Method and system for calculating isoline of airport noise map based on multi-source data fusion

Abstract

The invention relates to the technical field of airport noise prediction. The airport noise map isoline calculation method and system based on multi-source data fusion are provided, and the method comprises the following steps: carrying out space-time alignment fusion processing on multi-source heterogeneous data to generate a space-time synchronous standardized data set; processing through a track-noise coupling calculation unit to generate a single event noise value containing a meteorological correction factor; performing dynamic gridding accumulation calculation processing on the single event noise value to generate a noise evaluation quantity grid matrix with a time stamp; processing is carried out through a curvature-driven adaptive contour line generation unit, and a fidelity-optimized noise contour map is generated; and selecting a real-time lightweight channel or a distributed batch processing channel for processing based on the calculation mode decision-making unit, and outputting a noise contour map with optimized fidelity, so as to achieve the technical effects of improving noise positioning precision, reducing modeling errors in a dynamic flight environment and improving the fidelity of geographic features of contour lines.

Description

Technical Field

[0001] This invention relates to the field of airport noise prediction technology, and in particular to a method and system for calculating airport noise map contour lines using multi-source data fusion. Background Technology

[0002] With the rapid development of the civil aviation industry, the impact of airport noise pollution on the surrounding environment and residents' health is becoming increasingly prominent. High-precision noise maps, as a core tool for airport environmental assessment and management, play a crucial role in airspace planning, environmental compliance, and community governance.

[0003] However, the relevant airport noise prediction technologies have the following problems: noise localization errors caused by spatiotemporal mismatch of multi-source heterogeneous data; noise modeling errors in dynamic flight attitude and complex sound propagation environments; and geographical feature distortion in the contour line generation process. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and system for calculating airport noise map contour lines by fusing multi-source data to address the above-mentioned technical problems, so as to improve the accuracy of noise localization, reduce modeling errors in dynamic flight environments, and improve the fidelity of contour line geographic features.

[0005] Firstly, this application provides a method for calculating contour lines of airport noise maps based on multi-source data fusion, the method comprising:

[0006] The heterogeneous data collaborative processing unit performs spatiotemporal alignment and fusion processing on multi-source heterogeneous data to generate a standardized dataset that is spatiotemporally synchronized.

[0007] Based on a standardized dataset, the data is processed by a track-noise coupled computational unit to generate single-event noise values ​​that include meteorological correction factors.

[0008] The noise values ​​of a single event are dynamically gridded and accumulated to generate a grid matrix of noise evaluation metrics with time stamps.

[0009] Based on the noise evaluation grid matrix, a noise contour map with optimized fidelity is generated by processing it through a curvature-driven adaptive contour generation unit.

[0010] The decision unit based on the computational model selects either a real-time lightweight channel or a distributed batch processing channel for processing, and outputs a noise contour map with optimized fidelity.

[0011] Furthermore, based on a standardized dataset, the data is processed using a track-noise coupled computational unit to generate single-event noise values ​​that include meteorological correction factors, including:

[0012] Dynamic motion state parsing is performed on the track flow data in the standardized dataset to generate track motion state vectors;

[0013] Thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters;

[0014] Based on real-time thrust parameters and aircraft characteristic data in a standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping.

[0015] Based on the initial noise value, combined with micro-meteorological parameters and three-dimensional geospatial data in the standardized dataset, a single-event noise value containing a meteorological correction factor is generated through meteorological-topographic coupling correction processing.

[0016] Furthermore, based on real-time thrust parameters and aircraft characteristic data in the standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping, including:

[0017] Based on aircraft model feature data, multidimensional noise relationship feature vectors are extracted from the noise performance database;

[0018] Real-time thrust parameters are mapped to the noise feature space to generate thrust feature projection.

[0019] The initial noise value is generated by fusing the multidimensional noise relationship feature vector and thrust feature projection through the noise relationship surface reconstruction unit.

[0020] Furthermore, thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters, including:

[0021] The motion state vector of the trajectory is decomposed using kinematic features to generate decomposed motion parameters.

[0022] Based on the decomposed motion parameters, the reference thrust parameters are generated through the flight mechanics thrust mapping unit.

[0023] Based on atmospheric state parameters, the baseline thrust parameters are subjected to environmental adaptive correction to generate real-time thrust parameters.

[0024] Furthermore, the noise values ​​of single events are dynamically gridded and accumulated to generate a time-stamped noise evaluation grid matrix, including:

[0025] Spatial distribution features of noise are extracted based on single-event noise values ​​to generate spatial distribution features of noise.

[0026] Dynamic mesh generation is performed based on the spatial distribution characteristics of noise to generate adaptive mesh elements.

[0027] Spatiotemporal accumulation calculations are performed on each adaptive grid cell to generate the accumulated noise value of the grid cell;

[0028] The accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate a time-stamped noise evaluation grid matrix.

[0029] Furthermore, the accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate a time-stamped noise evaluation grid matrix, including:

[0030] The accumulated noise values ​​of the grid cells are subjected to time series correlation processing to generate a noise sequence with timestamps;

[0031] A time-dimensional matrix is ​​constructed based on the timestamped noise sequence to generate a time-extended grid matrix;

[0032] The time-extended grid matrix is ​​integrated with the spatial grid structure to generate a time-stamped noise evaluation grid matrix.

[0033] Furthermore, the noise evaluation grid matrix is ​​processed by a curvature-driven adaptive contour generation unit to generate a noise contour map with optimized fidelity, including:

[0034] Noise field gradient feature extraction is performed based on the noise evaluation grid matrix to generate noise gradient distribution features.

[0035] Based on the noise gradient distribution characteristics, contour line boundary point identification processing is performed to generate an initial boundary point set;

[0036] Curvature-driven compression is applied to the initial boundary point set to generate a feature-preserving compressed point set.

[0037] Topology generation is performed based on feature-preserving compressed point sets to generate optimized contour topology.

[0038] The optimized contour topology is converted into a vector graphic representation, generating a noise contour map with optimized fidelity.

[0039] Secondly, this application also provides a multi-source data fusion airport noise map contour line calculation system, which includes:

[0040] The data collaboration module is used to perform spatiotemporal alignment and fusion processing on multi-source heterogeneous data through the heterogeneous data collaboration processing unit to generate a standardized dataset that is spatiotemporally synchronized.

[0041] The noise calculation module is used to process the standardized dataset through the track-noise coupling calculation unit to generate single-event noise values ​​containing meteorological correction factors.

[0042] The grid processing module is used to perform dynamic gridded cumulative calculation on the noise values ​​of a single event, generating a grid matrix of noise evaluation metrics with time stamps.

[0043] The contour generation module is used to generate noise contour maps with optimized fidelity by processing the noise evaluation quantity grid matrix through a curvature-driven adaptive contour generation unit.

[0044] The output control module is used to select either the real-time lightweight channel or the distributed batch processing channel for processing based on the computing mode decision unit, and output noise contour maps with optimized fidelity.

[0045] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods in the first aspect of this application.

[0046] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods in the first aspect of this application.

[0047] This application provides a method and system for calculating airport noise map contour lines based on multi-source data fusion. The method includes: performing spatiotemporal alignment and fusion processing on multi-source heterogeneous data through a heterogeneous data collaborative processing unit to generate a standardized dataset with spatiotemporal synchronization; processing the standardized dataset through a track-noise coupling calculation unit to generate single-event noise values ​​including meteorological correction factors; performing dynamic gridded cumulative calculation processing on the single-event noise values ​​to generate a time-stamped noise evaluation quantity grid matrix; processing the noise evaluation quantity grid matrix through a curvature-driven adaptive contour line generation unit to generate a noise contour map with optimized fidelity; and selecting a real-time lightweight channel or a distributed batch processing channel for processing based on a calculation mode decision unit to output a noise contour map with optimized fidelity. This achieves the technical effects of improving noise localization accuracy, reducing modeling errors in dynamic flight environments, and improving the fidelity of contour line geographic features. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of an airport noise map contour line calculation method based on multi-source data fusion in one embodiment of the present invention.

[0050] Figure 2 The flowchart illustrates how a noise evaluation grid matrix, in one embodiment of the present invention, is processed by a curvature-driven adaptive contour generation unit to generate a noise contour map with optimized fidelity.

[0051] Figure 3 This is a structural diagram of an airport noise map contour line calculation system based on multi-source data fusion, according to one embodiment of the present invention. Detailed Implementation

[0052] To make the above-mentioned objects, features, and advantages of this application more apparent and understandable, the specific implementation methods of this application will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a full understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0053] First, the application scenarios of the embodiments of this application are described. In the embodiments of this application, a method for calculating airport noise map contour lines using multi-source data fusion, applicable to but not limited to such scenarios, is provided. For example, airport operations generate various noise-related information, including flight trajectories, characteristics of different aircraft types, meteorological conditions around the airport, and the geographical environment, all of which collectively influence the distribution of airport noise. Processing and analyzing the above information can provide a basis for airport noise management and the planning of surrounding areas.

[0054] As an illustration, the airport noise map contour line calculation method based on multi-source data fusion provided in this application embodiment can also be applied to other application scenarios. This is only an example and does not limit the specific application scenarios.

[0055] like Figure 1 As shown, this application provides a method for calculating contour lines of airport noise maps based on multi-source data fusion. The method includes:

[0056] S101: The heterogeneous data collaborative processing unit performs spatiotemporal alignment and fusion processing on multi-source heterogeneous data to generate a standardized dataset with spatiotemporal synchronization.

[0057] Specifically, the heterogeneous data collaborative processing unit collects multi-source heterogeneous data, including real-time flight trajectory data acquired by aviation surveillance systems, aircraft type characteristic data stored in airport basic databases, refined meteorological parameters recorded by meteorological monitoring terminals, and three-dimensional geospatial data containing terrain elevation information. Then, for the data from different sources and in different formats, timestamp synchronization processing is performed to ensure consistency across all data types in the time dimension, eliminating deviations caused by differences in data acquisition time. Simultaneously, coordinate system unification processing is performed, converting the spatial coordinates used in different data into a unified standard to resolve spatial mismatches. Through these spatiotemporal alignment operations, the data is integrated and standardized to generate a spatiotemporally synchronized standardized dataset.

[0058] S102: Based on a standardized dataset, the system processes the data through a track-noise coupled computation unit to generate single-event noise values ​​that include meteorological correction factors.

[0059] Specifically, the track-noise coupled computation unit dynamically analyzes the track flow data based on a standardized dataset, extracts motion state features reflecting the aircraft's flight attitude, and generates a track motion state vector. Based on the kinematic parameters obtained from this vector decomposition, and combined with flight mechanics principles, a baseline thrust parameter matching the current flight state is mapped out. This parameter is then adjusted for environmental adaptability based on atmospheric state parameters to obtain real-time thrust parameters.

[0060] Subsequently, combining the aircraft characteristic data in the standardized dataset, the corresponding multidimensional noise relationship features are retrieved from the noise performance database. The real-time thrust parameters are projected onto the noise feature space, and the initial noise value is generated by reconstructing the noise relationship surface. Micro-meteorological parameters and three-dimensional geospatial data from the standardized dataset are introduced to couple the initial noise value with meteorological factors and terrain conditions, incorporating meteorological correction factors to generate single-event noise values ​​containing meteorological correction factors.

[0061] S103: Perform dynamic gridded cumulative calculation on the noise value of a single event to generate a grid matrix of noise evaluation quantity with time stamp.

[0062] Specifically, the spatial distribution characteristics of noise from single events are extracted to clarify the diffusion patterns and intensity trends of noise in different regions. Then, based on these spatial distribution characteristics, and considering factors such as the terrain complexity and residential distribution of the computational area, adaptive grid cells are generated, allowing the grid density to be dynamically adjusted according to the criticality of the noise distribution.

[0063] Subsequently, for each adaptive grid cell, the spatiotemporal noise values ​​of all relevant single events within a certain time range are accumulated to obtain the cumulative noise value of each grid cell. The cumulative noise values ​​of each grid cell are then correlated and integrated with their corresponding time information to generate a noise evaluation grid matrix containing time stamps.

[0064] S104: Based on the noise evaluation grid matrix, it is processed by the curvature-driven adaptive contour generation unit to generate a noise contour map with optimized fidelity.

[0065] Specifically, gradient features are extracted from the noise data in the matrix, and the rate of change of noise values ​​in space is analyzed to clarify the transition trend of the noise field's strength. Based on the extracted noise gradient distribution features, the initial boundary point set of contour lines is identified; these point sets reflect the spatial contours with the same noise values.

[0066] Subsequently, the importance of features is evaluated based on the curvature changes of the boundary points. The initial boundary point set is then compressed, retaining key feature points such as terrain transitions and building outlines, while removing redundant nodes in flat areas to generate a feature-preserving compressed point set.

[0067] Based on the aforementioned compressed point set, a topological structure for contour lines is constructed to ensure their continuity and integrity, generating an optimized contour line topology. This optimized topology is then converted into vector graphics to more accurately represent geographical features such as airport boundaries and mountain outlines, generating a noise contour map with optimized fidelity.

[0068] S105: Based on the computing model decision unit, select the real-time lightweight channel or the distributed batch processing channel for processing, and output a noise contour map with optimized fidelity.

[0069] Specifically, the computational scenario is determined based on actual application needs. For scenarios requiring rapid response, such as real-time noise assessment of a single aircraft, the real-time lightweight channel is selected. This channel simplifies some non-critical computational steps, prioritizing processing speed to achieve second-level output of optimized-fidelity noise contour maps, meeting the immediacy requirements of real-time air traffic control decision-making.

[0070] For scenarios requiring high-precision batch processing, such as periodic noise statistical analysis of historical flight track data, the distributed batch processing channel is selected. This channel utilizes the collaborative computing capabilities of multiple nodes to perform comprehensive and detailed processing on large amounts of data, generating noise distribution results that include daily and monthly averages. Through targeted processing by the different channels mentioned above, noise contour maps with optimized fidelity are output.

[0071] One embodiment of this application provides a method for calculating airport noise map contour lines through multi-source data fusion, comprising: performing spatiotemporal alignment and fusion processing on multi-source heterogeneous data through a heterogeneous data collaborative processing unit to generate a spatiotemporally synchronized standardized dataset; processing the standardized dataset through a track-noise coupling calculation unit to generate single-event noise values ​​including meteorological correction factors; performing dynamic gridded cumulative calculation processing on the single-event noise values ​​to generate a time-stamped noise evaluation quantity grid matrix; processing the noise evaluation quantity grid matrix through a curvature-driven adaptive contour line generation unit to generate a noise contour map with optimized fidelity; and selecting a real-time lightweight channel or a distributed batch processing channel for processing based on a calculation mode decision unit to output a noise contour map with optimized fidelity, thereby achieving the technical effects of improving noise localization accuracy, reducing modeling errors in dynamic flight environments, and improving the fidelity of contour line geographic features.

[0072] Furthermore, based on a standardized dataset, the data is processed using a track-noise coupled computational unit to generate single-event noise values ​​that include meteorological correction factors, including:

[0073] Dynamic motion state parsing is performed on the track flow data in the standardized dataset to generate track motion state vectors;

[0074] Thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters;

[0075] Based on real-time thrust parameters and aircraft characteristic data in a standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping.

[0076] Based on the initial noise value, combined with micro-meteorological parameters and three-dimensional geospatial data in the standardized dataset, a single-event noise value containing a meteorological correction factor is generated through meteorological-topographic coupling correction processing.

[0077] Specifically, the processing is based on a standardized dataset, including dynamic motion state analysis of the flight path data. By extracting information such as changes in position, speed, and altitude during flight, the kinematic features reflecting different attitudes of the aircraft, such as acceleration, climb, and cruise, are analyzed to generate a flight path motion state vector.

[0078] Subsequently, based on the kinematic parameters contained in the above vectors, the correlation between the aircraft's thrust and motion state is mapped in combination with the principles of flight mechanics. The reference thrust parameters that match the current motion state are calculated, and then the reference thrust parameters are adaptively adjusted according to atmospheric state parameters such as atmospheric pressure and temperature to generate real-time thrust parameters.

[0079] Subsequently, based on the real-time thrust parameters and combined with the aircraft feature data in the standardized dataset, the multi-dimensional noise relationship features corresponding to the aircraft model, such as distance and thrust, are retrieved from the noise performance database. The real-time thrust parameters are mapped to the noise feature space to generate thrust feature projection. By reconstructing the noise relationship surface, multi-dimensional noise relationship mapping is achieved, and initial noise values ​​are generated.

[0080] Based on the initial noise value, micro-meteorological parameters such as wind speed and humidity from the standardized dataset, as well as three-dimensional geospatial data such as terrain elevation and building distribution, are introduced. Taking into account the influence of meteorological conditions on sound wave propagation and the effects of terrain on noise blocking and diffraction, meteorological-terrain coupling correction processing is performed, and meteorological correction factors are incorporated to generate single-event noise values ​​containing meteorological correction factors.

[0081] Furthermore, based on real-time thrust parameters and aircraft characteristic data in the standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping, including:

[0082] Based on aircraft model feature data, multidimensional noise relationship feature vectors are extracted from the noise performance database;

[0083] Real-time thrust parameters are mapped to the noise feature space to generate thrust feature projection.

[0084] The initial noise value is generated by fusing the multidimensional noise relationship feature vector and thrust feature projection through the noise relationship surface reconstruction unit.

[0085] Specifically, based on aircraft characteristic data (such as engine model, fuselage structure, etc.), multidimensional noise relationship feature vectors matching the aircraft model are extracted from the noise performance database. These vectors include the noise characteristic correlation under different thrust, distance and other conditions.

[0086] Subsequently, the real-time thrust parameters are transformed into the corresponding noise feature space, and a thrust feature projection reflecting the noise characteristics under the current thrust state is generated through parameter mapping. The noise relationship surface reconstruction unit performs deep fusion on the extracted multidimensional noise relationship feature vector and the generated thrust feature projection. By reconstructing the relationship surface between noise and parameters such as thrust and distance, the noise characteristic information contained in both is integrated to generate the initial noise value.

[0087] Furthermore, thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters, including:

[0088] The motion state vector of the trajectory is decomposed using kinematic features to generate decomposed motion parameters.

[0089] Based on the decomposed motion parameters, the reference thrust parameters are generated through the flight mechanics thrust mapping unit.

[0090] Based on atmospheric state parameters, the baseline thrust parameters are subjected to environmental adaptive correction to generate real-time thrust parameters.

[0091] Specifically, when performing kinematic feature decomposition on the trajectory motion state vector, key parameters reflecting the aircraft's motion state, including speed, acceleration, and rate of climb, are extracted from the vector. These parameters are then split and organized according to kinematic categories to generate decomposed motion parameters, which can more accurately characterize the aircraft's motion characteristics at different stages.

[0092] Based on the decomposed motion parameters, the flight mechanics thrust mapping unit establishes the correlation between motion parameters and engine thrust according to the principles of flight mechanics and the aerodynamic characteristics of the aircraft and the engine performance model. It calculates the reference thrust parameters that match the current motion state, which reflect the amount of thrust required by the aircraft under standard environmental conditions.

[0093] Subsequently, atmospheric state parameters, including air pressure, temperature, and air density, are introduced to analyze the influence of these parameters on the actual thrust output of the engine. The baseline thrust parameters are dynamically adjusted according to environmental differences to adapt to thrust changes under different atmospheric conditions, thereby generating real-time thrust parameters.

[0094] Furthermore, the noise values ​​of single events are dynamically gridded and accumulated to generate a time-stamped noise evaluation grid matrix, including:

[0095] Spatial distribution features of noise are extracted based on single-event noise values ​​to generate spatial distribution features of noise.

[0096] Dynamic mesh generation is performed based on the spatial distribution characteristics of noise to generate adaptive mesh elements.

[0097] Spatiotemporal accumulation calculations are performed on each adaptive grid cell to generate the accumulated noise value of the grid cell;

[0098] The accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate a time-stamped noise evaluation grid matrix.

[0099] Specifically, when extracting spatial distribution features based on single-event noise values, the diffusion range, intensity gradient, and distribution density of noise in different regions are analyzed, the attenuation law of noise values ​​with distance and the distribution differences affected by terrain are identified, and noise spatial distribution features that can reflect the spatial distribution law of noise are generated.

[0100] Based on the above characteristics, and combined with factors such as terrain complexity and residential density within the computational area, the mesh is densified in areas with drastic noise changes or areas of particular interest, while the mesh is simplified in areas with gentle noise distribution, generating adaptive mesh cells that can adaptively match the characteristics of noise distribution.

[0101] For each adaptive grid cell, the contribution of all single-event noise values ​​within a certain time range is summarized. Considering the cumulative effect of noise events in different time periods, a spatiotemporal superposition calculation is performed to obtain the total noise impact value of each grid cell within the corresponding time range, i.e., the cumulative noise value of the grid cell.

[0102] Then, the cumulative noise value of each grid cell is associated and bound with its corresponding time stamp (such as time period, date), and integrated into a matrix form according to the spatial grid structure and time series. Finally, a noise evaluation grid matrix with time stamp is generated.

[0103] Furthermore, the accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate a time-stamped noise evaluation grid matrix, including:

[0104] The accumulated noise values ​​of the grid cells are subjected to time series correlation processing to generate a noise sequence with timestamps;

[0105] A time-dimensional matrix is ​​constructed based on the timestamped noise sequence to generate a time-extended grid matrix;

[0106] The time-extended grid matrix is ​​integrated with the spatial grid structure to generate a time-stamped noise evaluation grid matrix.

[0107] Specifically, when performing time series correlation processing on the cumulative noise values ​​of grid cells, the cumulative noise value of each grid cell is matched one-to-one with the specific time information of its generation, and arranged in chronological order to generate an ordered noise sequence with timestamps, so as to clearly present the trajectory of the change of noise of each grid cell over time.

[0108] Based on timestamped noise sequences, the noise data is hierarchically sorted according to the time dimension, and the noise values ​​at different time nodes are mapped to the time dimension positions of grid cells. A time-extended grid matrix containing time levels is constructed, so that the noise data generates a structured distribution in the time dimension.

[0109] Then, the time-extended grid matrix is ​​merged with the original spatial grid structure, so that each spatial grid cell retains its spatial coordinate information and is associated with the corresponding time marker and the cumulative noise value at that time. Through precise matching of spatial and temporal dimensions, a time-marked noise evaluation grid matrix is ​​generated.

[0110] like Figure 2 As shown, based on the noise evaluation grid matrix, a curvature-driven adaptive contour generation unit is used to generate a noise contour map with optimized fidelity, including:

[0111] S201: Extract noise field gradient features based on the noise evaluation grid matrix to generate noise gradient distribution features;

[0112] S202: Based on the noise gradient distribution characteristics, perform contour line boundary point identification processing to generate an initial boundary point set;

[0113] S203: Perform curvature-driven compression on the initial boundary point set to generate a feature-preserving compressed point set;

[0114] S204: Based on feature-preserving compressed point sets, perform topology generation processing to generate optimized contour topology;

[0115] S205: Convert the optimized contour topology into a vector graphic representation to generate a noise contour map with optimized fidelity.

[0116] Specifically, when extracting noise field gradient features based on the noise evaluation grid matrix, the rate and direction of change of noise values ​​in the space are analyzed, the steepness and trend of the transition of noise from high value area to low value area are identified, and noise gradient distribution features that can reflect the spatial change characteristics of the noise field are generated.

[0117] Based on the characteristics of noise gradient distribution, spatial contour lines with the same noise value are located. By screening the critical points of regions with significant gradient changes, the initial set of boundary points that constitute the basic framework of contour lines is determined.

[0118] When performing curvature-driven compression on the initial boundary point set, the importance of each boundary point in representing geographical features is evaluated based on the curvature magnitude. Points at key features such as terrain transitions and building edges are retained, while redundant points in flat areas are removed, generating a feature-preserving compressed point set.

[0119] Based on the aforementioned compressed point set, the connection relationships between the points are constructed to ensure the continuity and closure of the contour lines, generating an optimized contour line topology that conforms to the noise distribution pattern and retains key geographical features. The optimized contour line topology is then converted into vector graphics, enabling the contour lines to accurately fit geographical elements such as airport runways and mountain outlines, generating a noise contour map with optimized fidelity.

[0120] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0121] In one embodiment, such as Figure 3 As shown, this application also provides an airport noise map contour line calculation system 300 based on multi-source data fusion, the system 300 comprising:

[0122] The data collaboration module 301 is used to perform spatiotemporal alignment and fusion processing on multi-source heterogeneous data through the heterogeneous data collaboration processing unit to generate a standardized dataset with spatiotemporal synchronization.

[0123] The noise calculation module 302 is used to generate single-event noise values ​​containing meteorological correction factors by processing the standardized dataset through the track-noise coupling calculation unit.

[0124] The grid processing module 303 is used to perform dynamic gridded cumulative calculation on the noise value of a single event, and generate a grid matrix of noise evaluation quantity with time stamp;

[0125] The contour generation module 304 is used to generate noise contour maps with optimized fidelity by processing the noise evaluation quantity grid matrix through a curvature-driven adaptive contour generation unit.

[0126] The output control module 305 is used to select the real-time lightweight channel or the distributed batch processing channel for processing based on the computing mode decision unit, and output noise contour maps with optimized fidelity.

[0127] Specifically, the data collaboration module 301 uses a heterogeneous data collaboration processing unit to process multi-source heterogeneous data, such as real-time flight trajectory data from the aviation surveillance system, aircraft characteristic data from the airport basic database, micro-meteorological parameters from the meteorological monitoring terminal, and three-dimensional geospatial data including terrain elevation. It unifies the data format, aligns the spatiotemporal coordinates, and generates a standardized dataset that is spatiotemporally synchronized.

[0128] The noise calculation module 302 is based on a standardized dataset. It uses a track-noise coupled calculation unit to analyze the track motion state to obtain thrust parameters, retrieves noise relationship features in combination with aircraft characteristics, and generates single-event noise values ​​containing meteorological correction factors after meteorological and terrain coupling correction.

[0129] The grid processing module 303 extracts the spatial distribution characteristics of the noise value of a single event to generate an adaptive grid cell, performs spatiotemporal accumulation calculation on each grid cell to obtain the cumulative noise value, and then associates it with time information to generate a noise evaluation grid matrix with time stamp.

[0130] The contour generation module 304 is based on a time-stamped noise evaluation grid matrix. Through a curvature-driven adaptive contour generation unit, it extracts noise gradient features to identify the initial boundary point set, retains key feature points through curvature-driven compression, constructs an optimized contour topology and converts it into vector graphics to generate a noise contour map with optimized fidelity.

[0131] The output control module 305, through the calculation mode decision unit, selects either the real-time lightweight channel (suitable for real-time evaluation of a single aircraft) or the distributed batch processing channel (suitable for periodic analysis of historical data) according to actual needs, and outputs noise contour maps with optimized fidelity.

[0132] The noise calculation module 302 is also used for:

[0133] Dynamic motion state parsing is performed on the track flow data in the standardized dataset to generate track motion state vectors;

[0134] Thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters;

[0135] Based on real-time thrust parameters and aircraft characteristic data in a standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping.

[0136] Based on the initial noise value, combined with micro-meteorological parameters and three-dimensional geospatial data in the standardized dataset, a single-event noise value containing a meteorological correction factor is generated through meteorological-topographic coupling correction processing.

[0137] The noise calculation module 302 is also used for:

[0138] Based on aircraft model feature data, multidimensional noise relationship feature vectors are extracted from the noise performance database;

[0139] Real-time thrust parameters are mapped to the noise feature space to generate thrust feature projection.

[0140] The initial noise value is generated by fusing the multidimensional noise relationship feature vector and thrust feature projection through the noise relationship surface reconstruction unit.

[0141] The noise calculation module 302 is also used for:

[0142] The motion state vector of the trajectory is decomposed using kinematic features to generate decomposed motion parameters.

[0143] Based on the decomposed motion parameters, the reference thrust parameters are generated through the flight mechanics thrust mapping unit.

[0144] Based on atmospheric state parameters, the baseline thrust parameters are subjected to environmental adaptive correction to generate real-time thrust parameters.

[0145] The mesh processing module 303 is also used for:

[0146] Spatial distribution features of noise are extracted based on single-event noise values ​​to generate spatial distribution features of noise.

[0147] Dynamic mesh generation is performed based on the spatial distribution characteristics of noise to generate adaptive mesh elements.

[0148] Spatiotemporal accumulation calculations are performed on each adaptive grid cell to generate the accumulated noise value of the grid cell;

[0149] The accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate a time-stamped noise evaluation grid matrix.

[0150] The mesh processing module 303 is also used for:

[0151] The accumulated noise values ​​of the grid cells are subjected to time series correlation processing to generate a noise sequence with timestamps;

[0152] A time-dimensional matrix is ​​constructed based on the timestamped noise sequence to generate a time-extended grid matrix;

[0153] The time-extended grid matrix is ​​integrated with the spatial grid structure to generate a time-stamped noise evaluation grid matrix.

[0154] The contour line generation module 304 is also used for:

[0155] Noise field gradient feature extraction is performed based on the noise evaluation grid matrix to generate noise gradient distribution features.

[0156] Based on the noise gradient distribution characteristics, contour line boundary point identification processing is performed to generate an initial boundary point set;

[0157] Curvature-driven compression is applied to the initial boundary point set to generate a feature-preserving compressed point set.

[0158] Topology generation is performed based on feature-preserving compressed point sets to generate optimized contour topology.

[0159] The optimized contour topology is converted into a vector graphic representation, generating a noise contour map with optimized fidelity.

[0160] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0161] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0162] In one embodiment, the effective perceived noise level L of a single aircraft EPN for:

[0163] L EPN =L(F,d)+ΔV-Λ(β,l,φ)-A atm +ΔL

[0164] Where L(F,d) represents the interpolated sound level of the aircraft at engine thrust F and the shortest distance d between the ground calculation point and the flight path, in dB; ΔV represents the speed correction factor; Indicates the lateral attenuation factor; A atm ΔL represents the attenuation caused by atmospheric absorption; ΔL represents the directional correction factor.

[0165] Maximum A-weighted sound level of a single aircraft Amax for:

[0166]

[0167] Among them, L Amax (F,d) represents the maximum interpolated Class A sound level of the aircraft at the shortest distance d between the engine thrust and the ground calculation point and the flight path, in dB(A). This represents the lateral attenuation factor.

[0168] The speed correction factor is:

[0169]

[0170] Among them, V r This indicates the reference airspeed, 160 knots.

[0171] During sound wave propagation, the lateral attenuation caused by the ground is as follows:

[0172]

[0173] in, Indicates engine position correction; G(l) indicates ground surface sound absorption correction; A Grd+Rs (β) represents the refraction and scattering correction of the sound wave; β represents the angle of depression, l represents the angle of elevation, and l represents the lateral distance.

[0174] Jet engines mounted on the fuselage of an aircraft; engine position correction. for:

[0175]

[0176] The surface sound absorption correction G(l) is:

[0177]

[0178] Correction for refraction and scattering of sound waves (A) Grd+Rs (β) is:

[0179]

[0180] In one embodiment, for an engine power P located between and , the noise level linear interpolation is:

[0181]

[0182] For a distance d between and , the logarithmic interpolation of the noise level is:

[0183]

[0184] When the distance d is less than the minimum distance in the NPD database:

[0185]

[0186] When the distance d is greater than the maximum distance in the NPD database:

[0187]

[0188] Where I represents the number of distance points in the NPD database.

[0189] In one embodiment, a method for calculating engine thrust was determined, wherein for rated thrust, the corrected net thrust of a single engine is:

[0190] F n / δ=E+F·V C +G A ·h+G B ·h 2 +H·T

[0191] Among them, F nThe net thrust of a single engine is expressed in pounds; δ represents the ratio of air pressure at the aircraft to standard air pressure at sea level, expressed in V. C Indicates the corrected airspeed, in knots; T represents the air temperature at the fan; E, F, G A G B H represents the engine thrust correction factor.

[0192] For non-rated thrust, the corrected net thrust of a single engine can be calculated from the engine pressure ratio EPR or the engine speed N1:

[0193] F n / δ=E+F·V C +G A ·h+G B ·h 2 +H·T+K1·EPR+K2·EPR 2

[0194]

[0195] Where EPR represents the engine pressure ratio; N1 represents the engine speed; and θ represents the ratio of the engine inlet temperature to the sea level temperature.

[0196] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0197] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A method for calculating contour lines of airport noise maps based on multi-source data fusion, characterized in that, The method includes: The heterogeneous data collaborative processing unit performs spatiotemporal alignment and fusion processing on multi-source heterogeneous data to generate a standardized dataset that is spatiotemporally synchronized. Based on the standardized dataset, the data is processed by a track-noise coupled computation unit to generate single-event noise values ​​that include meteorological correction factors. The noise values ​​of the single events are dynamically gridded and accumulated to generate a time-stamped noise evaluation grid matrix; Based on the noise evaluation grid matrix, a noise contour map with optimized fidelity is generated by processing it through a curvature-driven adaptive contour generation unit. The decision unit based on the computing model selects either a real-time lightweight channel or a distributed batch processing channel for processing, and outputs a noise contour map with optimized fidelity.

2. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 1, characterized in that, The process, based on the standardized dataset, involves processing the data through a track-noise coupling computation unit to generate single-event noise values ​​that include meteorological correction factors, including: Dynamic motion state parsing is performed on the track flow data in the standardized dataset to generate track motion state vectors; Thrust parameter mapping is performed based on the trajectory motion state vector to generate real-time thrust parameters. Based on the real-time thrust parameters and the aircraft characteristic data in the standardized dataset, initial noise values ​​are generated through multi-dimensional noise relationship mapping processing. Based on the initial noise value, combined with the micro-meteorological parameters and three-dimensional geospatial data in the standardized dataset, the single-event noise value containing the meteorological correction factor is generated through meteorological-terrain coupling correction processing.

3. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 2, characterized in that, The step of generating initial noise values ​​based on the real-time thrust parameters and aircraft characteristic data in the standardized dataset through multi-dimensional noise relationship mapping includes: Based on the aircraft model feature data, a multidimensional noise relationship feature vector is extracted from the noise performance database; The real-time thrust parameters are mapped to the noise feature space to generate a thrust feature projection. The initial noise value is generated by fusing the multidimensional noise relationship feature vector and the thrust feature projection through the noise relationship surface reconstruction unit.

4. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 2, characterized in that, The step of generating real-time thrust parameters by mapping the thrust parameters based on the trajectory motion state vector includes: The motion state vector of the trajectory is subjected to kinematic feature decomposition to generate decomposed motion parameters; Based on the decomposed motion parameters, the reference thrust parameters are generated by processing them through the flight mechanics thrust mapping unit. The reference thrust parameters are subjected to environmental adaptive correction based on atmospheric state parameters to generate the real-time thrust parameters.

5. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 1, characterized in that, The step of performing dynamic gridded cumulative calculation on the single-event noise value to generate a time-stamped noise evaluation grid matrix includes: Based on the single-event noise value, spatial distribution features are extracted to generate noise spatial distribution features; Based on the spatial distribution characteristics of the noise, dynamic mesh generation processing is performed to generate adaptive mesh elements; For each of the adaptive grid cells, spatiotemporal accumulation calculations are performed to generate the accumulated noise value of the grid cell. The accumulated noise values ​​of the grid cells are integrated with the corresponding time information to generate the time-stamped noise evaluation grid matrix.

6. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 5, characterized in that, The step of integrating the accumulated noise values ​​of the grid cells with the corresponding time information to generate the time-stamped noise evaluation grid matrix includes: The accumulated noise values ​​of the grid cells are subjected to time series correlation processing to generate a noise sequence with timestamps; Based on the timestamped noise sequence, a time-dimensional matrix is ​​constructed to generate a time-extended grid matrix; The time-extended grid matrix is ​​integrated with the spatial grid structure to generate the time-stamped noise evaluation grid matrix.

7. The method for calculating airport noise map contour lines based on multi-source data fusion according to claim 1, characterized in that, Based on the aforementioned noise evaluation grid matrix, a curvature-driven adaptive contour generation unit is used to generate a noise contour map with optimized fidelity, including: Based on the noise evaluation grid matrix, noise field gradient feature extraction is performed to generate noise gradient distribution features. Based on the noise gradient distribution characteristics, contour line boundary point identification processing is performed to generate an initial boundary point set; The initial boundary point set is subjected to curvature-driven compression to generate a feature-preserving compressed point set; Based on the aforementioned features, the compressed point set is preserved and a topology generation process is performed to generate an optimized contour topology. The optimized contour topology is converted into a vector graphic representation to generate the noise contour map with optimized fidelity.

8. A multi-source data fusion system for calculating airport noise map contour lines, characterized in that, The system includes: The data collaboration module is used to perform spatiotemporal alignment and fusion processing on multi-source heterogeneous data through the heterogeneous data collaboration processing unit to generate a standardized dataset that is spatiotemporally synchronized. The noise calculation module is used to process the standardized dataset through the track-noise coupling calculation unit to generate single-event noise values ​​containing meteorological correction factors. The grid processing module is used to perform dynamic gridded cumulative calculation on the single-event noise value to generate a time-stamped noise evaluation grid matrix. The contour generation module is used to generate a noise contour map with optimized fidelity by processing the noise evaluation quantity grid matrix through a curvature-driven adaptive contour generation unit. The output control module is used to select either the real-time lightweight channel or the distributed batch processing channel for processing based on the computing mode decision unit, and output the noise contour map with optimized fidelity.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the airport noise map contour line calculation method based on multi-source data fusion as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the airport noise map contour line calculation method based on multi-source data fusion as described in any one of claims 1 to 7.

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