Low-altitude flight service software and hardware platform fused with meteorological safety algorithm

Abstract

The invention relates to the technical field of low-altitude economic meteorological safety and aircraft communication navigation monitoring service, and discloses a low-altitude flight service software and hardware platform integrated with a meteorological safety algorithm. Comprising a foundation platform (a vertical take-off and landing point / an airport and the like), a ground / satellite / and various private networks, an aircraft communication guiding and monitoring module, a low-altitude flight service software and hardware platform fused with a meteorological safety algorithm and the like. The method can effectively solve the flight safety problem of various aircrafts, provides a safe, scientific and accurate flight service early warning decision management algorithm scheme, and can be widely applied to the low-altitude economy industry. For example, project construction of low-altitude economic unmanned aerial vehicles / manned aerial vehicle foundation platforms (vertical take-off and landing points / airports and the like) and application of a low-altitude economic flight service platform in scenes such as electric power / petroleum / communication / chemical engineering / civil aviation airports / meteorological stations / forests and the like are expanded, and the market huge application value is extremely high.

Description

Technical Field

[0001] This invention addresses the technical fields of low-altitude economic weather safety and aircraft communications, navigation, and surveillance services. It also discloses a software and hardware platform for low-altitude flight services, including weather positioning, monitoring, early warning, ground resistance and ground grid status monitoring, and aircraft communication and navigation monitoring. In particular, the software platform integrates a weather safety analysis component with a communications, navigation, and surveillance analysis component to achieve highly accurate positioning and advanced warning of weather threats around low-altitude aircraft, providing critical safety assurance for urban air mobility (UAM) and drone logistics. Background Art

[0002] The existing "low-altitude flight service system hardware and software platform" primarily focuses on "communication, navigation, and surveillance," but lacks integration with systems such as the "ground-based platform (vertical takeoff and landing point)" meteorological safety positioning, monitoring, early warning, and ground resistance and ground grid status monitoring modules. With the rapid development of the low-altitude economy, the number of drones and manned aircraft will increase exponentially, making low-altitude flight safety a pressing issue that must be addressed. Our company integrates meteorological safety analysis components with communication, navigation, and surveillance analysis components within the software platform. Through a combined algorithmic solution, we achieve highly accurate positioning and advanced warning of meteorological threats around low-altitude aircraft, providing a safe, scientific, and accurate early warning decision-making management solution and enabling the development of reasonable flight plans for aircraft management. Summary of the Invention

[0003] This paper proposes a low-altitude flight service hardware and software platform that integrates meteorological safety algorithms to address meteorological safety issues during low-altitude aircraft flight, achieve high-precision positioning and advance warning of meteorological threats around low-altitude aircraft, and provide key safety guarantees for urban air traffic (UAM) and drone logistics.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] The low-altitude flight service software and hardware platform integrating meteorological safety algorithms consists of: ground-based platform: vertical take-off and landing points / airports, etc., ground / satellite / and various private networks, aircraft communication and monitoring modules, and the low-altitude flight service software and hardware platform integrating meteorological safety algorithms;

[0006] The low-altitude flight service software and hardware platform integrating meteorological safety algorithms includes the following steps:

[0007] In step 1, the weather positioning module collects data such as the longitude and latitude, location, and time of lightning strikes through sensors and special algorithms. The weather monitoring module collects data such as lightning amplitude, lightning current, and time rate of change through sensors and acquisition modules. The weather warning module collects data such as atmospheric electric field distribution, wind speed, wind direction, rainfall, temperature, humidity, and air pressure through sensors. The ground resistance and ground grid status monitoring module collects ground resistance and ground grid status data such as vertical take-off and landing points through sensors. All of this weather safety data is ultimately uploaded to ground / satellite / and various private networks via interfaces such as RJ45 / fiber optic / wireless 4G5G / WI-FI / RS485 / RS422 / LoRa / satellite.

[0008] Step 2: The drone / man-machine communication / sensor / radar module data is uploaded to the ground / satellite / and various private networks via 4G5G and satellite communication modules, including the drone identification code, real-time location information, flight altitude, speed, heading, time and date, etc.

[0009] Step 3: The manned aircraft communication / sensor / radar module data is simultaneously broadcast via the ADS-B transmitter, including identity, high-precision position, speed, altitude, heading, status code, and other information, to the ADS-B ground station. The ADS-B ground station then forwards this information to the proprietary network via the ASTERIX CAT021 interface protocol.

[0010] Step 4: Ground / satellite / and various dedicated networks aggregate and transmit meteorological safety module data, drone / manned aircraft module data, and ADS-B data to the cloud database server;

[0011] Step 5: Software for weather positioning, monitoring, early warning, ground resistance and grounding network analysis, and communication navigation

[0012] The monitoring and analysis module obtains data from the cloud data server in real time and generates analysis data through a specific joint algorithm and reports it to the early warning decision management module;

[0013] Step 6: The software early warning decision management module makes early warning decision management based on the data in step 5 and outputs the result to the scheduling execution management software module;

[0014] Step 7: The scheduling execution management software module outputs the results to the flight plan management software module, and the flight plan management software module outputs a schedule for the aircraft's flight time, route, etc.

[0015] The beneficial effects of the present invention are as follows:

[0016] First: Integrate the "ground-based platform: vertical take-off and landing point" meteorological safety: positioning, monitoring, early warning and ground resistance and grounding grid status monitoring module systems to provide a safe, scientific and accurate early warning decision-making management plan and formulate a reasonable flight plan for aircraft management.

[0017] Second: Our company has integrated meteorological safety analysis components and communication, navigation, surveillance and analysis components in the software platform, and through a combined software algorithm technology solution, it can achieve high-precision positioning and advance warning of meteorological threats around low-altitude aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the network elements and system flow of the low-altitude flight service software and hardware platform integrating meteorological safety algorithms. DETAILED DESCRIPTION

[0019] Example 1:

[0020] The network elements and system flow diagram of the low-altitude flight service software and hardware platform integrating meteorological safety algorithms, such as Figure 1 As shown, the hardware and software and platform components include: ground-based platforms (vertical take-off and landing points / airports, etc.), ground / satellite / and various private networks, aircraft communication, navigation and monitoring models, and low-altitude flight service hardware and software platforms that integrate meteorological safety algorithms.

[0021] The implementation process of the low-altitude flight service software and hardware platform integrating meteorological safety algorithms is as follows:

[0022] The meteorological positioning module collects data such as the longitude and latitude, location, and time of lightning strikes through sensors and special algorithms. The meteorological monitoring module collects data such as lightning amplitude, lightning current, and the rate of change over time through sensors and acquisition modules. The meteorological warning module collects data such as atmospheric electric field distribution, wind speed, wind direction, rainfall, temperature, humidity, and air pressure through sensors. The ground resistance and ground grid status monitoring module collects ground resistance and ground grid status data such as vertical take-off and landing points through sensors. All of this meteorological safety data is ultimately uploaded to ground / satellite / and various private networks via interfaces such as RJ45 / fiber optic / wireless 4G5G / Wi-Fi / RS485 / RS422 / LoRa / satellite.

[0023] The drone / man-machine communication / sensor / radar module data is uploaded to the ground / satellite / and various private networks via 4G5G and satellite communication modules, including the drone identification code, real-time location information, flight altitude, speed, heading, time and date, etc.

[0024] The manned aircraft communication / sensor / radar module data is simultaneously broadcast through the ADS-B transmitter, including identity, high-precision position, speed, altitude, heading, status code, and other information to the ADS-B ground station. The ADS-B ground station forwards this information to the proprietary network via the ASTERIX CAT021 interface protocol.

[0025] Ground / satellite / and various dedicated networks aggregate and transmit meteorological safety module data, drone / manned aircraft module data, and ADS-B data to the cloud database server for storage;

[0026] The software's meteorological positioning, monitoring, early warning, ground resistance and grounding network analysis, and communication and navigation monitoring and analysis modules acquire cloud data server data in real time and generate analysis data through a specific joint algorithm and report it to the early warning decision management module;

[0027] The software early warning decision management module makes early warning decision management based on the data of the two analysis components of the platform software algorithm and outputs the results to the scheduling execution management software module;

[0028] The scheduling execution management software module outputs the results to the flight plan management software module, and the flight plan management software module outputs the plan table for the aircraft's flight time, route, etc.

[0029] Example 2:

[0030] The low-altitude flight service software and hardware platform integrates meteorological safety algorithms. Its characteristics are as follows: the flight service software platform integrates meteorological safety analysis components and communication, navigation, surveillance and analysis components. Through the three core technologies of airborne-ground collaborative perception, quantum intelligent optimization and dynamic grid, it can achieve high-precision positioning and advanced warning of meteorological threats around low-altitude aircraft. The specific implementation process is as follows:

[0031] Process 1: Dynamic Mesh Adaptation Technology (Solving Aircraft Mobility Issues)

[0032] A 3D grid is dynamically constructed based on the real-time position of the aircraft. The center of the grid moves along the trajectory, and the resolution is adaptively adjusted according to the threat level.

[0033] 1. Grid center binding formula

[0034] (λ_c(t), φ_c(t)) = (λ_u(t) + v_u·Δt·cosθ, φ_u(t) + v_u·Δt·sinθ), where vu is the vehicle velocity, θ is the heading angle, and Δt is the update time interval.

[0035] 2. Resolution Adaptive Rules

[0036] Res = {

[0037] 0.5km × 0.5km × 100m if E > 5kV / m

[0038] 1km × 1km × 200m if 1kV / m < E ≤ 5kV / m

[0039] 2km × 2km × 500m otherwise

[0040] }

[0041] The electric field strength threshold E is obtained in real time by the airborne sensor.

[0042] Process 2: Quantum-Genetic Hybrid Optimization Positioning Engine (Breaking Through Traditional Optimization Bottlenecks)

[0043] 1. Quantum-encoded lightning location probability

[0044] Map the lightning position (x,y,z) to a superposition of quantum states:

[0045] |ψ〉 = α0|0〉 + α1|1〉, |α0|² + |α1|² = 1, where αi represents the probability amplitude of the position existing in grid cell i.

[0046] 2. Hybrid fitness function design

[0047] Fusion of TOA error, multimodal feature similarity and terrain attenuation factor

[0048] L = ∑[k=1→K](Δt_k - ||x - x_k|| / c)² (This part is the TOA error)

[0049] + β·||F_model(x) - F_obs||² + γ·exp(-κ·H(x))

[0050] Fmodel: simulated electromagnetic / electric field / topographic characteristics at position x

[0051] H(x): SAR terrain elevation data (to suppress false positives in mountainous areas)

[0052] κ: Terrain attenuation coefficient (measured and calibrated)

[0053] 3. Quantum revolving door and genetic crossover collaborative update

[0054] Quantum Gate Update:

[0055] U(θ) = [ cosθ -sinθ ]

[0056] [ sinθ cosθ ]

[0057] θ = η·exp(-(L_i - L_best) / σ)

[0058] Genetic operation: Differential evolution (DE / best / 1 strategy) is introduced to the particle swarm that has stagnated in convergence to avoid local optimality.

[0059] Process 3: Cross-modal feature fusion model (improving robustness in low-altitude complex environments)

[0060] 1. Multi-source input features

[0061] Electromagnetic pulse time-frequency spectrum (STF) (VLF / LF band), atmospheric electric field gradient ∇E, radar reflectivity Z and SAR terrain elevation HSAR

[0062] 2. Gated Cross-Modal Attention Mechanism

[0063] F_fusion = ∑[i=1→N] g_i·W_i·F_i

[0064] g_i = σ(W_g · [F_i || F_ref])

[0065] gi: gate weight (sigmoid activation)

[0066] Fref: Reference features of the aircraft's surrounding environment (extracted from the dynamic grid)

[0067] Residual connections are introduced to prevent gradient disappearance.

[0068] Process 4: Reinforcement Learning-Optimized Detection Node Deployment (Improving System Perception Coverage)

[0069] For the layout of ground mobile detection nodes, build a virtual environment to train the intelligent agent:

[0070] 1. State Space

[0071] Deployed node coordinates {(xi,yi)}, real-time lightning activity heat map D(x,y)

[0072] 2. Action Space

[0073] Node movement, addition and deletion, and power adjustment

[0074] 3. Reward Function

[0075] R = λ·[∑[(x,y)∈G] D(x,y)·I_cover(x,y)] / ∑D(x,y)- μ·Cost_nodes-ν·Overlap_ratio

[0076] Icover: Whether the grid point (x,y) is covered

[0077] Overlapratio: Node detection overlap ratio (needs to be minimized).

[0078] Process 5: Real-time verification driven by sudden changes in the atmospheric electric field (reducing the false alarm rate)

[0079] Based on the sudden change characteristics of the atmospheric electric field before lightning occurs, the verification constraints are constructed:

[0080] Verification pass conditions:

[0081] |∂E(x,t) / ∂t| > Γ_E ∧ ∇²E(x,t) < 0 ∧ max_k(∂Z_k / ∂t) > Γ_Z

[0082] ΓE, ΓZ: Electric field / radar reflectivity change threshold (dynamic calibration using machine learning)

[0083] Positioning results that fail verification are directly filtered.

[0084] By combining the software algorithms of each part from process one to process five, the data is finally output to the early warning decision management module for decision-making.

Claims

1. Low-altitude flight service hardware and software platform integrating meteorological safety algorithms, characterized by: The software and hardware components include: various meteorological modules and ADS-B ground stations of the ground-based platform, ground / satellite / and various private networks, aircraft communication, navigation and monitoring modules, and low-altitude flight service software and hardware platforms integrating meteorological safety algorithms.

2. The low-altitude flight service software and hardware platform integrating the meteorological safety algorithm according to claim 1 is characterized in that: Multiple software and software algorithm modules are deployed on the cloud database server. These software modules collaborate with each other to store meteorological and communication navigation monitoring data on the cloud server, analyze, make decisions, schedule and formulate flight plans.

3. A low-altitude flight service hardware and software platform integrating a meteorological safety algorithm is characterized by comprising the following steps: In step 1, the weather positioning module collects data such as the longitude and latitude, location, and time of lightning strikes through sensors and special algorithms. The weather monitoring module collects data such as lightning amplitude, lightning current, and time rate of change through sensors and acquisition modules. The weather warning module collects data such as atmospheric electric field distribution, wind speed, wind direction, rainfall, temperature, humidity, and air pressure through sensors. The ground resistance and ground grid status monitoring module collects ground resistance and ground grid status data such as vertical take-off and landing points through sensors. All of this weather safety data is ultimately uploaded to ground / satellite / and various private networks via interfaces such as RJ45 / fiber optic / wireless 4G5G / WI-FI / RS485 / RS422 / LoRa / satellite. Step 2: The drone / man-machine communication / sensor / radar module data is uploaded to the ground / satellite / and various private networks via 4G5G and satellite communication modules, including the drone identification code, real-time location information, flight altitude, speed, heading, time and date, etc. Step 3: The manned aircraft communication / sensor / radar module data is simultaneously broadcast via the ADS-B transmitter, including identity, high-precision position, speed, altitude, heading, status code, and other information, to the ADS-B ground station. The ADS-B ground station then forwards this information to the proprietary network via the ASTERIX CAT021 interface protocol. Step 4: Ground / satellite / and various dedicated networks aggregate and transmit data from various meteorological safety modules, drone / manned aircraft modules, and ADS-B ground station data to the cloud database server; Step 5: Software for weather positioning, monitoring, early warning, ground resistance and grounding network analysis, and communication navigation The monitoring and analysis module obtains data from the cloud data server in real time and generates analysis data through a specific joint algorithm and reports it to the early warning decision management module; Step 6: The software early warning decision management module makes early warning decision management based on the data in step 5 and outputs the result to the scheduling execution management software module; Step 7: The scheduling execution management software module outputs the results to the flight plan management software module, and the flight plan management software module outputs a schedule for the aircraft's flight time, route, etc.

4. The low-altitude flight service hardware and software platform integrating the meteorological safety algorithm according to claim 2 is characterized in that: The flight service software platform integrates meteorological safety analysis components and communication, navigation, surveillance, and analysis components, namely, software algorithm modules for meteorological positioning, monitoring, early warning, ground resistance and grounding grid analysis, and communication, navigation, surveillance, and analysis. Leveraging three core technologies: airborne-ground collaborative sensing, quantum intelligent optimization, and dynamic grid, this platform achieves highly accurate positioning and proactive warning of meteorological threats around low-altitude aircraft, providing critical safety assurance for urban air mobility (UAM) and drone logistics.

5. Step 1: Dynamic Mesh Adaptation Technology (Solving Aircraft Mobility Issues) Dynamically construct a 3D grid based on the real-time position of the aircraft. The grid center moves along the trajectory, and the resolution is adaptively adjusted according to the threat level. This is implemented through a grid center binding formula and resolution adaptive rules. Step 2: Quantum-Genetic Hybrid Optimization Positioning Engine (Breaking Through Traditional Optimization Bottlenecks) It is implemented through quantum-encoded lightning position probability, hybrid fitness function design, and quantum revolving gate and genetic crossover collaborative update rules; Step 3: Cross-modal feature fusion model (improving robustness in low-altitude complex environments) Combined multi-source input features and gated cross-modal attention mechanism Step 4: Deploy detection nodes optimized by reinforcement learning (improve system perception coverage) Based on the layout of ground mobile detection nodes, a virtual environment is constructed to train the intelligent agent: state space, action space and reward function; Step 5: Real-time verification driven by sudden changes in the atmospheric electric field (reducing the false alarm rate) Based on the sudden change characteristics of the atmospheric electric field before lightning occurs, verification constraints are established: positioning results that fail the verification are directly filtered.

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