A grid-based unmanned aerial vehicle monitoring management system and a deployment method thereof

Abstract

The application discloses a kind of grid unmanned aerial vehicle monitoring management systems and its deployment method.The system includes a plurality of grid monitoring nodes and a cloud supervision platform, each monitoring node integrates signal detection, decoding analysis, self-registration and heartbeat, environmental assessment, beacon analog transmission and geographic fence module, cloud platform integrates equipment management, data fusion, link self-check and visualization display module.Deployment method includes six steps of grid node deployment, automatic registration and configuration, initial environment self-evaluation, networking link self-test, real-time monitoring and alarm, data fusion and tracking.The application realizes seamless monitoring in a large range through grid microcell architecture, realizes intelligent deployment and operation and maintenance through self-registration and self-test mechanism, realizes system state transparency through environmental assessment and link self-check, effectively solves the problem that the detection distance of prior art is limited, deployment is rigid, and operation and maintenance are difficult, and is suitable for city low-altitude unmanned aerial vehicle supervision.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) monitoring technology, specifically to a gridded UAV monitoring and management system and its deployment method. Background Technology

[0002] With the implementation of national standards such as the "Safety Requirements for Civil Unmanned Aerial Vehicle Systems" (GB 42590-2023) and the "Operational Identification Specification for Civil Unmanned Aerial Vehicle Systems" (GB 46750-2025), remote identification (Remote ID) has become a mandatory function for light and small unmanned aerial vehicles (UAVs) to connect to the network. This function requires UAVs to periodically broadcast their serial number (SN), location, speed, and pilot's location via Wi-Fi or Bluetooth during flight, enabling "identity recognition" and "operational monitoring" of the UAV. Current technology typically employs a single, wide-range UAV monitoring device to receive these broadcast signals, thereby enabling the detection and identification of UAVs within a certain range.

[0003] Defects and shortcomings of existing technology:

[0004] 1. Limited single-point detection range and susceptibility to environmental interference: Existing monitoring equipment based on Remote ID signal reception suffers significant attenuation in complex environments (such as urban core areas and locations with dense electronic devices). Because 2.4GHz / 5.8GHz Wi-Fi signals are highly susceptible to building obstruction, multipath effects, and electromagnetic interference from other devices operating in the same frequency band, the actual effective detection radius of a single device in urban environments is often only a few hundred meters, failing to meet the needs of large-scale, continuous low-altitude surveillance.

[0005] 2. Rigid deployment methods, lacking flexibility and adaptability: Traditional monitoring systems heavily rely on manual planning and configuration. Each monitoring point requires on-site network configuration, platform registration, and parameter calibration by professionals, resulting in low efficiency and high costs during large-scale deployments. Simultaneously, the system cannot perceive its own and its surrounding signal environment quality, nor can it perform self-checks on link integrity. When a node fails or is interfered with, the system cannot autonomously detect and notify maintenance personnel, leading to blind spots in the monitoring network without the system's knowledge.

[0006] 3. Lack of system transparency and difficulties in operation and maintenance: The existing system cannot assess the electromagnetic interference level around each monitoring node in real time and automatically, and it is also difficult to verify the effectiveness of communication and detection links between nodes after networking. Managers cannot quickly determine whether the decline in monitoring capabilities in a certain area is due to equipment failure, network interruption, or severe environmental interference, making troubleshooting and system optimization difficult.

[0007] Therefore, overcoming the aforementioned shortcomings has become an important issue that urgently needs to be addressed by those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a gridded drone monitoring and management system and its deployment method. It aims to solve the technical problems of existing single-point drone monitoring equipment, such as limited detection range, susceptibility to interference, rigid deployment, low efficiency, opaque system status, and difficult operation and maintenance.

[0009] To achieve the above objectives, the first aspect of this application proposes a grid-based unmanned aerial vehicle (UAV) monitoring and management system, comprising:

[0010] Multiple monitoring nodes, totaling 100, each being an independent monitoring device, are deployed in the target monitoring area to form a grid-like coverage.

[0011] A cloud-based monitoring platform 200 is communicatively connected to the multiple monitoring nodes 100;

[0012] Each monitoring node 100 includes:

[0013] Signal detection module 101 is used to scan and receive UAV operation identification broadcast signals that conform to the prescribed standards in the frequency band used for UAV communication;

[0014] The decoding and parsing module 102 is connected to the signal detection module 101 and is used to decode the received UAV operation identification broadcast signal and parse out the UAV-related information.

[0015] The self-registration and heartbeat module 103 is used to send registration requests to the cloud monitoring platform 200 and periodically report the node's own status;

[0016] The environmental assessment module 104 is used to control the signal detection module 101 to scan the surrounding signal environment in the same specific frequency band as the UAV's operation identification broadcast signal when there is no launch mission, assess the degree of environmental interference, and generate an environmental assessment report to be reported to the cloud monitoring platform 200.

[0017] The beacon simulation transmission module 105 is used to respond to the instructions of the cloud monitoring platform 200 or according to preset rules, simulate and generate test operation identification broadcast signals that meet the prescribed standards and transmit them to each monitoring node 100 for network link testing.

[0018] The geofencing module 106 is used to store the electronic fence data sent from the cloud monitoring platform 200. When the location information in the decoded drone-related information triggers the fence boundary, an alarm event is generated.

[0019] The cloud-based monitoring platform 200 includes:

[0020] Device management module 201 is used to maintain the global device list and status;

[0021] Data fusion module 202 is used to deduplicatize and correlate the UAV information reported by each node to generate continuous flight trajectories;

[0022] The link self-test module 203 is used to trigger the beacon simulation transmission module 105 of one or more monitoring nodes 100 to work, and to evaluate the effectiveness of the signal propagation link and the coverage overlap area between monitoring nodes based on the test signal received by other monitoring nodes.

[0023] The visualization module 204 is used to display the status of monitoring nodes, electromagnetic environment information, and the real-time location and trajectory of UAVs on a map.

[0024] Preferably, the signal detection module 101 operates in the 2.4GHz and / or 5.8GHz frequency band, scanning and receiving UAV operation identification broadcast signals conforming to GB 42590-2023 and GB 46750-2025 standards;

[0025] The drone-related information parsed by the decoding and parsing module 102 includes at least one of the following: drone's unique serial number, drone's real-time location, speed, heading, and pilot's location information.

[0026] Preferably, the self-registration and heartbeat module 103 automatically sends a registration request after the detection node is powered on for the first time or the network is restored, and reports its own status at a fixed frequency; the own status includes at least device ID, geographical location, IP address and online status; after the device management module 201 of the cloud monitoring platform 200 verifies that the monitoring node's registration request is approved, it sends configuration parameters, including electronic fence and working frequency band, to the node.

[0027] Preferably, the environmental assessment module 104 is used to scan the signal environment of the surrounding 2.4GHz and / or 5.8GHz frequency bands, record the received signal strength indication and noise floor value of each channel, and generate an environmental assessment report to be reported to the cloud monitoring platform 200; the cloud monitoring platform 200 is used to generate an electromagnetic environment heat map of the entire monitoring area based on the environmental assessment reports reported by multiple monitoring nodes 100.

[0028] Preferably, the data fusion module 202 is configured to: use the combination of the UAV's unique serial number and the reporting timestamp as the deduplication key value; within a preset spatiotemporal sliding window that includes a time window and a spatial distance window, deduplicate multiple reported data with the same serial number and whose time difference and position difference both fall within the threshold range, retaining only one of them; and then group the deduplicated data by serial number, sort by time, and connect each position point in sequence to generate a continuous flight trajectory for each UAV.

[0029] Preferably, when performing a link test, the link self-test module 203 instructs the selected first monitoring node to act as a beacon transmitter and simultaneously instructs at least one second monitoring node around the first monitoring node to act as a receiver for scanning; after the second monitoring node successfully receives the test signal, it reports the received signal strength indication information to the cloud monitoring platform 200.

[0030] The link self-test module 203 triggers a link test when any of the following conditions are met: the preset time period is reached, the device management module 201 detects that any monitoring node is offline, or the electromagnetic interference intensity in a certain area exceeds a preset threshold based on an environmental assessment report.

[0031] A second aspect of the present invention provides a deployment method for an unmanned aerial vehicle (UAV) monitoring and management system, applied to the system described in the first aspect, comprising the following steps:

[0032] S1. Node Deployment: Within the target monitoring area, multiple monitoring nodes 100 are installed according to the preset grid spacing or the principle of prioritizing key areas to form a grid-like coverage of monitoring nodes;

[0033] S2. Automatic registration and configuration: After any monitoring node is powered on and connected to the network, its self-registration and heartbeat module 103 automatically sends a registration request to the cloud monitoring platform 200. After the platform verifies the request, it adds the node to the device list and issues configuration parameters.

[0034] S3. Initial environmental self-assessment: After the environmental assessment module 104 of each monitoring node 100 is started, it scans the electromagnetic environment of the corresponding frequency band in the surrounding area, generates an environmental assessment report and reports it to the platform.

[0035] S4. Network link self-test: The link self-test module 203 of the cloud monitoring platform 200 initiates link tests periodically or as needed to evaluate the link status between nodes and coverage overlap areas.

[0036] S5. Real-time monitoring and alarm: When the system enters the working state, each monitoring node continuously receives and decodes the drone operation identification broadcast signal. When a target is detected entering the electronic fence, an alarm event and drone information are immediately reported to the platform.

[0037] S6. Data Fusion and Tracking: The platform's data fusion module 202 receives data reported by all nodes, performs fusion processing on the data reported by multiple nodes, and generates the drone's flight trajectory.

[0038] Preferably, in step S1, for ordinary urban areas, a backbone node network is used, and the spacing between backbone nodes is determined based on the actual effective detection radius of a single monitoring node; for key control areas, blind spot nodes are used to enhance coverage, and the spacing between blind spot nodes is smaller than the spacing between backbone nodes.

[0039] Preferably, the network link self-test specifically includes the following steps:

[0040] The cloud-based monitoring platform instructs the first monitoring node selected as the beacon transmitter to simulate and transmit a test operation identification broadcast signal that conforms to the prescribed standards.

[0041] At the same time, it instructs at least one second monitoring node around the first monitoring node to perform scanning as a receiving end;

[0042] After the second monitoring node successfully receives the test signal, it will report the signal quality information to the cloud monitoring platform.

[0043] The platform uses this information to determine the link status between nodes and the overlapping coverage areas.

[0044] Preferably, the data fusion and tracking specifically includes the following steps:

[0045] The platform's data fusion module uses the combination of the drone's unique identifier and the reporting time as the basis for deduplication;

[0046] Using a preset spatiotemporal sliding window, multiple reported data with the same identifier and matching spatiotemporal features are deduplicated, and only one of them is retained;

[0047] The deduplicated data is grouped by identifier, sorted by time, and then connected to each location point in sequence to generate a continuous flight trajectory for each drone.

[0048] One or more technical solutions proposed in this application have at least the following technical effects:

[0049] 1. Achieve low-cost, high-reliability full-area coverage: This invention effectively overcomes the shortcomings of single devices in complex urban electromagnetic environments, such as short detection distance and susceptibility to interference, by deploying a large number of low-cost, small-scale monitoring nodes in a grid-like cluster. Each node is responsible for a "microcell", and the coverage quality is achieved by leveraging the quantity advantage, thus realizing seamless and continuous monitoring of the target area.

[0050] 2. Highly Intelligent Deployment and Operation: This invention achieves plug-and-play and configuration-free deployment of monitoring nodes through self-registration and heartbeat modules, greatly reducing the engineering complexity of large-scale deployments. Environmental assessment and link self-checking modules enable the system to automatically perceive environmental interference and its own link health status, transforming passive operation and maintenance into proactive intelligent operation and maintenance. This allows managers to intuitively grasp the system's operating status and quickly locate blind spots or fault areas.

[0051] 3. Transparent system status and data-driven optimization: The environmental interference heat map and link test report generated by this invention provide accurate data basis for subsequent system optimization and node blind spot filling, enabling the construction of the low-altitude monitoring network to shift from experience-driven to data-driven.

[0052] 4. Enhanced system reliability and robustness: The grid-based deployment method brings natural system redundancy. When a single or a few nodes fail, their coverage area may still be partially covered by adjacent nodes, preventing the entire system from crashing and ensuring high overall system reliability. Attached Figure Description

[0053] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the structure of the gridded UAV monitoring and management system of the present invention.

[0056] Figure 2 This is a flowchart of the deployment and self-testing method of the present invention.

[0057] Figure 3 This is an example of a regional electromagnetic environment heat map generated by the present invention.

[0058] Figure 4 This is a schematic diagram illustrating the network link testing performed according to the present invention.

[0059] 100 - Grid-based monitoring nodes; 101 - Signal detection module; 102 - Decoding and parsing module; 103 - Self-registration and heartbeat module; 104 - Environmental assessment module; 105 - Beacon simulation transmission module; 106 - Geofencing module; 200 - Cloud monitoring platform; 201 - Equipment management module; 202 - Data fusion module; 203 - Link self-test module; 204 - Visualization module. Detailed Implementation

[0060] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0061] It should be noted that in the description of this application and the appended claims, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0062] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment provides a gridded drone monitoring and management system, including multiple gridded monitoring nodes 100 and a cloud-based monitoring platform 200. The monitoring nodes 100 establish a two-way communication connection with the cloud-based monitoring platform 200 via wireless or wired communication to achieve data reporting and command reception.

[0065] First, each grid-based monitoring node 100 is an independent, integrated monitoring device. In specific implementation, its hardware configuration may include:

[0066] RF front-end chip: Supports the reception and transmission of dual-band signals of 2.4GHz and 5.8GHz, has Wi-Fi sniffing function, and can capture the operation identification signals broadcast by drones;

[0067] Baseband signal processor: Connected to the RF front-end chip, it is responsible for demodulating and decoding the received RF signals, as well as modulating and encoding the transmitted signals;

[0068] GNSS positioning module: Supports positioning via multiple systems such as GPS and BeiDou, used to obtain the precise latitude, longitude and altitude information of the monitoring node in real time;

[0069] Central Processing Unit (CPU): As the core control unit of the node, it runs the algorithm programs of various software modules and coordinates the work of various hardware components;

[0070] Wireless / Wired Communication Module: Includes a 4G / 5G wireless communication module and an Ethernet interface for data interaction with the cloud monitoring platform.

[0071] Secondly, the software system of monitoring node 100 adopts a modular design, including the following functional modules:

[0072] Signal detection module 101: used to scan and receive UAV operation identification broadcast signals (Remote ID) conforming to GB 42590-2023 / GB 46750-2025 standards in the 2.4GHz and / or 5.8GHz frequency bands; specifically, by controlling the radio frequency front-end chip to scan in the specified frequency band, it captures UAV operation identification broadcast signals propagating in the air and transmits the raw signals to the decoding and parsing module;

[0073] Decoding and parsing module 102: Connected to the signal detection module 101, used to decode the received broadcast signal, parse out the information contained therein such as the drone's unique serial number (SN), the drone's real-time location (latitude, longitude, altitude), speed, heading, and pilot's location, and package it into a standardized drone detection dataset;

[0074] The self-registration and heartbeat module 103 is used to automatically send a registration request containing the device ID and geographical location to the cloud monitoring platform 200 after the node is powered on for the first time or the network is restored. After successful registration, it reports the device's online status, IP address and other heartbeat information to the platform at a fixed frequency. This module realizes plug-and-play and zero-configuration online monitoring of the node, eliminating the dependence of traditional systems on on-site debugging by professional personnel.

[0075] Environmental Assessment Module 104: When a node has no transmission task, the control signal detection module 101 scans the surrounding 2.4GHz and 5.8GHz full-band frequencies, records the Received Signal Strength Indication (RSSI) and noise floor value of each channel, generates an environmental assessment report, and uploads it to the cloud. This module enables the system to have real-time electromagnetic environment perception capabilities, comprehensively grasp the interference distribution within the monitoring area, and provides quantitative data basis for dynamic channel adjustment, node deployment optimization, and fault diagnosis. In specific implementation, during the idle period when the node has no transmission task, the environmental assessment module controls the control signal detection module to scan the full-band frequency channel by channel, measuring and recording the background noise and received signal strength of each channel. After the scan is completed, an environmental assessment report is generated and uploaded to the cloud. The platform summarizes the data from all nodes and generates an electromagnetic environment heat map covering the entire monitoring area through a spatial interpolation algorithm, visually displaying the interference intensity of each area with different colors.

[0076] Beacon simulation transmission module 105: Used to respond to the instructions of the cloud monitoring platform or according to a preset cycle, simulate and generate test operation identification broadcast signals conforming to GB 42590-2023 / GB 46750-2025 standards and transmit them to other monitoring nodes in the grid for network link testing;

[0077] Geofencing module 106: Stores electronic fence data sent from the cloud, compares and decodes the drone's location information in real time, and immediately generates and reports an alarm event when the drone enters or leaves the fence boundary.

[0078] Furthermore, the cloud-based monitoring platform 200 adopts a cloud-native architecture design, consisting of a cluster of multiple servers, supporting horizontal scaling to access massive monitoring nodes. Specifically, the cloud-native architecture design is based on a cloud environment, using technologies such as containerization, microservices, and dynamic orchestration to achieve a paradigm of high availability, elastic scaling, and continuous delivery for applications. Its core lies in deconstructing traditional monolithic applications into independently deployed modular services, combined with an automated toolchain to achieve full lifecycle management. The software system of the cloud-based monitoring platform 200 includes the following core modules:

[0079] Device Management Module 201: Receives and verifies registration requests from monitoring nodes, maintains a global device list, records basic information, online status, and configuration parameters of each node; and issues configuration instructions such as electronic fences and operating frequency bands to nodes.

[0080] Data fusion module 202: Receives UAV detection data reported by multiple monitoring nodes, uses the UAV's unique serial number as an identifier, and performs data deduplication and correlation processing in combination with a preset spatiotemporal window to connect discrete location points in chronological order and generate the UAV's continuous flight trajectory.

[0081] Link self-test module 203: Responsible for orchestrating and executing network link test tasks, instructing designated monitoring nodes to transmit test signals, collecting reception results from surrounding nodes, evaluating the effectiveness of signal propagation links and coverage overlap areas between nodes, and identifying potential monitoring blind spots;

[0082] Visualization module 204: Based on an electronic map, it provides a visual interface to display the distribution and status of all monitoring nodes, regional electromagnetic environment heat map, real-time location and flight trajectory of UAVs, and alarm event information.

[0083] In practice, multiple independent monitoring nodes are installed in a grid-like layout on building rooftops, lampposts, and other high locations within the target monitoring area. Each node establishes bidirectional communication with the cloud-based monitoring platform via 4G / 5G or Ethernet. The signal detection module within each monitoring node continuously scans the commonly used communication frequency bands for drones (limited to the nationally compliant 2.4GHz and / or 5.8GHz bands in this case), capturing the drone's operational identification broadcast signals. The decoding and parsing module then extracts relevant drone information. The self-registration and heartbeat module handles automatic network access and status reporting for nodes. The environmental assessment module scans the surrounding electromagnetic environment during idle periods. The beacon simulation transmission module transmits test signals according to platform instructions. The geofencing module determines in real-time whether a drone has entered the controlled area. The cloud platform centrally manages all nodes, integrates and processes the reported data, assesses link status, and provides visual displays.

[0084] As described above, the gridded drone monitoring and management system provided in this embodiment addresses the problems of limited detection range and susceptibility to interference, rigid deployment and low efficiency, and opaque system status and difficult operation and maintenance of existing single-point drone monitoring equipment in complex urban environments. It adopts a technical solution combining a gridded microcellular architecture, distributed intelligent nodes, and cloud-based collaborative management and control. This solution abandons the traditional approach of using a single high-power device to cover a large area. By deploying a large number of low-cost, small-area monitoring nodes to form a gridded cluster, each node is responsible for monitoring a specific area. Leveraging the advantage of quantity, it effectively overcomes the problems of building obstruction and co-frequency interference, achieving seamless and continuous monitoring of the target area and significantly reducing overall construction costs. Simultaneously, the integrated self-registration and heartbeat modules enable plug-and-play and configuration-free deployment of monitoring nodes. Unified cloud-based configuration management also eliminates the need for on-site debugging of each node when adjusting parameters, significantly shortening the large-scale deployment cycle and reducing engineering complexity and labor costs. More importantly… The system combines an environmental assessment module and a link self-test module to construct a multi-dimensional status assessment system. It not only generates intuitive electromagnetic environment heat maps but also proactively verifies actual detection capabilities through node-to-node test beacons. It automatically distinguishes between three types of problems: network failures, equipment hardware failures, and environmental interference, transforming passive operation and maintenance into proactive intelligent operation and maintenance, making the system status completely transparent. Furthermore, the inherent redundancy brought by the grid-based deployment ensures that even when a single or a few nodes fail, their coverage area can still be partially covered by adjacent nodes, preventing large-scale monitoring blind spots. The cloud can also automatically adjust the parameters of surrounding nodes to temporarily enhance the monitoring capabilities of the failed area, significantly improving the overall reliability and robustness of the system. Simultaneously, the environmental interference heat maps and link test reports generated by the system provide accurate data for node blind spot filling, channel adjustment, and continuous system optimization, driving the construction of low-altitude monitoring networks from experience-driven to data-driven, and providing scientific decision-making support for future urban low-altitude governance.

[0085] As a preferred implementation, by limiting the signal detection module to operate in the 2.4GHz and / or 5.8GHz frequency bands, it can capture all UAV operation identification broadcast signals that comply with the two current mandatory national standards in China, thereby ensuring the system's signal reception standards. Specifically, after demodulating the signal, the decoding and parsing module (102) extracts key information such as the UAV's unique identifier, three-dimensional position, flight attitude, and pilot position, and packages it into standardized data frames for reporting to the cloud, thus clarifying the data parsing scope. This ensures full compatibility with all compliant UAVs and avoids missed detections due to missing frequency bands or non-compliance with standards. It also defines the core information dimensions for parsing, providing the necessary data foundation for subsequent UAV identification, trajectory tracking, and violation alarms.

[0086] In a preferred implementation, when the monitoring node powers on for the first time or recovers from a network outage, the self-registration and heartbeat module automatically generates a registration request containing the device's unique identifier and its geographical location, and sends it to the cloud platform. After verifying the device's legitimacy, the platform adds it to the device management list and issues initial configuration parameters such as the electronic fence and operating frequency band. After successful registration, the node will continuously report heartbeat information such as device ID, geographical location, IP address, and online status to the platform at preset fixed time intervals. The platform updates the device status in real time based on the heartbeat information.

[0087] The specific implementation method is as follows: After the node is powered on or the network is restored, the module first reads the unique serial number of the node device, obtains the current geographical location through the GNSS positioning module, and obtains the IP address information of the communication module. It encapsulates this information into a registration message and sends it to the cloud device management module 201 via 4G / 5G or Ethernet. After the cloud verifies the legality of the device identity, it returns a registration success response containing configuration parameters such as electronic fence, working frequency band, and heartbeat cycle. The node completes local configuration accordingly. After successful registration, the module reports heartbeat information such as device ID, geographical location, IP address, and online status to the cloud at a fixed frequency (e.g., every 30 seconds). If the heartbeat is interrupted due to network failure, it will immediately re-register and synchronize the configuration updates during the offline period after the network is restored, thereby realizing plug-and-play and zero-configuration online monitoring nodes.

[0088] As described above, the self-registration and heartbeat module 103 in this case, in conjunction with the cloud-based device management module 201, enables monitoring nodes to automatically complete registration, verification, and configuration distribution after powering on and connecting to the network, completely eliminating the reliance on on-site debugging by professional personnel in traditional systems. The deployment time for a single node is reduced from several hours to minutes, and unified cloud-based configuration management allows for one-click adjustments to global parameters, significantly reducing the engineering complexity and labor costs of large-scale deployments. Simultaneously, the periodic heartbeat reporting mechanism enables the cloud platform to monitor the online status of all nodes in real time, promptly detect offline devices, and improve system manageability.

[0089] In a preferred implementation, during idle periods when the monitoring nodes are not performing transmission tasks, the environmental assessment module controls the signal detection module to perform a channel-by-channel scan of the entire 2.4GHz and 5.8GHz frequency bands, measuring and recording the received signal strength indication and background noise value for each channel. After the scan is completed, an environmental assessment report containing interference information for each channel is generated and uploaded to the cloud platform. The platform aggregates the environmental data reported by all nodes and generates an electromagnetic environment heat map covering the entire monitoring area using a spatial interpolation algorithm, visually displaying the interference intensity distribution in different areas with different colors.

[0090] As mentioned above, firstly, the environmental assessment module 104 in this case enables the system to perceive the surrounding electromagnetic environment in real time, allowing for a comprehensive understanding of interference within the monitoring area. The generated electromagnetic environment heat map provides crucial data for channel optimization, node deployment adjustments, and fault diagnosis, enabling managers to promptly identify areas with severe interference and take appropriate measures. Furthermore, the frequency band monitored and scanned by the environmental assessment module 104 is completely consistent with the frequency band used by the signal detection module 101 to receive UAV operation identification broadcast signals during normal operation (i.e., both are 2.4GHz and / or 5.8GHz bands). This allows the environmental assessment module to focus on assessing interference frequency bands that may have a real impact on UAV signal reception, rather than wasting computational resources scanning irrelevant frequency bands. The resulting environmental assessment report accurately reflects the interference intensity faced by the signal detection module within its operating frequency band, providing precise and effective reference for subsequent channel selection, frequency band switching, and link testing, avoiding the problem of distorted assessment results and reduced reference value due to inconsistent monitoring frequency bands.

[0091] In a preferred implementation, the data fusion module 202 is configured to: use the combination of the UAV's unique serial number and the reporting timestamp as the deduplication key; within a preset spatiotemporal sliding window containing a time window and a spatial distance window, deduplicate multiple reported data with the same serial number and whose time difference and position difference both fall within their respective threshold ranges, retaining only one of them (which can be selected based on signal quality parameters or detection node priority); and then group the deduplicated data by serial number, sort by time, and connect them sequentially to each location point to generate a continuous flight trajectory for each UAV. Specifically, for multiple detection stations receiving data reported by the same UAV simultaneously, deduplication is performed by using the UAV's SN code + reporting time (second-level or millisecond-level timestamp) as the core combination key, setting a spatiotemporal sliding window (e.g., window length 1 second, spatial distance tolerance 50 meters) at the receiving end: if multiple data with the same SN code and whose time difference and coordinate difference both fall within the threshold are received within the window, only the first one is retained (or the optimal one is selected based on signal quality / detector station priority). Meanwhile, by using this window to group continuously received data by SN code and sort it by time, the deduplicated location points can be connected in sequence to form the continuous flight trajectory of each drone.

[0092] The "spatiotemporal sliding window" described in this article refers to a dynamic data comparison interval that simultaneously incorporates both time and spatial dimensions. The time window limits the maximum time difference between two adjacent data points to be considered "simultaneous," while the spatial window limits the maximum distance difference between two location points to be considered "the same location." This window slides forward as the data processing progresses, used for real-time deduplication of continuously arriving reported data.

[0093] As mentioned above, the data fusion module achieves efficient deduplication of multi-node data through a spatiotemporal sliding window, effectively solving the data redundancy problem caused by overlapping coverage of multiple nodes. Simultaneously, it can correlate discrete single-point monitoring data into continuous flight trajectories, enabling managers to clearly and intuitively grasp the complete flight path of the UAV, thus improving the usability and value of the monitoring data.

[0094] In one preferred implementation, when the link self-test module performs the test, it selects a node as a beacon transmitter, instructs its beacon simulation transmission module to generate and transmit a standard test signal, and simultaneously instructs nodes within a preset range to enter scanning mode as receivers. After capturing the test signal, the receivers report quality parameters such as signal strength and signal-to-noise ratio to the platform. The platform evaluates the link effectiveness and coverage overlap area based on the reported results. The test can be automatically triggered at a preset period (e.g., every 24 hours) or automatically initiated when a node is offline or when environmental interference exceeds the limit. In this way, this module breaks through the limitation of traditional systems that only determine device online status through heartbeats. By exchanging test beacons between nodes, it can realistically verify the actual radio frequency detection capability of each node, actively distinguish between three types of problems: network faults, equipment faults, and environmental interference, and realize the system's proactive link self-test function. It transforms passive operation and maintenance into proactive operation and maintenance, and can periodically or as needed verify the actual detection capability and coverage of each node. Compared with simply relying on the heartbeat mechanism, it can more realistically reflect the system's operating status, enabling the system to promptly detect link anomalies and potential monitoring blind spots, thereby improving the system's reliability and stability.

[0095] Example 2

[0096] This embodiment 2 provides a deployment method for a gridded UAV monitoring and management system, specifically a complete process for deployment and self-testing using the system described in embodiment 1, with the following steps: Figure 2 As shown. This deployment method includes the following steps:

[0097] S1, Grid-based node deployment

[0098] Within the target monitoring area, multiple gridded monitoring nodes 100 are installed according to the preset grid spacing or the principle of prioritizing key areas.

[0099] S2, Automatic Node Registration and Configuration

[0100] After any monitoring node is powered on and successfully connected to the network, its self-registration and heartbeat module 103 automatically generates a registration message, which includes the device's unique serial number (SN) and latitude and longitude information obtained from GNSS positioning. This message is then sent to the device management module 201 of the cloud monitoring platform 200 via the communication module.

[0101] After verifying the validity of the monitoring node's SN, the platform's device management module (201) adds it to the global device list and issues default configuration parameters, including the operating frequency band (2.4GHz and 5.8GHz are enabled by default), electronic fence data, and heartbeat cycle. The entire process requires no manual on-site configuration, achieving plug-and-play functionality.

[0102] S3. Initial Environment Self-Assessment

[0103] After each monitoring node completes registration, the environmental assessment module 104 automatically starts, controlling the signal detection module 101 to scan the surrounding 2.4GHz and 5.8GHz full-band frequencies, sequentially measuring the received signal strength index and noise floor value of each channel, generating an environmental assessment report containing interference information for each channel, and uploading it to the cloud. For example, after a node successfully registers, the environmental assessment module (104) immediately starts a full-band scan, detecting the signal strength of 14 channels in the surrounding 2.4GHz and 25 channels in the 5.8GHz, recording the average RSSI value and noise floor level of each channel. After the scan is completed, an initial environmental assessment report is generated and uploaded to the platform. The cloud monitoring platform summarizes the environmental assessment data reported by all nodes and generates an electromagnetic environment heat map of the entire monitoring area through a spatial interpolation algorithm, providing data basis for subsequent node optimization deployment and channel adjustment. The heat map visually displays the interference intensity of each area with different colors; the darker the color in the heat map, the more severe the electromagnetic interference in that area. Maintenance personnel can adjust the node position or add blind spot nodes in high-interference areas based on the heat map. Specifically, such as Figure 3 The image shown is an example of a regional electromagnetic environment heat map generated by a gridded UAV monitoring system. The system uses a standardized 3×3 uniform grid layout to fully display the electromagnetic interference distribution in the coverage area of ​​the nine monitoring nodes.

[0104] The background depicts a simplified urban road grid consisting of two horizontal main roads and two vertical main roads. The target monitoring area is evenly divided into nine perfectly equal squares. A monitoring node is precisely positioned at the geometric center of each square, with a total of nine monitoring points labeled Node 1 to Node 9. Each monitoring node is represented by a solid black dot, with its corresponding node number clearly marked next to it. Each square is filled with different gray levels to visually correspond to the electromagnetic interference intensity of the area. A standardized legend is provided on the right, with gray levels ranging from light to dark:

[0105] White: No interference (such as the area where node 1 and node 6 are located)

[0106] Light gray: Slight interference (such as the area where nodes 2 and 7 are located).

[0107] Medium gray: Moderate interference (such as the area where nodes 3 and 8 are located)

[0108] Dark gray: Severe interference (such as the areas where nodes 4 and 9 are located).

[0109] Black: Extremely strong interference (such as the central area where node 5 is located).

[0110] The darker the grayscale in the heatmap, the more severe the electromagnetic interference in the 2.4GHz / 5.8GHz frequency bands in that area, intuitively reflecting the differences in the signal reception environment for drone operation in different areas. Maintenance personnel can use this data to quickly locate high-interference areas and adjust the working channels of corresponding nodes or deploy blind spot replacement nodes, providing intuitive quantitative data support for dynamic channel optimization, coverage improvement, and daily maintenance of the system.

[0111] S4, Network Link Self-Test

[0112] The cloud-based monitoring platform's link self-test module 203 initiates network link tests periodically or as needed, with the process as follows: Figure 4 As shown:

[0113] The platform selects any monitoring node A as the beacon transmitter and sends a link test command to it;

[0114] According to the instructions, the beacon simulation transmission module 105 of node A generates and transmits a test operation identification broadcast signal that conforms to the specified standards;

[0115] Meanwhile, multiple monitoring nodes (nodes B, C, etc.) within a preset range around node A, acting as receiving ends, switch to the designated channel for signal scanning.

[0116] Once the receiving node successfully receives the test signal, it will report the signal strength (RSSI), signal-to-noise ratio, and other quality parameters to the cloud platform.

[0117] Based on the reports from each receiver, the platform determines whether the detection links between node A and surrounding nodes (such as determining the A→B link and the A→C link) are working properly, and calculates the effective coverage area of ​​node A and the coverage overlap area with other nodes to identify whether there are monitoring blind spots caused by signal obstruction.

[0118] After the test is completed, the platform generates a link test report, recording the link status and coverage of each node. When a link anomaly or monitoring blind spot is detected at a node, it indicates that there is obstruction or interference between that node and its neighboring nodes, potentially resulting in a coverage blind spot. The platform will send blind spot filling suggestions to the operations and maintenance personnel, automatically generate an operations and maintenance alarm, and notify the management personnel to handle the situation.

[0119] Specifically, Figure 4This is a schematic diagram of the self-test of the network link in this invention. Corresponding to step S4 in the specification, it intuitively shows the complete process of the link self-test module of the cloud monitoring platform (200) initiating and executing the link test: The cloud monitoring platform (200) first selects the central node A as the beacon transmitter and issues a test command. At the same time, it instructs the nodes B and C within its preset range to switch to the designated channel for signal scanning as receivers. After receiving the command, node A generates and transmits a test operation identification broadcast signal that conforms to the national standard through the beacon simulation transmission module. The outermost thin dashed circle in the figure represents the limit detection radius of node A, the middle thick dashed circle represents the rated effective detection radius of node A, the middle solid circle represents the actual coverage range of node A obtained through this test, and the right solid circle represents the actual coverage range of the adjacent node C. The intersection area of ​​the two solid circles is the coverage overlap area of ​​node A and node C. After scanning and receiving the signal, the surrounding receiver nodes report the signal strength (RSSI), signal-to-noise ratio and other quality parameters to the cloud. The platform judges the link status and calculates the link status of node A based on this. The effective coverage area overlaps with the coverage areas of other nodes. Solid black arrows indicate "smooth" coverage, meaning the receiver successfully received the target signal; dashed black arrows indicate "disrupted" coverage, meaning the receiver did not receive a valid signal. The diagram clearly shows two typical link scenarios: Node B is within the maximum detection radius of Node A, but the link is not smooth, intuitively reflecting the problem of hidden monitoring blind spots caused by building obstruction between Node A and Node B—a core defect that traditional heartbeat mechanisms alone cannot detect. Node C is within the maximum detection radius of Node A, and the link is smooth, verifying that the device's basic detection capabilities meet design requirements. Furthermore, the actual coverage area of ​​Node C overlaps with that of Node A, intuitively demonstrating the inherent coverage redundancy of grid deployment, providing a foundation for multi-node data fusion and automatic fault takeover. After testing, the platform generates a complete link test report, recording the link status and actual coverage of each node. When link anomalies or monitoring blind spots are detected, it automatically sends blind spot repair suggestions and maintenance alarms to maintenance personnel.

[0120] S5, Real-time Monitoring and Alarm

[0121] After completing the above steps, the system enters normal operation. Each monitoring node continuously scans and decodes the drone operation identification broadcast signal, and reports the parsed drone information to the platform in real time. When a drone enters a pre-set electronic fence area, the node's geofence module (106) immediately generates an alarm event, including the drone's serial number (SN), location, speed, pilot's location, and trigger time, and reports it to the platform first. The platform's visualization module (204) pops up an alarm window and simultaneously issues an audible alarm to notify supervisory personnel to handle the situation promptly.

[0122] S6, Data Fusion and Tracking

[0123] The platform's data fusion module 202 receives UAV data reported by all nodes in real time. For the same UAV, it may be detected simultaneously by multiple adjacent nodes. The module associates the data using the UAV's unique serial number (SN code) and uses a preset spatiotemporal sliding window to deduplicate and remove duplicate reports. Then, it uses a Kalman filter algorithm to fuse the position, velocity, and other information observed by multiple nodes, effectively correcting measurement errors and generating a smooth, continuous UAV flight trajectory. Supervisory personnel can view the UAV's flight trajectory in real time on the electronic map of the visualization module 204.

[0124] As described above, the deployment method of the UAV monitoring and management system provided by the present invention,

[0125] First, through the automatic registration and configuration mechanism of step S2, monitoring nodes automatically complete registration, verification, and configuration distribution after powering on and connecting to the network. This eliminates the need for professional personnel to debug each node on-site, reducing single-node deployment time from hours to minutes. Unified cloud-based configuration management allows for one-click adjustments to global parameters, significantly reducing the engineering complexity and manpower costs of large-scale deployments. Second, through the dual self-checking mechanisms of S3 initial environment self-assessment and S4 network link self-testing, the system can comprehensively understand the electromagnetic environment of the monitoring area and the effectiveness of links between nodes before formal operation. The environment assessment report can identify areas with severe interference in advance, and the link test can pinpoint blind spots and communication anomalies between nodes, ensuring the system is in optimal working condition from the moment it goes online. This avoids the passive situation of "install and use, deal with problems later" common in traditional methods. Meanwhile, S1 to S6 form a complete closed-loop process for deployment, registration, evaluation, testing, monitoring, and integration. Most configuration and testing tasks can be completed automatically without manual intervention, ensuring consistent deployment quality and reducing the technical requirements for maintenance personnel. This transforms the construction of large-scale grid-based monitoring systems from customized projects to standardized delivery. Furthermore, due to the automatic registration mechanism of S2 and the self-testing mechanism of S4, new nodes only need to undergo physical installation to be automatically identified, configured, and verified by the system, without interrupting the normal operation of the existing system, giving the monitoring network excellent elastic expansion capabilities. Finally, through the electromagnetic environment heat map generated by S3 and the link test report generated by S4, administrators can intuitively understand the coverage and interference distribution of the entire monitoring network after system deployment, providing accurate data support for subsequent blind spot deployment, channel optimization, and fault diagnosis.

[0126] In this way, this deployment method transforms the traditional model that relies on manual on-site debugging and has an unknown deployment status into an automated, standardized model with verifiable deployment status, which greatly improves the construction efficiency, deployment quality and maintainability of the UAV monitoring network.

[0127] As a preferred implementation method, a backbone node networking mode is adopted for ordinary urban areas, with the node spacing determined based on the actual effective detection radius of a single device. For key control areas such as airports and government offices, smaller-spaced filler nodes are used to enhance coverage and ensure no monitoring blind spots. After all nodes are installed, power is connected and the network is established to form a multi-node gridded coverage network. Specifically, the backbone node spacing is set to 400 meters, and the filler node spacing is set to 150 meters. Nodes are preferentially installed in locations with open and unobstructed views, such as streetlight poles and building rooftops, with a height controlled between 5 and 15 meters.

[0128] As mentioned above, the deployment method in this case adopts a differentiated networking strategy combining backbone nodes and gap-filling nodes. For ordinary urban areas, the spacing between backbone nodes is determined based on the actual effective detection radius of a single monitoring node, ensuring the continuity of basic coverage while avoiding resource waste caused by excessive node density, achieving the best balance between coverage and construction costs. For key control areas such as airports and government agencies, coverage is enhanced by deploying gap-filling nodes with smaller spacing, ensuring that there are no monitoring blind spots in these critical areas, thus achieving precise allocation of monitoring resources within a limited budget. In addition, by linking node spacing to the actual detection radius, rather than using a fixed value, the deployment scheme can adapt to the specific environmental differences in different cities and regions (such as building density, road width, electromagnetic background, etc.), exhibiting strong generalization ability and promotional value. At the same time, prioritizing the use of existing public facilities (streetlight poles, rooftops) for node installation eliminates the need for additional pole erection or land acquisition, significantly reducing deployment coordination costs and construction difficulties. Finally, this deployment method is deeply integrated with the system's self-registration and self-testing mechanisms. After the nodes complete the physical installation and power-on network according to the above rules, the system's automatic registration and link self-testing process can automatically verify the actual coverage effect. If coverage gaps or link anomalies are found, the administrator can make targeted blind spot filling or position fine-tuning based on the original deployment plan according to the test report, thereby improving the accuracy and success rate of deployment.

[0129] As a preferred implementation, the network link self-test specifically includes the following steps:

[0130] The cloud-based monitoring platform instructs the first monitoring node selected as the beacon transmitter to simulate and transmit a test operation identification broadcast signal that conforms to the prescribed standards.

[0131] At the same time, it instructs at least one second monitoring node around the first monitoring node to perform scanning as a receiving end;

[0132] After the second monitoring node successfully receives the test signal, it will report the signal quality information to the cloud monitoring platform.

[0133] The platform uses this information to determine the link status between nodes and the overlapping coverage areas.

[0134] This clearly defines the specific execution steps for link testing, ensuring the standardization and repeatability of the testing process. By simulating the propagation path of real UAV signals, the status of the detection links between nodes can be accurately assessed, providing reliable measured data support for system operation and maintenance optimization and blind spot deployment.

[0135] In a preferred implementation, step S6, the data fusion and tracking specifically includes the following steps:

[0136] The platform's data fusion module uses the combination of the drone's unique identifier and the reporting time as the basis for deduplication;

[0137] Using a preset spatiotemporal sliding window, multiple reported data with the same identifier and matching spatiotemporal features are deduplicated. Data captured by multiple nodes at similar times and locations of the same UAV are identified as duplicate data, and only the one with the best signal quality is retained.

[0138] The deduplicated data is grouped by identifier, sorted by time, and then connected to each location point in sequence to generate a continuous flight trajectory for each drone.

[0139] Thus, the spatiotemporal matching mechanism ensures the accuracy and efficiency of deduplication. The generated continuous flight trajectory can completely reconstruct the drone's flight path, providing accurate and intuitive information support for drone tracking, control, and post-event evidence collection.

[0140] In summary, this invention discloses a gridded drone monitoring and management system and its deployment method, relating to the field of drone monitoring technology. The system includes multiple gridded monitoring nodes and a cloud-based monitoring platform. Each monitoring node integrates modules for signal detection, decoding and parsing, self-registration and heartbeat, environmental assessment, beacon simulation transmission, and geofencing. The cloud platform integrates modules for device management, data fusion, link self-testing, and visualization. The deployment method includes six steps: gridded node deployment, automatic registration and configuration, initial environmental self-assessment, network link self-testing, real-time monitoring and alarm, and data fusion and tracking. This invention achieves large-scale seamless monitoring through a gridded microcellular architecture, intelligent deployment and operation through self-registration and self-testing mechanisms, and transparent system status through environmental assessment and link self-testing. It effectively solves the problems of limited detection range, rigid deployment, and difficult operation and maintenance in existing technologies, and is suitable for urban low-altitude drone monitoring.

[0141] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A grid-based unmanned aerial vehicle (UAV) monitoring and management system, characterized in that, include: Multiple monitoring nodes (100), each node being an independent monitoring device, are deployed in the target monitoring area to form a grid-like coverage; A cloud-based monitoring platform (200) is communicatively connected to the plurality of monitoring nodes (100); Each monitoring node (100) includes: The signal detection module (101) is used to scan and receive UAV operation identification broadcast signals that conform to the prescribed standards in the frequency band used for UAV communication; The decoding and parsing module (102) is connected to the signal detection module (101) and is used to decode the received UAV operation identification broadcast signal and parse out the UAV-related information. The self-registration and heartbeat module (103) is used to send a registration request to the cloud monitoring platform (200) and periodically report the node's own status; The environmental assessment module (104) is used to control the signal detection module (101) to scan the surrounding signal environment in the same specific frequency band as the UAV operation identification broadcast signal when there is no launch mission, assess the degree of environmental interference, and generate an environmental assessment report to be reported to the cloud monitoring platform (200). The beacon simulation transmission module (105) is used to respond to the instructions of the cloud monitoring platform (200) or according to preset rules, simulate and generate test operation identification broadcast signals that meet the prescribed standards and transmit them to each monitoring node (100) for network link testing; The geofence module (106) is used to store the geofence data sent from the cloud monitoring platform (200). When the location information in the decoded drone-related information triggers the fence boundary, an alarm event is generated. The cloud-based monitoring platform (200) includes: The device management module (201) is used to maintain the global device list and status. The data fusion module (202) is used to deduplicatize and correlate the UAV information reported by each node to generate a continuous flight trajectory; The link self-test module (203) is used to trigger the beacon simulation transmission module (105) of one or more monitoring nodes (100) to work, and to evaluate the effectiveness of the signal propagation link and the coverage overlap area between monitoring nodes based on the test signal received by other monitoring nodes. The visualization module (204) is used to display the status of monitoring nodes, electromagnetic environment information, and the real-time location and trajectory of UAVs on a map.

2. The grid-based UAV monitoring and management system according to claim 1, characterized in that, The signal detection module (101) operates in the 2.4GHz and / or 5.8GHz frequency band, scanning and receiving UAV operation identification broadcast signals conforming to GB 42590-2023 and GB 46750-2025 standards; The drone-related information parsed by the decoding and parsing module (102) includes at least one of the following: drone's unique serial number, drone's real-time location, speed, heading, and pilot's location information.

3. The grid-based UAV monitoring and management system according to claim 1, characterized in that, The self-registration and heartbeat module (103) automatically sends a registration request after the detection node is powered on for the first time or the network is restored, and reports its own status at a fixed frequency; the self-status includes at least device ID, geographical location, IP address and online status; after the device management module (201) of the cloud monitoring platform (200) verifies that the registration request of the monitoring node is approved, it sends configuration parameters including electronic fence and working frequency band to the node.

4. The grid-based UAV monitoring and management system according to claim 1, characterized in that, The environmental assessment module (104) is used to scan the signal environment of the surrounding 2.4GHz and / or 5.8GHz frequency bands, record the received signal strength indication and noise floor value of each channel, and generate an environmental assessment report to be reported to the cloud monitoring platform (200); the cloud monitoring platform (200) is used to generate an electromagnetic environment heat map of the entire monitoring area based on the environmental assessment reports reported by multiple monitoring nodes (100).

5. The grid-based UAV monitoring and management system according to claim 2, characterized in that, The data fusion module (202) is configured as follows: using the combination of the UAV's unique serial number and the reporting timestamp as the deduplication key, within a preset spatiotemporal sliding window that includes a time window and a spatial distance window, multiple reported data with the same serial number and whose time difference and position difference both fall within the threshold range are deduplicated, and only one of them is retained; and the deduplicated data are grouped by serial number, sorted by time, and then connected to each position point in sequence to generate the continuous flight trajectory of each UAV.

6. The system according to claim 1, characterized in that, When performing a link test, the link self-test module (203) instructs the selected first monitoring node to act as a beacon transmitter and simultaneously instructs at least one second monitoring node around the first monitoring node to act as a receiver for scanning. After the second monitoring node successfully receives the test signal, it will report the received signal strength indication information to the cloud monitoring platform (200). The link self-test module (203) triggers a link test when any of the following conditions are met: the preset time period is reached, the device management module (201) detects that any monitoring node is offline, or the electromagnetic interference intensity in a certain area exceeds a preset threshold based on the environmental assessment report.

7. A deployment method for an unmanned aerial vehicle (UAV) monitoring and management system, characterized in that, Applied to the system according to any one of claims 1 to 6, comprising the following steps: S1. Node Deployment: Within the target monitoring area, multiple monitoring nodes (100) are installed according to the preset grid spacing or the principle of prioritizing key areas to form a grid-like coverage of monitoring nodes; S2. Automatic registration and configuration: After any monitoring node is powered on and connected to the network, its self-registration and heartbeat module (103) automatically sends a registration request to the cloud monitoring platform (200). After the platform verifies the registration, it adds the node to the device list and issues configuration parameters. S3. Initial environmental self-assessment: After the environmental assessment module (104) of each monitoring node (100) is started, it scans the electromagnetic environment of the corresponding frequency band in the surrounding area, generates an environmental assessment report and reports it to the platform. S4. Network link self-test: The link self-test module (203) of the cloud monitoring platform (200) initiates link tests periodically or as needed to evaluate the link status and coverage overlap area between nodes. S5. Real-time monitoring and alarm: When the system enters the working state, each monitoring node continuously receives and decodes the drone operation identification broadcast signal. When a target is detected entering the electronic fence, an alarm event and drone information are immediately reported to the platform. S6. Data Fusion and Tracking: The platform's data fusion module (202) receives the data reported by all nodes, performs fusion processing on the data reported by multiple nodes, and generates the drone's flight trajectory.

8. The deployment method according to claim 7, characterized in that, In step S1, for ordinary urban areas, a backbone node network is used, and the spacing between backbone nodes is determined based on the actual effective detection radius of a single monitoring node; for key control areas, blind spot nodes are used to enhance coverage, and the spacing between blind spot nodes is smaller than the spacing between backbone nodes.

9. The deployment method according to claim 7, characterized in that, In step S4, the network link self-test specifically includes the following steps: The cloud-based monitoring platform instructs the first monitoring node selected as the beacon transmitter to simulate and transmit a test operation identification broadcast signal that conforms to the prescribed standards. At the same time, it instructs at least one second monitoring node around the first monitoring node to perform scanning as a receiving end; After the second monitoring node successfully receives the test signal, it will report the signal quality information to the cloud monitoring platform. The platform uses this information to determine the link status between nodes and the overlapping coverage areas.

10. The deployment method according to claim 7, characterized in that, In step S6, the data fusion and tracking specifically includes the following steps: The platform's data fusion module uses the combination of the drone's unique identifier and the reporting time as the basis for deduplication; Using a preset spatiotemporal sliding window, multiple reported data with the same identifier and matching spatiotemporal features are deduplicated, and only one of them is retained; The deduplicated data is grouped by identifier, sorted by time, and then connected to each location point in sequence to generate a continuous flight trajectory for each drone.

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