Flight route planning method for avoiding noise pollution in low-altitude urban environment

Abstract

The invention relates to the technical field of flight route planning, and discloses a flight route planning method for avoiding noise pollution in a low-altitude urban environment. According to the method, the noise sensitive area data and the aircraft performance parameters are acquired, and the route is dynamically adjusted in combination with the real-time environment data, so that the noise pollution of the aircraft to the urban environment is effectively reduced. According to the method, a multi-objective optimization algorithm is adopted to generate an initial route, a genetic algorithm is utilized to optimize the route in real time, and flight parameters are finely adjusted in the flight process through an adaptive control algorithm to ensure that noise emission is always lower than a preset threshold value. In addition, the system supports multi-aircraft collaborative planning and a noise compensation mechanism, and the noise superposition effect and the influence on the sensitive area are further reduced. According to the method, the social acceptance of urban low-altitude flight is remarkably improved, and reliable technical guarantee is provided for standardized management of low-altitude traffic.

Description

Technical Field

[0001] The present invention relates to the technical field of flight route planning, and specifically to a flight route planning method for avoiding noise pollution in a low-altitude urban environment. Background Art

[0002] With the acceleration of the urbanization process, the application of low-altitude aircraft (such as drones, flying cars, etc.) in the urban environment is becoming increasingly widespread. However, the problem of noise pollution caused by low-altitude aircraft is gradually emerging. Especially in densely populated urban areas, noise not only affects the quality of life of residents but may also have a negative impact on the urban ecosystem. Existing flight route planning methods mainly focus on flight safety, efficiency, and energy consumption, while insufficient consideration is given to the avoidance of noise pollution. Traditional noise control methods mostly rely on the noise reduction design of the aircraft itself, but these methods have limited effects in complex urban environments and are difficult to cope with dynamically changing noise sources.

[0003] Therefore, there is an urgent need for an innovative flight route planning method that can effectively avoid noise pollution in a low-altitude urban environment while taking into account flight safety and efficiency. This method should combine multi-dimensional information such as real-time environmental data, noise source distribution, and aircraft performance parameters, and dynamically adjust the flight route through intelligent algorithms to minimize noise interference to urban residents and the environment. For this reason, a flight route planning method for avoiding noise pollution in a low-altitude urban environment is proposed. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a flight route planning method for avoiding noise pollution in a low-altitude urban environment to solve the problems in the background art.

[0005] To achieve the above object, the present invention provides the following technical solution: A flight route planning method for avoiding noise pollution in a low-altitude urban environment, including the following steps:

[0006] Step 1: Obtain data on noise-sensitive areas in the urban environment, including areas sensitive to noise such as residential areas, schools, hospitals, parks, etc., and the noise tolerance thresholds of these areas;

[0007] Step 2: Obtain the performance parameters of the aircraft, including noise emission characteristics, flight altitude range, speed range, and endurance;

[0008] Step 3: Generate an initial flight route based on the noise-sensitive area data and the aircraft performance parameters to ensure that the route avoids high-noise-sensitive areas or passes within their noise tolerance thresholds;

[0009] Step 4: Use real-time environmental data and dynamic noise source data to dynamically adjust the flight route through an optimization algorithm to further reduce the noise impact on noise-sensitive areas;

[0010] Step Five: During flight, continuously monitor the aircraft noise emission level and the changes in ambient noise in the surrounding environment, and make fine adjustments to the flight altitude, speed, or path through an adaptive control algorithm to ensure the minimization of noise pollution;

[0011] Step Six: After the flight mission is completed, generate a noise impact assessment report, record the noise impact data of the aircraft on the surrounding environment, and provide an optimization basis for subsequent flight route planning.

[0012] Preferably, the data of the noise-sensitive areas is obtained through the urban geographic information system (GIS) and combined with the noise monitoring data of the urban management department to form a dynamically updated map of noise-sensitive areas.

[0013] Preferably, the noise emission characteristics in the aircraft performance parameters are obtained through experimental tests and modeling, specifically including:

[0014] The noise levels at different flight altitudes;

[0015] The noise levels at different flight speeds;

[0016] The noise levels in different flight modes.

[0017] Preferably, the multi-objective optimization algorithm is used to generate the initial flight route, and the optimization objectives include:

[0018] Minimizing the noise impact of the aircraft on noise-sensitive areas;

[0019] Minimizing the flight distance and energy consumption;

[0020] Meeting the time window requirements of the flight mission.

[0021] Preferably, the intelligent optimization method based on the genetic algorithm is used for the optimization algorithm of dynamically adjusting the flight route, specifically including:

[0022] Generating multiple candidate route plans according to the real-time environmental data and dynamic noise source data;

[0023] Calculating the comprehensive noise impact index of each candidate plan, which comprehensively considers the noise intensity, duration, and sensitivity of the affected area;

[0024] Selecting the candidate plan with the lowest comprehensive noise impact index as the adjusted flight route.

[0025] Preferably, the adaptive control algorithm performs the following operations in real time during flight:

[0026] Real-time obtain the noise data of the surrounding environment of the aircraft through the noise sensors carried by the aircraft and the urban Internet of Things noise monitoring network;

[0027] Dynamically adjust the flight altitude, speed or path according to a preset noise tolerance threshold to ensure that the noise emissions of the aircraft do not exceed the tolerance range of the surrounding environment;

[0028] In case of an emergency, automatically trigger an alternate route or an emergency landing procedure.

[0029] Preferably, the noise impact assessment report includes the following:

[0030] Noise impact data of the aircraft on each noise-sensitive area during the mission;

[0031] Spatial and temporal distribution maps of the aircraft noise emissions;

[0032] Noise over-limit conditions and improvement suggestions for noise-sensitive areas.

[0033] Preferably, the method further includes a noise pollution warning and avoidance system, which can, when the aircraft approaches a noise-sensitive area, calculate in real time the potential impact of the aircraft noise emissions on the surrounding environment, and automatically generate avoidance suggestions or adjust flight parameters according to a preset noise tolerance threshold to avoid noise over-limit.

[0034] Preferably, the method also supports cooperative noise control of multiple aircraft, and coordinates the route planning of multiple aircraft through a distributed optimization algorithm to avoid the noise superposition effect of multiple aircraft in the same area, thereby reducing the overall impact on noise-sensitive areas.

[0035] Preferably, the method further includes a noise compensation and optimization mechanism. When the aircraft cannot completely avoid the noise impact on certain noise-sensitive areas, the system will automatically optimize the flight time or provide an alternative route plan to minimize the interference to residents and the environment.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] By obtaining data on noise-sensitive areas and aircraft performance parameters, and dynamically adjusting the route in combination with real-time environmental data, the present invention effectively reduces the noise pollution of the aircraft to the urban environment. The method uses a multi-objective optimization algorithm to generate an initial route, uses a genetic algorithm to optimize the path in real time, and fine-tunes flight parameters during flight through an adaptive control algorithm to ensure that the noise emissions are always lower than the preset threshold. In addition, the system supports cooperative planning of multiple aircraft and a noise compensation mechanism, further reducing the noise superposition effect and the impact on sensitive areas. The present invention significantly improves the social acceptance of low-altitude flight in cities and provides a reliable technical guarantee for the standardized management of low-altitude traffic.

[0038] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the specification, claims as well as the drawings. Description of the Drawings

[0039] Figure 1 It is a flowchart of the flight route planning method for avoiding noise pollution in the low-altitude urban environment of the present invention. Detailed Embodiments

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the technical field of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] Embodiment

[0042] Please refer to Figure 1 , this embodiment proposes a flight route planning method for avoiding noise pollution in the low-altitude urban environment. By combining noise-sensitive area data, aircraft performance parameters, real-time environmental data, and intelligent optimization algorithms, the flight route is dynamically planned to minimize noise pollution to the urban environment.

[0043] Hardware Composition:

[0044] Aircraft Side: Equipped with a noise sensor (such as Brüel& 4957 type microphone array), GPS module, flight controller (such as Pixhawk 6), communication module (4G / 5G or LoRaWAN).

[0045] Ground Side: Urban Internet of Things noise monitoring network (ReSpeaker microphone array deployed on street lights and buildings), central server (equipped with a GPU cluster for real-time computing).

[0046] User Terminal: Aircraft operator console, urban management platform (Web-based interactive interface).

[0047] Software Modules:

[0048] Noise-sensitive Area Dynamic Map Construction Module;

[0049] Multi-objective Route Optimization Engine (based on NSGA-III algorithm);

[0050] Real-time Genetic Algorithm Dynamic Adjustment Module;

[0051] Adaptive control logic (fuzzy PID controller);

[0052] Distributed multi-vehicle collaborative planning module (based on Apache Kafka message queue); Blockchain data storage and certification system (Hyperledger Fabric framework).

[0053] Based on the above hardware and software, the following steps are implemented:

[0054] Step 1: Construction of a high-precision noise-sensitive area map

[0055] 1. Data collection:

[0056] Extract the polygon boundaries (GeoJSON format) of residential areas, schools, etc. from the urban GIS database; access the real-time noise monitoring API of the Environmental Protection Bureau (sampling rate 1Hz, accuracy ±1dB);

[0057] Supplement blind spot data through in-vehicle mobile monitoring equipment (one sampling point is arranged every 500 meters)

[0058] 2. Data fusion algorithm:

[0059] Use the Kriging interpolation method to generate a heat map of noise tolerance thresholds:

[0060]

[0061] where λ i is the weight coefficient, which is optimized and calculated through the semi-variogram.

[0062] 3. Dynamic update mechanism:

[0063] Receive data from IoT nodes every 5 minutes through the MQTT protocol to trigger incremental map updates; 4. Output:

[0064] A vector map layer containing timestamps, and the attribute fields include:

[0065]

[0066]

[0067] Step 2: Modeling of aircraft noise characteristics

[0068] 1. Experimental test design:

[0069] Measure different working conditions taking a six-rotor UAV as an example in an anechoic chamber (background noise <20dB):

[0070] Height gradient: 30m, 60m, 90m (calibrated by a laser rangefinder);

[0071] Speed gradients: Hover (0 m / s), 5 m / s, 10 m / s;

[0072] Payload variations: 0 kg, 2 kg, 5 kg.

[0073] 2. Data fitting:

[0074] Use second-order polynomial regression to establish a noise prediction model:

[0075] L p = 68.3 + 0.15h - 0.002h 2 + 1.2v + 0.05v 2 (R 2 = 0.93)

[0076] where h is the height (meters) and v is the speed (m / s).

[0077] 3. Generate a noise configuration file (YAML format) specific to the aircraft:

[0078]

[0079]

[0080] Step three: Generate the initial route with multiple constraints

[0081] 1. Multi-objective optimization modeling:

[0082] Objective function:

[0083]

[0084] L p (i): Predicted noise level in the i-th sensitive area

[0085] T(i): Threshold in this area at the current time period

[0086] D: Total route distance (km)

[0087] E: Energy consumption (kWh)

[0088] Weight coefficient w 1 = 0.6, w 2 = 0.2, w 3 = 0.2.

[0089] 2. NSGA-III algorithm configuration:

[0090] Population size: 200 individuals

[0091] Number of iterations: 500 generations

[0092] Crossover probability: 0.85 (simulated binary crossover)

[0093] Mutation probability: 0.15 (polynomial mutation).

[0094] 3. Path encoding:

[0095] Using the Bezier curve control point encoding method (12 control points define the flight path): class Chromosome:

[0096] genes = [(lat1, lon1), (lat2, lon2),...] # Control point coordinates fitness = (noise_score, distance, energy).

[0097] 4. Constraint handling:

[0098] Soft constraints: Handling minor threshold violations through penalty functions (e.g., within 5% of the limit)

[0099] Hard constraints: Completely avoiding the core areas of hospitals and schools (buffer radius of 200 meters).

[0100] Step 4: Online dynamic flight path optimization

[0101] 1. Real-time data stream processing:

[0102] Using Apache Flink to process the following data streams (processing 10,000 messages per second):

[0103] Traffic congestion index (from map API);

[0104] Sudden noise events (such as concert, construction filing system);

[0105] Meteorological data (impact of wind speed and temperature gradient on noise propagation).

[0106] 2. Improved genetic algorithm:

[0107] Elite retention strategy: Retaining the top 10% of individuals in each generation Adaptive mutation rate: Adjusted according to the environmental change rate (0.1 - 0.3)

[0108] Fast evaluation mechanism: GPU-accelerated fitness calculation (CUDA kernel function).

[0109] 3. Candidate solution generation:

[0110] Generating 10 alternative flight paths, each flight path needs to meet:

[0111] Deviating from the original flight path by no more than ±15°;

[0112] Maximum altitude change rate ≤ 3 m / s

[0113] The emergency landing point coverage rate is 100% (one alternate landing site every 2 kilometers).

[0114] 4. Noise compensation and optimization mechanism:

[0115] Time period optimization:

[0116] If it is impossible to avoid during the day in residential areas, the task will be automatically postponed to night (the threshold is increased to 45 dB);

[0117] Combined with historical data, preferentially select low-wind-speed time periods for flight (reduce the noise propagation distance).

[0118] 5. Cooperative control of multiple aircraft

[0119] Cooperative strategy:

[0120] Space-time resource allocation: Allocate independent flight corridors for each aircraft (vertical interval 50 m, horizontal interval 100 m);

[0121] Noise superposition suppression: Coordinate flight routes through the ADMM algorithm to ensure that only 1 aircraft passes through the same sensitive area at the same time.

[0122] For example, when 3 drones are used for delivery, the system staggers their passing times through residential areas, reducing the superposed noise from 75 dB to 68 dB.

[0123] Step Five: Adaptive flight control

[0124] 1. Control logic design:

[0125] Double-layer control architecture:

[0126] Upper-layer decision-making: Fuzzy logic controller (inputs: noise exceedance rate, remaining flight range; outputs: height / speed adjustment amount);

[0127] Lower-layer execution: PID controller (parameters self-tuning, response time < 50 ms).

[0128] 2. Emergency avoidance protocol:

[0129]

[0130] 3. Sensor fusion:

[0131] Kalman filter fuses multi-source noise data:

[0132]

[0133] Among them, the state variable x includes the aircraft position, speed, and noise radiation intensity.

[0134] 4. Noise pollution warning and avoidance

[0135] Early warning trigger condition: when the predicted noise exceeds the threshold by 5% (e.g., predicted value 52.5 dB, threshold 50 dB);

[0136] Avoidance actions:

[0137] Generate altitude adjustment suggestions (e.g., "Ascending to 130 m can reduce the noise to 48 dB");

[0138] If it is still over the standard after adjustment, automatically call the standby route library (10 emergency paths are pre-stored).

[0139] Step 6: Noise assessment throughout the life cycle

[0140] 1. Spatiotemporal data analysis:

[0141] Use PySpark to process TB-level flight logs and generate:

[0142] Heat map: Use the Folium library to draw the dynamic noise distribution;

[0143] Spectrum analysis: Use STFT to calculate the main noise frequency components.

[0144] f, t, Sxx = scipy.signal.stft(noise_data, fs = 1000) plt.pcolormesh(t, f, np.abs(Sxx)).

[0145] 2. Traceability of over-standard events:

[0146] Non-tamperable log based on blockchain:

[0147]

[0148] 3. Automatic optimization:

[0149] Use reinforcement learning (PPO algorithm) to generate strategies:

[0150] If a certain hospital area exceeds the standard continuously for 3 times, adjust the buffer zone of this area to 300 meters; if the over-standard rate of night flights > 15%, forcibly enable the noise reduction propeller kit.

[0151] Test example:

[0152] Scenario: A logistics center dispatches 20 drones to deliver medicines to the community.

[0153] Input conditions:

[0154] Sensitive areas: 2 hospitals (threshold 50 dB), 3 residential areas (threshold 55 dB);

[0155] Task requirements: Deliver within 30 minutes and avoid school hours (07:00 - 18:00)

[0156] Implementation results:

[0157] Indicator Result Average noise exceeding standard rate 2.3% (18.7% for traditional method) Number of resident complaints 0 times (15 times for traditional method) Task completion rate 100% (average time-consuming 28 minutes) Number of emergency landing triggers 0 times

[0158] In this embodiment, by integrating urban GIS data, aircraft acoustic characteristics, and real-time environment perception, a hierarchical optimization architecture is constructed: a global noise avoidance route is generated based on a multi-objective algorithm, genetic algorithms are combined to dynamically respond to sudden noise events, and fuzzy-PID control is used to achieve millisecond-level adaptive adjustment. Systematically solve the problems of insufficient noise avoidance, multi-aircraft conflicts, and low data credibility in traditional methods.

[0159] The present invention provides a flight route planning method for avoiding noise pollution in a low-altitude urban environment by integrating urban noise-sensitive area data, aircraft performance parameters, real-time environment perception, and intelligent optimization algorithms. This method can dynamically generate and adjust flight routes to ensure that while the aircraft meets the mission requirements, it minimizes the noise impact on sensitive areas such as residential areas, schools, and hospitals. Through multi-objective optimization, adaptive control, and collaborative planning, the noise over-standard rate and the resident complaint rate are significantly reduced, while the safety and efficiency of flight missions are improved, providing technical support for the sustainable development of urban low-altitude transportation.

Claims

1. A flight route planning method for avoiding noise pollution in a low-altitude urban environment, characterized in that: The following steps are involved: Step 1: Obtain data on noise-sensitive areas in the urban environment, including residential areas, schools, hospitals, parks and other noise-sensitive areas, as well as the noise tolerance thresholds of these areas; Step 2: Obtain the performance parameters of the aircraft, including noise emission characteristics, flight altitude range, speed range, and endurance; Step 3: Generate an initial flight route based on noise-sensitive area data and aircraft performance parameters to ensure that the route avoids high noise-sensitive areas or passes within their noise tolerance threshold; Step 4: Using real-time environmental data and dynamic noise source data, the flight path is dynamically adjusted through an optimization algorithm to further reduce the noise impact on noise-sensitive areas; Step 5: During the flight, the aircraft noise emission level and the surrounding noise changes are monitored in real time, and the flight altitude, speed or path are fine-tuned through adaptive control algorithms to ensure that noise pollution is minimized; Step 6: After the flight mission is completed, a noise impact assessment report is generated to record the noise impact data of the aircraft on the surrounding environment and provide an optimization basis for subsequent route planning.

2. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1 is characterized in that: The noise-sensitive area data is obtained through the urban geographic information system (GIS) and combined with the noise monitoring data of the urban management department to form a dynamically updated noise-sensitive area map.

3. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The noise emission characteristics of the aircraft performance parameters are obtained through experimental testing and modeling, specifically including: Noise levels at different flight altitudes; Noise levels at different flight speeds; Noise levels in different flight modes.

4. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The generation of the initial flight route adopts a multi-objective optimization algorithm, and the optimization objectives include: Minimize the noise impact of aircraft on noise-sensitive areas; Minimize flight distance and energy consumption; Meet the time window requirements of the flight mission.

5. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The optimization algorithm for dynamically adjusting the flight route adopts an intelligent optimization method based on a genetic algorithm, which specifically includes: Generate multiple candidate route plans based on real-time environmental data and dynamic noise source data; Calculate a comprehensive noise impact index for each candidate, which takes into account noise intensity, duration, and sensitivity of the affected area; The candidate with the lowest comprehensive noise impact index is selected as the adjusted flight route.

6. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The adaptive control algorithm performs the following operations in real time during flight: Through the noise sensors carried by the aircraft and the urban IoT noise monitoring network, the noise data of the aircraft's surrounding environment can be obtained in real time; Dynamically adjust flight altitude, speed or path according to preset noise tolerance thresholds to ensure that aircraft noise emissions do not exceed the tolerance range of the surrounding environment; In an emergency, the alternate route or emergency landing procedure is automatically triggered.

7. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The noise impact assessment report includes the following: Data on the noise impact of the aircraft on each noise-sensitive area during the mission; Spatiotemporal distribution of aircraft noise emissions; Noise exceeding standards and improvement suggestions in noise-sensitive areas.

8. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The method also includes a noise pollution warning and avoidance system, which can calculate in real time the potential impact of aircraft noise emissions on the surrounding environment when the aircraft approaches a noise-sensitive area, and automatically generate avoidance suggestions or adjust flight parameters based on a preset noise tolerance threshold to avoid noise exceeding the standard.

9. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The method also supports collaborative noise control of multiple aircraft by coordinating the route planning of multiple aircraft through a distributed optimization algorithm to avoid the noise superposition effect of multiple aircraft in the same area, thereby reducing the overall impact on noise-sensitive areas.

10. The method for flight route planning for avoiding noise pollution in a low-altitude urban environment according to claim 1, characterized in that: The method also includes a noise compensation and optimization mechanism. When the aircraft cannot completely avoid the noise impact on certain noise-sensitive areas, the system will automatically optimize the flight time or provide alternative route plans to minimize interference with residents and the environment.

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