thereby obtaining signal characteristic data.

[0088] In this way, by identifying whether a broadcast signal is present and whether the identity matches, two typical black flight scenarios (no broadcast, fake identity) can be identified without additional data collection, thus improving the efficiency of judgment; multi-dimensional data collection is only initiated for drones with matching identities, reducing the processing of invalid data and reducing the consumption of system resources.

[0089] S405. Based on the first response information and the multi-dimensional data, determine the target flight state of the target UAV; the target flight state includes one of the following: normal state, unauthorized flight state.

[0090] In this embodiment of the application, the flight status of the target UAV can be analyzed based on the first response information and multi-dimensional data to obtain the target flight status.

[0091] Optionally, the first response information may also include: flight permit information and reported flight route; please refer to [link / reference]. Figure 6 , Figure 6 This is a flowchart illustrating a method for determining the flight state of a target drone according to an embodiment of this application. Step S405, determining the target flight state of the target drone based on the first response information and the multi-dimensional data, may include... Figure 6 The steps shown are as follows:

[0092] A1. Determine the airspace range to be reported in the flight permit information;

[0093] A2. If the reported airspace range does not include the preset airspace, then the target flight status is determined to include the illegal flight status.

[0094] A3. If the reported airspace range includes the preset airspace, determine the first motion trajectory based on the target motion data; determine the target flight status based on the reported flight route, the first motion trajectory, and the signal characteristic data.

[0095] In this embodiment, key parameters of the reported airspace, such as latitude and longitude boundaries and altitude range, can be extracted from the flight permit information to obtain the reported airspace range. Then, the latitude and longitude boundaries and altitude range of a preset airspace can be obtained. Next, it is determined whether the reported airspace range includes the preset airspace, as follows:

[0096] Planar range comparison: Determine whether the latitude and longitude boundaries of the preset airspace fall completely within the latitude and longitude boundaries of the reported airspace range (including cases of boundary overlap).

[0097] Altitude range comparison: Determine whether the altitude ranges of the preset airspace are all within the altitude range of the reported airspace.

[0098] The airspace range is determined to include the preset airspace only if both the plane range and the altitude range are fully included. If either condition is not met, the airspace is determined to be excluded.

[0099] If the reported airspace does not include the preset airspace, it means that the target drone is not allowed to fly in the preset airspace. In this case, the target's flight status can be directly determined as an unauthorized flight.

[0100] If the reported airspace includes the preset airspace, it means the target UAV can fly within the preset airspace. The first trajectory can then be determined based on the target's motion data. Specifically, key information can be extracted from the target's motion data: three-dimensional coordinates (latitude, longitude, and altitude) sorted by timestamp (accurate to milliseconds), filtering out invalid data (e.g., coordinate jumps caused by interference, duplicate or missing timestamps), and retaining continuous and valid "time-coordinate" data pairs. Next, latitude and longitude can be converted to decimal degrees, for example, 30°15′N to 30.25°, and altitude can be standardized to meters to ensure unit consistency. Data pairs can be arranged in ascending order by timestamp to ensure the temporal continuity of the trajectory (adjacent times). The stamp interval is usually less than or equal to 1 second (adapted to radar scanning frequency). Adjacent coordinate points are connected sequentially by line segments in chronological order to form the original motion trajectory (including features such as flight path, direction changes, and altitude fluctuations). The motion trajectory can be overlaid onto the geographic coordinate system using map software to visually present the spatial path. Then, filtering algorithms (such as Kalman filtering and moving average) can be used to optimize the original motion trajectory. For example, the average value of 3 to 5 consecutive coordinate points can be taken to eliminate the slight jitter caused by radar measurement errors, making the trajectory more consistent with the actual flight path of the UAV, thus obtaining the first motion trajectory. Finally, the target flight status can be determined based on the reported flight route, the first motion trajectory, and signal characteristic data.

[0101] In this way, by checking whether the reported airspace covers the preset airspace, obvious illegal flights can be quickly screened out, avoiding invalid subsequent analysis and improving regulatory efficiency.

[0102] Optionally, the first motion trajectory includes b coordinate points, where b is a positive integer. Step A3, determining the target flight state based on the reported flight route, the first motion trajectory, and the signal feature data, may include the following steps:

[0103] B1. Determine the shortest distance between each of the b coordinate points and the reported flight route to obtain b shortest distances;

[0104] B2. Determine c shortest distances that are greater than a preset distance from the b shortest distances, and d shortest distances that are not greater than the preset distance; c and d are both natural numbers less than or equal to b, and c + d = b;

[0105] B3. Determine the first ratio based on the c shortest distances and the d shortest distances;

[0106] B4. When the first ratio is greater than a preset ratio, it is determined that the target flight state includes the unauthorized flight state;

[0107] B5. When the first ratio is not greater than the preset ratio, determine c coordinate times based on the b coordinate points and the c shortest distances, with each shortest distance corresponding to a coordinate time; obtain the start time of the target UAV entering the preset airspace; determine the target deviation frequency based on the start time and the c coordinate times; if the target deviation frequency is greater than the preset deviation frequency, determine that the target flight state includes the unauthorized flight state; if the target deviation frequency is not greater than the preset deviation frequency, determine the target flight state based on the signal characteristic data.

[0108] In this embodiment, the preset distance, preset ratio, and preset deviation frequency can all be preset in advance or defaulted.

[0109] In a specific embodiment, the shortest distance between each of the b coordinate points and the reported flight route can be determined, resulting in b shortest distances. Specifically, for each coordinate point, the latitude and longitude of the coordinate point and the latitude and longitude of all inflection points of the reported flight route can be uniformly converted into Cartesian coordinates to eliminate the error of direct calculation using spherical coordinates. Then, the reported flight route can be divided into continuous line segments (e.g., A1A2, A2A3, ..., A...). n-1 A n (n is an integer greater than 1), where each line segment is the basic unit of the flight path. Then, the distance from the coordinate point to a single line segment can be determined:

[0110] If the coordinate point lies on a certain line segment (for example, A) i A i+1 Within the projection range of (projection point on the line segment), the distance is the perpendicular distance from the coordinate point to the line segment;

[0111] If the projection point is at A i The outer side, the distance from coordinate point A i The straight-line distance;

[0112] If the projection point is at A i+1 The outer side, the distance from coordinate point A i+1 The straight-line distance.

[0113] Next, the distances from the coordinate point to all line segments can be compared. The minimum distance is the shortest distance between that coordinate point and the reported flight path. This process is repeated b times to obtain b shortest distances. Then, each of the b shortest distances can be compared with a preset distance to obtain c shortest distances that are greater than the preset distance and d shortest distances that are not greater than the preset distance. Finally, based on the c shortest distances and d shortest distances, the following steps can be taken:

[0114] The first ratio = c / d;

[0115] According to the above formula, the first ratio can be obtained; when the first ratio is greater than the preset ratio (for example, 0.4), the target flight state is determined to be an unauthorized flight state.

[0116] When the first ratio is not greater than a preset ratio, c coordinate times are determined based on b coordinate points and c shortest distances. Specifically, since there is a one-to-one correspondence between coordinate points and shortest distances, c coordinate points corresponding to the c shortest distances among the b coordinate points can be determined. Then, b timestamps corresponding to the b coordinate points can be obtained from the target motion data. Then, c timestamps corresponding to the c coordinate points can be found from these b timestamps, which are the c coordinate times. Then, the start time of the target UAV entering the preset airspace can be obtained. Specifically, the earliest timestamp among the b timestamps can be found and used as the start time.

[0117] Furthermore, the target deviation frequency can be determined based on the start time and c coordinate times. Specifically, the latest time among the c coordinate times can be found, and the target duration can be obtained by subtracting the start time from the latest time. Then, the target deviation frequency is obtained by dividing the number of deviations c by the target duration. For example, assuming c=6 and the target duration is 60 minutes, the target deviation frequency is 0.1 times per minute. The target deviation frequency is compared with the preset deviation frequency. If the target deviation frequency is greater than the preset deviation frequency, the target flight state is determined to be an unauthorized flight state.

[0118] If the target's deviation frequency is not greater than the preset deviation frequency, the target's flight status can be determined based on the signal characteristic data.

[0119] In this way, the ratio of deviation distance is used to quickly identify illegal flight scenarios with serious deviations (high proportion of violations), then the deviation frequency is used to check for hidden violations in scenarios with slight deviations, and finally the signal characteristics are used as a backup, avoiding in-depth analysis of the whole process and improving the efficiency of judgment.

[0120] Optionally, the method may further include the following steps:

[0121] C1. Obtain the target airspace type corresponding to the preset airspace;

[0122] C2. Determine the reference deviation frequency corresponding to the target airspace type;

[0123] C3. Determine the target drone type and target operator based on the target drone identity information;

[0124] C4. Determine the reference performance parameters corresponding to the target UAV type;

[0125] C5. Determine the target drone operation parameters corresponding to the target operator;

[0126] C6. Obtain the target environment parameters corresponding to the preset airspace at the start time;

[0127] C7. Adjust the reference performance parameters according to the target environmental parameters and the target UAV operating parameters to obtain the target performance parameters;

[0128] C8. Determine the target fine-tuning factor corresponding to the target performance parameter;

[0129] C9. Adjust the reference deviation frequency according to the target fine-tuning factor to obtain the preset deviation frequency.

[0130] In this application embodiment, the target airspace type includes one of the following: restricted flight zone, reported flight zone, open flight zone, temporary control zone, etc., which are not limited here.

[0131] In a specific embodiment, the target airspace type corresponding to the preset airspace can be obtained. Specifically, an airspace management website or electronic airspace map database can be accessed to query the airspace type of the preset airspace and obtain the target airspace type. Then, the reference deviation frequency corresponding to the target airspace type can be determined. For example, the mapping relationship between the preset airspace type and the deviation frequency can be stored in advance, and the reference deviation frequency corresponding to the target airspace type can be determined based on the mapping relationship.

[0132] Next, a unique device identifier can be extracted from the target drone's identity information. This unique device identifier can then be used to query the drone type and operator information from a drone regulatory platform (e.g., a drone real-name registration system or an industry management database). This will yield the target drone type and the target operator. Then, the reference performance parameters corresponding to the target drone type can be determined. Specifically, the reference performance parameters may include at least one of the following: maximum flight altitude, maximum level flight speed, endurance, remote control radius, weight class, etc., without limitation. A pre-stored mapping relationship between drone types and performance parameters can be used to determine the reference performance parameters corresponding to the target drone type.

[0133] Furthermore, the target drone operation parameters corresponding to the target operator can be determined. Specifically, the target drone operation parameters may include at least one of the following: operator qualification level (e.g., the operator's drone operation certificate level), qualification validity period, historical operation compliance records, etc., which are not limited here. The target drone operation parameters can be obtained by querying the drone operation parameters of the target operator from the drone monitoring platform. Then, the target environmental parameters corresponding to the preset airspace at the start time can be obtained. Specifically, the target environmental parameters may include at least one of the following: temperature, humidity, wind speed, electromagnetic interference intensity, etc., which are not limited here. At the start time, the environmental acquisition device can be activated to collect the environmental parameters of the preset airspace to obtain the target environmental parameters. For example, if the target environmental parameter is wind speed, the environmental acquisition device can be an anemometer, which collects the wind speed of the preset airspace.

[0134] Next, the reference performance parameters can be adjusted based on the target environmental parameters and the target UAV operating parameters to obtain the target performance parameters. Specifically, a first adjustment factor corresponding to the target environmental parameters and a second adjustment factor corresponding to the target UAV operating parameters can be determined. For example, a pre-stored mapping relationship between preset environmental parameters and adjustment factors can be used to determine the first adjustment factor corresponding to the target environmental parameters. Similarly, a pre-stored mapping relationship between preset UAV operating parameters and adjustment factors can be used to determine the second adjustment factor corresponding to the target UAV operating parameters. Next, a first weight (e.g., 0.6) and a second weight (e.g., 0.4) corresponding to the target environmental parameters can be determined, with the sum of the first and second weights being 1. Further, a third adjustment factor can be determined based on the first adjustment factor, the second adjustment factor, the first weight, and the second weight, as follows:

[0135] Third adjustment factor = First adjustment factor × First weight + Second adjustment factor × Second weight;

[0136] Based on the above formula, the third adjustment factor can be obtained. It should be noted that the value range of each adjustment factor can be -0.15 to 0.15. Then, the reference performance parameters can be adjusted according to the third adjustment factor, as follows:

[0137] Target performance parameter = Reference performance parameter × (1 + Third adjustment factor);

[0138] Based on the above formula, the target performance parameters can be obtained. Then, the target fine-tuning factor corresponding to the target performance parameters can be determined. Specifically, a pre-stored mapping relationship between preset performance parameters and fine-tuning factors can be used to determine the target fine-tuning factor corresponding to the target performance parameters. The value range of the target fine-tuning factor can be -0.25 to 0.25. Finally, the reference deviation frequency can be adjusted according to the target fine-tuning factor, as follows:

[0139] Preset deviation frequency = Reference deviation frequency × (1 + Target fine-tuning factor);

[0140] Based on the above formula, the preset deviation frequency can be obtained.

[0141] In this way, by correcting environmental parameters (for example, if the deviation caused by strong winds is not a malicious violation, the deviation frequency threshold can be appropriately relaxed) and by adjusting the linkage between operating parameters and performance parameters (for example, the threshold for novice operators and low-performance drones can be reasonably relaxed), we can distinguish between "deviations caused by objective factors" and "malicious illegal flights" and reduce misjudgments and omissions.

[0142] Optionally, the signal characteristic data includes: frequency band usage data and signal strength data. The signal strength data includes multiple signal strengths and the acquisition time corresponding to each signal strength. Step B5, determining the target flight state based on the signal characteristic data, may include the following steps:

[0143] D1. Determine the e frequency bands and the number of frequency band switching times corresponding to the target UAV based on the frequency band usage data; e is a positive integer.

[0144] D2. If there is an illegal frequency band in the e frequency bands, and / or the number of frequency band switching is greater than the preset number of switching, it is determined that the target flight state includes the illegal flight state;

[0145] D3. When no illegal frequency band exists in the e frequency bands, and the number of frequency band switching is not greater than the preset number of switching, curve fitting is performed based on the multiple signal strengths and the acquisition time corresponding to each signal strength to obtain a target signal curve; the horizontal axis of the target signal curve is time, and the vertical axis is signal strength; the target similarity between the target signal curve and the preset signal curve is determined; if the target similarity is less than the preset similarity, the target flight state is determined to include the illegal flight state; if the target similarity is not less than the preset similarity, the target flight state is determined to include the normal state.

[0146] In this embodiment, the preset number of switching times and the preset signal curve can both be preset in advance or left as default.

[0147] In a specific embodiment, the target UAV is determined by the frequency band usage data, which includes e frequency bands and the number of frequency band switching. Specifically, it can be first determined that all frequency bands exist in the frequency band usage data, and these frequency bands are deduplicated to obtain e frequency bands. Then, the frequency band usage data can be sorted in ascending order by timestamp, and the frequency band values ​​of two adjacent data are compared in turn. If the frequency band of the later data is different from that of the previous data, it is counted as 1 frequency band switching. After traversing all data, the switching count is accumulated to obtain the number of frequency band switching.

[0148] If there is an illegal frequency band among the e frequency bands, and / or the number of frequency band switching is greater than the preset number of switching, the target's flight status will be determined as an unauthorized flight.

[0149] If no illegal frequency bands exist in the e frequency bands, and the number of frequency band switching times does not exceed the preset number of switching times, multiple coordinate points are obtained by combining multiple signal strengths with their corresponding acquisition times. A curve fitting method (e.g., polynomial fitting) is then used to fit these coordinate points to obtain the target signal curve. Next, the target similarity between the target signal curve and the preset signal curve can be determined. Specifically, the target signal curve and the preset signal curve can be standardized as follows:

[0150] Unified Time Axis: Based on the intersection of the time ranges of the two curves, if the time range of the target signal curve is [t1, t2]... n The preset signal curve is [t1',t]. m '], take the overlapping interval [t_start,t_end]; truncate the data outside the overlapping interval, and fill in the missing data with linear interpolation (for example, if a curve is missing data points in t∈[t_start,t_end], fill in the missing data according to the slope of the adjacent points).

[0151] Unified sampling frequency: Adjust the sampling frequency of the two curves to be consistent (for example, set them both to 1Hz, i.e., 1 data point per second). This can be achieved through interpolation or downsampling, which will not be elaborated here.

[0152] Standardize numerical units: Ensure that the signal strength units of the two curves are consistent (e.g., both are dBm) to avoid calculation errors caused by unit differences;

[0153] Next, a preset similarity algorithm can be used to calculate the target similarity between the target signal curve and the preset signal curve. The preset similarity algorithm can be one of the following: Pearson correlation coefficient method, normalized mean square error method, dynamic time warping method, etc., which are not limited here.

[0154] If the target similarity is less than the preset similarity, the target's flight status is determined to be an unauthorized flight; if the target similarity is not less than the preset similarity, the target's flight status is determined to be a normal flight.

[0155] In this way, obvious violations can be quickly screened out by frequency band compliance and number of switching (e.g., using illegal frequency bands or frequently switching frequency bands to evade supervision). This type of judgment does not require complex data processing and can quickly identify high-risk illegal flights. Only for scenarios that are frequency band compliant and have met the number of switching requirements, signal curve fitting and similarity comparison are performed to avoid in-depth analysis of the entire process and greatly improve the efficiency of judgment.

[0156] S406. When the target flight state includes the unauthorized flight state, determine the target warning level corresponding to the target drone based on the multi-dimensional data; determine the target processing scheme corresponding to the target warning level; process the target drone according to the target processing scheme so that the target drone can get out of the unauthorized flight state.

[0157] In this embodiment of the application, the target warning level may include one of the following: high warning level, medium warning level, low warning level, etc., which are not limited here.

[0158] In a specific embodiment, when the target's flight state includes unauthorized flight, the target warning level corresponding to the target drone can be determined based on multi-dimensional data. Then, the target handling scheme corresponding to the target warning level can be determined. Specifically, a pre-stored mapping relationship between preset warning levels and handling schemes can be used to determine the target handling scheme corresponding to the target warning level. For example:

[0159] Assuming the target warning level is low, the corresponding target handling solution can be: remote warning prompts and compliance guidance. Specifically, text or voice warnings can be sent to the target drone, and a compliance flight route can be pushed to guide the target drone into the compliance route.

[0160] Assuming the target warning level is medium warning level, the corresponding target handling scheme can be: communication interference and forced landing guidance. Specifically, low-power interference (power less than or equal to 10 watts to avoid affecting surrounding equipment) is implemented on the communication frequency band of the target UAV, and it is guided to a preset safe forced landing area (open suburbs or designated take-off and landing point) for landing.

[0161] Assuming the target warning level is high, the corresponding target handling plan can be: forced landing and ground control linkage. Specifically, the target drone is subjected to full-band directional jamming (power 10~30W, focusing on the target drone's position), the remote control signal is cut off, and ground control forces (e.g., drone countermeasure team) are activated to take over and force the target drone to fly to a safe area for forced landing.

[0162] Finally, the target drone is processed according to the target processing plan to remove it from its unauthorized flight status.

[0163] Optionally, step S406, determining the target early warning level corresponding to the target drone based on the multi-dimensional data, may include the following steps:

[0164] S61. Determine the current position, current altitude, current speed, and current acceleration of the target UAV at the current moment based on the target motion data;

[0165] S62. Determine the no-fly zones within a preset distance around the preset airspace to obtain f no-fly zones; f is a natural number.

[0166] S63. Determine the distances between the current location and the f no-fly zones to obtain f distances;

[0167] S64. Determine the minimum distance among the f distances, and determine the location risk value corresponding to the minimum distance;

[0168] S65. Determine the difference between the current height and the preset height to obtain a first difference;

[0169] S66. Determine the altitude risk value corresponding to the first difference;

[0170] S67. Determine the difference between the current speed and the preset speed to obtain a second difference;

[0171] S68. Determine the reference risk value corresponding to the second difference;

[0172] S69. Determine the target optimization factor corresponding to the current acceleration;

[0173] S610. Optimize the reference risk value according to the target optimization factor to obtain the speed risk value;

[0174] S611. Determine the target risk value based on the location risk value, the altitude risk value, and the speed risk value;

[0175] S612. Determine the target early warning level corresponding to the target risk value.

[0176] In this embodiment, the preset distance, preset altitude, and preset speed can all be preset in advance or defaulted to; the risk value is a quantitative assessment indicator of the degree to which the drone's flight status deviates from compliance requirements and the probability of causing a safety threat. For example, the risk value can range from 0 to 10, where 0 indicates that the flight is fully compliant and there is no safety threat, and 10 indicates that the flight deviates significantly from compliance requirements and the probability of a safety threat is extremely high.

[0177] In a specific embodiment, the current position, current altitude, current speed, and current acceleration of the target UAV at the current moment can be determined based on the target motion data. Specifically, since the target motion data collected by the radar unit is in spherical coordinates, it needs to be converted to a three-dimensional Cartesian coordinate system (with the radar as the origin, adapted to the position description of airspace surveillance) to clarify the horizontal position (latitude and longitude or planar coordinates) and altitude, thus obtaining the first motion data. Then, the current position, current altitude, and current speed of the target UAV at the current moment can be obtained from this first motion data. Next, the speed of the previous moment (e.g., 1 second before the current moment) can also be obtained from the first motion data to obtain the first speed. Calculations are then performed based on the previous moment, the current moment, the first speed, and the current speed, as follows:

[0178] Current acceleration = (current velocity - first velocity) / (current moment - previous moment);

[0179] Based on the above formula, the current acceleration can be obtained. Next, no-fly zones within a preset distance around the preset airspace can be identified, resulting in f no-fly zones. Specifically, no-fly zone data can be obtained by accessing an airspace management website, and no-fly zones within a preset distance around the preset airspace can be found from this data, resulting in f no-fly zones. Then, the distances between the current position and the f no-fly zones can be determined, resulting in f distances. Specifically, the center positions of the f no-fly zones can be obtained, resulting in f center positions. The distance between each of these f center positions and the current position can be calculated, resulting in f distances. Finally, the minimum distance among these f distances can be found, and the position risk value can be determined based on the minimum distance. For example, a preset mapping relationship between distances and risk values ​​can be stored in advance, and the position risk value corresponding to the minimum distance can be determined based on this mapping relationship. The smaller the minimum distance, the greater the position risk value.

[0180] Next, the first difference can be obtained by subtracting the preset height from the current height. Then, the height risk value corresponding to the first difference can be determined. For example, a preset mapping relationship between the difference and the risk value can be stored in advance, and the height risk value corresponding to the first difference can be determined based on this mapping relationship. The larger the first difference, the larger the height risk value. Then, the second difference can be obtained by subtracting the preset speed from the current speed. Then, the reference risk value corresponding to the second difference can be determined. Similarly, the reference risk value corresponding to the second difference can be determined based on the above mapping relationship between the difference and the risk value.

[0181] Furthermore, a target optimization factor corresponding to the current acceleration can be determined. For example, a pre-stored mapping relationship between acceleration and optimization factors can be used to determine the target optimization factor corresponding to the current acceleration. The target optimization factor can range from -0.3 to 0.3. The reference risk value is then optimized based on the target optimization factor, as follows:

[0182] Speed ​​risk value = Reference risk value × (1 + Target optimization factor);

[0183] Based on the above formula, the speed risk value can be obtained. Next, the target risk value can be determined based on the location risk value, altitude risk value, and speed risk value. Specifically, the average of the location risk value, altitude risk value, and speed risk value can be calculated and used as the target risk value. Alternatively, three risk weights corresponding to these three risk values ​​can be determined, with the sum of these three risk weights being 1. The target risk value is obtained by weighting these three risk weights, the location risk value, the altitude risk value, and the speed risk value. For example, assuming the location risk value is 6, the altitude risk value is 2, and the speed risk value is 3, the three risk weights are as follows:

[0184] Location risk weight Wp=0.5 (core risk, primary target risk);

[0185] High-risk weight Wh=0.2 (ultra-high risk is relatively controllable, secondary risk);

[0186] Speed ​​risk weight Wv=0.3 (speeding easily leads to collisions, secondary core risk);

[0187] Therefore, the target risk value = 6 × 0.5 + 2 × 0.2 + 3 × 0.3 = 4.3;

[0188] It should be explained that in practical applications, the drone control system can be configured with a human-machine interface, through which staff can manually adjust the three risk weights mentioned above.

[0189] Finally, a pre-stored mapping relationship between preset risk values ​​and warning levels can be used to determine the target warning level corresponding to the target risk value.

[0190] In this way, all types of risk values ​​are derived from objective data (distance, difference, acceleration), and speed risk is dynamically corrected by optimization factors, avoiding assessment bias caused by a single indicator, and the results are objective and traceable.

[0191] In summary, the UAV-based control method described in this application verifies the identity information in the first response information by pre-setting a UAV database. This filters out UAVs with non-compliant identities, ensuring that subsequent analysis targets are "registered UAVs with compliant identities," reducing misjudgments caused by false subject information. After successful identity verification, multi-dimensional data is collected through a data acquisition center, breaking through the limitations of traditional single radar data and analyzing the actual flight status of the UAV from multiple dimensions, thereby improving the accuracy of UAV flight status identification.

[0192] Please see Figure 7 , Figure 7 This is a functional unit block diagram of a drone-based control device 700 provided in an embodiment of this application. The drone-based control device 700 is applied to the control platform in a drone control system. The drone control system also includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The drone-based control device 700 includes: a monitoring unit 701, a status recognition unit 702, and an early warning processing unit 703, wherein:

[0193] The monitoring unit 701 is configured to send a first request message to the target drone when it detects that the target drone has entered a preset airspace; detect whether it receives a first response message from the target drone in response to the first request message within a preset time period; the first response message includes the target drone's identity information; if so, the target drone's identity information is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful or verification failed; when the first verification result includes verification successful, multi-dimensional data of the target drone is collected through the data acquisition center.

[0194] The state recognition unit 702 is used to determine the target flight state of the target UAV based on the first response information and the multi-dimensional data; the target flight state includes one of the following: normal state and unauthorized flight state;

[0195] When the target flight state includes the unauthorized flight state, the early warning processing unit 703 determines the target early warning level corresponding to the target drone based on the multi-dimensional data; determines the target processing scheme corresponding to the target early warning level; and processes the target drone according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

[0196] In specific implementations, the UAV-based control device 700 described in the embodiments of the present invention can also execute other implementations described in the UAV-based control method provided in the embodiments of the present invention, which will not be repeated here.

[0197] Please see Figure 8 , Figure 8This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, memory, and communication interface can be interconnected via a bus. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, the electronic device is applied to the control platform of a drone control system. The drone control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The programs include instructions for performing the following steps:

[0198] When a target drone is detected to have entered a preset airspace, a first request message is sent to the target drone;

[0199] Detect whether a first response message from the target drone in response to the first request message is received within a preset time period; the first response message includes the target drone's identity information.

[0200] If so, the identity information of the target drone is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful, verification failed;

[0201] When the first verification result includes successful verification, multi-dimensional data of the target UAV is collected through the data acquisition center.

[0202] Based on the first response information and the multi-dimensional data, the target flight state of the target UAV is determined; the target flight state includes one of the following: normal state, unauthorized flight state;

[0203] When the target flight state includes the unauthorized flight state, the target warning level corresponding to the target drone is determined based on the multi-dimensional data; the target processing scheme corresponding to the target warning level is determined; and the target drone is processed according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

[0204] In specific implementations, the electronic devices described in the embodiments of the present invention can also execute other implementation methods described in the UAV-based control method provided in the embodiments of the present invention, which will not be repeated here.

[0205] This application also provides a computer-readable storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0206] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0207] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0208] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0209] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0210] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0211] The steps of the methods or algorithms described in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, portable hard disk, read-only optical disk (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Furthermore, the ASIC can reside in a terminal device or management device. Alternatively, the processor and storage medium can exist as discrete components in the terminal device or management device.

[0212] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in the embodiments of this application can be implemented, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, in the form of a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0213] The aforementioned computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media.

[0214] The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0215] The modules / units included in the various devices and products described in the above embodiments can be software modules / units, hardware modules / units, or a combination of both. For example, for devices and products applied to or integrated into a chip, all modules / units can be implemented using hardware methods such as circuits, or at least some modules / units can be implemented using software programs that run on a processor integrated within the chip, while the remaining (if any) modules / units can be implemented using hardware methods such as circuits. For devices and products applied to or integrated into a chip module, all modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components of the chip module, or at least some modules / units can be implemented using hardware methods such as circuits. The implementation is achieved through a software program that runs on the processor integrated within the chip module. The remaining modules / units (if any) can be implemented using hardware methods such as circuits. For various devices and products applied to or integrated into terminal equipment, each of their modules / units can be implemented using hardware methods such as circuits. Different modules / units can be located in the same component (e.g., chip, circuit module, etc.) or different components within the terminal equipment. Alternatively, at least some modules / units can be implemented through a software program that runs on the processor integrated within the terminal equipment, while the remaining modules / units (if any) can be implemented using hardware methods such as circuits.

[0216] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above descriptions are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. A control method based on unmanned aerial vehicles (UAVs), characterized in that, A control platform applied in an unmanned aerial vehicle (UAV) control system, wherein the UAV control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center, the method comprising: When a target drone is detected to have entered a preset airspace, a first request message is sent to the target drone; Detect whether a first response message from the target drone in response to the first request message is received within a preset time period; the first response message includes the target drone's identity information. If so, the identity information of the target drone is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful, verification failed; When the first verification result includes successful verification, multi-dimensional data of the target UAV is collected through the data acquisition center. Based on the first response information and the multi-dimensional data, the target flight state of the target UAV is determined; the target flight state includes one of the following: normal state, unauthorized flight state; When the target flight state includes the unauthorized flight state, the target warning level corresponding to the target drone is determined based on the multi-dimensional data; the target processing scheme corresponding to the target warning level is determined; and the target drone is processed according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

2. The method as described in claim 1, characterized in that, The data acquisition center includes: a broadcast monitoring unit, a radar unit, and a spectrum detection unit; the multi-dimensional data includes: target motion data of the target UAV, target UAV broadcast signal corresponding to the target UAV, and signal characteristic data of the target UAV. The process of collecting multi-dimensional data of the target UAV through the data acquisition center includes: The broadcast monitoring unit listens to the drone broadcast signals within the preset airspace to obtain *a* drone broadcast signals; where *a* is a natural number. When the broadcast signals of the a drones do not include the broadcast signal of the target drone, it is determined that the target flight state includes the unauthorized flight state; When the broadcast signal of drone a includes the broadcast signal of the target drone, the identity information of the broadcasting drone corresponding to the broadcast signal of the target drone is determined; if the identity information of the broadcasting drone is inconsistent with the identity information of the target drone, the target flight state is determined to include the unauthorized flight state; if the identity information of the broadcasting drone is consistent with the identity information of the target drone, the target motion data is collected by the radar unit; and the signal feature data is collected by the spectrum detection unit.

3. The method as described in claim 2, characterized in that, The first response information also includes: flight permit information and reported flight routes; Determining the target flight state of the target UAV based on the first response information and the multi-dimensional data includes: Determine the scope of the airspace to be reported in the flight permit information; If the reported airspace range does not include the preset airspace, then the target flight status is determined to include the illegal flight status; If the reported airspace range includes the preset airspace, a first motion trajectory is determined based on the target motion data; the target flight status is determined based on the reported flight route, the first motion trajectory, and the signal characteristic data.

4. The method as described in claim 3, characterized in that, The first motion trajectory includes b coordinate points, where b is a positive integer. Determining the target flight state based on the reported flight path, the first motion trajectory, and the signal characteristic data includes: Determine the shortest distance between each of the b coordinate points and the reported flight route to obtain b shortest distances; Determine c shortest distances that are greater than a preset distance from the b shortest distances, and d shortest distances that are not greater than the preset distance; c and d are both natural numbers less than or equal to b, and c + d = b; The first ratio is determined based on the c shortest distances and the d shortest distances; When the first ratio is greater than a preset ratio, it is determined that the target flight state includes the unauthorized flight state; When the first ratio is not greater than the preset ratio, c coordinate times are determined based on the b coordinate points and the c shortest distances, with each shortest distance corresponding to a coordinate time; the start time of the target UAV entering the preset airspace is obtained; the target deviation frequency is determined based on the start time and the c coordinate times; if the target deviation frequency is greater than the preset deviation frequency, the target flight state is determined to include the unauthorized flight state; if the target deviation frequency is not greater than the preset deviation frequency, the target flight state is determined based on the signal characteristic data.

5. The method as described in claim 4, characterized in that, The method further includes: Obtain the target airspace type corresponding to the preset airspace; Determine the reference deviation frequency corresponding to the target airspace type; The target drone type and target operator are determined based on the target drone's identity information; Determine the reference performance parameters corresponding to the target UAV type; Determine the target UAV operating parameters corresponding to the target operator; Obtain the target environmental parameters corresponding to the preset airspace at the start time; The reference performance parameters are adjusted based on the target environmental parameters and the target UAV operating parameters to obtain the target performance parameters; Determine the target fine-tuning factor corresponding to the target performance parameter; The reference deviation frequency is adjusted according to the target fine-tuning factor to obtain the preset deviation frequency.

6. The method as described in claim 4, characterized in that, The signal characteristic data includes: frequency band usage data and signal strength data. The signal strength data includes multiple signal strengths and the acquisition time corresponding to each signal strength. Determining the target flight state based on the signal characteristic data includes: Based on the frequency band usage data, determine the e frequency bands corresponding to the target UAV and the number of frequency band switching; e is a positive integer. If there is an illegal frequency band among the e frequency bands, and / or the number of frequency band switching is greater than the preset number of switching, the target flight state is determined to include the illegal flight state; If no illegal frequency band exists in the e frequency bands, and the number of frequency band switching is not greater than the preset number of switching, a target signal curve is obtained by curve fitting based on the multiple signal strengths and the acquisition time corresponding to each signal strength; the horizontal axis of the target signal curve is time, and the vertical axis is signal strength; the target similarity between the target signal curve and the preset signal curve is determined; if the target similarity is less than the preset similarity, the target flight state is determined to include the illegal flight state; if the target similarity is not less than the preset similarity, the target flight state is determined to include the normal state.

7. The method according to any one of claims 2-6, characterized in that, Determining the target early warning level corresponding to the target drone based on the multi-dimensional data includes: The current position, current altitude, current speed, and current acceleration of the target UAV are determined based on the target motion data at the current moment. Determine f no-fly zones within a preset distance around the preset airspace, resulting in f no-fly zones; where f is a natural number. Determine the distances between the current location and the f no-fly zones to obtain f distances; Determine the minimum distance among the f distances, and determine the location risk value corresponding to the minimum distance; Determine the difference between the current height and the preset height to obtain the first difference; Determine the height risk value corresponding to the first difference; Determine the difference between the current speed and the preset speed to obtain a second difference; Determine the reference risk value corresponding to the second difference; Determine the target optimization factor corresponding to the current acceleration; The reference risk value is optimized based on the target optimization factor to obtain the speed risk value; The target risk value is determined based on the location risk value, the altitude risk value, and the speed risk value. Determine the target early warning level corresponding to the target risk value.

8. A control device based on an unmanned aerial vehicle (UAV), characterized in that, A control platform is used in an unmanned aerial vehicle (UAV) control system. The UAV control system further includes a data acquisition center, and the control platform is communicatively connected to the data acquisition center. The device includes: a monitoring unit, a status identification unit, and an early warning processing unit, wherein: The monitoring unit is configured to send a first request message to the target drone when it detects that the target drone has entered a preset airspace; detect whether it receives a first response message from the target drone in response to the first request message within a preset time period; the first response message includes the target drone's identity information; if so, the target drone's identity information is verified through a preset drone database to obtain a first verification result; the first verification result includes one of the following: verification successful or verification failed; when the first verification result includes verification successful, multi-dimensional data of the target drone is collected through the data acquisition center. The state recognition unit is used to determine the target flight state of the target UAV based on the first response information and the multi-dimensional data; the target flight state includes one of the following: normal state and unauthorized flight state; When the target flight state includes the unauthorized flight state, the early warning processing unit determines the target early warning level corresponding to the target drone based on the multi-dimensional data; determines the target processing scheme corresponding to the target early warning level; and processes the target drone according to the target processing scheme to enable the target drone to leave the unauthorized flight state.

9. An electronic device, characterized in that, include: Processor, memory, communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A computer program for storing electronic data interchange, wherein the computer program causes a computer to perform the method as described in any one of claims 1-7.

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