Stratosphere wind field analysis method, device and equipment

Abstract

The application provides a stratosphere wind field analysis method, device and equipment, including: a distance measurement unit, which calculates the similarity of historical wind fields between any regions (longitude, latitude, height) through a distance measurement algorithm; a space-time similarity analysis unit, which analyzes the space-time similarity of global stratosphere wind fields by taking a clustering algorithm as a core, and obtains the distribution of similar regions of global stratosphere wind fields; and an easy-to-stay space-time analysis unit, which finds the space and time in which a target region / similar region is more likely to stay by analyzing wind direction diversity. The mining of similar regions can play a role in the reuse of wind field observation data and airship navigation experience; and the time and space found by analyzing wind direction diversity, which are more suitable for the stay of an aircraft, can significantly improve the stay accuracy. Through the analysis of the stratosphere wind field, the technical solution can generally improve the stay time and performance stability to a certain extent, and thus enables the aircraft to perform better observation and communication.

Description

Technical Field

[0001] This invention relates to a meteorological big data analysis method, and more particularly to a stratospheric wind field analysis method, apparatus, and equipment. Background Technology

[0002] Stratospheric aerostats, situated above conventional aircraft and below spacecraft, possess unique advantages. As a high-resolution Earth observation platform, they offer wide coverage and have significant development potential in areas such as communication support, intelligence gathering, early warning, and civilian applications. They are widely used in meteorological data collection, monitoring, scientific investigation, and data communication.

[0003] To achieve continuous residence and stable performance of stratospheric aerostats in the stratosphere, accurate analysis of the stratospheric wind field environment is required, which in turn necessitates abundant wind field data. Currently, stratospheric meteorological observations mainly rely on weather balloons. In addition, stratospheric aerostats have also collected stratospheric wind field data during their historical loitering periods. However, the high cost of aerostats and the limited loitering time and space result in relatively limited usable data.

[0004] Transfer learning is the process of applying a model learned or trained in an old domain to a new domain by leveraging the similarity between data, tasks, or models. Analogously, similarity algorithms can be used to analyze stratospheric wind field data to identify regions with similar wind field environments, thereby achieving the goal of reusing meteorological observation data and navigation experience within these similar regions.

[0005] Current research on navigation algorithms for stratospheric aerostats primarily relies on analysis of the near-zero wind layer for wind field analysis and application. However, in aerostat stationing missions, it has been found that a broader range of useful wind patterns and low-speed areas can effectively improve stationing efficiency, not limited to the near-zero wind layer. By analyzing wind direction diversity and wind speed, these useful wind patterns and low-speed areas can be identified, revealing suitable spatiotemporal ranges for stationing. Experimental verification of the navigation algorithm demonstrates that the analysis conclusions can effectively improve aerostat stationing efficiency, achieving efficient utilization of national resources and enhancing the work efficiency of personnel. Summary of the Invention

[0006] This application provides a method, apparatus, and equipment for stratospheric wind field analysis, mainly including stratospheric wind field environmental similarity analysis and spatiotemporal detection of airships prone to lingering. Wind field similarity analysis can improve the utilization rate of stratospheric meteorological observation data and airship navigation experience; spatiotemporal detection of airships prone to lingering can increase the airship's dwell time, thereby making its performance more stable.

[0007] The stratospheric wind field analysis device described in this application includes:

[0008] Data preprocessing unit: Processes publicly available multi-source heterogeneous stratospheric wind field data, unifies spatial / temporal resolution, and constructs three-dimensional (longitude, latitude, altitude) gridded wind field data.

[0009] Distance metric unit: Calculates the similarity of historical wind fields between any regions (longitude, latitude, altitude) using distance metric algorithms (such as Euclidean distance and dynamic time warping algorithm).

[0010] Spatiotemporal similarity analysis unit: Based on clustering algorithm, it analyzes the spatiotemporal similarity of global stratospheric wind fields and obtains the distribution of similar regions of global stratospheric wind fields.

[0011] Easy-to-Stay Space-Time Analysis Unit: By analyzing the diversity of wind directions, it discovers the space and time that are more suitable for the airship to stay aloft.

[0012] Specifically, airships are more likely to remain airborne in a region when the wind direction varies at different pressure altitudes for most of the day.

[0013] Furthermore, 1) by calculating the average proportion of time ranges with wind direction diversity in a certain region (longitude, latitude, altitude) or similar wind field environments, the suitable time range for airships to be stationed in that region can be identified; 2) by calculating the historical wind direction diversity in various regions of the world during the target stationing period (e.g., May), suitable regions for airships to be stationed can be identified, thereby improving the effectiveness and efficiency of airship stationing.

[0014] The storage unit is used to store: 1) three-dimensional gridded wind field data; 2) the distribution of similar regions of global stratospheric wind field; and 3) spatiotemporal analysis results of easily stationary spaces.

[0015] The visualization unit is used to display: 1) the distribution of similar regions of the global stratospheric wind field, and 2) the spatiotemporal analysis results of the easily entrenched space.

[0016] A method for analyzing stratospheric wind fields includes:

[0017] Step 1: Data preprocessing, converting multi-source heterogeneous stratospheric wind field data into three-dimensional grid data with uniform temporal and spatial resolution.

[0018] Step 2: Spatiotemporal similarity analysis of stratospheric wind fields to obtain the distribution of similar regions of global stratospheric wind fields. Specific methods include:

[0019] 1) By employing various distance metric algorithms to calculate the similarity of time series data, the similarity of wind field sequence data (historical wind field environment) between any two regions can be measured. Specifically, taking Euclidean distance as an example, the similarity of the historical wind field environment between regions A and B over the past year can be analyzed by measuring the differences in their historical wind field data over the past year.

[0020] 2) By calculating the difference (i.e., distance value) between historical wind field data of any region in the world, clustering algorithms are applied to analyze the distribution pattern of similar wind field environments in the world.

[0021] Step 3: Regional wind field characteristics analysis. Analyze the wind field characteristics of any target area or similar areas, calculate the wind direction diversity of different historical time periods, and discover the suitable time range for air travel in the target area or similar areas.

[0022] Specifically, the analysis of similar regions of the target area is mainly used when the target area lacks detailed observation data of the stratospheric wind field, but its similar regions do exist. By analyzing the wind field characteristics of the similar regions, the analysis of the target area can be effectively supplemented.

[0023] Step 4: Global wind field characteristics analysis. Analyze the historical wind field characteristics of various regions around the world during the target time period, calculate wind direction diversity, and discover areas suitable for air travel during the target time period.

[0024] Step 5: Visualize the above conclusions using wind rose diagrams, line graphs, and map visualization methods, including: 1) the spatiotemporal distribution of stratospheric wind fields; 2) conclusions on regional wind field characteristics analysis; and 3) conclusions on global wind field characteristics analysis.

[0025] In addition, this application proposes a stratospheric wind field analysis device, characterized in that:

[0026] One or more processors; memory;

[0027] One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: perform the above-described stratospheric wind field analysis method.

[0028] As can be seen from the above technical solutions, this application has the following advantages:

[0029] (1) Through spatiotemporal similarity analysis of stratospheric wind fields, similar regions of global wind field environment can be found in any area, and wind field observation / forecast data and airship experience can be reused between regions. This can make up for the lack of stratospheric wind field data to a certain extent. At the same time, due to the high cost of airships and the limited number of experiments / applications, the reuse of navigation experience in similar wind field environments can improve the stationary time and performance stability to a certain extent.

[0030] (2) Analysis of regional wind field characteristics: By analyzing the wind direction diversity of the target area / similar areas, the time range within the target area that is more suitable for airship operation is identified. Analysis of global wind field characteristics: By analyzing the wind direction diversity of various regions globally during the target time period, regions suitable for airship operation during the target time period are identified. Under the same navigation strategy, compared to operating in any time and space, the airship operation accuracy (the proportion of time the airship operates within the specified range) of the suitable spatiotemporal range identified in this application can be significantly improved. This can significantly improve the efficiency of airship operation, thereby enabling the airship to achieve better observation and communication. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0032] Figure 1 Flowchart of Stratospheric Wind Field Analysis Device

[0033] Figure 2 Example map of similar wind field distribution areas

[0034] Figure 3 Example of wind direction distribution at various pressure levels in the stratosphere Figure 1

[0035] Figure 4 Example of wind direction distribution at various pressure levels in the stratosphere Figure 2

[0036] Figure 5 Example area wind direction diversity temporal distribution map

[0037] Figure 6 Example of wind direction diversity spatial distribution map over a given time period Detailed Implementation

[0038] The technical solutions of this disclosure will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the protection scope of this disclosure.

[0039] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be used to interpret the limitations of this application.

[0040] The following is an analysis of some of the terms and keywords used in this application.

[0041] Wind direction diversity: When the difference in wind direction angle at different pressure levels in the stratosphere exceeds a certain angle in a certain region at a certain time, that region at that time has wind direction diversity.

[0042] Example 1:

[0043] This embodiment will be combined with the appendix Figure 1 This paper provides a detailed explanation of the relevant details regarding the analysis of stratospheric wind field similarity regions. The detailed implementation steps in this embodiment are divided into three steps: historical wind field similarity analysis between arbitrary regions, global wind field environment similarity region analysis, and comparative analysis of wind field environment between similar regions and the target region. The specific steps are as follows:

[0044] Step 1: Calculate the similarity of historical wind fields between any regions

[0045] Based on the three-dimensional (longitude, latitude, altitude) wind field data generated by data preprocessing, this paper takes the Korla region of Xinjiang Uygur Autonomous Region (41.5°N, 86.0°E) as an example to analyze the similarity between the stratospheric wind field in other parts of the world and the Korla region.

[0046] Specifically, taking the 50 hPa pressure altitude as an example, analyzing the wind field data from May to June 2021, and using the Euclidean distance (as shown in the formula below), the similarity of the wind field environment between the Urumqi area (43.5°N, 87.5°E) and the Korla area in Xinjiang Uygur Autonomous Region can be calculated.

[0047] in:

[0048] Furthermore, the above and These represent wind field data for Korla and Urumqi during May-June 2021, respectively. and Wind field data representing time t in Korla, These represent the horizontal and vertical wind components, respectively. This indicates the difference in wind field data between the two regions. This indicates the similarity of wind field data between the two regions.

[0049] Step 2: Analysis of Similar Regions in Global Wind Field Environment

[0050] Based on the method described in step 1 for calculating the wind field environmental similarity between two regions, the similarity of wind fields between any regions in the world is calculated sequentially to obtain a similarity matrix.

[0051] Furthermore, based on the similarity matrix, a clustering algorithm was applied to obtain the distribution of similar regions in the global wind field environment at 50 hPa pressure altitude. Specifically, regions with the same category have relatively similar wind field environments.

[0052] Step 3: Comparative analysis of wind field environment between similar areas and target area

[0053] Step 1 provides the similarity between the wind fields of Korla and any other region globally during May-June 2021. Step 2 provides the distribution of similar wind field regions globally. We selected the top 20% of regions in the former similarity ranking and the regions in the latter category that Korla belongs to, respectively. The two groups of regions have a high degree of overlap.

[0054] Specifically, the analysis of similar regions is mainly to apply wind field observation data and navigation experience from similar regions to enrich our understanding of the target region and improve the navigation efficiency and accuracy of the target region.

[0055] Furthermore, 1) when the target area is large, such as the Xinjiang Uygur Autonomous Region, the global wind field similarity area distribution obtained in step 2 can be applied; 2) when the target area is small, such as the Korla area, the similarity distribution between other global regions and the Korla area obtained in step 1 can be applied.

[0056] Furthermore, taking the Kuer region as an example, the top 20% of areas in terms of visualized wind field environmental similarity (see appendix) Figure 2 A comparative analysis was conducted between similar areas and the Korla region, examining wind field environments including wind direction distribution and wind speed trends. The analysis revealed that the wind field environments were quite consistent.

[0057] Example 2:

[0058] This embodiment specifically illustrates the method for identifying suitable spatiotemporal regions for airship stationary. The detailed implementation steps will elaborate on three parts: wind direction diversity calculation, regional wind field characteristic analysis, and global wind field characteristic analysis. Specifically, regional wind field characteristic analysis can identify the time range within a region suitable for airship stationary, while global wind field characteristic analysis can identify regions globally suitable for airship stationary during target stationary periods.

[0059] Step 1: Calculate wind direction diversity

[0060] Analyze wind direction data at various pressure heights in a certain region (longitude and latitude) to determine whether it exhibits wind direction diversity at a given moment.

[0061] Specifically, taking a certain region (26°N, 119.25°E) as an example and using the global meteorological reanalysis data ERA5 as an example, this will be explained. In the calculation of wind direction diversity, when the difference in wind direction angles at multiple pressure heights in the stratosphere at a specified location and time exceeds a certain angle, that location and time exhibits wind direction diversity. Since the presence of winds in opposite directions in the stratospheric wind field greatly reduces the difficulty of an aerostat maintaining its position, we set the angle difference threshold to 180°. The following example uses this threshold to analyze whether the example exhibits wind direction diversity.

[0062] Furthermore, taking a specific moment on April 1, 2017 as an example, under a threshold of 180°, there is wind direction diversity. See attached details. Figure 3 As shown in the figure, the wind directions corresponding to each pressure altitude (30 hPa, 50 hPa, 70 hPa, 100 hPa, 125 hPa, 150 hPa) are (85.36°, 32.37°, 24.97°, 333.17°, 249.54°, 252.24°).

[0063] Taking a specific moment on September 7, 2017 as an example, under a threshold of 180°, there is no wind direction diversity. See attached details. Figure 4 As shown in the figure, the wind directions corresponding to each pressure height (30 hPa, 50 hPa, 70 hPa, 100 hPa, 125 hPa, 150 hPa) are (109.76°, 90.44°, 63.66°, 47.26°, 11.94°, 26.03°).

[0064] Step 2: Regional wind field characteristics analysis

[0065] By analyzing the wind field characteristics of any target area or similar areas, calculating the wind direction diversity over historical time periods, and identifying the suitable time range for air travel in the target area or similar areas.

[0066] Specifically, taking the airship stationary application scenario as an example, the method for calculating wind direction diversity in step 1 is applied to calculate the average proportion of time periods with wind direction diversity in the stationary area for each year / month / any time period.

[0067] Furthermore, taking a certain region (26°N, 119.25°E) as an example, and using a monthly time period, the average proportion of times with wind direction diversity in each month within a 50-kilometer radius was calculated. The analysis results are attached. Figure 5 It was found that the proportion of time with wind direction diversity in May and June was relatively high in all historical years (averaging more than 60% of the time with wind direction diversity).

[0068] Subsequent experiments verified that, based on the results of this example, under the same navigation strategy algorithm, May-June has a higher airspace accuracy (the proportion of time spent within a specified airspace range) than other times.

[0069] Step 3: Global Wind Field Characteristics Analysis

[0070] By analyzing the wind field characteristics of different regions around the world during the same historical period within the target time period, the diversity of wind direction is calculated, and regions suitable for air travel during the target time period are identified.

[0071] Specifically, taking the future stationing observation site selection mission of the airship as an example, the method of calculating wind direction diversity in step 1 is applied to analyze the global wind direction diversity during the planned stationing time.

[0072] Furthermore, if we plan to conduct stationary observations in May 2023 as an example, we can analyze the historical wind direction diversity across different regions of the world in May. The temporal distribution of global wind field wind direction diversity in May 2021 is shown below. Figure 5 For example, areas with darker colors have a higher proportion of time with diverse wind directions in May. Generally speaking, in May, areas near the equator and in the Northern Hemisphere with a high proportion of wind direction diversity experience wind direction diversity most of the time. Conducting aerial observations in these areas is easier and more accurate. Within the feasible aerial observation range, selecting areas with a higher proportion of time with diverse wind directions can improve the success rate of aerial observations, thereby saving manpower and financial resources for aerostat aerial experiments and enhancing the experimental results.

[0073] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0074] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A device for stratospheric wind field analysis, characterized in that The device comprises: a data preprocessing unit: for processing publicly available multi-source heterogeneous stratospheric wind field data, unifying spatial and temporal resolution, and constructing three-dimensional gridded wind field data defined by longitude, latitude and height; a distance measurement unit: for calculating the similarity of historical wind fields between any regions by distance measurement algorithms; a spatio-temporal similarity analysis unit: for analyzing the spatio-temporal similarity of global stratospheric wind fields with a clustering algorithm as the core, and obtaining the distribution of similar regions of global stratospheric wind fields; an easy-to-hold space-time analysis unit: for finding more suitable space and time for airships to hold by analyzing wind direction diversity; a storage unit: for storing the three-dimensional gridded wind field data, the distribution of similar regions of global stratospheric wind fields, and the easy-to-hold space-time analysis results; a visualization unit: for displaying the distribution of similar regions of global stratospheric wind fields and the easy-to-hold space-time analysis results.

2. The apparatus of claim 1, wherein, The distance measurement unit is specifically used to measure the similarity of wind field sequence data between historical wind field environments of any regions by various distance measurement algorithms for calculating the similarity of time series data.

3. The apparatus of claim 1, wherein, The easy-to-hold space-time analysis unit is specifically used to: find the time range suitable for aircraft to hold in a region or its similar region by calculating the time proportion of the region or its similar region having wind direction diversity in any time range; and find the region suitable for aircraft to hold by calculating the proportion of the region having wind direction diversity in the target holding time period in the history of each region in the world.

4. The apparatus of claim 1, wherein, The visualization unit is specifically used to: display the distribution of similar regions of global stratospheric wind fields for finding the wind field environment similar region distribution of the target flight or holding region; and visualize the easy-to-hold space-time analysis results, which include displaying the most easy-to-hold time distribution results of the specified region or its similar region, and displaying the most easy-to-hold space distribution of the specified holding time.

5. A method of stratospheric wind field analysis, characterized by, It comprises: S1: data preprocessing, converting multi-source heterogeneous stratospheric wind field data into three-dimensional gridded data with unified time and spatial resolution; S2: stratospheric wind field spatio-temporal similarity analysis, obtaining the distribution of similar regions of global stratospheric wind fields; S3: regional wind field characteristic analysis, calculating the wind direction diversity of each time period in history by analyzing the wind field characteristics of any target region or its similar region, to find the time range suitable for the target region or its similar region to hold; S4: global wind field characteristic analysis, analyzing the wind field characteristics of each region in the world in the target time period in history, and calculating the wind direction diversity, to find the region suitable for holding in the target time period; S5: applying wind rose diagram, line chart and map visualization methods to visualize the following conclusions: 1) the distribution of similar regions of global stratospheric wind fields obtained in step S2; 2) the regional wind field characteristic analysis conclusion obtained in step S3; and 3) the global wind field characteristic analysis conclusion obtained in step S4.

6. A stratospheric wind field analysis device, characterized by: one or more processors; a memory; one or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, the one or more programs configured to perform the analysis method of claim 5.

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