A low-altitude monitoring method based on multi-protocol fusion and a beacon terminal system

Abstract

This invention provides a low-altitude airspace monitoring method and beacon terminal system based on multi-protocol fusion, relating to the fields of low-altitude airspace monitoring, the Internet of Things, and communication scheduling. The method involves parsing the original positioning statement and battery voltage information to obtain positioning data and battery percentage data; filtering the positioning data to obtain noise-reduced trajectory data and flight trajectory records; performing boundary detection on the noise-reduced trajectory data and no-fly zone boundary data to obtain boundary judgment results; processing the noise-reduced trajectory data and preset flight path data to obtain collision avoidance interception judgment results and yaw markers, thereby obtaining flight compliance review results; and processing the flight trajectory records, flight compliance review results, and battery percentage data to obtain continuously active flight status display data and alarm trigger signals. This invention solves the problems of poor reliability, large positioning deviation, easy locking under high-concurrency read / write operations, and low efficiency of compliance review calculations in existing low-altitude airspace monitoring systems.

Description

Technical Field

[0001] This invention relates to the fields of low-altitude surveillance, Internet of Things and communication scheduling technology, and in particular to a low-altitude surveillance method and beacon terminal system based on multi-protocol fusion. Background Technology

[0002] In recent years, the low-altitude economy has rapidly emerged, with drones and other low-altitude aircraft being widely used in numerous fields such as power line inspection, urban security, agricultural plant protection, and emergency rescue. As the frequency and scope of low-altitude flights continue to expand, the need for safety management and control of low-altitude flights has become increasingly urgent. Meanwhile, standards stipulate tiered management and precise policy implementation for low-altitude flights, clearly requiring differentiated management between pilot and command terminals, full-process compliant traceability of flight data, and efficient dual management of no-fly zones. Contemporary society has four core needs for low-altitude regulation: accurate positioning and review, absolute reliability of equipment, multi-terminal collaboration, and scalability to adapt to multiple scenarios.

[0003] However, existing low-altitude airspace monitoring systems generally suffer from numerous technical bottlenecks, making it difficult to meet actual regulatory needs and standard control requirements. Specifically, these bottlenecks manifest in the following aspects: Firstly, at the hardware level, traditional low-altitude surveillance equipment often adopts a distributed modular design, which suffers from problems such as redundant size, severe internal electromagnetic interference, and poor overall reliability. In complex low-altitude environments, these devices are highly susceptible to network outages, system crashes, and other malfunctions, making it impossible to achieve stable 24-hour continuous surveillance.

[0004] Secondly, at the data acquisition and positioning level, the raw coordinates acquired by existing hardware are prone to severe offsets of hundreds of meters when directly applied to electronic maps; at the same time, the lack of an effective positioning signal quality assessment mechanism leads to significant deviations in monitoring and positioning. Furthermore, there is a security risk of unauthorized data transmission during data transfer, and memory overflows are prone to occur during the parsing stage, directly threatening system stability.

[0005] Secondly, in terms of cloud processing and compliance review, the existing system's cloud processing capabilities are weak. When faced with high-frequency data write requests from multiple IoT devices and large-scale queries on the web, single-file databases are prone to I / O read / write deadlocks; traditional solutions that introduce heavyweight geospatial databases will cause server resources to be quickly exhausted, resulting in extremely low efficiency and excessive latency in spatial compliance reviews (such as boundary detection and collision avoidance).

[0006] Finally, in terms of terminal collaboration and interaction, the existing terminal interaction experience is poor. Web monitoring terminals are prone to screen flickering when refreshing status; mobile terminals, due to directly handling highly complex polygon mathematical calculations, often lead to full CPU load, severe overheating, and lag. Simultaneously, frequent background switching on mobile terminals often results in zombie threads causing memory overflow (OOM) and abnormal power consumption, and network fluctuations can easily cause temporary freezes. Furthermore, existing systems generally lack hierarchical permission control and end-to-end data traceability capabilities, failing to effectively distinguish the operational permissions of management and pilots. No-fly zone control also fails to balance the rigid, fixed controls stipulated in standards with the customized needs of actual business scenarios, making it difficult to meet the standardized management requirements for low-altitude flights as stipulated in the standards. Summary of the Invention

[0007] To address the aforementioned shortcomings in existing technologies, this invention provides a low-altitude surveillance method and beacon terminal system based on multi-protocol fusion, which solves the problems of poor reliability, large positioning deviation, easy locking during high-concurrency read / write operations, and low computational efficiency for compliance review in existing low-altitude surveillance systems.

[0008] To achieve the aforementioned objectives, the technical solution adopted by this invention is: a low-altitude surveillance method based on multi-protocol fusion, comprising: S1: Based on the acquired original location statement and battery voltage information, a parsing algorithm with pre-allocated static memory is used to obtain location data containing real-time latitude, longitude and altitude, as well as battery percentage data. S2: Based on the positioning data, the timestamp and distance comparison algorithm is used for filtering and log mode writing to obtain noise-reduced trajectory data and concurrently stored flight trajectory records; S3: Using a ray-mapping computational mechanism, cross-boundary detection is performed on the noise-reduced trajectory data and the preset no-fly zone boundary data to obtain the cross-boundary determination result; S4: Using line segment intersection verification and spherical curvature conversion mechanism, the noise-reduced trajectory data and preset route data are processed to obtain the collision avoidance interception judgment result and yaw mark; S5: Based on the boundary crossing judgment result, the collision avoidance interception judgment result, and the yaw mark, the result summary logic is used to process the results and obtain the flight compliance review result. S6: Utilizing spatial dimensionality reduction filtering and lifecycle detection mechanisms, it processes flight trajectory records, flight compliance review results, and battery percentage data to obtain continuously active flight status display data and alarm trigger signals, thus completing low-altitude supervision based on multi-protocol fusion.

[0009] Further, S2 includes: Based on the location data, the stationary time data and drift noise data are removed by the timestamp comparison and distance threshold judgment algorithm to obtain the noise-reduced trajectory data; By utilizing the write-ahead log mode mechanism and the connection-level queuing sleep lock mechanism, concurrent write requests are processed for the noise-reduced trajectory data to obtain concurrently stored flight trajectory records.

[0010] Further, S3 includes: Based on the noise-reduced trajectory data and the polygon vertex coordinate sequence used as the preset no-fly zone boundary data, a ray-based judgment algorithm with a small constant offset is used to filter the preconditions and obtain the boundary judgment result.

[0011] Further, S4 includes: Based on the denoised trajectory data and the boundary line segments used as preset flight path data, the cross-verification of line segment intersections is performed using a three-point counterclockwise sorting judgment algorithm based on vector cross product, and the collision avoidance interception judgment result is obtained. Based on the line segments in the noise-reduced trajectory data and the preset flight path data, the planar projection ratio from the positioning point to the line segment is calculated using a dimension reduction projection algorithm to extract the shortest distance point. The spherical curvature conversion formula is then called for the shortest distance point to obtain the converted distance. The calculated distance is compared with the preset dynamic yaw threshold. If the calculated distance is greater than the dynamic yaw threshold, a yaw marker is output.

[0012] Furthermore, the expression for the result of the vector cross product is: CCW(A,B,C)=(Cy-Ay)*(Bx-Ax)-(By-Ay)*(Cx-Ax); Wherein, CCW(A,B,C) represents the result of the cross product of the vectors formed by the three coordinate points, used to determine the counterclockwise arrangement relationship, and serves as an intermediate calculation parameter; Ax represents the longitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Ay represents the latitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Bx represents the longitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; By represents the latitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Cx represents the longitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data; Cy represents the latitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data.

[0013] Further, S6 includes: Based on the extreme points in the pre-parsed preset no-fly zone boundary data, a bounding box cache object is constructed using an extreme value comparison algorithm; Based on the bounding box cache object and the real-time positioning data in the flight trajectory record, the bounding box numerical determination process is performed first using a logic short-circuit mechanism. When the real-time positioning data enters the area of ​​the bounding box cache object, the polygon edge calculation algorithm is used to calculate and obtain the output result of the polygon edge calculation algorithm. Based on the received asynchronous update task, the redundant hanging process is intercepted and removed before the interface rendering process by using the life cycle state detection mechanism. Based on the exception capture mechanism, the next round of timer is forcibly loaded in the closed loop statement. Combined with the output of the polygon edge calculation algorithm, the flight status display data that is kept alive is obtained. Based on the flight compliance review results and battery percentage data, an alarm trigger signal is obtained when the flight compliance review result is a violation, thus completing low-altitude supervision based on multi-protocol fusion.

[0014] Furthermore, the expression for the output result of the polygon edge calculation algorithm is as follows: lon_{cross}=(pj.lon-pi.lon)*(lat-pi.lat) / (pj.lat-pi.lat+1e-9)+pi.lon; Wherein, lon_{cross} represents the longitude of the intersection point of the ray and the polygon edge at the current latitude, which is the output result of the polygon edge calculation algorithm and is an intermediate calculation parameter; lat represents the real-time latitude of the target aircraft, obtained from the positioning data uploaded by the hardware acquisition terminal; pi.lat represents the latitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pi.lon represents the longitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lat represents the latitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lon represents the longitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; 1e-9 represents a small offset constant, which is a self-designed anti-collapse and anti-shaking constant to prevent the division-to-zero anomaly caused by the two vertices being on the same horizontal line.

[0015] This invention provides a low-altitude surveillance beacon terminal system based on multi-protocol fusion, comprising: The data acquisition and parsing module is used to obtain positioning data containing real-time latitude, longitude and altitude, as well as battery percentage data, based on the acquired original positioning statement and battery voltage information using a parsing algorithm with pre-allocated static memory. The data denoising and storage module is used to filter and write in log mode based on the positioning data using a timestamp and distance comparison algorithm to obtain denoised trajectory data and concurrently stored flight trajectory records. The boundary crossing detection module is used to perform boundary crossing detection based on the noise-reduced trajectory data and the preset no-fly zone boundary data, using a ray-mapping computation mechanism to obtain the boundary crossing judgment result. The collision avoidance and yaw calculation module is used to obtain the collision avoidance interception judgment result and yaw mark based on the noise-reduced trajectory data and the preset route data, using the line segment intersection verification and spherical curvature conversion mechanism. The compliance review summary module is used to obtain the flight compliance review result based on the boundary crossing judgment result, the collision avoidance interception judgment result, and the yaw marker, using the result summary logic; The situation display and alarm triggering module is used to obtain continuously active flight situation display data and alarm triggering signals based on the flight trajectory record, the flight compliance review result and the battery percentage data, using spatial dimensionality reduction filtering and life cycle detection mechanisms, and to complete low-altitude supervision based on multi-protocol fusion.

[0016] Furthermore, the data acquisition and parsing module is specifically implemented by an integrated hardware acquisition terminal, which includes a customized printed circuit board and a microcontroller unit, a positioning and communication integrated module, a charge and discharge management module, a hardware watchdog module and an expansion interface module carried on the customized printed circuit board. The integrated positioning and communication module is connected to the microcontroller unit via a universal asynchronous transceiver interface, and is used to extract standard positioning message statements as the original positioning statements. The charge / discharge management module is electrically connected to the microcontroller unit and is used to monitor the power supply status and output voltage data as battery voltage information. The hardware watchdog module is connected to the microcontroller unit and is used to trigger a hardware-level restart when the main loop times out; The microcontroller unit acquires the original positioning statement and the battery voltage information, and uses a parsing algorithm with pre-allocated static memory to extract and transform the data to obtain positioning data containing real-time latitude, longitude, and altitude, as well as the battery percentage data.

[0017] Furthermore, the customized printed circuit board is a four-layer printed circuit board, wherein: The microcontroller unit and the expansion interface module are deployed at the top level; The second layer houses the charge and discharge management module, and integrates reverse connection protection, overcurrent and overvoltage protection circuits, and a voltage regulator chip. The third layer is separately deployed with the integrated positioning and communication module, and the third layer is covered with a grounded electromagnetic shielding layer for suppressing signal interference. The bottom layer is a fully covered grounding layer; The customized printed circuit board is encapsulated within an engineering plastic sealed housing with a built-in waterproof sealing ring and a dustproof and waterproof protection rating.

[0018] The beneficial effects of this invention are as follows: This invention provides a low-altitude surveillance method based on multi-protocol fusion, which uses a static memory pre-allocation parsing algorithm to process the original positioning statement and battery voltage information, avoiding memory fragmentation and overflow problems caused by frequent dynamic memory allocation, and ensuring stable output of basic data.

[0019] By using a timestamp and distance comparison algorithm for filtering, invalid location data is accurately eliminated, keeping statistical errors within an extremely low range. Combined with log-mode writing, concurrent write request processing is achieved during high-frequency data uploads, improving the system's concurrent processing throughput.

[0020] Employing a ray-based computational mechanism based on pure mathematical calculations, line segment intersection verification, and spherical curvature conversion mechanism, this system processes noise-reduced trajectory data and no-fly zone and flight route data. This eliminates the high computational cost of traditional space review, ensuring that the output flight compliance review results are highly real-time and accurate.

[0021] By utilizing spatial dimensionality reduction filtering and lifecycle detection mechanisms, irrelevant data is filtered and redundant suspension processes are intercepted when processing flight trajectory records and compliance review results, reducing terminal computing power consumption and ensuring the continuous survival of flight status display data and the timeliness and accuracy of alarm trigger signals. Attached Figure Description

[0022] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is a schematic diagram of a low-altitude surveillance beacon terminal system based on multi-protocol fusion, as shown in some embodiments of this specification. Figure 2 This is an exemplary flowchart of a low-altitude regulatory method based on multi-protocol fusion, as shown in some embodiments of this specification. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] Example 1 Figure 1 This is a schematic diagram of a low-altitude surveillance beacon terminal system based on multi-protocol fusion, as shown in some embodiments of this specification.

[0025] In some embodiments, the low-altitude surveillance beacon terminal system based on multi-protocol fusion may include a data acquisition and parsing module, used to obtain positioning data including real-time latitude, longitude, and altitude, as well as battery percentage data, based on the acquired original positioning statement and battery voltage information using a parsing algorithm with pre-allocated static memory; a data denoising and storage module, used to filter and log-mode write the positioning data using a timestamp and distance comparison algorithm to obtain denoised trajectory data and concurrently stored flight trajectory records; and a boundary crossing detection module, used to perform boundary crossing detection using a ray-matrix algorithm based on the denoised trajectory data and preset no-fly zone boundary data to obtain a boundary crossing determination result. The collision avoidance and yaw calculation module is used to obtain the collision avoidance interception judgment result and yaw mark based on the noise-reduced trajectory data and preset flight path data, using line segment intersection verification and spherical curvature conversion mechanism; the compliance review summary module is used to obtain the flight compliance review result based on the boundary violation judgment result, the collision avoidance interception judgment result and the yaw mark, using result summary logic; the situation display and alarm triggering module is used to obtain continuously active flight situation display data and alarm triggering signal based on the flight trajectory record, the flight compliance review result and the battery percentage data, using spatial dimensionality reduction filtering and life cycle detection mechanism, to complete low-altitude supervision based on multi-protocol fusion.

[0026] In some embodiments, the data acquisition and parsing module is specifically implemented by an integrated hardware acquisition terminal, which includes a customized printed circuit board and a microcontroller unit, a positioning and communication integrated module, a charge and discharge management module, a hardware watchdog module, and an expansion interface module mounted on the customized printed circuit board. The positioning and communication integrated module is connected to the microcontroller unit through a universal asynchronous transceiver interface and is used to extract standard positioning message statements as the original positioning statements. The charge and discharge management module is electrically connected to the microcontroller unit and is used to monitor the power supply status and output voltage data as battery voltage information. The hardware watchdog module is connected to the microcontroller unit and is used to trigger a hardware-level restart when the main loop times out. The microcontroller unit acquires the original positioning statements and the battery voltage information, and uses a parsing algorithm with pre-allocated static memory to extract and transform them to obtain positioning data containing real-time latitude, longitude, and altitude, as well as the battery percentage data.

[0027] In some embodiments, the customized printed circuit board is a four-layer printed circuit board, wherein: the top layer houses the microcontroller unit and the expansion interface module; the second layer houses the charge and discharge management module, and integrates reverse connection protection, overcurrent and overvoltage protection circuits, and a voltage regulator chip; the third layer houses the positioning and communication integrated module separately, and the third layer is covered with a grounding electromagnetic shielding layer for suppressing signal interference; the bottom layer is a fully covered grounding layer; the customized printed circuit board is entirely encapsulated in an engineering plastic sealed shell with a built-in waterproof sealing ring and a dustproof and waterproof protection rating.

[0028] In some specific embodiments, the microcontroller unit may use the ESP32-C3 core chip, the integrated positioning and communication module may use the Air780EG communication module that integrates 4G and GNSS dual-mode positioning, the charging and discharging management module may use the TP4056 chip, the universal asynchronous transceiver interface is a UART interface, the standard positioning message statement is an NMEA statement, and the engineering plastic sealed shell with dustproof and waterproof protection level may be an IP67-rated protective shell.

[0029] In some embodiments, the data denoising and storage module can be deployed in a cloud processing hub. The cloud processing hub is built from an API cluster and uses a time-series spatial database (such as SQLite and WAL mode). After receiving location data, this module compares timestamps to remove stationary periods where two reports exceed a preset threshold, filters noise points where the drift distance is less than a preset threshold, obtains denoised trajectory data, and uses WAL write-ahead log mode and a queuing lock mechanism to complete high-concurrency writing, forming a flight trajectory record.

[0030] In some embodiments, the IoT beacon hardware acquisition terminal for the data acquisition and parsing module has specific parameters in terms of physical structure and power consumption control to adapt to complex low-altitude environments. Specifically, the overall size of the hardware is controlled within 80mm × 50mm × 15mm, and the weight is less than or equal to 50g, so as not to affect the payload and endurance of the target aircraft. In terms of power consumption control, combined with the battery voltage information extracted by the parsing algorithm based on pre-allocated static memory, the system sets the operating power consumption to be less than or equal to 100mW and the standby power consumption to be less than or equal to 10mW. At the same time, the power management module integrates reverse connection protection, overcurrent protection, and overvoltage protection circuits to ensure that the hardware is not damaged due to abnormal current when acquiring the original positioning statement. In addition, when performing device identity updates, this module introduces Flash physical wear protection logic: when receiving an OTA instruction to dynamically modify the device identifier, the processor will first check whether the new identifier sent is consistent with the existing local identifier before writing to non-volatile memory (NVS). If they are consistent, the instruction will be discarded directly, thereby avoiding invalid high-frequency erasure and writing caused by the MQTT message retention mechanism, extending the hardware life and ensuring the stable output of subsequent positioning data.

[0031] In some embodiments, the boundary crossing detection module specifically runs on the spatial geometry calculation engine of the cloud processing center. Based on denoised trajectory data and preset no-fly zone boundary data (including standard fixed no-fly zones and custom no-fly zones), this module uses a ray casting method with a small constant offset to perform boundary crossing detection and outputs the boundary crossing judgment result.

[0032] In some embodiments, for the determination of the high-velocity bounding box in the boundary crossing detection module, due to the high maneuverability of low-altitude aircraft, relying solely on static AABB bounding boxes may lead to alarm lag. Therefore, the system introduces a dynamic look-ahead bounding box expansion algorithm based on velocity vectors. The preset no-fly zone boundary data includes fixed no-fly zone data and custom no-fly zone data. Among them, the fixed no-fly zone data is loaded into local memory during the system initialization phase according to preset external security standards and is protected by the access control and interception mechanism; the custom no-fly zone data is generated by the Web dispatch and command center based on graphical drawing instructions and synchronized to the temporal spatial database via the cloud processing center.

[0033] Specifically, the processor not only pre-calculates the maximum and minimum latitude and longitude of the no-fly zone to construct the basic bounding box cache object, but also obtains the time difference and position offset of two adjacent positioning data in the flight trajectory record in real time, and calculates the instantaneous velocity vector and yaw angle of the target aircraft. Based on this, the bounding box boundary determination condition is optimized as follows: the four extreme boundaries of the bounding box cache object are extended along the instantaneous velocity vector direction by a look-ahead distance, the magnitude of which is proportional to the instantaneous velocity.

[0034] The extended dynamic judgment logic can trigger a logic short circuit in advance, entering a high-precision polygon edge calculation algorithm before the target aircraft approaches the no-fly zone boundary during a high-altitude, high-speed dive or sharp turn, thus adding a high-risk collision warning state to the boundary violation judgment result. This improvement not only reduces the computing power consumption during normal cruise, but also solves the problem of lag in compliance review under high-maneuverability flight.

[0035] In some embodiments, the collision avoidance and yaw calculation module specifically runs on the spatial geometry calculation engine of the cloud processing center. This module uses a three-point counterclockwise sorting judgment algorithm based on vector cross product to perform line segment intersection verification, and verifies the relationship between the denoised trajectory data and the preset flight path data to output the collision avoidance interception judgment result; at the same time, it uses a dimensionality reduction projection algorithm and a spherical curvature conversion formula to calculate the shortest distance, and compares it with the dynamic yaw threshold to output a yaw mark.

[0036] In some embodiments, for short-range dimensionality reduction projection calculations in the collision avoidance and yaw calculation module, considering that the Earth is an irregular ellipsoid, conventional planar projections will produce deformation errors at different latitudes, leading to inaccurate short-range yaw judgments. To improve creativity and computational accuracy, the processor introduces an Earth curvature compensation factor that is dynamically adjusted with real-time latitude when executing the dimensionality reduction projection algorithm.

[0037] Specifically, the calculation process for the converted distance includes: first, extracting the shortest distance plane projection point from the positioning point to the flight path segment; then, calculating the curvature compensation factor based on the real-time latitude of the target aircraft. The expression for the curvature compensation factor is: K_c = 1 + (e^2 * cos^2(lat)) / (1 - e^2); Where K_c is the curvature compensation factor, e is the Earth's first eccentricity constant, and lat is the real-time latitude of the target spacecraft.

[0038] Finally, the preliminary converted distance obtained by calling the spherical curvature conversion formula is multiplied by the curvature compensation factor K_c to obtain the final converted distance. This mechanism ensures the absolute accuracy of short-distance yaw marker triggering in cross-latitude, large-scale inspection scenarios.

[0039] In some embodiments, the compliance review summary module operates in a cloud-based processing hub. This module integrates the boundary crossing determination results, collision avoidance interception determination results, yaw markers, and no-fly zone verification to arrive at the final flight compliance review result.

[0040] In some embodiments, the situation display and alarm triggering module can be deployed distributed across a web-based dispatch and command center and an Android mobile command and control terminal. The web terminal uses a front-end technology stack and map API to achieve flicker-free incremental updates; the mobile terminal uses a native architecture and map SDK. This module utilizes a pre-computed zone cache to construct bounding box cache objects, performs axis-aligned bounding box (AABB) logic short-circuit filtering, and combines a lifecycle detection mechanism to intercept background processes. When a compliance review signal indicating a violation is received, the terminal triggers an alarm signal combining sound, light, and vibration, continuously updating the flight situation display data.

[0041] In some embodiments, the web-based dispatch and command center is built on HTML5 / JS and a map JS API. This center integrates a mouse drawing tool (such as AMap.MouseTool), supporting manual configuration of preset no-fly zone boundary data and preset flight path data, and setting asynchronous update tasks including yaw alarm thresholds. After configuration, the center uses time selection and bounding box filtering logic to replay and render the flight situation display data.

[0042] The Android mobile command and control terminal adopts a native architecture and integrates a map SDK. Based on a preset permission-based hierarchical interception mechanism, the terminal only provides a display view containing real-time location data and flight compliance review results. By introducing a latitude and longitude linear interpolator (such as LatLngEvaluator) and an animation execution object (such as ValueAnimator), the terminal generates a uniform sliding animation within a two-frame asynchronous update task interval. Simultaneously, for boundary violation judgment results, yaw markers, and abnormal no-fly periods, the terminal calls a hardware interface to trigger alarm signals including sound, light, and vibration.

[0043] In some embodiments, for the situation display and alarm triggering module, the Android mobile command and control terminal implements a strict anti-freeze mechanism in its underlying code logic to ensure the continuous persistence of flight situation display data. Specifically, upon receiving an asynchronous update task based on flight trajectory records, a lifecycle state detection mechanism is used to perform a judgment before interface rendering: the state of the current user interface (UI) fragment is determined logically, i.e., the "!isAdded() ||getActivity() == null" detection is executed. If it is determined that the user interface has failed or has been switched to the background, a callback removal function is immediately called to intercept and clear redundant suspended processes to prevent memory overflow (OOM). At the same time, the underlying automatic retry mechanism is disabled in the network communication engine (RetryOnConnectionFailure=False) to avoid silent retry blocking for tens of seconds in weak network environments. When processing the output results of the polygon edge calculation algorithm, based on the exception capture mechanism, the next round of data polling timer, for example, 1000ms, is forcibly encapsulated in the "finally" statement of the core execution block. Regardless of whether the network request is successful, the parsing fails, or the user switches to the background, the mounting of the next round of timer is guaranteed, completely eliminating the phenomenon of data polling freeze.

[0044] In some embodiments, for near-field low-altitude countermeasures and detection in offline environments, the situation display and alarm triggering module integrates a detailed local sniffing and detection protocol in the mobile command and control terminal. In addition to conventional network reception, the Bluetooth scanner on the mobile command and control terminal supports Legacy broadcast, Coded PHY long-range coded broadcast, and Extended Broadcast (Ext Adv) protocols; the Wi-Fi detection function supports Wi-Fi Beacon and Wi-Fi NaN Aware protocols. When the network is interrupted, the system switches to local sniffing mode, using the aforementioned protocols to receive broadcast signals from nearby drones as an alternative source of location data, ensuring that the alarm triggering signal remains effective even in extreme emergency scenarios.

[0045] In some embodiments, the core processes of a low-altitude regulatory beacon terminal system based on multi-protocol fusion include: Initialization process: The hardware acquisition terminal performs a power-on self-test, starts the state machine, and initializes the communication connection; the cloud loads no-fly zone data; the terminal completes map initialization and permission verification. Acquisition and upload process: The hardware acquisition terminal periodically acquires positioning and power data, processes it through static memory parsing logic, uploads it to the cloud via multiple protocols, and resets the hardware watchdog. Cloud processing and command issuance process: The cloud performs coordinate correction, boundary detection, and yaw calculation, and synchronously issues compliance review results to the dispatch terminal and mobile terminal; the dispatch terminal renders the situation in real time, and the mobile terminal performs animation interpolation and alarm judgment. Anomaly recovery and data archiving process: The device triggers a hard restart of the watchdog when offline; the cloud performs long-term storage and archiving of complete flight data and generates statistical reports.

[0046] In some embodiments, a low-altitude surveillance beacon terminal system based on multi-protocol fusion can be used to execute a low-altitude surveillance beacon terminal method based on multi-protocol fusion, including: S1: Based on the acquired original positioning statement and battery voltage information, using a statically pre-allocated memory parsing algorithm, obtaining positioning data containing real-time latitude, longitude, and altitude, as well as battery percentage data; S2: Based on the positioning data, using a timestamp and distance comparison algorithm for filtering and log mode writing, obtaining noise-reduced trajectory data and concurrently stored flight trajectory records; S3: Using a ray-mapping mechanism, comparing the noise-reduced trajectory data with preset no-fly zone boundary data... S4: Using line segment intersection verification and spherical curvature conversion mechanism, the noise-reduced trajectory data and preset route data are processed to obtain the collision avoidance interception judgment result and yaw marker; S5: Based on the boundary violation judgment result, collision avoidance interception judgment result and yaw marker, the result summary logic is used to process the data to obtain the flight compliance review result; S6: Using spatial dimensionality reduction filtering and life cycle detection mechanism, the flight trajectory record, flight compliance review result and battery percentage data are processed to obtain the flight status display data and alarm trigger signal that are kept alive, thus completing low-altitude supervision based on multi-protocol fusion.

[0047] In some embodiments, the system can be applied to the following scenarios: Drone inspection and plant protection supervision: The hardware data acquisition terminal is installed on inspection or plant protection drones; the dispatch terminal plans the operation route and operation area; the mobile terminal allows pilots to view compliance status in real time; and the cloud automatically archives operation mileage and duration data. Urban low-altitude and campus security: Dedicated no-fly zone boundary data is set in core areas; the dispatch terminal globally monitors devices entering this airspace; and alarm signals are immediately pushed to security terminals upon detecting violations. Emergency rescue and event scheduling: Temporary safe routes are quickly planned through the dispatch terminal; the mobile terminal initiates local sniffing and detection of device status in offline environments, ensuring compliance and safety of multi-device collaborative flight.

[0048] Drone Inspection and Supervision: IoT beacons are installed on drones used for power or oil and gas inspections. A web-based dispatch and command center plans specific pre-set inspection routes, and pilots can view flight compliance review results in real time on mobile devices. The entire flight path is recorded in the cloud, ensuring comprehensive traceability throughout the inspection process.

[0049] Urban low-altitude security: Fixed no-fly zone boundary data are established in key areas such as government buildings and train stations. The web-based system provides global monitoring of aircraft entering this airspace. Once the boundary detection module outputs a boundary violation determination, an alarm trigger signal is immediately pushed to the mobile devices of security personnel for rapid interception.

[0050] Emergency rescue dispatch: At the disaster site, temporary safe pre-set routes can be quickly planned via the web interface to avoid dangerous areas. The mobile terminal utilizes the integrated Bluetooth and Wi-Fi NaN Aware protocols to achieve near-field drone collaborative monitoring in areas with network outages, ensuring that rescue formations do not encounter collision avoidance and interception decisions.

[0051] Agricultural plant protection monitoring: Customized polygonal boundaries are set for agricultural drones to serve as temporary no-fly zones. The pilot's terminal receives yaw markers in real time to prevent drones from spraying across areas or accidentally entering residential areas, while the cloud automatically calculates the actual plant protection area and operation time based on noise-reduced trajectory data.

[0052] Low-altitude event management: At drone racing or performance events, to address the risk of overlapping trajectories caused by multiple drones taking off simultaneously, the system sets up a dedicated no-fly zone boundary for the event. Collision avoidance and interception decisions are calculated in real time, and violations are simultaneously recorded in the cloud-based flight trajectory log for post-event adjudication.

[0053] Campus Low-Altitude Control: This involves designating the area surrounding the campus as a dedicated no-fly zone. By deploying a low-altitude monitoring system, abnormal location data of drones intruding into campus airspace is processed in real time. Security terminals, guided by alarm trigger signals, quickly locate and handle drones that have illegally entered the campus.

[0054] In some embodiments of this specification, the processor utilizes a low-altitude surveillance beacon terminal system based on multi-protocol fusion to execute a low-altitude surveillance method based on multi-protocol fusion. First, underlying hardware defects are addressed through integrated design and layered protection. The system employs a deeply integrated design of microprocessor, positioning and communication module, and power management. Combined with layered PCB layout and electromagnetic shielding technology, mutual interference between 4G and GPS signals is physically suppressed. IP67-level sealing protection and extremely low standby power consumption enable the hardware to fully adapt to harsh high and low temperature environments, as well as rain, snow, and dust, achieving long-lasting operation. The built-in watchdog hardware-level hard reboot mechanism completely eliminates the persistent problem of device network outages and apparent freezing, ensuring stable operation of the system base around the clock.

[0055] Second, a seamless closed loop of end-edge-cloud-network collaboration is constructed. The system achieves end-to-end control, including pre-event identity registration and association, millisecond-level compliance review and trajectory tracking during the event, and automatic post-event archiving and aggregation. The web terminal features global, flicker-free incremental updates, while mobile terminals display smooth interpolated animations. The cloud handles core workloads, effectively distributing the computing load. This not only ensures high data synchronization across multiple terminals but also enables near-field detection on mobile devices using local protocols in offline environments, greatly expanding the system's availability in extreme scenarios such as network outages and field operations.

[0056] Third, the system implements tiered and dual control in accordance with standards. Strict access control is implemented in the business logic. The web interface, acting as the dispatch and command center, has exclusive configuration permissions for no-fly zone mapping, flight route planning, and data auditing; while the mobile interface only retains data viewing and unauthorized data reception permissions. Combined with a dual-layer mechanism of fixed no-fly zones as stipulated by standards and flexible custom no-fly zones, this satisfies both the absolute prohibition of flight in core areas as required by standards and the personalized area isolation needs of different business units such as power line inspection and campus security. This design eliminates safety hazards caused by pilot misoperation or malicious modification. Specifically, the system performs full lifecycle control encompassing pre-event, during-event, and post-event dimensions. In the pre-event control phase, the hardware terminal device ID and location information are entered and persistently stored in the cloud-based flight trajectory record via commands. In the during-event control phase, based on the flight compliance review results continuously output by the compliance review summary module, the flight status data is displayed on the terminal without flickering. During the post-event control phase, the flight trajectory records and noise-reduced trajectory data corresponding to specific device IDs are extracted using a database reading mechanism, and statistical reports containing cumulative flight time and flight distance are generated through dimensionality reduction calculations.

[0057] Fourth, it possesses extremely strong application versatility and deployment economy. This system avoids the need for expensive hardware servers and computing power, relying solely on short-circuit detection in the underlying software logic, log pattern reconstruction, and a purely mathematical, minimalist calculation engine. This allows it to achieve compliance review efficiency comparable to heavy-duty military-grade systems on conventional servers and ordinary mobile terminals. Its contactless and lightweight characteristics enable seamless installation on most industrial and civilian drones on the market, facilitating rapid deployment in areas such as event security, agricultural plant protection, and urban security, demonstrating significant market promotion and technological and economic benefits.

[0058] Example 2 Figure 2 This is an exemplary flowchart illustrating a low-altitude regulatory method based on multi-protocol fusion, according to some embodiments of this specification. Figure 2 As shown, the process includes the following steps. In some embodiments, the process may be executed by a processor.

[0059] S1: Based on the acquired original location statement and battery voltage information, a parsing algorithm with pre-allocated static memory is used to obtain location data containing real-time latitude, longitude, and altitude, as well as battery percentage data.

[0060] The raw location statement is the unparsed raw location message data generated by the hardware module. For example, the raw location statement may include an NMEA format string containing raw characters such as time, latitude and longitude, altitude, and number of satellites.

[0061] In some embodiments, the IoT Beacon hardware acquisition terminal can obtain the original location statement from the integrated positioning and communication module via AT and CGNSF commands.

[0062] Battery voltage information is the current voltage status data of the device's power management module. For example, battery voltage information may include the real-time analog or digital discharge voltage value of a 3.7V lithium battery.

[0063] In some embodiments, the microprocessor can obtain battery voltage information from the charge / discharge management module via AT and CBC instructions.

[0064] Location data consists of spatial coordinates and altitude information with clear physical meaning extracted after parsing. For example, location data may include truncated longitude, latitude, altitude, and the number of available satellites.

[0065] In some embodiments, the processor can extract and transform the original location statement to obtain location data through a parsing algorithm with pre-allocated static memory.

[0066] Battery percentage data is the percentage of remaining usable battery power calculated based on the current absolute voltage. For example, battery percentage data can include a value between 0% and 100%, along with the absolute voltage value.

[0067] In some embodiments, the processor can obtain the raw battery voltage information by traversing through the character pointer and convert it into a percentage to obtain the battery percentage data.

[0068] S2: Based on the location data, the system uses a timestamp and distance comparison algorithm for filtering and log mode writing to obtain noise-reduced trajectory data and concurrently stored flight trajectory records.

[0069] Noise-reduced trajectory data is a set of valid flight trajectory points after invalid time elimination and spatial jump filtering. For example, noise-reduced trajectory data can include a continuous sequence of coordinate points with time intervals of less than 5 minutes and spatial displacements of more than 3 meters.

[0070] In some embodiments, the cloud processing hub can process location data and obtain noise-reduced trajectory data through timestamp comparison and distance threshold determination algorithms.

[0071] Flight trajectory records are persistent trajectory information stored in a database for post-event tracing and concurrent reading by multiple devices. For example, flight trajectory records may include device ID, processed location data, timestamps, and corresponding appended log records.

[0072] In some embodiments, the cloud processing hub can concurrently write the noise-reduced trajectory data into the time-series spatial database to obtain flight trajectory records through a write-ahead log mode mechanism.

[0073] In some embodiments, the processor can use timestamp comparison and distance threshold determination algorithms to remove stationary time data and drift noise data based on positioning data to obtain denoised trajectory data; and use a write-ahead log mode mechanism and a connection-level queuing sleep lock mechanism to process concurrent write requests to obtain concurrently stored flight trajectory records.

[0074] Stationary time data refers to the period during which an aircraft is in a stationary state or undergoing battery replacement due to power failure. For example, stationary time data may include timestamp records of two consecutive position reports with a time difference greater than 5 minutes.

[0075] In some embodiments, the processor can identify stationary time data by comparing the timestamp differences of consecutive positioning data.

[0076] Drift noise data refers to abnormally small spatial displacement characteristics caused by fluctuations in GPS signals. For example, drift noise data can include noisy coordinate points where the spherical drift distance between two consecutively reported coordinates is less than 3 meters but shows a slight change.

[0077] In some embodiments, the processor can identify drift noise data by calculating the spherical distance of continuous positioning data and comparing it with a preset distance threshold.

[0078] S3: Using a ray-mapping computational mechanism, cross-boundary detection is performed on the noise-reduced trajectory data and the preset no-fly zone boundary data to obtain the cross-boundary determination result.

[0079] No-fly zone boundary data is the spatial geometric boundary information that constitutes the outline of the no-fly zone. For example, no-fly zone boundary data may include a standard-defined coordinate sequence around an airport or a vertex coordinate sequence of a custom hand-drawn polygon.

[0080] In some embodiments, the system can obtain no-fly zone boundary data through automatic initialization or user input operations from the Web dispatch and command center.

[0081] The boundary violation determination result is a status indicator that indicates whether the aircraft's current position has entered a no-fly zone. For example, the boundary violation determination result can include a logical value indicating "entered" or "not entered".

[0082] In some embodiments, the processor can use a ray-mapping mechanism to calculate the boundary violation determination result from the noise-reduced trajectory data and the no-fly zone boundary data.

[0083] In some embodiments, the processor can use a ray-mapping algorithm with a small constant offset to perform precondition screening based on the noise-reduced trajectory data and the polygon vertex coordinate sequence as preset no-fly zone boundary data, and obtain the boundary crossing judgment result.

[0084] S4: Using line segment intersection verification and spherical curvature conversion mechanism, the noise-reduced trajectory data and preset route data are processed to obtain the collision avoidance interception judgment result and yaw mark.

[0085] Preset flight path data is spatial line segment information of the pre-planned expected flight path of an aircraft. For example, preset flight path data may include the coordinates of the starting point and ending point of the flight path, as well as the sequence of connecting flight path segments.

[0086] In some embodiments, the system can obtain preset route data through the map route planning tool of the Web dispatch and command center.

[0087] Collision avoidance interception results are safety verification feedback information indicating whether a planned flight path has entered or crossed the boundary of a no-fly zone. For example, collision avoidance interception results may include an interception rejection signal and the specific no-fly zone name involved in the collision.

[0088] In some embodiments, the processor can process preset flight path data and no-fly zone boundary data through a line segment intersection verification mechanism to obtain collision avoidance interception judgment results.

[0089] A yaw marker is an alarm status message indicating that the aircraft's actual flight trajectory has deviated from a preset route by a certain threshold. For example, a yaw marker may include a Boolean value or information containing the specific direction and distance of deviation.

[0090] In some embodiments, the processor can obtain a yaw marker by comparing the converted distance with a dynamic yaw threshold.

[0091] In some embodiments, the processor can perform cross-validation of line segment intersections based on the denoised trajectory data and the boundary line segments used as preset flight path data, using a three-point counterclockwise sorting algorithm based on vector cross product to obtain the collision avoidance interception judgment result; based on the line segments in the denoised trajectory data and preset flight path data, it can use a dimensionality reduction projection algorithm to calculate the planar projection ratio from the positioning point to the line segment to extract the shortest distance point, and call the spherical radian conversion formula for the shortest distance point to obtain the converted distance; the converted distance is compared with a preset dynamic yaw threshold, and if the converted distance is greater than the dynamic yaw threshold, a yaw mark is output.

[0092] The converted distance is the shortest radii of the Earth between the aircraft's current actual position and its planned flight path. For example, the converted distance can include a value in meters calculated using spherical dimensionality reduction.

[0093] In some embodiments, the processor can obtain the converted distance by calling the spherical radian conversion formula to process the shortest distance plane projection point extracted from the positioning point and the flight path.

[0094] In some embodiments, the expression for the result of the vector cross product is: CCW(A,B,C)=(Cy-Ay)*(Bx-Ax)-(By-Ay)*(Cx-Ax); Wherein, CCW(A,B,C) represents the result of the cross product of the vectors formed by the three coordinate points, used to determine the counterclockwise arrangement relationship, and serves as an intermediate calculation parameter; Ax represents the longitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Ay represents the latitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Bx represents the longitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; By represents the latitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Cx represents the longitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data; Cy represents the latitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data.

[0095] S5: Based on the boundary crossing judgment result, the collision avoidance interception judgment result, and the yaw mark, the result summary logic is used to process the results and obtain the flight compliance review result.

[0096] Flight compliance review results are the final conclusion data derived from a comprehensive set of spatial and temporal verifications, determining whether the aircraft's current behavior is compliant. For example, flight compliance review results may include status classification information such as "normal flight," "boundary violation," "deviation violation," and "flight during prohibited periods."

[0097] In some embodiments, the processor can obtain flight compliance review results by processing the boundary violation determination results, collision avoidance interception determination results, and yaw markers through result aggregation logic.

[0098] S6: Utilizing spatial dimensionality reduction filtering and lifecycle detection mechanisms, it processes flight trajectory records, flight compliance review results, and battery percentage data to obtain continuously active flight status display data and alarm trigger signals, thus completing low-altitude supervision based on multi-protocol fusion.

[0099] Flight situation display data is a collection of data used to smoothly render the real-time position and animation of an aircraft on a terminal map. For example, flight situation display data may include a coordinate grid of uniformly sliding animations and status update data that takes over from older animations.

[0100] In some embodiments, the mobile command and control terminal can process flight trajectory records, flight compliance review results, and battery percentage data through spatial dimensionality reduction filtering and lifecycle detection mechanisms to obtain flight status display data.

[0101] An alarm trigger signal is a control command used to activate the physical alarm device of a terminal device. For example, an alarm trigger signal may include a command code to activate sound, light, and vibration, as well as a text message displaying the alarm reason simultaneously.

[0102] In some embodiments, the system can obtain an alarm trigger signal through an event triggering mechanism when the flight compliance review result is a violation.

[0103] In some embodiments, the processor can construct a bounding box cache object based on the extreme points in the pre-parsed preset no-fly zone boundary data and using an extreme value comparison algorithm; based on the bounding box cache object and the real-time positioning data in the flight trajectory record, a logic short-circuit mechanism is used to prioritize the bounding box value determination processing; when the real-time positioning data enters the area of ​​the bounding box cache object, a polygon edge calculation algorithm is used to calculate and obtain the output result of the polygon edge calculation algorithm; based on the received asynchronous update task, a life cycle state detection mechanism is used to intercept and remove redundant hanging processes before the interface rendering process; and based on the exception capture mechanism, a method of forcibly loading the next round of timers in the closed-loop statement is used, combined with the output result of the polygon edge calculation algorithm, to obtain continuously kept-alive flight status display data; based on the flight compliance review result and battery percentage data, when the flight compliance review result is a violation, an alarm trigger signal is obtained to complete low-altitude supervision based on multi-protocol fusion.

[0104] A bounding box cache object is a pre-computed, rectangular region data structure containing polygon extrema points, pre-calculated and stored in local memory. For example, a bounding box cache object may include a data set extracted from a no-fly zone, consisting of the maximum and minimum latitude, maximum and minimum longitude.

[0105] In some embodiments, the mobile processor can obtain a bounding box cache object by pre-parsed vertices of the no-fly zone boundary data.

[0106] In some embodiments, the expression for the bounding box boundary determination result is: ; in, This represents the bounding box boundary determination result, a Boolean value used to determine whether real-time positioning data has entered the region of the bounding box cache object. This indicates the real-time latitude of the target aircraft, obtained from flight trajectory records. This indicates the real-time longitude of the target aircraft, obtained from flight trajectory records. This represents the minimum dimension within the bounding box cache object, obtained based on pre-parsed, preset no-fly zone boundary data. This represents the maximum dimension within the bounding box cache object, obtained based on pre-parsed, preset no-fly zone boundary data. This represents the minimum longitude within the bounding box cache object, obtained based on pre-parsed, preset no-fly zone boundary data. This represents the maximum longitude within the bounding box cache object, obtained based on pre-parsed, preset no-fly zone boundary data. Represents the logical AND operator.

[0107] The output of a polygon edge calculation algorithm is the specific value or state of the actual intersection between a ray and a polygon edge in a high-precision calculation. For example, the output of a polygon edge calculation algorithm may include the parity of the number of intersections, or the longitude of the intersection point between a ray and a specific polygon edge at the current latitude.

[0108] In some embodiments, the processor can process real-time positioning data and no-fly zone boundary data using a polygon edge calculation algorithm to obtain the output results of the polygon edge calculation algorithm.

[0109] In some embodiments, the expression for the output of the polygon edge calculation algorithm is: lon_{cross}=(pj.lon-pi.lon)*(lat-pi.lat) / (pj.lat-pi.lat+1e-9)+pi.lon; Wherein, lon_{cross} represents the longitude of the intersection point of the ray and the polygon edge at the current latitude, which is the output result of the polygon edge calculation algorithm and is an intermediate calculation parameter; lat represents the real-time latitude of the target aircraft, obtained from the positioning data uploaded by the hardware acquisition terminal; pi.lat represents the latitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pi.lon represents the longitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lat represents the latitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lon represents the longitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; 1e-9 represents a small offset constant, which is a self-designed anti-collapse and anti-shaking constant to prevent the division-to-zero anomaly caused by the two vertices being on the same horizontal line.

[0110] An asynchronous update task refers to a callback operation instruction that triggers a front-end interface refresh after a background network request or processing is completed. For example, an asynchronous update task may include a Runnable thread that retrieves cloud coordinates and updates the user interface (UI), or a timer task that attaches to the next round of polling.

[0111] In some embodiments, the terminal processor can obtain asynchronous update tasks through the response callback mechanism of the network polling engine.

[0112] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

Claims

1. A low-altitude surveillance method based on multi-protocol fusion, characterized in that, include: S1: Based on the acquired original location statement and battery voltage information, a parsing algorithm with pre-allocated static memory is used to obtain location data containing real-time latitude, longitude and altitude, as well as battery percentage data. S2: Based on the positioning data, the timestamp and distance comparison algorithm is used for filtering and log mode writing to obtain noise-reduced trajectory data and concurrently stored flight trajectory records; S3: Using a ray-mapping computational mechanism, cross-boundary detection is performed on the noise-reduced trajectory data and the preset no-fly zone boundary data to obtain the cross-boundary determination result; S4: Using line segment intersection verification and spherical curvature conversion mechanism, the noise-reduced trajectory data and preset route data are processed to obtain the collision avoidance interception judgment result and yaw mark; S5: Based on the boundary crossing judgment result, the collision avoidance interception judgment result, and the yaw mark, the result summary logic is used to process the results and obtain the flight compliance review result. S6: Utilizing spatial dimensionality reduction filtering and lifecycle detection mechanisms, it processes flight trajectory records, flight compliance review results, and battery percentage data to obtain continuously active flight status display data and alarm trigger signals, thus completing low-altitude supervision based on multi-protocol fusion.

2. The low-altitude surveillance method based on multi-protocol fusion according to claim 1, characterized in that, S2 includes: Based on the location data, the stationary time data and drift noise data are removed by the timestamp comparison and distance threshold judgment algorithm to obtain the noise-reduced trajectory data; By utilizing the write-ahead log mode mechanism and the connection-level queuing sleep lock mechanism, concurrent write requests are processed for the noise-reduced trajectory data to obtain concurrently stored flight trajectory records.

3. The low-altitude surveillance method based on multi-protocol fusion according to claim 1, characterized in that, S3 includes: Based on the noise-reduced trajectory data and the polygon vertex coordinate sequence used as the preset no-fly zone boundary data, a ray-based judgment algorithm with a small constant offset is used to filter the preconditions and obtain the boundary judgment result.

4. The low-altitude surveillance method based on multi-protocol fusion according to claim 1, characterized in that, S4 includes: Based on the denoised trajectory data and the boundary line segments used as preset flight path data, the cross-verification of line segment intersections is performed using a three-point counterclockwise sorting judgment algorithm based on vector cross product, and the collision avoidance interception judgment result is obtained. Based on the line segments in the noise-reduced trajectory data and the preset flight path data, the planar projection ratio from the positioning point to the line segment is calculated using a dimension reduction projection algorithm to extract the shortest distance point. The spherical curvature conversion formula is then called for the shortest distance point to obtain the converted distance. The calculated distance is compared with the preset dynamic yaw threshold. If the calculated distance is greater than the dynamic yaw threshold, a yaw marker is output.

5. The low-altitude surveillance method based on multi-protocol fusion according to claim 4, characterized in that, The expression for the result of the vector cross product is: CCW(A,B,C)=(Cy-Ay)*(Bx-Ax)-(By-Ay)*(Cx-Ax); Wherein, CCW(A,B,C) represents the result of the cross product of the vectors formed by the three coordinate points, used to determine the counterclockwise arrangement relationship, and serves as an intermediate calculation parameter; Ax represents the longitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Ay represents the latitude of the starting point A of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Bx represents the longitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; By represents the latitude of the ending point B of the line segment, obtained based on preset flight route data or no-fly zone boundary data; Cx represents the longitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data; Cy represents the latitude of the verification point C, obtained based on preset flight route data or no-fly zone boundary data.

6. The low-altitude surveillance method based on multi-protocol fusion according to claim 1, characterized in that, S6 includes: Based on the extreme points in the pre-parsed preset no-fly zone boundary data, a bounding box cache object is constructed using an extreme value comparison algorithm; Based on the bounding box cache object and the real-time positioning data in the flight trajectory record, the bounding box numerical determination process is performed first using a logic short-circuit mechanism. When the real-time positioning data enters the area of ​​the bounding box cache object, the polygon edge calculation algorithm is used to calculate and obtain the output result of the polygon edge calculation algorithm. Based on the received asynchronous update task, the redundant hanging process is intercepted and removed before the interface rendering process by using the life cycle state detection mechanism. Based on the exception capture mechanism, the next round of timer is forcibly loaded in the closed loop statement. Combined with the output of the polygon edge calculation algorithm, the flight status display data that is kept alive is obtained. Based on the flight compliance review results and battery percentage data, an alarm trigger signal is obtained when the flight compliance review result is a violation, thus completing low-altitude supervision based on multi-protocol fusion.

7. The low-altitude surveillance method based on multi-protocol fusion according to claim 6, characterized in that, The expression for the output result of the polygon edge calculation algorithm is: lon_{cross}=(pj.lon-pi.lon)*(lat-pi.lat) / (pj.lat-pi.lat+1e-9)+pi.lon; Wherein, lon_{cross} represents the longitude of the intersection point of the ray and the polygon edge at the current latitude, which is the output result of the polygon edge calculation algorithm and is an intermediate calculation parameter; lat represents the real-time latitude of the target aircraft, obtained from the positioning data uploaded by the hardware acquisition terminal; pi.lat represents the latitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pi.lon represents the longitude of the first vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lat represents the latitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; pj.lon represents the longitude of the second vertex of the current edge of the polygon, obtained from the preset no-fly zone boundary data; 1e-9 represents a small offset constant, which is a self-designed anti-collapse and anti-shaking constant to prevent the division-to-zero anomaly caused by the two vertices being on the same horizontal line.

8. A low-altitude surveillance beacon terminal system based on multi-protocol fusion, used to execute the low-altitude surveillance method based on multi-protocol fusion as described in any one of claims 1 to 7, characterized in that, include: The data acquisition and parsing module is used to obtain positioning data containing real-time latitude, longitude and altitude, as well as battery percentage data, based on the acquired original positioning statement and battery voltage information using a parsing algorithm with pre-allocated static memory. The data denoising and storage module is used to filter and write in log mode based on the positioning data using a timestamp and distance comparison algorithm to obtain denoised trajectory data and concurrently stored flight trajectory records. The boundary crossing detection module is used to perform boundary crossing detection based on the noise-reduced trajectory data and the preset no-fly zone boundary data, using a ray-mapping computation mechanism to obtain the boundary crossing judgment result. The collision avoidance and yaw calculation module is used to obtain the collision avoidance interception judgment result and yaw mark based on the noise-reduced trajectory data and the preset route data, using the line segment intersection verification and spherical curvature conversion mechanism. The compliance review summary module is used to obtain the flight compliance review result based on the boundary crossing judgment result, the collision avoidance interception judgment result, and the yaw marker, using the result summary logic; The situation display and alarm triggering module is used to obtain continuously active flight situation display data and alarm triggering signals based on the flight trajectory record, the flight compliance review result and the battery percentage data, using spatial dimensionality reduction filtering and life cycle detection mechanisms, and to complete low-altitude supervision based on multi-protocol fusion.

9. The low-altitude surveillance beacon terminal system based on multi-protocol fusion according to claim 8, characterized in that, The data acquisition and parsing module is specifically implemented by an integrated hardware acquisition terminal, which includes a customized printed circuit board and a microcontroller unit, a positioning and communication integrated module, a charge and discharge management module, a hardware watchdog module and an expansion interface module carried on the customized printed circuit board. The integrated positioning and communication module is connected to the microcontroller unit via a universal asynchronous transceiver interface, and is used to extract standard positioning message statements as the original positioning statements. The charge / discharge management module is electrically connected to the microcontroller unit and is used to monitor the power supply status and output voltage data as battery voltage information. The hardware watchdog module is connected to the microcontroller unit and is used to trigger a hardware-level restart when the main loop times out; The microcontroller unit acquires the original positioning statement and the battery voltage information, and uses a parsing algorithm with pre-allocated static memory to extract and transform the data to obtain positioning data containing real-time latitude, longitude, and altitude, as well as the battery percentage data.

10. The low-altitude surveillance beacon terminal system based on multi-protocol fusion according to claim 9, characterized in that, The customized printed circuit board is a four-layer printed circuit board, wherein: The microcontroller unit and the expansion interface module are deployed at the top level; The second layer houses the charge and discharge management module, and integrates reverse connection protection, overcurrent and overvoltage protection circuits, and a voltage regulator chip. The third layer is separately deployed with the integrated positioning and communication module, and the third layer is covered with a grounded electromagnetic shielding layer for suppressing signal interference. The bottom layer is a fully covered grounding layer; The customized printed circuit board is encapsulated within an engineering plastic sealed housing with a built-in waterproof sealing ring and a dustproof and waterproof protection rating.

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