Intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system

Abstract

The invention provides an intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system which comprises a system formed by an airborne subsystem, an edge node and a cloud control platform in a cooperative mode and is in service linkage with a digital twin airspace. Wherein the airborne subsystem comprises a flight control and task calculation unit, a communication multilink module, an equipment health monitoring module and a load interface module; the edge node comprises a machine nest and an edge gateway, and has the functions of taking off, landing, energy supply, data aggregation and evidence anchoring; the cloud control platform comprises task arrangement and scheduling, compliance semantic compilation, airspace risk prediction, monitoring and intervention implementation, evidence management and an open interface; the problems that the implementation process of an existing monitoring system is invisible and cannot be verified, sensing fragmentation is achieved, algorithm splitting is achieved, and multi-task and multi-machine cooperation is difficult are solved.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicles (UAVs), and more particularly to an intelligent low-altitude economic UAV flight monitoring system. Background Technology

[0002] This invention addresses the large-scale operation of unmanned aerial vehicles (UAVs) in various scenarios within the "low-altitude economy" (logistics delivery, urban inspection, ecological and environmental protection, energy operation and maintenance, cultural tourism aerial photography, emergency rescue, etc.), and proposes systematic improvements to address the following pain points commonly found in the existing flight "implementation process": The implementation process is invisible and unverifiable: whether flight routes are executed as approved, whether restricted airspace boundaries are violated, whether payload / energy meets mission redundancy requirements, and whether there are control risks caused by long-latency links—there is a lack of unified online monitoring and a verifiable post-event data chain. Especially in complex urban scenarios and electromagnetic disturbance environments, communication link jitter can easily cause path deviations and control instability.

[0003] Fragmented perception and disjointed algorithms: Inconsistent standards for air-ground collaborative information (GNSS / IMU / visual, meteorological, ground IoT sensing, airspace rules, temporary no-fly notices, etc.) make it difficult to form an integrated closed loop of "mission-communication-security-compliance".

[0004] Multi-task and multi-machine collaboration is difficult: When different machine types, different loads and different task procedures run concurrently, there is a lack of unified risk measurement and scheduling benchmarks, making it impossible to achieve "real-time perception → dynamic assessment → closed-loop intervention".

[0005] Therefore, it is necessary to provide a new intelligent low-altitude economic drone flight monitoring system to solve the above-mentioned technical problems. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an intelligent low-altitude economic drone flight monitoring system, which solves the problems of existing monitoring systems such as the lack of visibility and verification during the flight process, fragmented perception, disjointed algorithms, and difficulties in multi-task and multi-drone collaboration.

[0007] The intelligent low-altitude economic UAV flight monitoring system provided by this invention consists of three collaborative parts: an airborne subsystem, edge nodes, and a cloud control platform, and is linked with a digital twin airspace service; wherein: The airborne subsystem includes: a flight control and mission computing unit, a communication multi-link module, an equipment health monitoring module, and a payload interface module; Edge nodes include: a housing and an edge gateway, which have take-off and landing, power supply, data aggregation and evidence anchoring functions; The cloud control platform includes: task orchestration and scheduling, compliant semantic compilation, airspace risk prediction, implementation monitoring and intervention, evidence management and open interfaces; Its core lies in: The compliance semantics compilation module translates regulations / temporary notices / privacy and noise boundaries into airborne executable spatiotemporal cost fields and logical constraints, and injects them into the trajectory optimization and controller; The communication health perception and path-control linkage module generates a health score based on latency, packet loss, jitter and interference intensity. When the score is below the threshold, it performs candidate link switching and partial path replacement without interrupting the task, and injects feedforward compensation into the controller to suppress latency effects. The implementation verification module records flight track / telemetry / command / payload results and compliance events in both the flight control layer and the mission layer, and generates verifiable flight implementation evidence using dual clock verification and segmented hash / Merkel tree. The device health and electrical load module uses an MCU to monitor power supply / current / temperature and load matrix, triggering cascaded load reduction, return to home, or alternate landing strategies; The airspace risk prediction and flexible flight corridor module outputs low-risk flight corridors that are updated over time based on a spatiotemporal model, and together with the cost field and linkage module, it constrains the entire flight implementation process.

[0008] Furthermore, the communication health perception and path-control linkage module includes: The health score calculation unit is used to normalize and weight latency, packet loss, jitter, and interference intensity to obtain a health score. ; Dual threshold trigger, setting pre-switching threshold and forced switching threshold: when When the flow is below the pre-switch threshold, enable critical flow caching and candidate link mirroring; when the flow is below the forced threshold, perform seamless switching. The path local replacement generator generates local replacement segments on the original track based on communication degradation areas; The control feedforward compensator is used to inject link switching timing and estimate delay to achieve phase alignment of attitude / velocity commands; Events and parameters throughout the switching process are synchronously written into the implementation proof module.

[0009] Furthermore, the compliance semantic compilation module includes: The compliance ontology and rule parser are used to parse local regulations, temporary airspace notices, payload usage restrictions, and privacy and noise boundaries into triplets of applicable subject-triggering condition-constraining behavior; Cost field generator, used to generate cost layers for no-fly zones, altitude limits, speed limits, imaging restrictions, and time-based noise / privacy constraints in three-dimensional space and time dimensions; The weighted adaptive mechanism adjusts the cost weights based on task level, time period, and event state to achieve flexible compliance. The constraint injection interface synchronously injects the cost layer and logical constraints into the trajectory optimization and flight control laws.

[0010] Furthermore, the implementation proof module includes: Dual clock verification unit: the onboard high-stability local clock and the authoritative cloud time beacon stamp and mutually verify key events; Segmented hashing and Merkle tree generation units are used to generate roots by rolling hashing of flight and mission data in 2–10 second windows, and periodically anchored at edge nodes. The evidence packager encapsulates flight tracks, telemetry data, links, compliance events, alarms and handling, payload summaries, etc., into a "flight implementation evidence package"; Privacy-preserving exporter for generating evidence summaries without original sensitive images for third-party verification.

[0011] Furthermore, the device health and electrical load module includes: The MCU acquisition chain is used to sample voltage, current, temperature, and load switching status at the millisecond level. A power supply anomaly estimator estimates the probability of busbar outage and predicts temperature rise trends based on a multi-state model. The cascaded load reduction strategy shuts down non-critical loads in sequence according to priority, ensuring redundancy in the "navigation-obstacle avoidance-communication" three lines. The safety handler triggers power limiting, speed limiting, and return / alternate landing when the power safety margin is below a threshold, and records the handling link to the implementation verification module.

[0012] Furthermore, the airspace risk prediction and flexible flight corridor module includes: The city's 3D rasterized risk modeler performs grid-based modeling of buildings, roads, population heat, micro-topography, and near-real-time micro-weather. The spatiotemporal predictor uses spatiotemporal maps / temporal series units to predict short-term risk distribution and population / vehicle flow migration trends; The corridor solver, under the constraints of dynamics and the cost field, outputs an elastic flight corridor that is updated over time. The corridor and compliance linkage system automatically tightens noise and privacy boundaries and adjusts cruise altitude and speed during residents' sensitive periods.

[0013] Furthermore, the airborne subsystem also includes a multi-source data fusion and mission-level AI monitoring module, configured as follows: Data from visible light, infrared, gas, inertial navigation, and visual odometry are integrated, and redundant compression and feature stitching are performed. A cascaded process of "super-resolution reconstruction + lightweight small target detection" is executed at the edge to improve the recognition effect of high-altitude small targets and reduce flight time; The wind field / diffusion model is introduced into the environmental monitoring task for reverse reasoning, outputting suspected source points and credibility scores, and the monitoring conclusions are written into the implementation proof module.

[0014] Furthermore, the edge node includes a smart nest and an edge gateway, configured as follows: It achieves automatic take-off and landing and positioning through RTK, and is equipped with automatic charging / battery swapping and health inspection; During the link switching phase, it undertakes critical flow mirroring and buffering, and performs periodic anchoring of evidence roots; It interfaces with the task orchestration and scheduling module of the cloud control platform to support dual-state collaboration between regular high-frequency tasks and emergency seizure tasks.

[0015] Furthermore, the cloud control platform maintains a unified data model and secure access control. This unified data model includes core entities such as "flight segments, mission segments, compliance events, equipment events, and evidence segments," and supports: Version and timeline consistency management enables data alignment and traceability between different modules; Open interfaces for controlled sharing with regulatory / insurance / industry platforms; Hardware-based key signing and encryption, combined with privacy-preserving evidence digests, enables external verification without exposing sensitive raw data.

[0016] The beneficial effects of this invention are: 1. Implementing visibility and verifiability, the chain of evidence makes it traceable and arbitrable to determine "who, when, where, with what configuration, and through what link completed what action".

[0017] 2. Steady-state communication and safe trajectory: Link feedforward and path replacement make the trajectory more stable and reduce control risks in high-altitude canyon / complex electromagnetic environments.

[0018] 3. It is portable across industries and models. The plug-in risk identification decouples the general implementation monitoring framework from industry knowledge, shortening the deployment cycle.

[0019] 4. Edge intelligence and low cost: "Super-resolution plus detection plus small sample adaptation" is achieved on the embedded platform, which balances accuracy and cost-effectiveness and is suitable for large-scale deployment of the low-altitude economy.

[0020] 5. Enhanced by both human factors and compliance: Reduce the likelihood of misoperation and violations through behavioral prediction and dynamic thresholds. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall system architecture and data flow structure provided by the present invention; Figure 2 This invention provides a communication-path-control linkage state machine and timing diagram; Figure 3 This is a schematic diagram illustrating the time sequence and abnormal location evidence collection for urban grid-based patrol tasks provided by the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Please refer to the following: Figure 1 , Figure 2 as well as Figure 3 ,in Figure 1 This is a schematic diagram of the overall system architecture and data flow structure provided by the present invention; Figure 2 This invention provides a communication-path-control linkage state machine and timing diagram; Figure 3 This is a schematic diagram illustrating the time sequence and abnormal location evidence collection for urban grid-based patrol tasks provided by the present invention.

[0024] In the specific implementation process, such as Figures 1-3 As shown, the system consists of three collaborative parts: an airborne subsystem, edge nodes, and a cloud control platform, and is linked with a digital twin airspace service; among which: The airborne subsystem includes: a flight control and mission computing unit, a communication multi-link module, an equipment health monitoring module, and a payload interface module; Edge nodes include: a housing and an edge gateway, which have take-off and landing, power supply, data aggregation and evidence anchoring functions; The cloud control platform includes: task orchestration and scheduling, compliant semantic compilation, airspace risk prediction, implementation monitoring and intervention, evidence management and open interfaces; Its core lies in: The compliance semantics compilation module translates regulations / temporary notices / privacy and noise boundaries into airborne executable spatiotemporal cost fields and logical constraints, and injects them into the trajectory optimization and controller; The compliance semantic compilation module includes: The compliance ontology and rule parser are used to parse local regulations, temporary airspace notices, payload usage restrictions, and privacy and noise boundaries into triplets of applicable subject-triggering condition-constraining behavior; Cost field generator, used to generate cost layers for no-fly zones, altitude limits, speed limits, imaging restrictions, and time-based noise / privacy constraints in three-dimensional space and time dimensions; The weighted adaptive mechanism adjusts the cost weights based on task level, time period, and event state to achieve flexible compliance. The constraint injection interface synchronously injects the cost layer and logical constraints into the trajectory optimization and flight control laws.

[0025] The communication health perception and path-control linkage module generates a health score based on latency, packet loss, jitter and interference intensity. When the score is below the threshold, it performs candidate link switching and partial path replacement without interrupting the task, and injects feedforward compensation into the controller to suppress latency effects. The communication health awareness and path-control linkage module includes: The health score calculation unit is used to normalize and weight latency, packet loss, jitter, and interference intensity to obtain a health score. ; Dual threshold trigger, setting pre-switching threshold and forced switching threshold: when When the flow is below the pre-switch threshold, enable critical flow caching and candidate link mirroring; when the flow is below the forced threshold, perform seamless switching. The path local replacement generator generates local replacement segments on the original track based on communication degradation areas; The control feedforward compensator is used to inject link switching timing and estimate delay to achieve phase alignment of attitude / velocity commands; Events and parameters throughout the switching process are synchronously written into the implementation proof module.

[0026] The implementation verification module records flight path / telemetry / command / payload results and compliance events at both the flight control and mission layers, and generates verifiable flight implementation evidence using dual clock verification and segmented hash / Merkel tree methods. The implementation verification module includes: Dual clock verification unit: the onboard high-stability local clock and the authoritative cloud time beacon stamp and mutually verify key events; Segmented hashing and Merkle tree generation units are used to generate roots by rolling hashing of flight and mission data in 2–10 second windows, and periodically anchored at edge nodes. The evidence packager encapsulates flight tracks, telemetry data, links, compliance events, alarms and handling, payload summaries, etc., into a "flight implementation evidence package"; Privacy-preserving exporter for generating evidence summaries without original sensitive images for third-party verification.

[0027] The equipment health and electrical load module uses an MCU to monitor power supply / current / temperature and the load matrix, triggering cascaded load reduction, return-to-home, or alternate landing strategies. The equipment health and electrical load module includes: The MCU acquisition chain is used to sample voltage, current, temperature, and load switching status at the millisecond level. A power supply anomaly estimator estimates the probability of busbar outage and predicts temperature rise trends based on a multi-state model. The cascaded load reduction strategy shuts down non-critical loads in sequence according to priority, ensuring redundancy in the "navigation-obstacle avoidance-communication" three lines. The safety handler triggers power limiting, speed limiting, and return / alternate landing when the power safety margin is below a threshold, and records the handling link to the implementation verification module.

[0028] The airspace risk prediction and flexible flight corridor module outputs low-risk flight corridors that are updated over time based on a spatiotemporal model, and together with the cost field and linkage module, it constrains the entire flight implementation process.

[0029] The airspace risk prediction and flexible flight corridor module includes: The city's 3D rasterized risk modeler performs grid-based modeling of buildings, roads, population heat, micro-topography, and near-real-time micro-weather. The spatiotemporal predictor uses spatiotemporal maps / temporal series units to predict short-term risk distribution and population / vehicle flow migration trends; The corridor solver outputs an elastic flight corridor that is updated over time, while satisfying the constraints of dynamics and cost field. The corridor and compliance linkage system automatically tightens noise and privacy boundaries and adjusts cruise altitude and speed during residents' sensitive periods.

[0030] The airborne subsystem also includes a multi-source data fusion and mission-level AI monitoring module, configured as follows: Data from visible light, infrared, gas, inertial navigation, and visual odometry are integrated, and redundant compression and feature stitching are performed. A cascaded process of "super-resolution reconstruction + lightweight small target detection" is executed at the edge to improve the recognition effect of high-altitude small targets and reduce flight time; The wind field / diffusion model is introduced into the environmental monitoring task for reverse reasoning, outputting suspected source points and credibility scores, and the monitoring conclusions are written into the implementation proof module.

[0031] The edge node includes a smart nest and an edge gateway, configured as follows: It achieves automatic take-off and landing and positioning through RTK, and is equipped with automatic charging / battery swapping and health inspection; During the link switching phase, it undertakes critical flow mirroring and buffering, and performs periodic anchoring of evidence roots; It interfaces with the task orchestration and scheduling module of the cloud control platform to support dual-state collaboration between regular high-frequency tasks and emergency seizure tasks.

[0032] The cloud control platform maintains a unified data model and secure access control. The unified data model includes core entities such as "flight segments, mission segments, compliance events, equipment events, and evidence segments," and supports: Version and timeline consistency management enables data alignment and traceability between different modules; Open interfaces for controlled sharing with regulatory / insurance / industry platforms; Hardware-based key signing and encryption, combined with privacy-preserving evidence digests, enables external verification without exposing sensitive raw data.

[0033] This system adopts a four-layer collaborative architecture: "airborne subsystem - edge node - cloud control platform - digital twin airspace service". 1. Airborne Subsystems Flight control and mission computing unit: heterogeneous SoC (including NPU / GPU) + RTOS / container runtime environment, running perception, prediction and control feedforward compensation.

[0034] Multi-link communication modules: cellular (4G / 5G), private network, Mesh, satellite, etc., supporting concurrent dialing and hot standby switching.

[0035] Equipment health monitoring module: MCU (such as STM32) collects voltage / current / temperature / load switching data, and supports priority load reduction and self-test.

[0036] Payload interface modules: visible light / infrared / multispectral / gas sensing / laser ranging, etc., plug and play (unified driver layer).

[0037] Airborne PoE recorder: Dual-channel rolling hashing at the flight control and mission layers, caching key segments and synchronizing with edge node time beacons.

[0038] 2. Edge nodes Intelligent Nest: RTK take-off and landing, automatic charging / battery swapping, automatic data transmission and health inspection.

[0039] Edge gateway: responsible for link mirroring and critical flow buffering, evidence root anchoring, local twin computation and rapid alarm.

[0040] 3. Cloud control platform Task orchestration and scheduling: Generates task specifications (route / payload / time window / evidence requirements), supporting both normal and emergency states.

[0041] Compliance semantic compilation: compiles regulations / announcements / privacy and noise boundaries into spatiotemporal cost fields and logical constraints.

[0042] Airspace risk prediction: risk extrapolation and resilient corridor generation driven by multi-source data.

[0043] Implement monitoring and intervention: calculate task-communication coupling risk (TCR), and issue actions such as speed limit / altitude limit / return to base / alternate landing.

[0044] Evidence management and open interfaces: evidence package generation, verification and controlled sharing.

[0045] 4. Digital Twin Airspace Services Maintain a database of elements such as 3D buildings, roads, population heat maps, temporary control zones, and weather / wind corridors, and provide high-speed spatial query and conflict detection.

[0046] The key modules and data model are as follows: 1. Unified data model The core entities are "flight footage - mission footage - compliance incidents - equipment incidents - evidence footage".

[0047] Flight footage: includes attitude / position / velocity / track sequence and timestamps; Mission segment: includes payload actions, target area, and operational parameters; Compliance events: No-fly zone verification, speed / altitude limit triggering, privacy / noise boundary constraint changes; Equipment events: power failure, temperature rise exceeding threshold, load reduction sequence, return to base / alternate landing decision; Fragments of evidence: hash root, time beacon, signature, and derived fingerprint.

[0048] All entities share dual clocks (onboard local high stability / cloud authoritative) and spatial indexes (UTM + height layer) to ensure spatiotemporal consistency.

[0049] 2. Communication health status and linkage control State vector (Delay, packet loss, jitter, interference) are normalized to [0,1].

[0050] Health rating: ,

[0051] Dual threshold triggering: Pre-switching threshold : Initiate critical flow buffering and candidate link mirroring; Forced threshold : Perform atomic switching and partial path replacement; Control feedforward compensation: Estimate the equivalent time delay Δt, align the attitude / velocity command phase, and avoid oscillation.

[0052] Hysteresis design: Set the value to no less than 0.15 to suppress frequent jittering during switching.

[0053] 3. Airspace Risk Prediction and Flexible Corridors Element library: 3D building volume, road network, population heat map, wind corridor / rain corridor, temporary control zone.

[0054] Spatiotemporal model: STGCN+LSTM for predicting short-term risks; Corridor solution: Minimize under dynamic and cost field constraints Receives data that is updated over time.

[0055] 4. Equipment health and load reduction Data Acquisition: The MCU acquires voltage / current / temperature / load status at the millisecond level; Prediction: Estimating the probability of disconnection based on a multi-state Markov model With the rising temperature trend; Load reduction sequence: non-critical loads → auxiliary lighting → redundant imaging → image transmission secondary link → main imaging frame reduction → limiting peak thrust → return to base / alternate landing.

[0056] Safety threshold: SOC≥30%, bus voltage drop>15% / 200ms triggers power limit.

[0057] 5. Implementation certificate Dual clock synchronization: mutual verification between the airborne high-stability clock and the edge time beacon; Rolling hashing: 2–10s window segmented hashing, constructing Merkle tree root; Edge anchoring: Edge gateway periodically uploads data to the blockchain / locally securely stores evidence (without exposing the original image); Evidence package: includes flight track, telemetry, link, compliance / device events, payload summary, signature and fingerprint; Privacy Digest: Optional zero-knowledge digest, proving "when and where the specified action was performed" without exposing image details.

[0058] In actual use, low-noise, compliant, high-frequency patrols are conducted on the urban grid (500m×500m) to identify issues such as temporary buildings exceeding height limits, road encroachment, and illegal nighttime construction.

[0059] Hardware configuration Aircraft type: Quadrotor (diagonal wheelbase ≈ 650mm), maximum takeoff weight ≤ 7kg; Payload: Visible light + low-light camera, array microphone (noise measurement optional), ranging radar; Communication: 5G+Mesh dual-link; Nest: RTK takeoff and landing, automatic charging (600W), enclosed silent air duct.

[0060] process: S101 mission orchestration: platform selects grid, time period, cruising altitude (80–120m) and evidence requirements (image summary + compliance record).

[0061] S102 Compliance Translation: Height and noise restrictions in residential areas from 22:00 to 6:00; speed restrictions around schools from 8:00 to 17:00 on weekdays.

[0062] S103 Takeoff Self-Test: Electrical / Load / Communication Tests (Results) ), generating a pre-flight evidence snapshot.

[0063] S104 Loading Corridor: Twin service combined with crowd heat / air corridor output

[0064] S105 cruise execution: If The edge gateway begins mirroring and buffering. like Perform link switching and partial path replacement; Compliance costs may vary depending on time of day / region, with speed and height limits adjusted accordingly. The vision module uses "super-resolution + small target detection" to capture newly added roof volumes, scaffolding, and abnormal nighttime light spots; All events are written to PoE.

[0065] S106 Return and Evidence Issuance: Upon landing, a PDF / JSON evidence package is automatically generated, including a grid list, anomaly locations, a timeline, and an evidence summary.

[0066] Key parameters: , ; Mirror buffer window 200ms; Noise threshold: Equivalent sound level LAeq ≤ 55dB (nighttime); Exposure redundancy ≥1.5×, triggers super-resolution reconstruction in cloud shadow / backlight conditions.

[0067] When identifying foreign objects in wires / hardware defects / thermal anomalies and automatically verifying "corridor safety distance - obstacle intrusion limit", the following configuration is required: Payload: Visible light + thermal imaging dual-channel, laser ranging module; Flight path: parallel to the center line of the corridor, divided into height levels (45m, 60m).

[0068] process: S1. Task Arrangement: Import the corridor centerline and tower coordinates to generate checkpoints.

[0069] S2. Compliance and Risk: Construct a "hard constraint layer" for safe distances from charged bodies and establish a "soft cost layer" for wind corridors.

[0070] S3. Implementation: When crossing risk areas, prioritize the use of Mesh links (low-altitude forest areas); Visual reasoning: wire detection → hardware positioning → foreign object / loose strand / bird's nest identification; Thermal imaging: Thresholding and timing anomaly detection for temperature differences in contact wires / fittings; Output "Defect Point - Distance from Conductor - Thermal Difference - Re-inspection Recommendation" and include it in the evidence package along with flight / compliance records.

[0071] S4. Abnormal Handling: If the wind corridor changes abruptly or communication continues to deteriorate, the path will be partially replaced to a higher contour level and the speed will be limited.

[0072] The circuits and controls involved in this invention are all existing technologies and will not be described in detail here.

[0073] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. An intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system, characterized in that, The system is composed of three parts: an airborne subsystem, an edge node, and a cloud control platform, which are coordinated and linked with digital twin airspace services. The airborne subsystem includes: flight control and task computing unit, communication multi-link module, device health monitoring module, and payload interface module. The edge node includes: nest and edge gateway, with take-off and landing and power supply, data aggregation and evidence anchoring functions. The cloud control platform includes: task scheduling and scheduling, compliance semantic compilation, airspace risk prediction, implementation monitoring and intervention, evidence management and open interface. Its core is: The compliance semantic compilation module translates regulations / temporary announcements / privacy and noise boundaries into space-time cost fields and logical constraints that can be executed on board and injected into trajectory optimization and controller; The communication health awareness and path-control linkage module generates a health score based on delay, packet loss, jitter, and interference intensity. When the score is below the threshold, it performs candidate link switching and local path replacement without interrupting the task, and injects feedforward compensation to the controller to suppress the delay effect; The implementation proof module rolls up the flight trajectory / telemetry / instructions / payload results and compliance events at the flight control and task levels, respectively, and generates verifiable flight implementation evidence using double clock verification and segmented hash / Merkle tree; The device health and electrical load module monitors the power / current / temperature and load matrix with the MCU, triggering cascading load reduction, return or standby landing strategies; The airspace risk prediction and elastic flight corridor module outputs a low-risk flight corridor that is updated in time based on the space-time model, and jointly constrains the entire flight implementation process with the cost field and linkage module. 2.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 1, characterized in that, The communication health awareness and path-control linkage module includes: a health degree calculation unit configured to normalize and weight the time delay, the packet loss, the jitter and the interference intensity to obtain a health degree score, ; Dual threshold trigger, set pre-switch threshold and forced switch threshold: when Turn on key flow cache and candidate link mirroring below the pre-switch threshold; perform seamless switching when below the forced threshold; Local path replacer, which generates a local replacement segment on the original flight path based on the communication degradation area; Control feedforward compensator, used to inject link switching timing and estimated delay to achieve phase alignment of attitude / speed instructions; Events and parameters during the entire switching process are written synchronously to the implementation proof module. 3.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 2, characterized in that, The compliance semantic compilation module includes: Compliance ontology and rule parser, which parses local regulations, temporary airspace announcements, payload usage restrictions, privacy, and noise boundaries into triples of applicable subjects-triggering conditions-constraint behaviors; Cost field generator, used to generate cost layers of no-fly, height limit, speed limit, imaging restrictions, and time-based noise / privacy constraints in three-dimensional space and time dimensions; Weight adapter, which adjusts the cost weight based on task level, time period, and event state to achieve elastic compliance; Constraint injection interface, which synchronously injects the cost layer and logical constraints into the trajectory optimization and flight control law. 4.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 3, characterized in that, The implementation proof module includes: Double clock verification unit, which stamps and mutually verifies key events between the onboard high-stability local clock and the cloud authoritative time beacon; Segmented hash and Merkle tree generation unit, which rolls up the hash and generates the root of the flight and task data in 2-10 second windows and periodically anchors them at the edge node; Evidence packager, which encapsulates the flight trajectory, telemetry, link, compliance events, alarms and disposal, payload summary, etc. into "flight implementation evidence package"; Privacy preserving exporter, used to generate evidence summaries without original sensitive images for third-party verification. 5.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 4, characterized in that, The device health and electrical load module includes: MCU acquisition chain for millisecond-level sampling of voltage, current, temperature, and load switch state; Power supply anomaly estimator to estimate bus outage probability and predict temperature rise trend based on multi-state model; Cascade load shedding strategy to sequentially shut down non-critical loads in priority sequence, ensuring "navigation-obstacle avoidance-communication" three-line redundancy; Safety handler to trigger power limiting, speed limiting, and return / home landing when power supply safety margin is below threshold, and record handling link to implementation proof module. 6.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 5, characterized in that, The airspace risk prediction and elastic flight corridor module includes: Urban three-dimensional gridding risk modeler to grid model building bodies, roads, crowd heat, micro-topography, and near-real-time micro-meteorology; Space-time predictor to predict short-term risk distribution and crowd / vehicle flow migration trend using space-time graph / sequential unit; Corridor solver to output time-rolling updated elastic flight corridor under dynamic and cost field constraints; Corridor and compliance linker to automatically tighten noise and privacy boundaries and adjust cruising altitude and speed during resident sensitive periods. 7.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 6, characterized in that, The airborne subsystem also includes a multi-source data fusion and task-level AI monitoring module configured to: Fuse visible light, infrared, gas, inertial navigation, and visual odometry data for redundant compression and feature splicing; Perform "super-resolution reconstruction + lightweight small target detection" cascade process on edge side to improve high-altitude small target recognition effect and reduce flight time; Introduce wind field / diffusion model for environmental monitoring tasks for backward reasoning, output suspected source points and credibility scores, and write monitoring conclusions into the implementation proof module. 8.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 7, characterized in that, The edge node includes an intelligent drone nest and an edge gateway configured to: Realize automatic take-off and landing and positioning through RTK, with automatic charging / exchange and health inspection; Take over key flow mirroring and buffering during link switching and perform periodic anchoring of evidence roots; Interface with the task scheduling and dispatching module of the cloud control platform to support dual-state collaboration of normal high-frequency tasks and emergency preemptive tasks. 9.The intelligent low-altitude economic unmanned aerial vehicle flight implementation monitoring system according to claim 8, characterized in that, The cloud control platform maintains a unified data model and security access control, and the unified data model includes core entities such as "flight segment, task segment, compliance event, device event, evidence segment", and supports: Version and timeline consistency management to achieve data alignment and traceability between different modules; Open interface for controlled sharing with regulatory, insurance, and industry platforms; Signature and encryption based on hardware keys, combined with privacy-preserving evidence summary to implement external verification without exposing sensitive original data.

Images