A method and system for analyzing messages of a drone

Abstract

This invention provides a method and system for parsing UAV messages. By configuring three independent receiving channels, after acquiring the message, multi-source signal interference is filtered to obtain a denoised message. Protocol feature analysis is performed on the denoised message, and a parsing algorithm is automatically matched and used for parsing. The parsed data is then filtered to obtain filtered data. Based on the identified communication protocol category, the data stream is deeply parsed, and structured information in the message is extracted. The data stream consists of filtered data. The deeply parsed data is converted into a standardized format and output through a preset communication method. This solves the problem of high false alarm rates and frequent missed detections caused by the high difficulty in reliably capturing remote ID messages from long-distance or weak signals in traditional UAV monitoring equipment due to its limited protocol support and weak anti-interference capabilities.

Description

Technical Field

[0001] This invention belongs to the field of UAV message parsing technology, specifically relating to a UAV message parsing method and system. Background Technology

[0002] Drone messages are standardized data units used for two-way communication between drones and ground stations, cloud platforms, and monitoring platforms. They carry critical information such as identity, location, status, and commands, serving as the "digital language" of low-altitude flight. Message parsing, on the other hand, is the process of decoding binary / string messages into readable and usable information, and it forms the foundation for drone monitoring, flight control, scheduling, and safety.

[0003] Currently, drone message parsing equipment on the market generally suffers from the following problems: Incomplete protocol parsing: Existing devices typically only support one or a few communication protocols (such as only supporting 2.4GHz WiFi or only supporting 5.8GHz WiFi), and cannot simultaneously parse drone messages of multiple frequency bands and protocols, resulting in the inability to fully capture drone signals in complex environments; Weak signal interference handling capability: In urban environments, there is a lot of wireless signal interference. Existing equipment lacks effective signal filtering algorithms, resulting in a high false alarm rate and inaccurate target identification. In particular, the performance drops sharply when receiving small signals at long distances. High processing latency: Existing equipment has high latency in parsing and processing messages, which cannot meet the needs of real-time monitoring, especially in low-altitude safety management scenarios that require rapid response; Limited networking capabilities: Existing devices typically lack flexible networking and communication capabilities, and cannot dynamically select wired or wireless communication methods according to environmental requirements, which limits the application of devices in different scenarios; Insufficient data format standardization: The parsed data formats are not uniform, making it difficult to directly access various low-altitude management platforms and increasing the complexity of system integration. Summary of the Invention

[0004] Based on this, embodiments of the present invention provide a method and system for parsing unmanned aerial vehicle (UAV) messages, which aims to solve at least one of the above-mentioned problems.

[0005] A first aspect of this invention provides a method for parsing unmanned aerial vehicle (UAV) messages, the method comprising: Three independent receiving channels are configured to capture packets in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, multi-source signal interference is filtered to obtain the noise-reduced packets. The protocol feature analysis is performed on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID, and the parsing algorithm is automatically matched and parsed. The parsed data is then filtered to obtain filtered data. The filtering process includes filtering out false and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. Based on the identified communication protocol category, the data stream is subjected to deep parsing, and structured information in the message is extracted, wherein the data stream is composed of the filtered data; The data after deep analysis is converted into a standardized format and output through a preset communication method.

[0006] Furthermore, in the step of configuring three independent receiving channels for capturing messages in the 2.4GHz and 5GHz frequency bands respectively, filtering multi-source signal interference after acquiring the messages, and obtaining the noise-reduced messages, hardware-level noise reduction, software-level noise reduction, and interference suppression are used to filter multi-source signal interference. In hardware-level noise reduction, each RF front-end integrates a proprietary narrowband filter and a low-noise amplifier to suppress adjacent channel interference and improve small signal reception sensitivity. The narrowband filter parameters are: center frequency 2.412GHz, bandwidth 5MHz, suppression ratio >40dB, and low-noise amplifier parameters are: noise figure <2dB, gain >15dB. In software-level noise reduction, random noise is reduced through adaptive threshold detection and signal averaging algorithms; In interference suppression, digital filter banks are used to suppress interference signals at specific frequencies.

[0007] Furthermore, in the step of performing protocol feature analysis on the denoised message to identify the communication protocol category used by the UAV Remote ID and automatically matching the parsing algorithm, the denoised message is sequentially subjected to frame type detection, OUI verification, protocol version verification and data structure analysis to complete the protocol feature analysis.

[0008] Furthermore, in the automatic matching and parsing algorithm, the step of parsing using the matching and parsing algorithm first performs protocol feature matching on the collected wireless messages based on a pre-built protocol feature library to identify and determine the wireless communication protocol type corresponding to the wireless messages. Then, based on the probability of message occurrence corresponding to different wireless communication protocol types, the corresponding processing priority is configured for the message parsing task corresponding to each wireless communication protocol type. Finally, a multi-threaded pool technique is used to drive the concurrent operation of multiple protocol parsers to improve packet parsing throughput. The number of threads in the multi-threaded pool is dynamically adjusted according to the number of CPU cores of the running device.

[0009] Furthermore, in the step of filtering the parsed data to obtain filtered data, filtering false signals and interference signals includes time window filtering, geographic clustering analysis, and power distribution analysis. Identifying and filtering duplicate messages includes sequence number checking, content hashing, and time window deduplication. Optimizing signal strength and channel selection includes signal-to-noise ratio assessment and dynamic channel switching.

[0010] Furthermore, in the step of performing deep analysis of the data stream based on the identified communication protocol category, the data stream is subjected to frame structure decomposition, information element extraction, and bit field parsing. For frame structure decomposition, the complete wireless frame is split according to the field function to obtain multiple frame fields corresponding to different service functions. The frame fields contain information element fields used to carry Remote ID data. For information element extraction, in the split frame field, information elements related to Remote ID are located and filtered, the format specifications of information elements are identified, and the type identifier and the affiliated organization identifier of information elements are matched and verified. If the verification is successful, it is determined as the target Remote ID information element. For bit field parsing, according to the Remote ID standard protocol specification, the target Remote ID information element is parsed bit by bit to restore the business meaning and business data corresponding to each bit field.

[0011] Furthermore, in the step of extracting structured information from the message, the structured information includes at least basic ID information, location information, operator information, timestamp information, and authentication information.

[0012] A second aspect of this invention provides a UAV message parsing system for implementing the UAV message parsing method provided in the first aspect of this invention. The system includes: The configuration module is used to configure three independent receiving channels for packet capture in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, it filters out multi-source signal interference to obtain noise-reduced packets. The analysis module is used to perform protocol feature analysis on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID, and automatically match the parsing algorithm and use the matched parsing algorithm for parsing; The filtering module is used to filter the parsed data to obtain filtered data. The filtering process includes filtering spoof signals and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. The parsing module is used to perform deep parsing of the data stream based on the identified communication protocol category and extract structured information from the message, wherein the data stream is composed of the filtered data; The conversion module is used to convert the data after deep parsing into a standardized format and output the data through a preset communication method.

[0013] A third aspect of the present invention provides a computer-readable storage medium, comprising: The readable storage medium stores one or more programs that, when executed by a processor, implement the UAV message parsing method as described in the first aspect.

[0014] A fourth aspect of the present invention provides an electronic device, the electronic device including a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the UAV message parsing method as described in the first aspect.

[0015] The UAV message parsing method and system provided in this embodiment of the invention have the following beneficial effects: 1. Multi-band and multi-protocol support: It supports the parsing of three signal types: 2.4GHz WiFi, 2.4GHz Bluetooth and 5.8GHz WiFi, while existing devices usually only support one or a few protocols; 2. Intelligent signal filtering: Adopting a self-developed detection and filtering algorithm, it effectively filters out interference from multiple sources of signals and improves the recognition accuracy. 3. Real-time processing capability: Processing latency is controlled within 10ms to meet real-time monitoring requirements; 4. Flexible communication methods: Supports both wired network (TCP / UDP) and 4G network (TCP / UDP / MQTT / HTTP) communication methods, which can be dynamically selected according to environmental requirements; 5. Standardized data output: It adopts JSON format and low-altitude monitoring platform protocol messages, which facilitates seamless integration with various low-altitude management platforms. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of a UAV message parsing method according to Embodiment 1 of the present invention. Figure 2 This is a structural block diagram of a UAV message parsing system provided in Embodiment 3 of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 Embodiment 1 of this invention provides a method for parsing UAV messages. Please refer to [link / reference]. Figure 1 This is a flowchart of the implementation of a UAV message parsing method, specifically including steps S01 to S05.

[0021] Step S01: Configure three independent receiving channels for packet capture in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, filter out multi-source signal interference to obtain the noise-reduced packets.

[0022] Specifically, each receiving channel is equipped with a dedicated signal processing chip, which can simultaneously receive multiple types of drone messages. In addition, it adopts a custom high-gain antenna (2.4GHz 6dBi, 5GHz 9dBi) and digital noise reduction technology to effectively filter multi-source signal interference.

[0023] In this embodiment of the invention, the first receiving channel (2.4GHz WiFi signal receiving channel): Signal processing chip: ESP32-C3 chip; Processor: RISC-V 32-bit single-core processor with a clock speed of up to 120MHz; Memory: 512KB SRAM (built-in); Wireless functionality: Supports 802.11 b / g / n protocols and operates in the 2.4GHz band; Features: It has powerful WiFi packet capture capabilities, supports promiscuous mode for full packet monitoring, and the chip's built-in hardware accelerator can effectively improve packet processing efficiency; Second receiving channel (2.4GHz Bluetooth Low Energy signal receiving channel): Signal processing chip: ESP32-C3 chip; Processor: RISC-V 32-bit single-core processor with a clock speed of up to 120MHz; Memory: 512KB SRAM (built-in); Wireless functionality: Supports Bluetooth 5.0 (LE) protocol, operating in the 2.4GHz band; Features: Supports Extended Scan mode, which can scan both broadcast and data channels simultaneously; Built-in proprietary BLE link layer hardware acceleration engine. Third receiving channel (5GHz WiFi signal receiving channel): Signal processing chip: RTL8711DAx chip; Processor: ARM® Cortex®-M4 32-bit processor with a clock speed of up to 166MHz; Memory: 256KB SRAM (built-in); Wireless features: Supports 802.11 a / b / g / n / ac protocols and operates in the 5GHz band; Features: Employs Realtek RTL8711DAx, supporting a wider bandwidth and higher transmission rates, and features a proprietary WiFi Media Access Control (MAC) hardware acceleration unit capable of capturing drone Remote ID signals in the 5GHz band.

[0024] It should be noted that digital noise reduction technology includes hardware-level noise reduction, software-level noise reduction, and interference suppression. In hardware-level noise reduction, each RF front-end integrates a proprietary narrowband filter and a low-noise amplifier (LNA) to suppress adjacent channel interference and improve small signal reception sensitivity (typical value: -110dBm @ 2.4GHz). The narrowband filter parameters are: center frequency 2.412GHz, bandwidth 5MHz, suppression ratio >40dB, and the low-noise amplifier parameters are: noise figure <2dB, gain >15dB. In software-level noise reduction, random noise is reduced through adaptive threshold detection and signal averaging algorithms. Specifically, adaptive threshold detection dynamically adjusts the detection threshold based on the real-time channel noise level. The threshold calculation formula is as follows: ,in The noise mean. The standard deviation of noise. This is an empirical coefficient (usually taken as 2.5~3.5). The threshold is used for signal averaging: A moving average is applied to continuously sampled data, with the moving window size being: 'samples' represents the sampled data points, and the averaging formula is: Avg[n] is the moving average output value at time n, N is the sliding window size, i is the summation index, and x[n] is the summation index. i] is the nth digit in the original continuously sampled data sequence. i sampled values; In interference suppression, a digital filter bank is used to suppress interference signals at specific frequencies. Specifically, an FIR filter is used, with the filter coefficients as follows: The filtering formula is: y[n] is the filter output value at time n, x[n] is the filter output value at time n. k] is the nth The input signal sample values ​​at k time points, where M is the length (order) of the filter.

[0025] Step S02: Perform protocol feature analysis on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID, and automatically match the parsing algorithm for parsing.

[0026] It should be noted that the denoised message undergoes frame type detection, OUI verification, protocol version verification, and data structure analysis sequentially to complete protocol feature analysis. Frame type detection checks the frame control field to determine if it is a Beacon frame. The Beacon frame control field value is 0x0080 (binary: 10000000000000000). The detection logic is as follows: ; OUI verification is used to verify whether the vendor information field matches the Remote ID standard (FA:0B:BC), where the OUI byte sequence is [0xFA, 0x0B, 0xBC]. Verification conditions are as follows: ; The protocol version verification is used to check the protocol version field to confirm the Remote ID message format. Data structure analysis is used to analyze whether a standard Remote ID message structure exists in the payload. Among them, message type check: (byte>>4)&0x0F is a valid Remote ID message type (0-5, 0x0F). Message length verification: IE length should be greater than or equal to 5 bytes (OUI 3 bytes + type 1 byte + data 1 byte).

[0027] Furthermore, in the step of parsing using a matching parsing algorithm, firstly, based on a pre-built protocol feature library, protocol feature matching is performed on the collected wireless packets to identify and determine the wireless communication protocol type corresponding to the wireless packets. Specifically, for WiFi features, [the following steps are set]. For Bluetooth features, set Then, based on the probability of message occurrence corresponding to different wireless communication protocol types, a corresponding processing priority is configured for the message parsing task corresponding to each wireless communication protocol type. Among them, the priority for WiFi 2.4GHz is as follows: Bluetooth LE priority: WiFi 5.8GHz priority: Finally, a multi-threaded pool technique is used to drive the concurrent operation of multiple protocol parsers to improve packet parsing throughput. The number of threads in the multi-threaded pool is dynamically adjusted according to the number of CPU cores of the running device. The thread pool size = number of CPU cores × 2 + 1.

[0028] In this embodiment of the invention, the parsing algorithm includes a 2.4GHz WiFi protocol parsing algorithm, a 2.4GHz Bluetooth protocol parsing algorithm, and a 5GHz WiFi protocol parsing algorithm. Specifically, for the 2.4GHz WiFi protocol parsing algorithm, the WiFi packet parsing is based on the 802.11 protocol standard and combined with a channel polling algorithm, using the following parsing process: (1) Channel Polling and Frame Acquisition: This embodiment of the invention adopts a serial channel polling scheme based on the ESP32-C3 chip. Since the WiFi module of the ESP32-C3 only supports single-channel operation mode, a time-division polling mechanism is used to scan each channel of the 2.4GHz frequency band sequentially. The specific implementation is as follows: - Polling the channel list: Iterate through the three non-overlapping channels CH1, CH6, and CH11 in order (CH1: 2412MHz, CH6: 2437MHz, CH11: 2462MHz). These three channels are the most commonly used channels with the least interference in the 2.4GHz band. - Single channel listening time: The listening time T for each channel is set to 200ms by default and can be adjusted through software configuration (range 100ms-500ms). - Channel handover: Each handover operation incurs an overhead of approximately 50-100ms; - Promiscuous Mode: Enable promiscuous mode to capture all 802.11 management frames; - RSSI Acquisition: Acquires the signal strength for each frame; (2) Frame type identification: Identify the Beacon frame in the 802.11 management frame (frame control field subtype = 0x1000), and extract the source MAC address (Source Address, offset 10) and BSSID (offset 16) in the frame header. (3) Vendor Specific IE parsing: Starting from the fixed parameter part of the Beacon frame (offset 36), traverse all information elements, search for the Vendor Specific IE (Element ID = 0xDD), and verify the following characteristics: - OUI (Bytes 2-4): Verify if it is 0xFA0BBC (Wi-Fi Alliance assigned Remote ID OUI); - Vendor Type (Byte 5): Verify if it is 0x0D (ASTM Remote ID); - MsgCounter (Byte 6): Message counter used for subsequent deduplication; - MsgPack (starting from Byte 7): A data packet containing one or more Remote ID messages; (4) Message unpacking: Parse the Message Pack data and extract Remote ID messages of various types, including BasicID, Location, Authentication, SelfID, System, Operator ID and other message types; (5) Data field parsing: According to the ASTM F3411 standard, bit field parsing is performed on the data fields of each message type to extract key information such as UAV ID, location, altitude, speed, heading, and flight status; (6) RSSI Threshold Filtering: During the signal strength filtering process of the filtering module, a minimum signal strength threshold $RSSI_{threshold}$ (recommended value -85dBm) is set to filter weak signal interference. When the RSSI of the captured frame is lower than this threshold, the frame is discarded. The RSSI range that the ESP32-C3 can acquire is -100dBm to -20dBm.

[0029] This channel polling algorithm utilizes the WiFi promiscuous mode of the ESP32-C3 to achieve frame capture, traversing multiple channels through serial polling. It can be practically implemented on the ESP32-C3 chip. Echoing the previously mentioned technique of "optimizing signal strength and channel selection, including signal-to-noise ratio evaluation and dynamic channel switching," it achieves multi-channel coverage capture of drone signals in the 2.4GHz WiFi band.

[0030] For the 2.4GHz Bluetooth protocol parsing algorithm, BLE message parsing is based on the ASTM F3411-22 standard (Remote ID and Tracking) and adopts the following parsing process: (1) Protocol feature identification: Detect whether the BLE broadcast data contains the OpenDroneID signature code, specifically detecting the following two features: - Service UUID matching: Detect whether the Service UUID in the broadcast data is OpenDroneIDService UUID (0xFED90000-F315-4F60-9A18-C8FF689C9F6D). - ASTM Header Inspection: Checks whether the broadcast data contains the 0x16FAFF0D (ASTM Remote IDHeader Prefix) signature. (2) Message structure parsing: Parse the Message Pack structure in the broadcast data and extract the following fields: - Message counter (MsgCounter): Used for deduplication and message order maintenance; - Single Message Size: The number of bytes per message; - Message count (MsgPackSize): The total number of messages in the message packet; (3) Message type parsing: Parse each message in the Message Pack one by one and extract the RemoteID messages of each type, including message types such as Basic ID, Location, Authentication, Self ID, System, Operator ID, etc.; (4) OUI verification: Verify whether the OUI (Organization Unique Identifier) ​​in the data is a known drone manufacturer's OUI in order to filter out non-drone BLE devices.

[0031] For the 5GHz WiFi protocol parsing algorithm, the 5GHz WiFi protocol parsing algorithm is an extension of the 2.4GHz WiFi protocol parsing algorithm, and adopts a serial channel polling scheme that can be implemented on the RTL8711DAx chip.

[0032] (1) Channel Polling and Frame Acquisition: The 5GHz band has richer channel resources, but is limited by the single-channel operation mode of the RTL8711DAx chip, and adopts a serial polling mechanism similar to that of 2.4GHz. The specific implementation is as follows: - Polling Channel List: In accordance with the regulations for the 5GHz band in China, five commonly used non-DFS channels are selected for polling: - CH36: 5180MHz (UNII-1); - CH40: 5200MHz (UNII-1); - CH44: 5220MHz (UNII-1); - CH48: 5240MHz (UNII-1); - CH149: 5745MHz (UNII-3); Note: Avoid DFS channels (CH52-CH144) to prevent forced handover caused by radar signal interference; - Single channel listening time: The listening time T for each channel is set to 200ms by default and can be adjusted through software configuration (range 100ms-500ms). - Channel switching: Channel switching is performed using the WiFi driver interface of RTL8711DAx, with each switching taking approximately 80-150ms. - Promiscuous mode: Enable Monitor mode to capture 802.11 management frames in the 5GHz band; - RSSI Acquisition: Obtain signal strength through the RSSI interface provided by the driver; - DFS avoidance: The RTL8711DAx chip has a built-in hardware-level DFS function, which automatically switches channels when a radar signal is detected. This algorithm avoids this problem by excluding the DFS channel list. (2) Frame type identification: Same as the 2.4GHz WiFi parsing algorithm, identify the Beacon frame in the 802.11 management frame (frame control field subtype = 0x1000), and extract the source MAC address and BSSID in the frame header.

[0033] We also capture and analyze additional data frames supported by the 5GHz band (such as Action frames), because some drones use the NAN (Neighborhood Awareness Network) protocol to send Remote ID information through Action frames.

[0034] (3) Vendor Specific IE Resolution: Same as the 2.4GHz WiFi resolution algorithm, it searches for and verifies the RemoteID signature (OUI=0xFA0BBC, Vendor Type=0x0D). For the 5GHz band, the recognition of the following extended signatures has been added: - 802.11ah (Sub-1GHz) signature detection: suitable for long-range drone monitoring scenarios; - 802.11ax (Wi-Fi 6) signature detection: Remote ID parsing for new drones; (4) Message unpacking and data field parsing: Similar to the 2.4GHz WiFi parsing algorithm, each message type is parsed according to the ASTM F3411 standard. For the high-speed characteristics of the 5GHz band, support for the following extended message types has been added: - Enhanced Location message: Supports higher precision location information parsing; - Multi-hop message forwarding: Supports Remote ID resolution in drone swarm scenarios; (5) RSSI Threshold Filtering: Similar to the 2.4GHz WiFi parsing algorithm, set a minimum signal strength threshold RSSI_{threshold} (recommended value -80dBm) to filter weak signal interference. Due to the greater propagation loss in the 5GHz band, the threshold setting is slightly higher than that in the 2.4GHz band.

[0035] The 5GHz band has less interference and a wider channel bandwidth compared to the 2.4GHz band, enabling it to capture Remote ID signals transmitted by more drone models. In particular, some high-end drone models mainly use the 5GHz band for Remote ID broadcasting.

[0036] Step S03: The parsed data is filtered to obtain filtered data. The filtering process includes filtering false signals and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection.

[0037] In this embodiment of the invention, a self-developed detection and filtering algorithm is used to intelligently filter the parsed data. Filtering for false and interference signals includes time window filtering, geographic clustering analysis, and power distribution analysis. During time window filtering, verification is performed based on the message sending periodicity. The periodic detection formula is as follows: Normal cycle range: Verification logic: , t current t represents the timestamp of the current message being sent. previous Here, Δt is the timestamp of the previous message, and Δt is the time interval between two adjacent messages. In the geographic clustering analysis process, signals with similar geographical locations are clustered, and Euclidean distance is used to calculate the distance between two points. Where R is the Earth's radius (6371 km), lat1 is the latitude coordinate of the first point (usually in degrees or radians), lat2 is the latitude coordinate of the second point, lon1 is the longitude coordinate of the first point, lon2 is the longitude coordinate of the second point, and the clustering threshold is: In the power distribution analysis process, signals with abnormal power are excluded. Specifically, the moving average of RSSI is calculated. Anomaly detection: (Usually the 3σ principle), RSSI avg The moving average of RSSI, RSSI i Let N be the raw RSSI sample value at time i, and N be the sliding window size. curren t is the real-time RSSI sample value at the current moment; Identifying and filtering duplicate messages includes sequence number checking, content hashing, and time window deduplication. Specifically, during sequence number checking, a message counter is used to determine if a message is a duplicate. The duplicate determination is: if (msg_counter_new ≤ msg_counter_old) then duplicate. The time window allows messages with the same sequence number to be received again within 3 seconds. During content hashing, the SHA-256 algorithm is used to generate the message content hash value. A hash table is used to store the hash values ​​of the N most recent messages. During the deduplication process within a time window, messages from the same source are merged within a specified time window. The time window size is: Deduplication algorithm: based on a two-dimensional index of MAC address and serial number; Optimizing signal strength and channel selection includes signal-to-noise ratio (SNR) assessment and dynamic channel switching. During SNR assessment, the SNR metric for each channel is calculated. SNR calculation: Channel quality score: ,in, For channel stability factor (0-1), SNR dB RSSI is the signal-to-noise ratio value expressed in decibels (dB). dBm To receive signal strength indication, Noise_Floor dBm Using noise as the basis, Quality as the channel quality score, and Stability as the channel stability factor, during dynamic channel switching, the channel with the best signal-to-noise ratio is selected for continuous monitoring. The switching condition is: when the channel quality score drops by more than 20% for 5 seconds, a switch is performed. The new channel is selected as follows: , The channel quality score for the nth channel.

[0038] Step S04: Based on the identified communication protocol category, perform deep parsing on the data stream and extract structured information from the message, wherein the data stream is composed of the filtered data.

[0039] It should be noted that the data stream is subjected to frame structure decomposition, information element extraction, and bit field parsing in order to complete deep parsing; For frame structure decomposition, the complete wireless frame is split according to field functions to obtain multiple frame fields corresponding to different service functions. The frame fields contain information element fields used to carry Remote ID data. In the embodiment of the present invention, the complete 802.11 frame is separated by field in frame structure decomposition, including frame control field, duration field, address field, sequence control field, marker field, beacon interval field, capability field and information element. Specifically, the frame control field (2 bytes) contains information such as type and subtype; the duration field (2 bytes) is the frame duration; the address field (3×6 bytes) is the destination address, source address, and BSSID; the sequence control field (2 bytes) is the sequence number and fragment number; the marker field (2 bytes) is the timestamp, interval, and function marker; the beacon interval field (2 bytes) is the beacon transmission interval; the capability field (2 bytes) is the device capability information; and the information element (variable length) contains Remote ID data. For information element extraction, within the split frame fields, information elements related to the Remote ID are located and filtered. The format specifications of the information elements are identified, and the type identifier and the affiliated organization identifier of the information element are matched and verified. If the verification passes, it is determined as the target Remote ID information element. The information element format is: [Type (1 byte), Length (1 byte), Data (N bytes)], Vendor-Specific IE type: Remote ID OUI: Check the logic: ; For bit-field parsing, according to the Remote ID standard protocol specification, the target Remote ID information element is parsed bit by bit to restore the business meaning and business data corresponding to each field. This includes message type parsing. Status bit analysis: Direction encoding: .

[0040] In this embodiment of the invention, the structured information includes at least basic ID information, location information, operator information, timestamp information, and authentication information. The basic ID information includes drone type, ID type, and ID string. The drone type enumeration is as follows: ID type enumeration: ID string: maximum 20 bytes, UTF-8 encoded; Location information includes latitude and longitude (accurate to 7 decimal places), altitude, speed, heading, and status. Latitude resolution is as follows: Longitude analysis: High-resolution: Horizontal speed: Vertical velocity: lat_degrees is the parsed latitude value, lat_encoded is the encoded original latitude value, lon_degrees is the parsed longitude value, lon_encoded is the encoded original longitude value, alt_meters is the parsed altitude value, alt_encoded is the encoded original altitude value, speed_h is the parsed horizontal speed value, speed_horiz is the encoded original horizontal speed value, speed_mult is the mode selection bit, speed_v is the parsed vertical speed value, and speed_vert is the encoded original vertical speed value. Operator information: Operator location, operator ID; Timestamp information: sending time, timestamp precision; Timestamp parsing: timestamp_sec is the parsed actual timestamp value, and timestamp_encoded is the original encoded timestamp value transmitted in the message; Authentication information includes: authentication type, authentication data, and message integrity verification. The authentication type is: .

[0041] Step S05: Convert the data after deep parsing into a standardized format and output the data through a preset communication method.

[0042] It should be noted that the standardized formats include JSON format and low-altitude surveillance platform protocol messages, outputting data through Ethernet interfaces (supporting TCP / UDP protocols) and 4G networks (supporting TCP / UDP / MQTT / HTTP protocols). The Ethernet interface data packet format is as follows: 4G network data packet format: .

[0043] In summary, the UAV message parsing method proposed in this invention involves configuring three independent receiving channels for message capture in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the messages, multi-source signal interference is filtered to obtain denoised messages. Protocol feature analysis is performed on the denoised messages to identify the communication protocol category used by the UAV Remote ID, and a parsing algorithm is automatically matched and used for parsing. The parsed data is then filtered to obtain filtered data, including filtering false and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. Based on the identified communication protocol category, the data stream is deeply parsed to extract structured information from the messages, where the data stream consists of filtered data. The deeply parsed data is converted into a standardized format and output through a preset communication method. Specifically, this method solves the problem that traditional UAV monitoring equipment, due to its single protocol support and weak anti-interference capability, struggles to stably capture remote ID messages from UAVs at long distances or with weak signals, resulting in high false alarm rates and frequent false alarms.

[0044] Example 2 Embodiment 2 of this invention describes the application of a drone message parsing method in urban low-altitude safety management. Specifically, in large urban central areas (such as commercial districts), there is a large number of civilian and industrial drone activities, and the electromagnetic environment is complex (dense Wi-Fi hotspots, numerous Bluetooth devices, and strong interference from 5G / 4G base stations). Therefore, the first step is to deploy and configure the equipment. Specifically, multiple drone message receiving and parsing devices are deployed on the rooftops of high-rise buildings in the city to form a grid-like monitoring network. The effective coverage radius of a single device is approximately 500m (under urban conditions). It should be noted that each device contains: Two 2.4GHz radio frequency channels (to handle WiFi and Bluetooth signals respectively); One 5.8GHz radio frequency channel (processes 5.8GHz WiFi signals); Each RF front-end integrates a proprietary narrowband filter and low-noise amplifier (LNA) to suppress adjacent channel interference and improve small signal reception sensitivity (typical value: -95dBm @ 2.4GHz).

[0045] Subsequently, message capture and protocol identification were performed. Specifically, at the same time, the system captured three types of drone signals: Drone A: Broadcasts its Remote ID using 2.4GHz Wi-Fi Beacon frames; Drone B: Broadcasts Remote ID using a 2.4GHz Bluetooth LE Advertising packet; Drone C: Broadcasts Remote ID using 5.8GHz Wi-Fi Beacon frames; In addition, the main control chip performs protocol feature fingerprint analysis on the raw signal (such as MAC layer format, channel hopping mode, frame structure) and automatically matches it to the corresponding parsing engine.

[0046] Furthermore, the data was filtered and amplified. Specifically, it detected more than 20 Wi-Fi hotspots and more than 50 Bluetooth devices nearby, generating a large number of false messages. Through filtration: Packets that do not conform to the standard Remote ID format will be excluded. Merge messages sent repeatedly by the same drone within 1 second (deduplication); Dynamically select the channel with the highest signal-to-noise ratio for tracking; Ultimately, only 3 valid objectives (A, B, and C) were retained.

[0047] Finally, structured extraction and standardized output are performed. Specifically, the valid messages are deeply decoded to extract the following information:

[0048] The data is packaged in JSON format and uploaded in real time to the municipal low-altitude monitoring platform via MQTT protocol through a 4G LTE module.

[0049] It should be noted that, using the aforementioned drone message parsing method, the platform completes the entire process from signal reception to visual display within 10ms; a single device can stably track 50+ targets simultaneously, with an identification accuracy of 98.2%; and the system triggers an alarm and locates the target within 2 seconds.

[0050] Example 3 Embodiment 3 of the present invention provides a UAV message parsing system 200. Please refer to [link / reference]. Figure 2 Here is a structural block diagram of a UAV message parsing system 200, which includes: Configuration module 21 is used to configure three independent receiving channels for packet capture in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, multi-source signal interference is filtered to obtain the noise-reduced packets. The filtering of multi-source signal interference is achieved by using hardware-level noise reduction, software-level noise reduction, and interference suppression. In hardware-level noise reduction, each RF front-end integrates a proprietary narrowband filter and a low-noise amplifier to suppress adjacent channel interference and improve small signal reception sensitivity. The narrowband filter parameters are: center frequency 2.412GHz, bandwidth 5MHz, suppression ratio >40dB, and low-noise amplifier parameters are: noise figure <2dB, gain >15dB. In software-level noise reduction, random noise is reduced through adaptive threshold detection and signal averaging algorithms; In interference suppression, digital filter banks are used to suppress interference signals at specific frequencies; Analysis module 22 is used to perform protocol feature analysis on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID and automatically match the parsing algorithm. The matched parsing algorithm is used for parsing. Specifically, the noise-reduced message is sequentially subjected to frame type detection, OUI verification, protocol version verification and data structure analysis to complete the protocol feature analysis. In addition, based on the pre-built protocol feature library, the collected wireless message is first matched with the protocol features to identify and determine the wireless communication protocol type corresponding to the wireless message. Then, based on the probability of message occurrence corresponding to different wireless communication protocol types, the corresponding processing priority is configured for the message parsing task corresponding to each wireless communication protocol type. Finally, a multi-threaded pool technique is used to drive the concurrent operation of multiple protocol parsers in order to improve the packet parsing throughput. The number of threads in the multi-threaded pool is dynamically adjusted according to the number of CPU cores of the running device. The filtering module 23 is used to filter the parsed data to obtain filtered data. The filtering process includes filtering false signals and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. Filtering false signals and interference signals includes time window filtering, geographic clustering analysis, and power distribution analysis. Identifying and filtering duplicate messages includes sequence number checking, content hashing, and time window deduplication. Optimizing signal strength and channel selection includes signal-to-noise ratio assessment and dynamic channel switching; The parsing module 24 is used to perform deep parsing of the data stream according to the identified communication protocol category and extract structured information from the message, wherein the data stream is composed of the filtered data. In addition, the data stream is subjected to frame structure decomposition, information element extraction and bit field parsing. For frame structure decomposition, the complete wireless frame is split according to the field function to obtain multiple frame fields corresponding to different service functions. The frame fields contain information element fields used to carry Remote ID data. For information element extraction, in the split frame field, information elements related to Remote ID are located and filtered, the format specifications of information elements are identified, and the type identifier and the affiliated organization identifier of information elements are matched and verified. If the verification is successful, it is determined as the target Remote ID information element. For bit field parsing, according to the Remote ID standard protocol specification, the target Remote ID information element is parsed bit by bit to restore the business meaning and business data corresponding to each bit field; The structured information includes at least basic ID information, location information, operator information, timestamp information, and authentication information; The conversion module 25 is used to convert the data after deep parsing into a standardized format and output the data through a preset communication method.

[0051] Example 4 Embodiment 4 of the present invention proposes an electronic device, please refer to [link / reference]. Figure 3 This is a structural block diagram of an electronic device, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the UAV message parsing method described above.

[0052] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0053] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0054] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the UAV message parsing method described above.

[0055] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0056] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0057] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0058] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0059] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for parsing unmanned aerial vehicle (UAV) messages, characterized in that, The method includes: Three independent receiving channels are configured to capture packets in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, multi-source signal interference is filtered to obtain the noise-reduced packets. The protocol feature analysis is performed on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID, and the parsing algorithm is automatically matched and parsed. The parsed data is then filtered to obtain filtered data. The filtering process includes filtering out false and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. Based on the identified communication protocol category, the data stream is subjected to deep parsing, and structured information in the message is extracted, wherein the data stream is composed of the filtered data; The data after deep analysis is converted into a standardized format and output through a preset communication method.

2. The UAV message parsing method according to claim 1, characterized in that, The configuration includes three independent receiving channels for capturing messages in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the messages, the process of filtering multi-source signal interference to obtain the denoised messages employs hardware-level noise reduction, software-level noise reduction, and interference suppression to filter multi-source signal interference. In hardware-level noise reduction, each RF front-end integrates a proprietary narrowband filter and a low-noise amplifier to suppress adjacent channel interference and improve small signal reception sensitivity. The narrowband filter parameters are: center frequency 2.412GHz, bandwidth 5MHz, suppression ratio >40dB, and low-noise amplifier parameters are: noise figure <2dB, gain >15dB. In software-level noise reduction, random noise is reduced through adaptive threshold detection and signal averaging algorithms; In interference suppression, digital filter banks are used to suppress interference signals at specific frequencies.

3. The UAV message parsing method according to claim 2, characterized in that, In the process of performing protocol feature analysis on the denoised messages to identify the communication protocol category used by the UAV Remote ID and automatically matching the parsing algorithm, the denoised messages are sequentially subjected to frame type detection, OUI verification, protocol version verification, and data structure analysis to complete the protocol feature analysis.

4. The UAV message parsing method according to claim 3, characterized in that, The automatic matching and parsing algorithm first performs protocol feature matching on the collected wireless packets based on a pre-built protocol feature library to identify and determine the wireless communication protocol type corresponding to the wireless packets. Then, based on the probability of message occurrence corresponding to different wireless communication protocol types, the corresponding processing priority is configured for the message parsing task corresponding to each wireless communication protocol type. Finally, a multi-threaded pool technique is used to drive the concurrent operation of multiple protocol parsers to improve packet parsing throughput. The number of threads in the multi-threaded pool is dynamically adjusted according to the number of CPU cores of the running device.

5. The UAV message parsing method according to claim 4, characterized in that, In the step of filtering the parsed data to obtain filtered data, filtering false signals and interference signals includes time window filtering, geographic clustering analysis, and power distribution analysis. Identifying and filtering duplicate messages includes sequence number checking, content hashing, and time window deduplication. Optimizing signal strength and channel selection includes signal-to-noise ratio assessment and dynamic channel switching.

6. The UAV message parsing method according to claim 5, characterized in that, In the step of performing deep parsing of the data stream based on the identified communication protocol category, the data stream is subjected to frame structure decomposition, information element extraction, and bit field parsing. For frame structure decomposition, the complete wireless frame is split according to the field function to obtain multiple frame fields corresponding to different service functions. The frame fields contain information element fields used to carry Remote ID data. For information element extraction, in the split frame field, information elements related to Remote ID are located and filtered, the format specifications of information elements are identified, and the type identifier and the affiliated organization identifier of information elements are matched and verified. If the verification is successful, it is determined as the target Remote ID information element. For bit field parsing, according to the Remote ID standard protocol specification, the target Remote ID information element is parsed bit by bit to restore the business meaning and business data corresponding to each bit field.

7. The UAV message parsing method according to claim 6, characterized in that, In the step of extracting structured information from the message, the structured information includes at least basic ID information, location information, operator information, timestamp information, and authentication information.

8. A UAV message parsing system, characterized in that, For implementing the UAV message parsing method as described in any one of claims 1-7, the system comprises: The configuration module is used to configure three independent receiving channels for packet capture in the 2.4GHz and 5GHz frequency bands respectively. After acquiring the packets, it filters out multi-source signal interference to obtain noise-reduced packets. The analysis module is used to perform protocol feature analysis on the noise-reduced message to identify the communication protocol category used by the UAV Remote ID, and automatically match the parsing algorithm and use the matched parsing algorithm for parsing; The filtering module is used to filter the parsed data to obtain filtered data. The filtering process includes filtering spoof signals and interference signals, identifying and filtering duplicate messages, and optimizing signal strength and channel selection. The parsing module is used to perform deep parsing of the data stream based on the identified communication protocol category and extract structured information from the message, wherein the data stream is composed of the filtered data; The conversion module is used to convert the data after deep parsing into a standardized format and output the data through a preset communication method.

9. A computer-readable storage medium, characterized in that, include: The readable storage medium stores one or more programs that, when executed by a processor, implement the UAV message parsing method as described in any one of claims 1-7.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the UAV message parsing method according to any one of claims 1-7.

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