Radio air monitoring system and method for drones

Abstract

The application discloses a radio air monitoring system and method for a UAV, a mapping relationship library of radio equipment types and monitoring parameters is established in a cloud server, and a target airspace is divided into three-dimensional grid monitoring sub-areas. For single equipment or multi-equipment cooperative monitoring types, a radio monitoring container and a time-sequenced data container sequence are generated, a UAV is driven to carry a monitoring unit to perform signal collection, and finally a monitoring report is generated. The method realizes three-dimensional grid planning, intelligent task identification and time-sequenced collection, solves problems such as many blind areas in traditional monitoring, data redundancy and the like, improves monitoring coverage and data quality, and has the advantages of efficient and accurate monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of unmanned aerial vehicles, in particular to a radio air monitoring system and method for unmanned aerial vehicles. BACKGROUND

[0002] Traditional ground fixed radio monitoring stations have many problems such as many monitoring blind areas and poor deployment flexibility, and are difficult to adapt to complex terrain and rapidly changing electromagnetic environment. In recent years, air monitoring technology based on unmanned aerial vehicles has gradually emerged, but its monitoring efficiency and data processing capacity still need to be improved.

[0003] The existing unmanned aerial vehicle radio monitoring method mainly has the following defects:

[0004] The monitoring task planning is rough: most of them use simple grid division method, and do not fully consider the relationship between the flight height of the unmanned aerial vehicle, the signal detection radius and the terrain shielding, resulting in many monitoring blind areas.

[0005] The device type recognition accuracy is low: the recognition ability of the multi-device type cooperative work scene is insufficient, and it is difficult to accurately distinguish the signal characteristics of different device types.

[0006] The data collection timing is chaotic: there is no effective timing collection mechanism, resulting in problems such as inconsistent time stamp and incomplete parameters of the collected data.

[0007] The data processing efficiency is low: the repeated monitoring parameters are not optimized, resulting in high data redundancy and increasing the difficulty of subsequent analysis. SUMMARY

[0008] The present application relates to the field of unmanned aerial vehicles, in particular to a radio air monitoring system and method for unmanned aerial vehicles.

[0009] Step S1, establishing a mapping relationship library of radio device types and monitoring parameters on a cloud server; obtaining a monitoring task planning of a target airspace, dividing the target airspace into a plurality of grid monitoring sub-regions according to the single flight monitoring coverage range of the unmanned aerial vehicle, and generating a sub-region sequence;

[0010] Step S2, analyzing the monitoring task of the current sub-region and identifying the monitoring type: if it is single device type monitoring, execute step S3; if it is multi-device type cooperative monitoring, execute step S4;

[0011] Step S3, the cloud server generates a radio monitoring container for the current sub-region; generates a device operation sequence according to the device operation list and operation timing of the sub-region; creates a corresponding radio parameter monitoring data container sequence based on the mapping relationship library according to the device operation sequence, and associates the sequence to the radio monitoring container of the current sub-region, and enters step S5;

[0012] Step S4, the cloud server generates a radio monitoring container for the current sub-region; extracts a stage execution sequence according to the multi-stage monitoring task of the sub-region; for each stage: generates a device operation sub-sequence according to the device operation list and operation timing in the stage, and creates a corresponding radio parameter monitoring data container sub-sequence; integrates all data container sub-sequences according to the stage execution sequence to form a complete radio parameter monitoring data container sequence, and associates the sequence to the radio monitoring container of the current sub-region;

[0013] Step S5, the radio monitoring unit carried by the unmanned aerial vehicle performs time-sequenced signal collection in the corresponding sub-region according to the radio parameter monitoring data container sequence; when the monitoring of all sub-regions is completed, a radio monitoring report of the target airspace is generated.

[0014] Further, the mapping relationship library between radio equipment types and monitoring parameters established in the cloud server comprises: associating corresponding monitoring parameters according to the signal characteristics of the radio equipment, and the monitoring parameters include spectrum occupancy, signal strength, bandwidth, modulation type and signal source direction.

[0015] Further, the monitoring task planning of the target airspace comprises: dividing the target airspace into a plurality of grid monitoring sub-regions according to the single flight monitoring coverage range of the unmanned aerial vehicle, and generating a sub-region sequence, which comprises: performing three-dimensional grid segmentation on the target airspace based on the effective signal detection radius and flight height of the unmanned aerial vehicle, and generating a sub-region sequence according to the monitoring task priority.

[0016] Further, the analysis of the monitoring task of the current sub-region comprises: if there is only a single radio equipment type in the sub-region, it is determined as single device type monitoring; if there are multiple devices of different types that need to be time-sequenced and cooperatively operated, it is determined as multi-device type cooperative monitoring; the device types include civil communication base stations, aviation navigation equipment, radars and Internet of Things terminals.

[0017] Further, the creation of the corresponding radio parameter monitoring data container sequence based on the mapping relationship library according to the device operation sequence comprises: extracting a corresponding monitoring parameter set from the mapping relationship library according to the device type in the device operation sequence, generating a parameter collection instruction queue according to the operation timing, and constructing a container sequence to store time-sequenced monitoring data.

[0018] Furthermore, the stage execution sequence integrates all data container subsequences to form a complete radio parameter monitoring data container sequence, including: aligning and splicing the monitoring data container subsequences of each stage in time axis according to the stage execution order to form a continuous monitoring data container sequence.

[0019] Furthermore, if the same monitoring parameter container exists in the monitoring data container subsequences of different stages, only the container of the first stage is retained and its effective monitoring period is extended to cover the same parameter collection requirements of subsequent stages.

[0020] A radio air monitoring system for unmanned aerial vehicles (UAVs) applies the radio air monitoring method for UAVs, comprising: a cloud server, a UAV cluster, an airborne radio monitoring unit, a control module, and an air-to-ground communication module; the cloud server, UAV cluster, airborne radio monitoring unit, and control module are respectively connected to the air-to-ground communication module; the cloud server is used to perform monitoring mission planning, container sequence generation, and data analysis; the UAV cluster receives monitoring instructions through the air-to-ground communication module and flies autonomously according to a sub-area sequence; the airborne radio monitoring unit has a built-in spectrum analyzer and direction recognition module for real-time acquisition and return of time-series radio parameter data.

[0021] The present invention provides the following benefits: It proposes a mapping relationship library based on the device operating state transition model, enabling dynamic and adaptive configuration of monitoring parameters. It also designs a hierarchical scheduling architecture for multi-stage monitoring tasks, solving the timing optimization problem of collaborative monitoring of multiple devices in complex electromagnetic environments. It also develops a resource allocation algorithm based on a conflict graph, effectively resolving frequency band contention and device conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a radio aerial monitoring method for UAVs is provided;

[0023] Figure 2 Schematic diagram of the implementation flow of the radio aerial monitoring method for UAVs. DETAILED DESCRIPTION

[0024] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following.

[0025] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0026] like Figure 1 As shown, the radio air monitoring method for UAV includes the following steps:

[0027] Step S1, a mapping relationship library of radio equipment types and monitoring parameters is established in a cloud server; a monitoring task plan of a target airspace is acquired, the target airspace is divided into a plurality of grid monitoring sub-areas according to a single flight monitoring coverage range of a UAV, and a sub-area sequence is generated;

[0028] Step S2, a monitoring task of a current sub-area is analyzed, and a monitoring type is identified: if it is single equipment type monitoring, step S3 is executed; if it is multi-equipment type cooperative monitoring, step S4 is executed;

[0029] Step S3, the cloud server generates a radio monitoring container of the current sub-area; a device work sequence is generated according to a device work list and a work timing sequence of the sub-area; a corresponding radio parameter monitoring data container sequence is created based on the mapping relationship library according to the device work sequence, and the sequence is associated to the radio monitoring container of the current sub-area, and step S5 is entered;

[0030] Step S4, the cloud server generates a radio monitoring container of the current sub-area; a stage execution sequence is extracted according to a multi-stage monitoring task of the sub-area; for each stage: a device work sub-sequence is generated according to a device work list and a work timing sequence in the stage, and a corresponding radio parameter monitoring data container sub-sequence is created; all data container sub-sequences are integrated according to the stage execution sequence to form a complete radio parameter monitoring data container sequence, which is associated to the radio monitoring container of the current sub-area;

[0031] Step S5, a radio monitoring unit carried by the UAV performs time-sequenced signal collection in the corresponding sub-area according to the radio parameter monitoring data container sequence; when all sub-area monitoring is completed, a radio monitoring report of the target airspace is generated.

[0032] The mapping relationship library of radio equipment types and monitoring parameters established in the cloud server comprises: according to the signal characteristics of the radio equipment, the corresponding monitoring parameters are associated, and the monitoring parameters comprise spectrum occupancy, signal strength, bandwidth, modulation type and signal source direction.

[0033] The monitoring task plan of the target airspace is acquired, the target airspace is divided into a plurality of grid monitoring sub-areas according to the single flight monitoring coverage range of the UAV, and the sub-area sequence is generated, which comprises: based on the effective signal detection radius and the flight height of the UAV, the target airspace is three-dimensionally grid segmented, and the sub-area sequence is generated according to the monitoring task priority

[0034] The monitoring task of the current sub-region is analyzed, and the monitoring type is identified, including: if there is only a single radio device type in the sub-region, it is determined as a single device type monitoring; if there are multiple devices types that need to be time-sequenced and cooperated, it is determined as a multi-device type cooperative monitoring; the device types include communication base stations, aviation navigation devices, radars and Internet of Things terminals.

[0035] The mapping relationship library is used to create a corresponding radio parameter monitoring data container sequence according to the device working sequence, including: according to the device type in the device working sequence, extracting the corresponding monitoring parameter set from the mapping relationship library, generating a parameter collection instruction queue according to the working time sequence, and constructing a container sequence to store the time-sequenced monitoring data.

[0036] The stage execution sequence integrates all data container subsequences to form a complete radio parameter monitoring data container sequence, including: aligning and splicing the monitoring data container subsequences of each stage according to the stage execution order to form a continuous monitoring data container sequence.

[0037] If there are the same monitoring parameter containers in the monitoring data container subsequences of different stages, the container of the first stage is retained and its effective monitoring period is extended to cover the same parameter collection requirements of the subsequent stages.

[0038] A radio air monitoring system based on a UAV applies the radio air monitoring method for the UAV, including: a cloud server, a UAV cluster, an airborne radio monitoring unit, a control module and an air-ground communication module; the cloud server, the UAV cluster, the airborne radio monitoring unit and the control module are connected with the air-ground communication module; the cloud server is used to execute monitoring task planning, container sequence generation and data analysis; the UAV cluster receives monitoring instructions through the air-ground communication module and flies autonomously according to the sub-region sequence; the airborne radio monitoring unit is internally provided with a spectrum analyzer and a direction recognition module, which are used to collect and return time-sequenced radio parameter data in real time.

[0039] Specifically, as shown in Figure 2 The radio air monitoring method for the UAV includes:

[0040] Step S1: task planning and airspace gridding

[0041] A dynamic updated radio device type and monitoring parameter mapping relationship library is constructed in the cloud server. The device signal feature database includes typical signal features of ADS-B transmitters, secondary radars, radios and LoRaWAN terminals; the typical signal features include center frequency, bandwidth range, modulation mode, frame structure, pulse characteristics, power spectral density, etc.

[0042] According to the above signal characteristics, the structured monitoring parameter set is associated and defined, including: spectrum occupancy: the energy distribution and occupancy state of a specific frequency band / channel in the time-frequency two-dimensional space. Signal strength: received power level, which needs to mark the measurement bandwidth and reference point. Signal bandwidth: the actual frequency bandwidth occupied by the signal. Modulation type identification: automatically identify the modulation method of the signal (such as QPSK, 16QAM, GFSK, FM, AM, LFM pulse, etc.). Signal source direction: using airborne array antenna or multi-channel receiver, the signal incident angle (azimuth angle, elevation angle) is determined by the direction of arrival estimation algorithm.

[0043] Receiving the target airspace monitoring task planning issued by the user or the upper system. The planning content includes: target airspace geographical range (three-dimensional boundary), concerned frequency range, key monitoring device type list, task priority, time constraint, accuracy requirement, etc.

[0044] Based on the effective signal detection model of the carried radio monitoring unit (considering the receiver sensitivity, antenna gain pattern, minimum detectable signal level MDS, environmental noise floor, line-of-sight propagation loss model) and the flight performance parameters of the unmanned aerial vehicle (cruise speed, maximum endurance time, hovering stability, maximum flight altitude, climb / descent rate).

[0045] Based on the effective detection radius of the unmanned aerial vehicle, considering the task accuracy requirement (such as higher accuracy requiring smaller grid), the horizontal projection of the airspace is divided into regular or irregular (considering the terrain / flight restricted area) grid cells.

[0046] According to the task requirements (such as low-altitude Internet of Things monitoring vs. high-altitude aviation navigation monitoring) and the flight envelope of the unmanned aerial vehicle, the airspace is divided into several layers in the vertical direction (for example: 0-100m, 100-500m, 500-1200m).

[0047] Each grid cell is a monitoring sub-region, which is a three-dimensional space.

[0048] According to the monitoring task priority (such as high-priority area, urgent interference elimination area), unmanned aerial vehicle path optimization (minimizing total flight distance / time), airspace regulation restrictions, and possible unmanned aerial vehicle cluster coordination strategies, all sub-regions are sorted to generate a sub-region sequence. The sequence needs to ensure that the unmanned aerial vehicle can access all sub-regions in sequence.

[0049] Step S2: Sub-region task analysis and monitoring type determination

[0050] Analyzing the current sub-region task: the cloud server loads the detailed task information of the current sub-region to be monitored, including its spatial coordinates, volume, and list of expected radio equipment types (and their expected operating frequency bands / periods).

[0051] Single device type monitoring: The task of the current sub-region only involves monitoring a single type of radio device (e.g., only monitoring 4G LTE base station signals in the region). Determination basis: It is explicitly specified in the task planning or inferred from the device list that only one device type is the main monitoring target and its working period covers the entire sub-region monitoring window.

[0052] Multi-device type collaborative monitoring: The task of the current sub-region involves monitoring multiple different types of radio devices, and the work of these devices has timing characteristics (e.g.: mainly monitoring civilian communication and Internet of Things devices during the day, monitoring aviation navigation beacons at night, and monitoring radar during a specific period). Determination basis: The task planning requires monitoring multiple device types, and the work schedules of these devices overlap or have specific sequence relationships within the sub-region monitoring window, requiring the UAV to switch monitoring configurations at different time points.

[0053] Step S3: Container sequence generation for single device type monitoring

[0054] The cloud server creates a logical radio monitoring container for the sub-region as a collection carrier for all monitoring data and metadata of the sub-region. The container contains meta-information such as sub-region ID, spatial coordinates, planned monitoring period, etc.

[0055] Based on the task list of the sub-region (explicitly specified device types) and its work timing model (standard work period of devices, special period specified by the task, predicted active period), generate a device work sequence in a timeline. The sequence elements are (device type, start time, end time).

[0056] Iterate through each (device type, work period) element in the device work sequence generated in step S2. According to the device type, query the mapping relationship library to obtain the complete structured monitoring parameter set corresponding to the device type and its collection requirements (such as sampling rate, resolution bandwidth RBW, FFT points, integration time).

[0057] For each work period: Create a dedicated radio parameter monitoring data container for the period and device type. This container pre-allocates storage space and configures the storage structure (such as timestamp, parameter 1 data, parameter 2 data,..., quality flag). Generate an accurate parameter collection instruction queue to drive the on-board radio monitoring unit during the period. The instructions include: center frequency, bandwidth, RBW, VBW, detector type, modulation analysis mode, DoA calculation mode, sampling duration, trigger condition, etc. Bind the data container with the collection instruction queue.

[0058] The time-sequentially arranged dedicated data containers (each corresponding to a working period) are concatenated to form a radio parameter monitoring data container sequence for the sub-region. The generated radio parameter monitoring data container sequence is associated (mounted) to the sub-region radio monitoring container created in step S1. Turn to step S5 to perform data collection.

[0059] Step S4: Container sequence generation for multi-device type cooperative monitoring

[0060] A top-level radio monitoring container is created for the sub-region. The extraction phase performs a sequence: analyze the multi-stage monitoring tasks for the sub-region (such as "Stage A: monitoring type X devices", "Stage B: monitoring type Y and Z devices", "Stage C: monitoring type W devices"). The time boundaries (start / end time) and execution order of each stage are determined, and a stage execution sequence is generated. The sequence elements are (stage ID, stage start time, stage end time, list of target device types in this stage).

[0061] According to the target device type list of the stage and its working time sequence within the stage time window (devices may work in parallel or in series), the device working sub-sequence within the stage is generated. The sub-sequence elements are still (device type, working start time, working end time), but these times must be within the stage time window.

[0062] Each (device type, working time period) element in the device working sub-sequence: query the mapping relationship library to obtain the monitoring parameter set and collection requirements corresponding to the device type. Create a dedicated radio parameter monitoring data container for the time period and device type. Generate a corresponding parameter collection instruction queue. Bind the container with the instruction queue. Concatenate all the time-sequentially arranged dedicated data containers in the stage to form a radio parameter monitoring data container sub-sequence for the stage.

[0063] Integrate the stage container sub-sequences: concatenate the container sub-sequences of each stage generated in step S3 strictly according to the time axis order defined by the stage execution sequence. During the concatenation process, container merging optimization is performed: the system checks whether there are dedicated data containers for the same monitoring parameter set (determined by device type and parameter set) and time-continuous or overlapping in the container sub-sequences of different stages. If such a situation is found, the first appearing dedicated container for the parameter set is retained, and its effective monitoring period is extended to cover the effective period of the same parameter set container in the subsequent stage. The same parameter set container in the subsequent stage that is covered will be marked as redundant and deleted, and its corresponding collection instruction period is merged into the first container's instructions. Avoids repeated configuration and data storage of the same set of parameters by the UAV in adjacent or overlapping periods, reduces the amount of instruction issuance, storage overhead, and potential processing delay.

[0064] After splicing and optimization, a continuous complete radio parameter monitoring data container sequence covering the entire sub-area monitoring time window is formed. This sequence contains the monitoring data storage structure and collection instructions required for all stages and all device types. The final integrated and optimized complete radio parameter monitoring data container sequence is associated with the sub-area radio monitoring container created in step 1.

[0065] Step S5: Timing signal collection and report generation

[0066] Through the air-ground communication link, the radio monitoring container of the current sub-area (including its associated data container sequence and embedded collection instruction queue) is delivered to the unmanned aerial vehicle performing the task of the sub-area.

[0067] The radio monitoring unit carried by the unmanned aerial vehicle receives and analyzes the instructions. The control module in the unit strictly follows the timeline defined by the data container sequence and configures the parameters of the spectrum analyzer, signal processing unit, and direction identification module (frequency, bandwidth, RBW, detector, DoA algorithm, etc.) at the specified precise time point when the unmanned aerial vehicle arrives and stabilizes in (or flies through according to the predetermined trajectory) the sub-area. Switch to the monitoring mode required for the current period.

[0068] Execute the collection instruction queue to perform high-precision, time-synchronized signal collection and parameter measurement. Fill the collected raw data or pre-processed parameter data (spectrum graph, RSSI value, bandwidth estimation value, modulation identification result, DoA angle, etc.) into the corresponding radio parameter monitoring data container in real time according to the timestamp and parameter identifier.

[0069] Through the air-ground communication link, the filled data is returned to the cloud server or stored in the on-board storage for later return after the task. The return process includes data verification.

[0070] The unmanned aerial vehicle completes all timing collection tasks defined by the current sub-area container sequence and confirms the success of data return / storage. The unmanned aerial vehicle navigates to the next sub-area according to the sub-area sequence and repeats steps S2-S5 (the cloud server determines to execute S3 or S4 again according to the new sub-area task type).

[0071] When the unmanned aerial vehicle completes the monitoring tasks of all sub-area sequences, the cloud server collects all data in the sub-area radio monitoring container. Interpolate or splice the spatially discrete sub-area data in three-dimensional space to form a continuous spatial distribution map of the target airspace (such as a spectrum occupation heat map, a signal strength distribution map, and an interference source positioning map).

[0072] Based on the mapping relationship library and the preset rules, abnormal detection (such as illegal signals, excessive emission), spectrum efficiency evaluation, device working state diagnosis, electromagnetic environment situation generation, etc. are performed. The output includes comprehensive target airspace radio monitoring reports containing raw data summaries, analysis results, visual charts (spectrum chart, spatial distribution chart, trend chart), abnormal alarm information, and compliance check conclusions.

[0073] Example 1: Urban Overhead Civilian Communication Spectrum Survey

[0074] A certain city radio management agency needs to continuously monitor the 10km x 8km x 300m (length x width x height) airspace in the central city for 24 hours, focusing on the spectrum occupancy rate and signal distribution of 5G base stations (3.4-3.6GHz), Wi-Fi hotspots (2.4 / 5GHz), and Internet of Things devices (LoRa / NB-IoT).

[0075] The cloud calls the preset device-parameter mapping library: 5G base station associated parameters: spectrum occupancy rate, signal strength, bandwidth, OFDMA modulation identification, signal direction; Wi-Fi associated parameters: spectrum occupancy rate, channel occupancy rate, RSSI, modulation type (OFDM); LoRa / NB-IoT associated parameters: spectrum occupancy rate, signal strength, spreading factor (only LoRa).

[0076] According to the 500m effective detection radius of the unmanned aerial vehicle, the airspace is horizontally divided into 4x8 grids and vertically into a single layer (0-300m). A priority sequence is generated: commercial area (Grid-01~08) > residential area (Grid-09~24) > industrial area (Grid-25~32).

[0077] Commercial area core grid monitoring (Grid-05)

[0078] Task analysis: identified as multiple device cooperative monitoring (different devices need to be monitored day and night)

[0079] Container generation

[0080] Stage 1 (daytime 08:00-20:00):

[0081] 08:00-12:00: Create a 5G monitoring container (center frequency 3.5GHz, RBW=100kHz);

[0082] 12:00-18:00: Create a Wi-Fi monitoring container (dual-frequency scanning, RBW=1MHz);

[0083] Stage 2 (night 20:00-08:00):

[0084] 20:00-24:00: Create a LoRa monitoring container (frequency band 868MHz);

[0085] 02:00-06:00: Create NB-IoT monitoring container (band 900 MHz);

[0086] Since LoRa and NB-IoT share the "spectrum occupancy" parameter, merge into a single container (band extended to 868-900 MHz, time period 20:00-06:00).

[0087] Data collection

[0088] Drones hover over business district coordinate points (116.4°E, 39.9°N, 200m)

[0089] Time sequence execution:

[0090] 12:00: Switch to Wi-Fi monitoring mode, scan 2.4GHz / 5GHz bands;

[0091] 20:00: Switch to Sub-GHz joint monitoring mode;

[0092] Abnormal discovery:

[0093] Night 2.4GHz band occupancy rate continues to reach 95% (threshold 70%); through directional positioning (DoA) to lock a certain mall (116.41°E, 39.91°N) illegal deployment of high-power Wi-Fi repeater.

[0094] Generate a spatiotemporal thermal map of the spectrum: show the serious congestion of the 2.4GHz band at night in the business district;

[0095] Output interference source forensics report: contains illegal device coordinates, signal spectrum template, 24-hour occupancy curve.

[0096] Example 2: Airline frequency band interference tracing around the airport

[0097] The airport tower reports that the 108-137MHz aviation communication frequency band is intermittently pulsed by interference, and needs to be quickly located in the 5km radius, 0-1000m height of the clear area.

[0098] Emergency response deployment

[0099] Map the library to load the special parameter set for civil aviation: signal strength, modulation type anomaly detection, high-precision direction positioning (DoA), pulse feature analysis

[0100] The reference grid is divided according to a 1km detection radius, and the grid in the high-interference area (east side of the airport) is encrypted to 200m; generate a spiral scanning sequence: take the tower report location as the center and radiate outward.

[0101] High-risk area monitoring (Grid-12)

[0102] Task resolution: determined as single-device monitoring (only capture 108-137MHz abnormal signals);

[0103] Create a continuous monitoring container: frequency band: 108-137MHz; Parameters: RSSI, modulation type, DoA, pulse width / repetition interval; Acquisition instructions: RBW=10kHz, sampling rate 10MS / s, trigger threshold -110dBm

[0104] Multi-machine cooperative positioning: 3 UAVs enter Grid-12 (coordinates P1: 116.701°E, 40.101°N; P2: 116.704°E, 40.099°N; P3: 116.699°E, 40.097°N) simultaneously; UAV 1 / 2 captures signal time difference Δt 12 =1.2μs→distance difference 360 meters; UAV 1 / 3 time difference Δt 13 =2.1μs→distance difference 630 meters; Three-machine DoA ray intersection at ground coordinates (116.705°E, 40.102°N).

[0105] Interference source confirmation: detected 109.5MHz high-power pulse signal (pulse width 20μs, repetition interval 2ms) in the freight warehouse, matching illegal UAV image transmission equipment characteristics.

[0106] Push a three-level alarm to the air traffic control system: contains the precise coordinates of the interference source (116.705°E, 40.102°N). Automatically generate a forensic package: spectrum waterfall chart, pulse timing analysis, signal fingerprint comparison report.

Claims

1. A radio air monitoring method for unmanned aerial vehicles, characterized in that: The steps include: Step S1: Establish a mapping relationship library between radio equipment types and monitoring parameters on a cloud server; obtain a monitoring mission plan for the target airspace, divide the target airspace into multiple gridded monitoring sub-areas based on the single flight monitoring coverage of the UAV, and generate a sub-area sequence; Step S2: parse the monitoring task of the current sub-area and identify the monitoring type: if it is single-device type monitoring, execute step S3; if it is multi-device type collaborative monitoring, execute step S4; In step S3, the cloud server generates a radio monitoring container for the current sub-region; generates an equipment working sequence based on the equipment working list and working sequence of the sub-region; creates a corresponding radio parameter monitoring data container sequence based on the mapping relationship library according to the equipment working sequence, and associates the sequence with the radio monitoring container of the current sub-region, and then proceeds to step S5; In step S4, the cloud server generates a radio monitoring container for the current sub-region. Based on the multi-stage monitoring tasks for the sub-region, the cloud server extracts the stage execution sequence. For each stage, the cloud server generates an equipment work sub-sequence based on the equipment work list and work sequence within the stage, and creates a corresponding radio parameter monitoring data container sub-sequence. All data container sub-sequences are integrated according to the stage execution sequence to form a complete radio parameter monitoring data container sequence, which is then associated with the radio monitoring container for the current sub-region. In step S5, the radio monitoring unit carried by the UAV performs timed signal acquisition in the corresponding sub-area according to the radio parameter monitoring data container sequence; when the monitoring of all sub-areas is completed, a radio monitoring report of the target airspace is generated.

2. The radio air monitoring method for UAV according to claim 1, characterized in that: The mapping relationship library between radio equipment types and monitoring parameters established on the cloud server includes: associating corresponding monitoring parameters according to the signal characteristics of the radio equipment, and the monitoring parameters include spectrum occupancy, signal strength, bandwidth, modulation type and signal source direction.

3. The radio air monitoring method for UAV according to claim 2, characterized in that: The monitoring task planning for obtaining the target airspace divides the target airspace into multiple gridded monitoring sub-areas based on the single flight monitoring coverage of the UAV and generates a sub-area sequence, including: performing three-dimensional grid segmentation of the target airspace based on the effective signal detection radius and flight altitude of the UAV, and generating a sub-area sequence according to the monitoring task priority.

4. The radio air monitoring method for UAV according to claim 3, characterized in that: The analysis of the monitoring tasks of the current sub-area and the identification of the monitoring type include: if there is only a single radio equipment type in the sub-area, it is determined to be single-equipment type monitoring; if there are multiple equipment types that need to work together in a timed manner, it is determined to be multi-equipment type collaborative monitoring; the equipment types include communication base stations, aviation navigation equipment, radars and Internet of Things terminals.

5. The radio air monitoring method for UAV according to claim 4, characterized in that: The method of creating a corresponding radio parameter monitoring data container sequence based on the mapping relationship library according to the device working sequence includes: extracting the corresponding monitoring parameter set from the mapping relationship library according to the device type in the device working sequence, generating a parameter acquisition instruction queue according to the working sequence, and constructing a container sequence to store the time-series monitoring data.

6. The radio air monitoring method for UAV according to claim 4, characterized in that: The stage execution sequence integrates all data container subsequences to form a complete radio parameter monitoring data container sequence, including: aligning and splicing the monitoring data container subsequences of each stage in time axis according to the stage execution order to form a continuous monitoring data container sequence.

7. The radio air monitoring method for UAV according to claim 6, characterized in that: If the same monitoring parameter container exists in the monitoring data container subsequences of different stages, the container of the first stage is retained and the effective monitoring period of the container is extended to cover the same parameter collection requirements of subsequent stages.

8. Radio air monitoring system for unmanned aerial vehicles, characterized in that, The radio air monitoring method for drones according to any one of claims 1 to 7 includes: a cloud server, a drone cluster, an airborne radio monitoring unit, a control module and an air-to-ground communication module; the cloud server, drone cluster, airborne radio monitoring unit and control module are respectively connected to the air-to-ground communication module; the cloud server is used to perform monitoring task planning, container sequence generation and data analysis; the drone cluster receives monitoring instructions through the air-to-ground communication module and flies autonomously according to the sub-area sequence; the airborne radio monitoring unit has a built-in spectrum analyzer and a direction recognition module for real-time collection and transmission of time-series radio parameter data.

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