Extreme climate multi-source data fusion monitoring method based on low-altitude detection

Abstract

The invention discloses an extreme climate multi-source data fusion monitoring method based on low-altitude detection, relates to the technical field of climate data detection, and solves the problem that the vertical coupling relation of meteorological elements of different height layers is not fully considered. According to a unique height difference, time difference and translation distance calculation mode, an affiliated space area is effectively locked. According to the method, regions with different space heights are comprehensively covered, it is guaranteed that climate data of multiple different spaces can be determined, key regions can be accurately positioned, a solid foundation is provided for subsequent data collection and analysis, and the integrity and accuracy of an extreme climate monitoring range are remarkably improved; in the process, values of different space point location data are fully mined, key data can be effectively screened out, monitoring data are more representative, high-quality data support is provided for meteorological feature analysis, and the accuracy of extreme climate feature recognition is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of climate data detection, and in particular to a method for fusion monitoring of extreme climate multi-source data based on low-altitude detection. Background Art

[0002] Against the backdrop of intensifying global climate change, the frequent occurrence of extreme climate events (such as strong typhoons, thunderstorms, and short-term heavy rainfall) has put forward higher requirements for meteorological monitoring and early warning; traditional single data source monitoring methods (such as relying solely on meteorological satellites or ground radars) have significant limitations: although satellite data has a wide coverage area, its spatial resolution is low (usually at the kilometer level), making it difficult to capture the fine structure of small and medium-scale extreme weather; although ground radar can provide high-resolution observations, it is blocked by terrain and limited by detection range, and cannot achieve full coverage of three-dimensional space; in addition, different types of data sources (such as satellite remote sensing data, radar echo data, and low-altitude drone detection data) differ in spatiotemporal benchmarks, data formats, and physical meanings, resulting in complex spatiotemporal registration and difficulty in feature correlation when fusing multi-source data, making it difficult to form a unified and accurate extreme climate monitoring system.

[0003] The application with publication number CN118821074A discloses a big data method and application platform for climate change monitoring in the Third Pole region, which relates to the field of climate data management technology. The method includes: collecting real-time data from ground observation stations and satellite remote sensing data to obtain multi-source climate data; obtaining the terrain characteristics of the Third Pole region; weighted fusion of multi-source climate data to generate climate monitoring data; correcting abnormal data to generate corrected data; using a climate change prediction model to predict climate change based on the corrected data to generate predicted climate change results; displaying the predicted climate change results through a visualization window and generating a climate report; solving the technical problem of the existing climate monitoring management that it is difficult to accurately and comprehensively capture the subtle differences and dynamic evolution of multi-source data in the Third Pole region, which leads to insufficient accuracy and reliability of climate change predictions, and achieving the technical effect of improving the accuracy and reliability of climate change predictions in the Third Pole region.

[0004] In the existing technology, multi-source data fusion often adopts simple weighting or traditional interpolation methods, which lack the ability to dynamically adapt to the spatiotemporal variability of extreme climate. For example, when processing the fusion of low-altitude sounding data and space-based data, the vertical coupling relationship between meteorological elements at different altitudes is not fully considered, resulting in the fusion result being unable to accurately reflect the three-dimensional structure of extreme weather (such as the vertical wind shear of the typhoon eyewall). At the same time, the existing methods are not sufficiently optimized for the efficiency of data format conversion, and processing delays are prone to occur when faced with massive heterogeneous data, making it difficult to meet the needs of real-time warning of extreme climate. Therefore, there is an urgent need for a monitoring method that can integrate multi-source heterogeneous data, accurately depict the three-dimensional characteristics of extreme climate, and realize efficient data processing, so as to improve the refined monitoring and early warning capabilities of extreme climate. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an extreme climate multi-source data fusion monitoring method based on low-altitude detection, which solves the problem of insufficient consideration of the vertical coupling relationship of meteorological elements at different altitudes.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-source data fusion monitoring method for extreme climate based on low-altitude detection, comprising the following steps:

[0007] Based on the determined detection area and detection time, the subsidiary spatial areas associated with the detection area are calibrated in sequence from the spatial features associated with the detection area. The specific method is as follows:

[0008] The determined detection area is recorded as the main area, and other areas in the same vertical range as the main area are recorded as subsidiary feature areas, each of which has different height data, and the height data are all preset data;

[0009] The determined detection time is recorded as the initial time, and the wind direction and wind speed associated with different subsidiary feature areas at the initial time are determined: the middle height associated with the subsidiary feature area is recorded as G i , where i represents different subsidiary space regions, and then confirm G i Height difference H from the center of the main area i , using: T i =H i ×C1 confirms the time difference T associated with its subordinate feature area i , where C1 is the preset fixed coefficient factor, and the monitored wind speed belonging to the corresponding subsidiary characteristic area is recorded as F i , using: F i ×T i =L i Confirm the translation distance L corresponding to the attached feature area i, and then take the corresponding wind direction as the specific direction, so that the corresponding subsidiary feature area is translated L in the specific direction i Lock the associated area and use the locked associated area as the subsidiary space area of ​​the main area;

[0010] Determine the characteristics of the detection data associated with the main area and the auxiliary space area, determine the regional characteristics associated with each area, integrate the determined groups of regional characteristics, and confirm the characteristic data package. The specific method is as follows:

[0011] The determined main area and the associated multiple groups of subsidiary spatial areas are marked as pending areas, the detection data detected at different spatial points in the pending area are confirmed, and the different data items in the detection data are recorded as single data, and the different single data associated with different spatial points are recorded as SJ q , where q represents different spatial points, SJ q The spatial point associated with max is recorded as the feature point;

[0012] Confirm the different feature points associated with different individual data in the area to be determined in turn, mark them in the area to be determined, and connect the confirmed feature points in the area to be determined. Confirm that the specific area associated with several feature points is recorded as a feature area;

[0013] Quantize multiple groups of detection data associated with the feature area: confirm the individual data associated with several groups of detection data in turn, confirm the minimum and maximum data values ​​from the confirmed groups of individual data, confirm the data interval belonging to the corresponding individual data, and then quantize the data interval with the quantization interval [0, 10] to confirm the quantization value associated with the corresponding individual data. Using this quantization method, quantize the individual data associated with the detection data in turn to confirm the quantization value associated with the corresponding individual data;

[0014] Confirm the total quantitative value associated with the detection data associated with a single spatial point in the feature area, calibrate the different quantitative values ​​associated with different individual data in the detection data as LHo, where o represents different individual data, and sum up several groups of quantitative values ​​LHo to confirm the total quantitative value;

[0015] The detection data that meets the quantization total value: the quantization total value ≥ 18 is calibrated as feature data, and several groups of feature data calibrated in the pending area are averaged: several groups of single item data belonging to the same single item are averaged to confirm the single item mean feature, and the several groups of single item mean features confirmed in the pending area are used as the selected data of this pending area;

[0016] Then, the selected data associated with different pending areas are bundled together to generate the feature data package confirmed in this detection process;

[0017] The confirmed characteristic data packets are formatted and the detection data in different data formats in the characteristic data packets are uniformly converted to obtain meteorological data. The specific method is as follows:

[0018] Confirm the data of different data formats in the characteristic data package and calibrate the data capacity of different formats of data as R p , where p represents different data formats;

[0019] Confirm the data formats that the meteorological system can receive, use this data format as the format to be converted, identify the average conversion speed when converting data of different formats to the format to be converted from historical conversion data, and record the confirmed average conversion speed as the characteristic average speed;

[0020] Randomly select a set of formats to be converted as the formats to be processed: based on the data capacity R of the corresponding format data p and the associated characteristic average speed, confirm the conversion time associated with the corresponding format data, and confirm the conversion time of different format data in turn, sum up the confirmed multiple groups of conversion time, confirm the total time, and use the confirmed total time as the characteristic time of the format to be processed;

[0021] Sequentially taking different formats to be converted as formats to be processed, and sequentially confirming characteristic times associated with different formats to be processed, selecting a minimum value from the confirmed sets of characteristic times, and recording the format to be processed associated with the minimum value as the selected format;

[0022] Convert different data in the feature data packet into a selected format to obtain meteorological data.

[0023] Preferably, the middle height is the middle value of the corresponding height data.

[0024] Preferably, the detection data with the total quantization value less than 18 is not calibrated in any way.

[0025] The present invention provides a multi-source data fusion monitoring method for extreme climate based on low-altitude detection. Compared with the existing technology, it has the following advantages:

[0026] This invention precisely calibrates the detection area and its associated spatial regions, effectively locking in the associated spatial regions based on unique height differences, time differences, and translation distance calculations. This not only comprehensively covers regions at different spatial heights, ensuring that climate data from multiple different spaces can be determined, but also accurately locates key areas, providing a solid foundation for subsequent data collection and analysis, significantly improving the completeness and accuracy of extreme climate monitoring.

[0027] The detection data from the main area and the auxiliary spatial area are systematically characterized, from the quantification of individual data items to the calculation of the quantified total value, and then to the screening and mean verification of the characteristic data to form a characteristic data package. This process fully taps the value of data from different spatial points, effectively screening key data, making the monitoring data more representative, providing high-quality data support for meteorological characteristic analysis, and improving the accuracy of identifying extreme climate characteristics;

[0028] When processing the format of characteristic data packets, an innovative format conversion strategy is used to determine the conversion time of data in different formats based on data capacity and average conversion speed, and the selected format with the shortest conversion time is selected for unified conversion. This method greatly shortens data conversion time and improves data processing efficiency, enabling the meteorological system to conduct verification and analysis based on meteorological data more quickly, output assessment results in a timely manner, and enhance the timeliness of extreme climate monitoring and response. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Schematic diagram of the process of the present invention;

[0030] Figure 2 Schematic diagram for determining characteristic data of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] First embodiment

[0033] See also Figure 1 , this application provides a multi-source data fusion monitoring method for extreme climate based on low-altitude detection, including the following steps:

[0034] Step 1: Based on the determined detection area and detection time, the subsidiary spatial areas associated with the detection area are calibrated in sequence from the spatial features associated with the detection area. Specifically, the so-called subsidiary spatial areas are other areas with different spatial altitudes. After the detection area is confirmed, it is generally determined by the corresponding radar satellite. Then, based on the top-down method, the detection areas with the same spatial features are also calibrated to ensure that the climate data of multiple different spaces can be determined. The specific method of calibrating the subsidiary spatial areas is as follows:

[0035] The determined detection area is recorded as the main area, and other areas in the same vertical range as the main area are recorded as subsidiary feature areas. The subsidiary feature areas all have different height data, and their height data are all preset data, which are prepared in advance by the operator based on experience;

[0036] The determined detection time is recorded as the initial time, and the wind direction and wind speed associated with different subsidiary feature areas at the initial time are determined: the middle height associated with the subsidiary feature area is recorded as G i , where i represents different auxiliary space areas, the so-called middle height is the middle value of the corresponding height data, and then confirm G i Height difference H from the center of the main area i , using: T i =H i ×C1 confirms the time difference T associated with its subordinate feature area i , where C1 is a preset fixed coefficient factor, the specific value of which is determined by the operator based on experience, and the monitored wind speed belonging to the corresponding subsidiary characteristic area is recorded as F i , using: F i ×T i =L i Confirm the translation distance L corresponding to the attached feature area i , and then take the corresponding wind direction as the specific direction, so that the corresponding subsidiary feature area is translated L in the specific direction i Lock the associated area and use the locked associated area as the subsidiary space area of ​​the main area;

[0037] Specifically, according to the set longitude, latitude and specific altitude, the corresponding detection area is locked from the corresponding atmosphere. According to the associated detection area, the specific subsidiary feature area of ​​the same longitude and latitude associated with the corresponding detection area can be determined from top to bottom and the associated altitude data. Then, according to the wind speed and wind direction associated with the corresponding area at the corresponding time, the translation process of the corresponding area can be effectively confirmed, and the corresponding subsidiary space area can be accurately locked, which is convenient for subsequent numerical verification.

[0038] Step 2: Determine the characteristics of the detection data associated with the main area and the subsidiary spatial areas, determine the regional characteristics associated with each area, integrate the determined groups of regional characteristics, and confirm the feature data package. Specifically, different spatial points exist in different spatial areas, and the detection data associated with each different spatial point is different. The detection data generally includes different data in multiple dimensions, generally meteorological parameters such as temperature, humidity, and air pressure;

[0039] The specific method for determining the characteristic data packet is as follows:

[0040] The determined main area and the associated multiple groups of subsidiary spatial areas are marked as pending areas, the detection data detected at different spatial points in the pending area are confirmed, and the different data items in the detection data are recorded as single data, and the different single data associated with different spatial points are recorded as SJ q , where q represents different spatial points, SJ q The spatial point associated with max is recorded as the feature point;

[0041] Different feature points associated with different single data in the pending area are confirmed in turn and marked in the pending area, and the confirmed feature points in the pending area are connected, and the specific area associated with several feature points is confirmed and recorded as a feature area. Specifically, the associated meteorological parameters include at least four groups, so four groups of feature points can be confirmed in the corresponding pending area. During the connection process of the four groups of feature points, a triangular area can be confirmed in the corresponding pending area, and the confirmed triangular area is the feature area associated with the corresponding pending area;

[0042] Quantify the multiple groups of detection data associated with the feature area: confirm the individual data associated with several groups of detection data in turn, confirm the minimum and maximum data values ​​from the confirmed groups of individual data, confirm the data interval belonging to the corresponding individual data, and then quantize the data interval and the quantization interval [0, 10] to confirm the quantization value associated with the corresponding individual data. Using this quantization method, quantize the individual data associated with the detection data in turn to confirm the quantization value associated with the corresponding individual data. Specifically, the individual data to be quantized is humidity data. In the corresponding feature area, there is a minimum humidity and a maximum humidity, so the humidity interval can be confirmed. The humidity interval to be confirmed is [10, 70]. Then 10 is quantized to 0, and the corresponding 70 is quantized to 10. After the specific quantization characteristics, the quantization values ​​associated with different humidity data can be determined in turn.

[0043] Confirm the total quantitative value associated with the detection data associated with a single spatial point in the feature area, calibrate the different quantitative values ​​associated with different individual data in the detection data as LHo, where o represents different individual data, and sum up several groups of quantitative values ​​LHo to confirm the total quantitative value;

[0044] Combine Figure 2 , calibrate the detection data that meet the quantization total value: the quantization total value ≥ 18 as feature data, perform mean verification on several groups of feature data calibrated in the undetermined area: perform mean processing on several groups of single item data belonging to the same single item, confirm the single item mean feature, and use the several groups of single item mean features confirmed in the undetermined area as the selected data of this undetermined area;

[0045] Then, the selected data associated with different pending areas are bundled together to generate the feature data package confirmed in this detection process;

[0046] Specifically, each undetermined area has several different spatial points, and different spatial points have different detection data. Each set of detection data has several sets of individual data. Then, from the several sets of individual data, the data characteristics of the individual data can be verified and analyzed, and the minimum and maximum values ​​of the corresponding individual data can be confirmed, and numerical quantization can be performed, so as to confirm the quantitative value of the corresponding individual data, and thus the specific quantitative value associated with each detection data can be confirmed, so as to determine the corresponding quantitative total value;

[0047] Based on the numerical performance of the total quantitative value of different spatial points, the optimal spatial point can be selected in the corresponding undetermined area, which makes it easier to obtain more accurate data in the corresponding undetermined area and achieve better data monitoring effects.

[0048] Step 3: Format the confirmed feature data packet and uniformly convert the detection data of different data formats in the feature data packet to obtain meteorological data. Specifically, the corresponding feature data packet includes a large amount of data in different formats, such as image data, feature data (temperature, humidity), etc., so it is necessary to uniformly convert the corresponding data and confirm the specific data after conversion. The specific method of performing the unified conversion processing is as follows:

[0049] Confirm the data of different data formats in the characteristic data package and calibrate the data capacity of different formats of data as R p , where p represents different data formats;

[0050] Confirm the data formats that the meteorological system can receive, use this data format as the format to be converted, identify the average conversion speed when converting data of different formats to the format to be converted from historical conversion data, and record the confirmed average conversion speed as the characteristic average speed;

[0051] Randomly select a set of formats to be converted as the formats to be processed: based on the data capacity R of the corresponding format data p and the associated characteristic average speed, confirm the conversion time associated with the corresponding format data, and confirm the conversion time of different format data in turn, sum up the confirmed multiple groups of conversion time, confirm the total time, and use the confirmed total time as the characteristic time of the format to be processed;

[0052] Then, different formats to be converted are sequentially used as formats to be processed, and characteristic times associated with different formats to be processed are sequentially confirmed. From the confirmed groups of characteristic times, the minimum value is selected, and the format to be processed associated with the minimum value is recorded as the selected format;

[0053] Convert different data in the feature data packet into a selected format to obtain meteorological data;

[0054] Specifically, the formats that can be processed by the proposed meteorological system are A, B and C respectively. Then, after feature processing, A is prioritized as the format to be processed, and then B or C is taken as the corresponding format to be processed. According to the confirmed format to be processed and the associated format data, in the unified conversion processing process, the format with the fastest processing process can be confirmed and selected, which can ensure that the conversion time of the obtained meteorological data is the shortest during data conversion, and can achieve faster data conversion processing effect.

[0055] After the meteorological data is obtained, the corresponding meteorological system will verify and analyze the meteorological characteristics of the relevant area based on such meteorological data, and directly output the corresponding assessment results. Since such operation processes can be completed within the existing meteorological system, they will not be elaborated here.

[0056] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0057] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The extreme climate multi-source data fusion monitoring method based on low-altitude detection is characterized by: The following steps are involved: Based on the determined detection area and detection time, the subsidiary spatial areas associated with the detection area are sequentially calibrated from the spatial features associated with the detection area; Determine the characteristics of the detection data associated with the main area and the auxiliary spatial area, determine the regional characteristics associated with each area, integrate the determined groups of regional characteristics, and confirm the characteristic data package; The confirmed characteristic data packets are formatted, and the detection data of different data formats in the characteristic data packets are uniformly converted to obtain meteorological data.

2. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 1 is characterized in that: The specific method of calibrating the auxiliary space area is: The determined detection area is recorded as the main area, and other areas in the same vertical range as the main area are recorded as subsidiary feature areas, each of which has different height data, and the height data are all preset data; The determined detection time is recorded as the initial time, and the wind direction and wind speed associated with different subsidiary feature areas at the initial time are determined: the middle height associated with the subsidiary feature area is recorded as G i , where i represents different subsidiary space regions, and then confirm G i Height difference H from the center of the main area i , using: T i =H i ×C1 confirms the time difference T associated with its subordinate feature area i , where C1 is the preset fixed coefficient factor, and the monitored wind speed belonging to the corresponding subsidiary characteristic area is recorded as F i , using: F i ×T i =L i Confirm the translation distance L corresponding to the attached feature area i , and then take the corresponding wind direction as the specific direction, so that the corresponding subsidiary feature area is translated L in the specific direction i Lock the associated area and use the locked associated area as the subsidiary space area of ​​the main area.

3. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 2 is characterized in that: The middle height is the middle value of the corresponding height data.

4. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 1 is characterized in that: The specific method for determining the regional characteristics associated with each region is as follows: The determined main area and the associated multiple groups of subsidiary spatial areas are marked as pending areas, the detection data detected at different spatial points in the pending area are confirmed, and the different data items in the detection data are recorded as single data, and the different single data associated with different spatial points are recorded as SJ q , where q represents different spatial points, SJ q The spatial point associated with max is recorded as the feature point; Different feature points associated with different single data in the pending area are confirmed in turn and marked in the pending area. The feature points confirmed in the pending area are connected, and the specific area associated with several feature points is recorded as the feature area.

5. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 1 is characterized in that: The specific method for confirming the characteristic data packet is: Quantize multiple groups of detection data associated with the feature area: confirm the individual data associated with several groups of detection data in turn, confirm the minimum and maximum data values ​​from the confirmed groups of individual data, confirm the data interval belonging to the corresponding individual data, and then quantize the data interval with the quantization interval [0, 10] to confirm the quantization value associated with the corresponding individual data. Using this quantization method, quantize the individual data associated with the detection data in turn to confirm the quantization value associated with the corresponding individual data; Confirm the total quantitative value associated with the detection data associated with a single spatial point in the feature area, calibrate the different quantitative values ​​associated with different individual data in the detection data as LHo, where o represents different individual data, and sum up several groups of quantitative values ​​LHo to confirm the total quantitative value; The detection data that meets the quantization total value: the quantization total value ≥ 18 is calibrated as feature data, and several groups of feature data calibrated in the pending area are averaged: several groups of single item data belonging to the same single item are averaged to confirm the single item mean feature, and the several groups of single item mean features confirmed in the pending area are used as the selected data of this pending area; The selected data associated with different pending areas are then bundled together to generate the feature data package confirmed in this detection process.

6. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 5 is characterized in that: The detection data with the total quantization value less than 18 is not calibrated.

7. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 1 is characterized in that: The specific method of formatting the confirmed feature data packet is as follows: Confirm the data of different data formats in the characteristic data package and calibrate the data capacity of different formats of data as R p , where p represents different data formats; Confirm the data formats that the meteorological system can receive, use this data format as the format to be converted, identify the average conversion speed when converting data of different formats to the format to be converted from historical conversion data, and record the confirmed average conversion speed as the characteristic average speed; Randomly select a set of formats to be converted as the formats to be processed: based on the data capacity R of the corresponding format data p And the associated characteristic average speed, confirm the conversion time associated with the corresponding format data, and confirm the conversion time of different format data in turn, sum up the confirmed multiple groups of conversion times, confirm the total time, and use the confirmed total time as the characteristic time of the format to be processed.

8. The extreme climate multi-source data fusion monitoring method based on low-altitude detection according to claim 7 is characterized in that: The specific method for confirming the meteorological data is as follows: Sequentially taking different formats to be converted as formats to be processed, and sequentially confirming characteristic times associated with different formats to be processed, selecting a minimum value from the confirmed groups of characteristic times, and recording the format to be processed associated with the minimum value as the selected format; Convert different data in the feature data packet into a selected format to obtain meteorological data.

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