CN112924974B - Method and device for identifying cloud cluster by using DBSCAN clustering algorithm and electronic equipment - Google Patents
Method and device for identifying cloud cluster by using DBSCAN clustering algorithm and electronic equipment Download PDFInfo
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Abstract
The invention discloses a method for identifying cloud clusters by using a DBSCAN clustering algorithm, which comprises the following steps: obtaining reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle; setting different echo threshold values according to the difference between the reflectivity factors of different clouds; establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value; classifying and identifying the cloud cluster according to the DBSCAN algorithm model; the cloud cluster classification method can classify and identify the cloud clusters, so that the cloud cluster classification result is more visual, and the edges, the sizes and the types of the cloud clusters are obvious.
Description
Technical Field
The invention relates to the field of atmospheric science research, in particular to a method and a device for identifying cloud clusters by using a DBSCAN clustering algorithm and electronic equipment.
Background
Rainstorm has serious harm to the economy and the society of China, and is one of the main disastrous weather phenomena of China. China's rainstorm has the characteristics of paroxysmal, persistent and frequent occurrence, and effective and real-time monitoring of rainfall is one of powerful means for improving the rainstorm early warning capability. Rainfall systems are generally cloud-accumulated mixed cloud precipitation, and convection cloud precipitation is embedded in large lamellar cloud precipitation, wherein convection cloud areas are the main reasons for strong convection weather processes, and the lamellar cloud areas control the duration and precipitation amount of rainstorm. Because the generation mechanism, the life and consumption change and the moving speed of the convection cloud and the layered cloud are different, and the contribution degree of the convection cloud and the layered cloud in the atmosphere is different, the convection cloud and the layered cloud in the storm rain are identified and judged, so that the mechanism of rainfall occurrence can be better understood, the rainfall estimation capability is improved, and monitoring and early warning of disastrous weather, aerospace, operation command of artificially influencing weather and the like are greatly facilitated.
To date, much work has been done by meteorologists both at home and abroad with respect to the research for identifying convection clouds and lamellar clouds. Early lamellar cloud precipitation identification is mostly carried out through a zero-degree lamellar light band, but the method has certain limitation, namely lamellar cloud can be accurately identified only after the lamellar cloud is developed to a relatively mature stage. Many other methods have been developed based on rain gauge data, which set a reflectivity factor threshold, and determine a convective cloud in the area with precipitation echo as long as the echo reaches the set threshold, and the rest are lamellar clouds, which is called Background-enhanced Technique (BET), and the BET Technique can be generally used to determine the center of the convective cloud precipitation, but has a disadvantage that the boundary range of the convective cloud precipitation cannot be determined.
Disclosure of Invention
Objects of the invention
The invention aims to provide a method, a device and electronic equipment for identifying cloud clusters by using a DBSCAN clustering algorithm, which can be used for classifying and identifying the cloud clusters, so that the cloud cluster classification result is more visual, and the edges, the sizes and the types of the cloud clusters are obvious.
(II) technical scheme
In order to solve the above problem, in one aspect, the present invention provides a method for identifying a cloud cluster by using a DBSCAN clustering algorithm, including the following steps: acquiring reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle; setting different echo threshold values according to the difference between the reflectivity factors of different clouds; establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value; and classifying and identifying the cloud cluster according to the DBSCAN algorithm model.
Optionally, the obtaining reflectivity factor data for the first single layer elevation angle and the first multi-layer elevation angle includes: outputting reflectivity factor data of a second single-layer elevation angle for convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster; processing the reflectivity factor data of the second single-layer elevation to obtain the reflectivity factor data of the first single-layer elevation; outputting reflectivity factor data of a second multilayer elevation angle for the vigorously developed convection cloud and multilayer laminar cloud in the cloud cluster; processing the reflectivity factor data of the second multilayer elevation to obtain the reflectivity factor data of the first multilayer elevation; wherein, the cloud with not exuberant development is a cloud cluster which is less than 6km away from the ground, and the cloud with exuberant development is a cloud cluster which is 6-8 km away from the ground.
Optionally, the reflectivity factor data of the second single-layer elevation angle is all reflectivity factor values within a first preset value; the reflectivity factor data for the second multi-layer elevation is all reflectivity factor values within a second preset value.
Optionally, the echo threshold is set to be Z more than or equal to 45dBZ, Z more than or equal to 37dBZ and less than 45dBZ, Z more than or equal to 30dBZ and less than 37dBZ, Z more than or equal to 25dBZ and less than 30dBZ, and respectively corresponds to a strong convection cloud, a weak convection cloud, a strong laminar cloud and a weak laminar cloud; where Z is the echo threshold.
Optionally, establishing a DBSCAN algorithm model for cloud cluster identification according to the reflectivity factor data and the echo threshold, including: according to the reflectivity factor data of the first single-layer elevation angle and the echo threshold value, carrying out DBSCAN algorithm modeling on the convection cloud and the single-layer layered cloud which are not vigorously developed; and according to the reflectivity factor data of the first multilayer elevation angle and the echo threshold value, carrying out DBSCAN algorithm modeling on the vigorously developed convection cloud and the multilayer laminar cloud.
Optionally, the performing DBSCAN algorithm modeling on convection clouds and single-layer layered clouds which are not actively developed according to the reflectivity factor of the first single-layer elevation angle and the echo threshold includes: and the DBSCAN creates a cluster taking one echo point as a core object by checking the number of the echo points contained in a third preset value neighborhood of each echo point in the single-layer elevation reflectivity factor data set, wherein the number of the echo points is more than a fourth preset value, and the echo points exceed the set echo intensity threshold.
Optionally, the modeling DBSCAN algorithm for the strongly developed convection cloud and the multi-layer laminar cloud according to the reflectivity factor of the first multi-layer elevation angle and the echo threshold includes: dividing the multilayer elevation into a plurality of distance sections corresponding to the multilayer elevation according to the number of distance bins; modeling DBSCAN algorithm according to the reflectivity factor data of the first multi-layer elevation, the plurality of distance segments and the echo threshold value.
Optionally, modeling DBSCAN algorithm according to the reflectivity factor data of the first multi-layer elevation angle, the plurality of distance segments and the echo threshold, including: DBSCAN simultaneously checks a fifth preset value neighborhood of each echo point in a corresponding position in a multilayer elevation to search clusters, and if the number of the echo points contained in the fifth preset value neighborhood of a position G is more than the number of MinPts and the number of the echo points exceeds a set echo intensity threshold, a cluster taking G as a core object is created; the position G is the position of the same distance library for each layer of elevation.
On the other hand, the invention also provides a device for identifying cloud clusters by using the DBSCAN clustering algorithm, which comprises the following steps: the acquisition module acquires reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle; the echo threshold setting module is used for setting different echo thresholds according to the difference between different cloud cluster reflectivity factors; the model module is used for establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value; and the identification module is used for classifying and identifying the cloud cluster according to the DBSCAN algorithm model.
Optionally, the obtaining module includes a single-layer elevation processing unit and a multi-layer elevation processing unit; the single-layer elevation angle processing unit outputs the reflectivity factor data of a second single-layer elevation angle to convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster, and processes the reflectivity factor data of the second single-layer elevation angle to obtain the reflectivity factor data of the first single-layer elevation angle; the multilayer elevation angle processing unit outputs the reflectivity factor data of a second multilayer elevation angle to convection clouds and multilayer layered clouds which are vigorously developed in cloud clusters, and processes the reflectivity factor data of the second multilayer elevation angle to obtain the reflectivity factor data of the first multilayer elevation angle.
Optionally, the model module comprises a single layer elevation model unit and a multi-layer elevation model unit; the single-layer elevation model unit is used for carrying out DBSCAN algorithm modeling on the convection cloud and the single-layer laminar cloud which are not developed vigorously according to the reflectivity factor data of the first single-layer elevation and the echo threshold; and the multilayer elevation model unit performs DBSCAN algorithm modeling on the vigorously developed convection cloud and the multilayer layered cloud according to the reflectivity factor data of the first multilayer elevation and the echo threshold.
In another aspect, the present invention also provides a storage medium having a computer program stored thereon, which when executed by a processor implements the steps of the above method.
In another aspect, the present invention further provides an electronic device, which includes a memory, a display, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method when executing the program.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
compared with the existing cloud cluster identification algorithm, the cloud cluster identification method is scientific and effective in design, utilizes radar data combined with a DBSCAN clustering algorithm, can effectively classify and identify convection clouds and layered clouds, further improves precipitation estimation capacity in meteorological services in China, is more suitable for cloud clusters detected by weather radars in China, has universal data sources, and can meet the requirements of popularization and use of meteorological service departments in China.
The invention also comprehensively considers the difference of the reflectivity factors of different cloud clusters (strong convection cloud, weak convection cloud, strong laminar cloud and weak laminar cloud), extracts the relevant threshold value, designs the method capable of realizing classification and identification of the convection cloud and the laminar cloud, and provides an objective tool for focusing on the effective identification of different cloud clusters.
In the clustering process of the same cloud cluster, the clustering center and the number of clusters do not need to be specified, clusters with any shapes can be found in the space with invalid echoes, convection clouds and layered clouds are preferentially identified from the cloud cluster according to the density, and the method has a good application prospect in the application of analyzing and predicting precipitation, thunderstorms and the like.
The invention uses the radar reflectivity factor to combine with the DBSCAN clustering algorithm in the machine learning algorithm to classify and identify the cloud cluster, the cloud cluster classification result is more visual, and the edge, the size and the type of the cloud cluster are obvious.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic diagram of the DBSCAN clustering algorithm for identifying strong convection cloud on a layer of elevation angle.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
In the description of the present invention, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
FIG. 2 is a schematic diagram of the DBSCAN clustering algorithm identifying strong convection clouds in an elevation angle, Z < 45dBZ in FIG. 2 (1): strong convection cloud core point, C: strong convection cloud boundary, N: a noise point.
As shown in fig. 1 to fig. 2, in an embodiment of the present invention, the present invention provides a method for identifying a cloud cluster by using a DBSCAN clustering algorithm, including the following steps:
step S10: obtaining reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle;
step S20: setting different echo threshold values according to the difference between the reflectivity factors of different clouds;
step S30: establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value;
step S40: and classifying and identifying the cloud cluster according to the DBSCAN algorithm model.
Compared with the existing cloud cluster identification algorithm, the cloud cluster identification method is scientific and effective in design, utilizes radar data combined with a DBSCAN clustering algorithm, can effectively classify and identify convection clouds and layered clouds, further improves precipitation estimation capacity in meteorological services in China, is more suitable for cloud clusters detected by weather radars in China, has universal data sources, and can meet the requirements of popularization and use of meteorological service departments in China. Wherein the radar material refers to weather radar reflectivity factor data.
The method is suitable for various types of weather radar data such as CINRAD/SA, CINRAD/SB, CINRAD/CC and the like in an observation system of a Chinese meteorological system, realizes the identification of convection cloud and laminar cloud by using the method, belongs to the field of atmospheric science research, and is used for the identification of different cloud clusters.
In the embodiment of the invention, in order to ensure that convection clouds with different heights and layered clouds with different layers are identified, two kinds of processing are carried out on the weather radar reflectivity factor. Namely, acquiring the reflectivity factor data of the first single-layer elevation angle and the first multi-layer elevation angle, comprising: outputting reflectivity factor data of a second single-layer elevation angle for convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster; processing the reflectivity factor data of the second single-layer elevation to obtain the reflectivity factor data of the first single-layer elevation; outputting reflectivity factor data of a second multilayer elevation angle for the convection cloud and the multilayer layered cloud which are vigorously developed in the cloud cluster; and processing the reflectivity factor data of the second multilayer elevation to obtain the reflectivity factor data of the first multilayer elevation. In this embodiment, the reflectivity factor data of the second single-layer elevation is all reflectivity factor values within the first preset value; the reflectivity factor data for the second multi-layer elevation is the total reflectivity factor value within a second predetermined value.
Wherein the un-exuberant convection clouds are clouds with a distance of less than 6km from the ground, and the exuberant convection clouds are clouds with a distance of 6-8 km from the ground; in this embodiment, the cloud cluster with a distance of 5km from the ground is not the vigorous convection cloud, and the cloud cluster with a distance of 7km from the ground is the vigorous convection cloud; in other embodiments, the non-vigorous-development convection cloud may be a cloud cluster 3km or 4km away from the ground, and the vigorous-development convection cloud may be a cloud cluster 6km or 8km away from the ground. In the present embodiment, the first preset value and the second preset value are both set to 230km, in other embodiments, the first preset value and the second preset value may also be set to 190km, 200km, 210km, 220km, and of course, the first preset value and the second preset value may be set according to the measurement requirement.
In the embodiment of the invention, the echo threshold is set to be Z more than or equal to 45dBZ, Z more than or equal to 37dBZ and less than 45dBZ, Z more than or equal to 30dBZ and less than 37dBZ, Z more than or equal to 25dBZ and less than 30dBZ, and respectively corresponds to strong convection cloud, weak convection cloud, strong laminar cloud and weak laminar cloud; where Z is the echo threshold. In the embodiment, the echo threshold is set according to the echo intensity difference of the convection cloud and the laminar cloud, so as to realize better modeling. In an embodiment of the present invention, modeling the cloud by the DBSCAN algorithm according to the reflectivity factor data of the elevation angle and the echo threshold includes: according to the reflectivity factor data of the single-layer elevation and the echo threshold, modeling the convection cloud and the single-layer layered cloud which are not developed vigorously by a DBSCAN algorithm; and according to the reflectivity factor data of the multilayer elevation and the echo threshold, carrying out DBSCAN algorithm modeling on the vigorously developed convection cloud and the multilayer laminar cloud.
In the present invention, the reflectance factor data for the first single layer elevation is exemplified by the elevation reflectance factor data of 0.50 ° as an embodiment of the present invention; the reflectance factor data for the first multi-layer elevation angle is illustrated with the reflectance factor data for the elevation angles of 0.50 °, 1.45 °, 2.40 ° as an embodiment of the present invention.
In the embodiment of the invention, DBSCAN algorithm modeling is carried out on convection cloud and single-layer layered cloud which are not vigorously developed according to the reflectivity factor of the first single-layer elevation angle and the echo threshold, and the method comprises the following steps: and the DBSCAN creates a cluster taking one echo point as a core object by checking that the number of the echo points contained in the third preset value neighborhood of each echo point in the single-layer elevation reflectivity factor data set is more than the number of the echo points in the fourth preset value, and the echo points exceed the set echo intensity threshold. In the present embodiment, the third preset value is set to 1km, and in other embodiments, the third preset value may also be set to 2km, 3km, 4km, and the like. In this embodiment, the fourth preset value is set to be 5, and in other embodiments, the fourth preset value may also be set to be 3, 4, 6, 7, 8, 9, or the like.
According to the invention, DBSCAN algorithm modeling is carried out on convection cloud and single-layer layered cloud which are not developed vigorously according to the reflectivity factor and the echo threshold of the first single-layer elevation, and the following steps are exemplified:
(1) Randomly selecting an echo point A (as shown in fig. 2) from the processed elevation angle reflectivity factor data of 0.50 degrees of the DBSCAN, searching at least 5 echo points which are more than or equal to 45dBZ in a neighborhood 1km of the echo point A, marking the echo point A as a strong convection cloud core point (if at least 5 echo points which are more than or equal to 37dBZ and less than 45dBZ are in the neighborhood 1km of the echo point A, marking the echo point A as a weak convection cloud core point, marking the echo point A as a strong laminar cloud core point if at least 5 echo points which are more than or equal to 30dBZ and less than 37dBZ are in the neighborhood 1km of the echo point A. If at least 5 echo points which are more than or equal to 25dBZ and less than 30dBZ are in the neighborhood 1km of the echo point A, marking the echo point A as a weak laminar cloud core point);
(2) Judging each echo point in the neighborhood of the echo point A (supposing that the echo point B is judged), if at least 5 echo points which are more than or equal to 45dBZ exist in the neighborhood 1km which meets the echo point B, marking the echo point as a strong convection cloud core point (if at least 5 echo points which are more than or equal to 37dBZ and less than 45dBZ exist in the neighborhood 1km which meets the echo point B, marking the echo point as a weak convection cloud core point), if at least 5 echo points which are more than or equal to 30dBZ and less than 37dBZ exist in the neighborhood 1km which meets the echo point B, marking the echo point as a strong laminar cloud point, and if at least 5 echo points which are more than or equal to 25dBZ and less than 30dBZ exist in the neighborhood 1km which meets the echo point B, marking the echo point as a weak laminar cloud core point);
(3) Continuing to judge echo points in the neighborhood of the echo point A (assuming to judge echo point C), if at least 5 echo points which are more than or equal to 45dBZ are not satisfied in the neighborhood of 1km of the echo point C, marking the echo point C as a strong convection cloud boundary point (if at least 5 echo points which are more than or equal to 37dBZ and less than 45dBZ are not satisfied in the neighborhood of 1km of the echo point C, marking the echo point C as a weak convection cloud boundary point, if at least 5 echo points which are more than or equal to 30dBZ and less than 37dBZ are not satisfied in the neighborhood of 1km of the echo point C, marking the echo point C as a strong laminar cloud boundary point, and if at least 5 echo points which are more than or equal to 25dBZ and less than 30dBZ are not satisfied in the neighborhood of 1km of the echo point C, marking the echo point C as a weak convection cloud boundary point);
(4) If the echo point N exists, and neither the core point nor the boundary point is satisfied, the echo point P is marked as a noise point, and when no new point is marked, the process ends.
In the embodiment of the present invention, according to the reflectivity factor of the first multi-layer elevation and the echo threshold, the DBSCAN algorithm modeling is performed on the convection cloud and the multi-layer laminar cloud which are vigorously developed, and the method includes: dividing the multilayer elevation into a plurality of distance sections corresponding to the multilayer elevation according to the number of distance bins; modeling DBSCAN algorithm according to the reflectivity factor data of the first multi-layer elevation, the plurality of distance segments and the echo threshold value. In one embodiment of the invention, the elevation angles of three layers of 0.50 degrees, 1.45 degrees and 2.40 degrees are divided into three distance sections of 0-120km,120-180km and 180-230km according to the number of distance bins.
In an embodiment of the invention, modeling DBSCAN algorithm according to reflectivity factor data of a first multi-layer elevation, a plurality of distance segments and an echo threshold comprises: the DBSCAN simultaneously checks a fourth preset value neighborhood of each echo point in a corresponding position in the multi-layer elevation to search a cluster, and if the number of the echo points contained in the fifth preset value neighborhood of the position G is more than the number of MinPts and the echo points exceed a set echo intensity threshold, a cluster taking G as a core object is created; wherein the position G is the position of the same distance bin for each layer of elevation. In the present embodiment, the fifth preset value is set to 1km, and in other embodiments, the fifth preset value may also be set to 2km, 3km, 4km, and the like.
The invention carries out DBSCAN algorithm modeling on the reflectivity factor data, a plurality of distance segments and an echo threshold value according to a first multilayer elevation angle, and exemplifies that:
(1) Dividing the three-layer elevation angle into three distance sections of 0-120km,120-180km and 180-230km according to the number of distance bins, and respectively carrying out DBSCAN algorithm modeling by utilizing the reflectivity factor data of the three-layer elevation angle of 0.50 degrees, 1.45 degrees and 2.40 degrees, the three distance sections and the echo threshold value.
(2) The DBSCAN randomly selects an echo point A (three layers correspond to the same position) from reflectivity factor data of three-layer elevation angles, searches for at least 10 echo points which are more than or equal to 45dBZ in the echo point A neighborhood 1km of the corresponding position in the three-layer elevation angle in a 0-120km distance section, marks the echo point A as a strong convection cloud core point (if at least 10 echo points which are more than or equal to 37dBZ and less than 45dBZ are in the echo point A neighborhood 1km, marks the echo point A as a weak convection cloud core point, if at least 10 echo points which are more than or equal to 30dBZ and less than 37dBZ are in the echo point A neighborhood 1km, marks the echo point A as a strong convection cloud core point, and marks the echo point A as a weak convection cloud core point if at least 10 echo points which are more than or equal to 25dBZ and less than 30dBZ are in the echo point A neighborhood 1 km).
The distance range of 120-180km finds that one in the neighborhood of 1km of echo point has at least 8 echo points, and the distance range of 180-230km finds that one in the neighborhood of 1km has at least 5 echo points.
(3) Then, judging each echo point in the neighborhood of the echo point A at the corresponding position in the three-layer elevation (supposing that the echo point B is judged), in a 0-120km distance section, if at least 10 echo points which are more than or equal to 45dBZ exist in the neighborhood 1km of the echo point B, marking the echo point as a strong convection cloud core point (if at least 10 echo points which are more than or equal to 37dBZ and less than 45dBZ exist in the neighborhood 1km of the echo point B, marking the echo point as a weak convection cloud core point, if at least 10 echo points which are more than or equal to 30dBZ and less than 37dBZ exist in the neighborhood 1km of the echo point B, marking the echo point as a strong laminar cloud core point, and if at least 10 echo points which are more than or equal to 25dBZ and less than 30dBZ exist in the neighborhood 1km of the neighborhood of the echo point B, marking the echo point as a weak laminar cloud core point); at least 8 echo points are found in a 1km neighborhood in the 120-180km distance section, and at least 5 echo points are found in a 1km neighborhood in the 180-230km distance section.
(4) And continuously judging echo points in the neighborhood of the echo point A corresponding to the position in the three-layer elevation (supposing that the echo point C is judged), in a 0-120km distance range, if at least 10 echo points which are more than or equal to 45dBZ are not satisfied in the neighborhood 1km of the echo point C, marking the echo point C as a strong convection cloud boundary point (if at least 10 echo points which are more than or equal to 37dBZ and less than 45dBZ are not satisfied in the neighborhood 1km of the echo point C, marking the echo point C as a weak convection cloud boundary point; if at least 10 echo points which are more than or equal to 30dBZ and less than 37dBZ are not satisfied in the neighborhood 1km of the echo point C, marking the echo point C as a strong laminar cloud boundary point; and if at least 10 echo points which are more than or equal to 25dBZ and less than 30dBZ are not satisfied in the neighborhood 1km of the echo point C, marking the echo point C as a weak convection cloud boundary point).
The 120-180km distance section searches for at least 8 echo points in a 1km neighborhood, and the 180-230km distance section searches for at least 5 echo points in a 1km neighborhood.
(5) If the echo point N exists, and neither the core point nor the boundary point is satisfied, the echo point P is marked as a noise point, and when no new point is marked, the process is ended.
In the embodiment of the invention, the cloud cluster is classified and identified according to the model, specifically, the cloud cluster can be identified into four types of strong convection cloud, weak convection cloud, strong laminar cloud and weak laminar cloud according to the established model, and the model can identify the core region and the edge region of each cloud type.
In the embodiment of the invention, the invention also provides a device for identifying cloud clusters by using the DBSCAN clustering algorithm, which comprises an acquisition module, a data acquisition module and a data acquisition module, wherein the acquisition module is used for acquiring the reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle; the echo threshold value setting module is used for setting different echo threshold values according to the difference between different cloud cluster reflectivity factors; the model module is used for establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value; and the identification module is used for classifying and identifying the cloud cluster according to the DBSCAN algorithm model.
In an embodiment of the invention, the acquisition module includes a single layer elevation processing unit and a multi-layer elevation processing unit. The single-layer elevation unit outputs the reflectivity factor data of a second single-layer elevation angle to convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster, and processes the reflectivity factor data of the second single-layer elevation angle to obtain the reflectivity factor data of the first single-layer elevation angle. The multi-layer elevation angle unit outputs the reflectivity factor data of a second multi-layer elevation angle to the convection cloud and the multi-layer layered cloud which are developed vigorously in the cloud cluster, and processes the reflectivity factor data of the second multi-layer elevation angle to obtain the reflectivity factor data of the first multi-layer elevation angle.
In the embodiment of the invention, the echo threshold of the echo threshold setting module is set to be Z more than or equal to 45dBZ, Z more than or equal to 37dBZ and less than 45dBZ, Z more than or equal to 30dBZ and less than 37dBZ, Z more than or equal to 25dBZ and less than 30dBZ, and the echo threshold is respectively corresponding to a strong convection cloud, a weak convection cloud, a strong laminar cloud and a weak laminar cloud; where Z is the echo threshold.
In the embodiment of the invention, the single-layer elevation model unit carries out DBSCAN algorithm modeling on convection cloud and single-layer layered cloud which are not vigorously developed according to the reflectivity factor data of the first single-layer elevation and the echo threshold.
In an embodiment of the invention, the modeling process of the single layer elevation model element: and the DBSCAN creates a cluster taking one echo point as a core object by checking the number of the echo points contained in a third preset value neighborhood of each echo point in the single-layer elevation reflectivity factor data set, wherein the number of the echo points is more than a fourth preset value, and the echo points exceed the set echo intensity threshold.
In an embodiment of the invention, the multi-layer elevation model unit performs DBSCAN algorithm modeling based on the reflectivity factor data of the first multi-layer elevation, the plurality of distance segments, and the echo threshold. The specific multilayer elevation model unit is that firstly, multilayer elevation is divided into a plurality of distance sections corresponding to the multilayer elevation according to the number of distance libraries; and modeling the DBSCAN algorithm according to the reflectivity factor data of the first multilayer elevation angle, the plurality of distance segments and the echo threshold value.
Modeling process of the multi-layer elevation model unit: and simultaneously checking a fourth preset value neighborhood of each echo point in a corresponding position in the multi-layer elevation angle by the DBSCAN to search a cluster, and if the number of the echo points contained in the fifth preset value neighborhood of the position G is more than the number of MinPts and the echo points exceed a set echo intensity threshold, creating a cluster taking G as a core object.
In an embodiment of the present invention, the present invention further provides a storage medium, on which a computer program is stored, and the program realizes the steps of the above method when executed by a processor.
In an embodiment of the present invention, the present invention further provides an electronic device, which includes a memory, a display, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the above method when executing the program.
According to the method, the difference of the reflectivity factors of different cloud clusters (strong convection cloud, weak convection cloud, strong laminar cloud and weak laminar cloud) is comprehensively considered, the relevant threshold is extracted, the method capable of realizing classification and identification of the convection cloud and the laminar cloud is designed, and an objective tool is provided for focusing on effective identification of the different cloud clusters.
In the clustering process of the same cloud cluster, the clustering center and the number of clusters do not need to be specified, clusters with any shapes can be found in the space with invalid echoes, convection clouds and layered clouds are preferentially identified from the cloud cluster according to the density, and the method has a good application prospect in the application of analyzing and predicting precipitation, thunderstorms and the like.
The invention uses the radar reflectivity factor to combine with the DBSCAN clustering algorithm in the machine learning algorithm to classify and identify the cloud cluster, the cloud cluster classification result is more visual, and the edge, the size and the type of the cloud cluster are obvious.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A method for identifying cloud clusters by using a DBSCAN clustering algorithm is characterized by comprising the following steps:
obtaining reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle;
setting different echo threshold values according to the difference between the reflectivity factors of different clouds;
establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value;
classifying and identifying the cloud cluster according to the DBSCAN algorithm model;
the obtaining reflectivity factor data for a first single layer elevation angle and a first multi-layer elevation angle includes:
outputting reflectivity factor data of a second single-layer elevation angle for convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster;
processing the reflectivity factor data of the second single-layer elevation to obtain the reflectivity factor data of the first single-layer elevation;
outputting reflectivity factor data of a second multilayer elevation angle for the vigorously developed convection cloud and multilayer laminar cloud in the cloud cluster;
processing the reflectivity factor data of the second multilayer elevation to obtain the reflectivity factor data of the first multilayer elevation;
establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold, wherein the DBSCAN algorithm model comprises the following steps:
according to the reflectivity factor data of the first single-layer elevation angle and the echo threshold value, carrying out DBSCAN algorithm modeling on the convection cloud and the single-layer layered cloud which are not vigorously developed;
and according to the reflectivity factor data of the first multilayer elevation angle and the echo threshold value, carrying out DBSCAN algorithm modeling on the vigorously developed convection cloud and the multilayer laminar cloud.
2. The method of claim 1,
the cloud cluster with less than 6km away from the ground is not developed vigorously, and the cloud cluster with 6-8 km away from the ground is developed vigorously.
3. The method of claim 2,
the reflectivity factor data of the second single-layer elevation angle is all reflectivity factor values within a first preset value;
the reflectivity factor data for the second multi-layer elevation is all reflectivity factor values within a second preset value.
4. The method of claim 1,
the echo threshold values are set to be Z more than or equal to 45dBZ, Z more than or equal to 37dBZ and less than 45dBZ, Z more than or equal to 30dBZ and less than 37dBZ, and Z more than or equal to 25dBZ and less than 30dBZ, and respectively correspond to strong convection clouds, weak convection clouds, strong laminar clouds and weak laminar clouds; where Z is the echo threshold.
5. The method of claim 1,
according to the reflectivity factor of the first single-layer elevation angle and the echo threshold value, DBSCAN algorithm modeling is carried out on convection cloud and single-layer layered cloud which are not developed vigorously, and the method comprises the following steps:
and the DBSCAN creates a cluster taking one echo point as a core object by checking the number of the echo points contained in the third preset value neighborhood of each echo point in the single-layer elevation reflectivity factor data set, wherein the number of the echo points is more than the fourth preset value, and the echo points exceed the set echo threshold value.
6. The method of claim 1,
according to the reflectivity factor of the first multilayer elevation angle and the echo threshold value, modeling of a DBSCAN algorithm is carried out on a vigorously developed convection cloud and a multilayer laminar cloud, and the modeling comprises the following steps:
dividing the multilayer elevation into a plurality of distance sections corresponding to the multilayer elevation according to the number of distance bins;
and carrying out DBSCAN algorithm modeling according to the reflectivity factor data of the first multilayer elevation angle, the plurality of distance segments and the echo threshold value.
7. The method of claim 6,
modeling DBSCAN algorithm according to the reflectivity factor data of the first multi-layer elevation angle, the plurality of distance segments and the echo threshold value, comprising:
the DBSCAN simultaneously checks a fifth preset value neighborhood of each echo point in a corresponding position in the multi-layer elevation to search a cluster, and if the number of the echo points contained in the fifth preset value neighborhood of the position G is more than the MinPts number and the echo points exceed a set echo threshold, a cluster taking the G as a core object is created;
the position G is the position of the same distance library for each layer of elevation.
8. A cloud cluster recognition device using a DBSCAN clustering algorithm, comprising:
the acquisition module acquires reflectivity factor data of a first single-layer elevation angle and a first multi-layer elevation angle;
the echo threshold value setting module is used for setting different echo threshold values according to the difference between different cloud cluster reflectivity factors;
the model module is used for establishing a DBSCAN algorithm model for identifying the cloud cluster according to the reflectivity factor data and the echo threshold value;
the identification module is used for classifying and identifying the cloud cluster according to the DBSCAN algorithm model;
the acquisition module comprises a single-layer elevation processing unit and a multi-layer elevation processing unit;
the single-layer elevation angle processing unit outputs the reflectivity factor data of a second single-layer elevation angle to convection clouds and single-layer layered clouds which are not developed vigorously in the cloud cluster, and processes the reflectivity factor data of the second single-layer elevation angle to obtain the reflectivity factor data of the first single-layer elevation angle;
the multilayer elevation angle processing unit outputs the reflectivity factor data of a second multilayer elevation angle to the convection cloud and the multilayer layered cloud which are developed vigorously in the cloud cluster, and processes the reflectivity factor data of the second multilayer elevation angle to obtain the reflectivity factor data of the first multilayer elevation angle;
the model module comprises a single-layer elevation model unit and a multi-layer elevation model unit;
the single-layer elevation model unit is used for carrying out DBSCAN algorithm modeling on the convection cloud and the single-layer layered cloud which are not vigorously developed according to the reflectivity factor data of the first single-layer elevation and the echo threshold;
and the multilayer elevation model unit performs DBSCAN algorithm modeling on the vigorously developed convection cloud and the multilayer layered cloud according to the reflectivity factor data of the first multilayer elevation and the echo threshold.
9. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
10. An electronic device comprising a memory, a display, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 7 when the program is executed by the processor.
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