A low-altitude flight evaluation method, system and device based on multi-source meteorological data

Abstract

The present application relates to the technical field of low-altitude flight evaluation, and particularly relates to a low-altitude flight evaluation method, system and equipment based on multi-source meteorological data. The method comprises the following steps: obtaining multi-source meteorological data of a target area; dividing the target area into spatial grids, and mapping the meteorological data to each spatial grid unit; determining each spatial grid unit in a continuous time sequence to generate a corresponding flyable state sequence; calculating a low-altitude flight potential index for each spatial grid unit; standardizing the low-altitude flight potential index to obtain a normalized index feature; and generating a low-altitude flight availability evaluation result of the target area. The present application realizes fine quantitative evaluation of the availability of urban low-altitude flight by fusing multi-source meteorological data and combining spatial gridding analysis and time sequence feature extraction, which can effectively improve the utilization efficiency of low-altitude airspace and provide technical support for low-altitude flight path planning and operation management.

Description

Technical Field

[0001] This invention relates to the field of low-altitude flight assessment technology, and in particular to a low-altitude flight assessment method, system and equipment based on multi-source meteorological data. Background Technology

[0002] With the rapid development of the low-altitude economy, including drone logistics and urban air traffic, the safety and availability assessment of low-altitude airspace has become a key technical issue. Meteorological conditions, as an important factor affecting low-altitude flight safety, directly determine the feasibility and operational efficiency of flight missions.

[0003] In existing technologies, meteorological analysis for low-altitude flight mostly focuses on the monitoring or short-term warning of single meteorological elements, such as real-time detection and alerts for wind speed, precipitation, or visibility. However, such methods typically suffer from the following problems:

[0004] (1) It lacks the ability to comprehensively analyze multi-source meteorological data, making it difficult to fully reflect the complexity of the low-altitude flight environment;

[0005] (2) There is a lack of a unified flightability determination mechanism, making it difficult to conduct standardized assessments of flight capabilities under different weather conditions;

[0006] (3) The lack of detailed analysis of the spatial dimension makes it impossible to quantitatively characterize the low-altitude flight potential of different areas within the city;

[0007] Therefore, there is a need for a low-altitude flight availability analysis method that can integrate multi-source meteorological data, achieve spatial gridded analysis, and have assessment capabilities, in order to improve the utilization efficiency of low-altitude airspace and flight safety. Summary of the Invention

[0008] To address the problems of incomplete meteorological assessment of low-altitude flight, lack of unified judgment standards, and inability to perform refined spatial analysis in existing technologies, this invention provides a method, system, and device for low-altitude flight assessment based on multi-source meteorological data.

[0009] A method for assessing low-altitude flight based on multi-source meteorological data includes:

[0010] Acquire multi-source meteorological data for the target area, wherein the meteorological data includes at least wind speed, precipitation, and temperature;

[0011] The target area is divided into spatial grids to obtain multiple spatial grid units, and meteorological data is mapped to each of the spatial grid units.

[0012] Based on preset meteorological threshold conditions, each of the spatial grid units is determined on a continuous time series to generate a corresponding flightable state sequence.

[0013] Based on the flightable state sequence, low-altitude flight potential indicators are calculated for each spatial grid cell, including availability, failure frequency, average failure duration and longest continuous availability.

[0014] The low-altitude flight potential index is standardized to obtain normalized index characteristics;

[0015] Based on the normalized index characteristics, a comprehensive evaluation index is constructed, and the low-altitude flight availability assessment results for the target area are generated.

[0016] Preferably, the flyable state sequence is a binary sequence, which takes the value of 1 when the meteorological conditions meet the threshold, and takes the value of 0 otherwise.

[0017] Preferably, the meteorological threshold conditions include:

[0018] Wind speed threshold V0, precipitation threshold P0, and temperature range [T1,T2];

[0019] When V ≤ V0 and P ≤ P0 and T ∈ [T1,T2].

[0020] Preferably, the formula for calculating the availability rate is:

[0021]

[0022] Where A is the availability rate, S i Let N represent the flyable state at time i, and N be the total number of time steps.

[0023] Preferably, the failure frequency is obtained by counting the number of times the flyable state sequence transitions from 1 to 0.

[0024] Preferably, the longest continuous available time is the length of the longest consecutive 1 in the continuous flyable state sequence.

[0025] Preferably, the comprehensive evaluation index is calculated using a weighted method:

[0026] Where: A is the availability rate, N f Where is the failure frequency, D is the average failure duration, L is the longest continuous availability duration, and w1 to w4 are weights.

[0027] Preferably, the meteorological data is preprocessed;

[0028] Specifically:

[0029] Time alignment of data from different sources;

[0030] Spatial interpolation or resampling of meteorological data;

[0031] Outliers are removed or replaced, and missing values ​​are filled by interpolation.

[0032] A low-altitude flight assessment system based on multi-source meteorological data also includes:

[0033] The data acquisition module is used to acquire multi-source meteorological data and perform preprocessing.

[0034] The grid partitioning module is used to partition the target area into spatial grids and complete the meteorological data mapping.

[0035] The state determination module is used to generate a flightable state sequence based on meteorological thresholds;

[0036] The index calculation module is used to calculate low-altitude flight potential indexes;

[0037] The comprehensive analysis module is used to construct comprehensive evaluation indicators and generate usability assessment results.

[0038] A low-altitude flight assessment device based on multi-source meteorological data also includes:

[0039] At least one memory is used to store a computer program that executes a low-altitude flight assessment method based on multi-source meteorological data;

[0040] At least one processor for executing a computer program in memory.

[0041] Compared with existing technologies, the present invention provides a method, system, and device for low-altitude flight assessment based on multi-source meteorological data, which has the following beneficial effects:

[0042] 1. This invention achieves quantitative assessment of low-altitude flight availability by constructing a meteorological threshold determination mechanism, a spatial gridding analysis method, and a multi-index evaluation system, thereby providing technical support for low-altitude flight path planning and airspace management.

[0043] 2. This invention achieves a refined quantitative assessment of urban low-altitude flight availability by integrating multi-source meteorological data and combining spatial gridding analysis with time series feature extraction. This can effectively improve the utilization efficiency of low-altitude airspace and provide technical support for low-altitude flight path planning and operation management. Attached Figure Description

[0044] Figure 1 This is a flowchart of a low-altitude flight assessment method based on multi-source meteorological data according to the present invention;

[0045] Figure 2 This is a schematic diagram of the spatial grid division of the target area for a low-altitude flight assessment method based on multi-source meteorological data according to the present invention.

[0046] Figure 3This is a module structure diagram of a low-altitude flight assessment system based on multi-source meteorological data according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1:

[0049] like Figure 1 and Figure 2 A low-altitude flight assessment method based on multi-source meteorological data includes: acquiring multi-source meteorological data of the target area, wherein the meteorological data includes at least wind speed, precipitation and temperature, and other relevant data;

[0050] Meteorological data sources include meteorological reanalysis data, meteorological observation station data, or numerical weather prediction data.

[0051] The acquired meteorological data undergoes preprocessing, including:

[0052] Time alignment is performed on data from different sources to give them a uniform time resolution;

[0053] Spatial interpolation or resampling of meteorological data is performed to adapt it to a uniform spatial resolution;

[0054] Outliers are removed or replaced, and missing values ​​are filled by interpolation.

[0055] After processing, a standardized meteorological dataset is obtained.

[0056] The target area is divided into spatial grids to obtain multiple spatial grid units, and meteorological data is mapped to each spatial grid unit.

[0057] The grid division method includes regular square grids or hexagonal grids. Based on the spatial location of meteorological data, the meteorological data is mapped to each grid cell to obtain the meteorological time series data corresponding to each grid cell.

[0058] Each grid cell corresponds to a set of meteorological data sequences sorted by time, which are used for subsequent analysis.

[0059] Based on preset meteorological threshold conditions, each spatial grid unit is judged on a continuous time series to generate a corresponding flightable state sequence.

[0060] Meteorological threshold conditions are set, including wind speed threshold V0, precipitation threshold P0, and temperature range [T1, T2]. For each grid cell, at each time step t, a determination is made based on the corresponding meteorological data:

[0061] When the following conditions are met:

[0062] V(t) ≤ V0 and P(t) ≤ P0 and T(t) ∈ [T1,T2]

[0063] Then, the time step is determined to be a flyable state, denoted as S(t)=1;

[0064] Otherwise, it is determined to be a non-flying state, denoted as S(t)=0.

[0065] For all time steps, a flightable state time series is generated for each grid cell. The flightable state series is a binary sequence, and the value is 1 when the meteorological conditions meet the threshold, and 0 otherwise.

[0066] Based on the flightable state sequence, low-altitude flight potential indicators are calculated for each spatial grid cell, including availability, failure frequency, average failure duration and longest continuous availability.

[0067] The following will explain each point in detail:

[0068] (1) Availability A:

[0069] Availability is defined as the proportion of time that is flyable, and its calculation formula is as follows:

[0070]

[0071] Where A is the availability rate, S i Let N represent the flyable state at time i, and N be the total number of time steps.

[0072] (2) Failure frequency N f :

[0073] The failure frequency is obtained by counting the number of times S(t)=1 changes to S(t)=0 in the state sequence.

[0074] The failure frequency is obtained by counting the number of transitions from 1 to 0 in the flyable state sequence.

[0075] (3) Mean time to failure (D):

[0076] Statistical analysis was conducted on continuous periods of non-flyability, the duration of each period was calculated, and the average value was taken to obtain the average failure duration.

[0077] (4) Longest continuous available time L:

[0078] The longest consecutive available time is the length of the longest consecutive 1 in the continuous flyable state sequence.

[0079] The longest continuous available time is obtained by counting the longest continuous time in the continuous flyable state sequence;

[0080] The comprehensive evaluation indicators are calculated using a weighted method:

[0081]

[0082] Where: A is the availability rate, N f Where is the failure frequency, D is the average failure duration, L is the longest continuous availability duration, and w1 to w4 are weights.

[0083] For the above indicators A and N f Normalize D and L to bring all indicators to a uniform dimension;

[0084] Based on preset weights w1, w2, w3, and w3, the indicators are weighted and combined to obtain the comprehensive evaluation index F:

[0085] Based on the range of values ​​for the comprehensive evaluation index F, each grid cell is classified into different levels, for example:

[0086] (1) F ≥ T high High Availability Zone;

[0087] (2)T low ≤ F < T high Medium availability zone;

[0088] (3)F < T low Low availability zone.

[0089] This generates the spatial distribution results of low-altitude flight availability in the target area.

[0090] Statistical analysis was performed on the low-altitude flight potential indicators of each grid unit; and each grid unit was classified into different levels based on comprehensive evaluation indicators.

[0091] Generate a low-altitude flight availability distribution map or availability classification results based on the classification results.

[0092] The low-altitude flight potential indicators are standardized to obtain normalized indicator characteristics. The low-altitude flight potential indicators are then normalized and weighted based on preset weights to construct a comprehensive evaluation index.

[0093] A comprehensive evaluation index is constructed based on the characteristics of normalized indicators. According to the comprehensive evaluation index, each grid unit is divided into levels to obtain the spatial distribution results of low-altitude flight availability in the target area, and the low-altitude flight availability assessment results of the target area are generated.

[0094] As can be seen, this invention achieves a quantitative assessment of low-altitude flight availability by constructing a meteorological threshold determination mechanism, a spatial gridding analysis method, and a multi-index evaluation system, thereby providing technical support for low-altitude flight path planning and airspace management.

[0095] The method of the present invention will be described below using a specific target area as the research object.

[0096] I. Meteorological Data Acquisition and Preprocessing:

[0097] Acquire multi-source meteorological data for the target area within a preset time range. The meteorological data includes:

[0098] (1) Surface air temperature;

[0099] (2) Wind speed at a height of 10m;

[0100] (3) Precipitation.

[0101] Meteorological data are preferably sourced from the ERA5 reanalysis dataset, which has globally consistent spatiotemporal resolution and high accuracy.

[0102] Preprocessing of meteorological data includes:

[0103] (1) Time is unified to hourly resolution;

[0104] (2) Spatial interpolation and resampling;

[0105] (3) Outlier removal and missing value imputation.

[0106] II. Spatial Grid Division and Data Mapping:

[0107] like Figure 3 As shown, the target area is divided into spatial grids:

[0108] (1) Mesh type: Hexagonal mesh;

[0109] (2) Grid scale: 1km × 1km.

[0110] Hexagonal grids have spatial uniformity and adjacency consistency.

[0111] Meteorological data is mapped to each grid cell to form a grid-time series meteorological data structure.

[0112] III. Meteorological Threshold Determination and Flightability Status Construction:

[0113] Based on the meteorological suitability conditions for low-altitude flight, the following thresholds are set:

[0114] (1) Wind speed threshold: ≤15 m / s;

[0115] (2) Temperature range: 0℃~37.8℃;

[0116] (3) Precipitation conditions: No precipitation;

[0117] For grid i at time t:

[0118] When the conditions are met:

[0119] S(i,t) = 1 (flying is possible);

[0120] otherwise:

[0121] S(i,t) = 0 (cannot fly);

[0122] As shown in Table 1, the binary time series is obtained.

[0123] Table 1

[0124]

[0125] Table 1 above is an example of a gridded binary time series table.

[0126] IV. Calculation of Low-Altitude Flight Potential Indicators:

[0127] Based on the state sequence, the following metrics are calculated for each grid cell:

[0128] (1) Availability A:

[0129] This indicates the percentage of time that can be flown out of the total time:

[0130]

[0131] Where A is the availability rate, S(t) is the flyable state at time t, and N is the total number of time steps.

[0132] (2) Failure frequency N f :

[0133] In the statistical state sequence: the number of consecutive non-flying segments formed by "1→0".

[0134] (3) Mean time to failure (D):

[0135] Calculate the average duration of each non-flyable event.

[0136] (4) Longest continuous available time L:

[0137] Calculate the length of the longest consecutive S=1.

[0138] To illustrate the specific implementation process of the method of the present invention, some representative grid cells were selected from the target area for calculation, and the results are shown in Table 2.

[0139] Table 2

[0140]

[0141] Table 2 above shows the results of the grid-based low-altitude flight potential index.

[0142] V. Convergence from grid scale to regional scale:

[0143] After the grid-scale calculations are completed, all grid indicators are statistically aggregated using an equal-weighted average to obtain regional-level indicators: availability A and failure frequency N. f Mean failure duration D and longest continuous availability L.

[0144] VI. Indicator Normalization and Comprehensive Evaluation:

[0145] Normalize the indicators:

[0146]

[0147] Construct comprehensive evaluation indicators:

[0148]

[0149] VII. Generation of Low-Altitude Flight Availability Assessment Results:

[0150] Based on the comprehensive evaluation index F, the availability level of each grid unit is classified.

[0151] Preferably, the grading rules are set as follows:

[0152] (1) F ≥ 0.8: High availability zone;

[0153] (2) 0.6 ≤ F < 0.8: Medium to high availability zone;

[0154] (3) 0.4 ≤ F < 0.6: Available area;

[0155] (4) F < 0.4: Low availability zone.

[0156] The evaluation results of some representative grid cells are shown in Table 3.

[0157] Table 3

[0158]

[0159] Table 3 above shows the results of the grid-based low-altitude flight availability assessment.

[0160] In summary, this embodiment acquires multi-source meteorological data and performs gridding processing on the target area based on preset meteorological threshold conditions to construct a binary time series of the flyability status of each grid unit. On this basis, it extracts low-altitude flight potential indicators such as availability rate, failure frequency, average failure duration, and longest continuous availability duration. Through normalization and weighting, it constructs a comprehensive evaluation index, thereby realizing a quantitative assessment and spatial distribution analysis of urban low-altitude flight availability.

[0161] As can be seen from the example results shown in Tables 1 to 3, the method of this invention can effectively reflect the differences in flight availability of different grid cells under meteorological conditions, demonstrating good discriminative ability and stability. Furthermore, this method, based on time-series characteristics, can characterize the dynamic changes in the low-altitude flight environment, thereby improving the reliability and practicality of the evaluation results.

[0162] Therefore, this invention enables a refined assessment of the availability of low-altitude flights in cities, providing technical support for the rational utilization of low-altitude airspace resources and the planning and operation management of low-altitude flight paths.

[0163] Therefore, this invention, by integrating multi-source meteorological data and combining spatial gridding analysis with time series feature extraction, achieves a refined quantitative assessment of urban low-altitude flight availability, which can effectively improve the utilization efficiency of low-altitude airspace and provide technical support for low-altitude flight path planning and operation management.

[0164] Example 2:

[0165] like Figure 1 Based on Example 1, a low-altitude flight assessment system based on multi-source meteorological data is proposed, which also includes:

[0166] The data acquisition module is used to acquire multi-source meteorological data and perform preprocessing.

[0167] The grid partitioning module is used to partition the target area into spatial grids and complete the meteorological data mapping.

[0168] The state determination module is used to generate a flightable state sequence based on meteorological thresholds;

[0169] The index calculation module is used to calculate low-altitude flight potential indexes;

[0170] The comprehensive analysis module is used to construct comprehensive evaluation indicators and generate usability assessment results.

[0171] Through the above-described embodiments, the present invention achieves a refined spatial assessment of low-altitude flight availability, improves the utilization efficiency of low-altitude airspace, and provides a reliable decision-making basis for flight path planning and operation scheduling.

[0172] Example 3:

[0173] like Figures 1-3 Based on Example 2, a low-altitude flight assessment device based on multi-source meteorological data is proposed, which also includes:

[0174] At least one memory is used to store a computer program that executes a low-altitude flight assessment method based on multi-source meteorological data;

[0175] At least one processor for executing computer programs in memory.

Claims

1. A method for assessing low-altitude flight based on multi-source meteorological data, characterized in that, include: Acquire multi-source meteorological data for the target area, wherein the meteorological data includes at least wind speed, precipitation, and temperature; The target area is divided into spatial grids to obtain multiple spatial grid units, and meteorological data is mapped to each of the spatial grid units. Based on preset meteorological threshold conditions, each of the spatial grid units is determined on a continuous time series to generate a corresponding flightable state sequence. Based on the flightable state sequence, low-altitude flight potential indicators are calculated for each spatial grid cell, including availability, failure frequency, average failure duration and longest continuous availability. The low-altitude flight potential index is standardized to obtain normalized index characteristics; Based on the normalized index characteristics, a comprehensive evaluation index is constructed, and the low-altitude flight availability assessment results for the target area are generated.

2. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The flyable state sequence is a binary sequence, which takes the value of 1 when the meteorological conditions meet the threshold, and takes the value of 0 otherwise.

3. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The meteorological threshold conditions include: Wind speed threshold Precipitation threshold and temperature range ; When satisfied and and .

4. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The formula for calculating the availability rate is: Where A is the availability rate. Let N represent the flyable state at time i, and N be the total number of time steps.

5. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The failure frequency is obtained by counting the number of times the flyable state sequence transitions from 1 to 0.

6. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The longest continuous available time is the length of the longest consecutive 1 in the continuous flyable state sequence.

7. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The comprehensive evaluation index is calculated using a weighted method: Where: A represents availability rate Where is the failure frequency, D is the average failure duration, and L is the longest continuous availability. As weight.

8. The low-altitude flight assessment method based on multi-source meteorological data according to claim 1, characterized in that, The meteorological data is preprocessed; Specifically: Time alignment of data from different sources; Spatial interpolation or resampling of meteorological data; Outliers are removed or replaced, and missing values ​​are filled by interpolation.

9. A low-altitude flight assessment system based on multi-source meteorological data, based on the low-altitude flight assessment method based on multi-source meteorological data according to any one of claims 1-8, characterized in that, Also includes: The data acquisition module is used to acquire multi-source meteorological data and perform preprocessing. The grid partitioning module is used to partition the target area into spatial grids and complete the meteorological data mapping. The state determination module is used to generate a flightable state sequence based on meteorological thresholds; The index calculation module is used to calculate low-altitude flight potential indexes; The comprehensive analysis module is used to construct comprehensive evaluation indicators and generate usability assessment results.

10. A low-altitude flight assessment device based on multi-source meteorological data, characterized in that, Also includes: At least one memory is used to store a computer program that executes any one of the low-altitude flight assessment methods based on multi-source meteorological data according to claims 1-8; At least one processor for executing a computer program in memory.

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