CN112924975B - Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar - Google Patents

Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar Download PDF

Info

Publication number
CN112924975B
CN112924975B CN202110248001.8A CN202110248001A CN112924975B CN 112924975 B CN112924975 B CN 112924975B CN 202110248001 A CN202110248001 A CN 202110248001A CN 112924975 B CN112924975 B CN 112924975B
Authority
CN
China
Prior art keywords
radar
area
strong convection
information
height
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110248001.8A
Other languages
Chinese (zh)
Other versions
CN112924975A (en
Inventor
尹春光
马雷鸣
戴建华
岳彩军
张军平
林红
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Central Observatory
Shanghai Ecometeorology And Satellite Remote Sensing Center
Shanghai Meteorological Information And Technical Support Center
Original Assignee
Shanghai Central Observatory
Shanghai Ecometeorology And Satellite Remote Sensing Center
Shanghai Meteorological Information And Technical Support Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Central Observatory, Shanghai Ecometeorology And Satellite Remote Sensing Center, Shanghai Meteorological Information And Technical Support Center filed Critical Shanghai Central Observatory
Priority to CN202110248001.8A priority Critical patent/CN112924975B/en
Publication of CN112924975A publication Critical patent/CN112924975A/en
Application granted granted Critical
Publication of CN112924975B publication Critical patent/CN112924975B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
    • G01S13/958—Theoretical aspects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/418—Theoretical aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention discloses a self-adaptive observation method and a self-adaptive observation system for AOI (automated optical inspection) applicable to a networking weather radar, and relates to the technical field of networking radars. The method comprises the following steps: constructing a three-dimensional grid point field of a weather radar net, and analyzing coverage information on an equal-height surface and between equal-height layers to obtain radar coverage information of each grid point; determining the final reflectivity value of each grid point according to the radar coverage information of each grid point; acquiring layer combination reflectivity information of each layer of equal-height surface on a set spatial height, and carrying out identification on a strong convection center and a strong convection region based on fusion information of the multilayer layer combination reflectivity information; and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation or triggering a radar body scanning mode based on strong convection center and area identification by combining radar data to be added. The invention enables the volume scanning mode of the newly added radar to realize better matching aiming at the strong convection center and the area, and fully utilizes the detection capability of the newly added radar.

Description

Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar
Technical Field
The invention relates to the technical field of networking radars, in particular to an AOI (automatic optical inspection) adaptive observation method and system applicable to a networking weather radar.
Background
The networking weather radar can increase the measurement dimension and confidence, improve the fault tolerance and robustness of the system, and is widely applied to the modern meteorological comprehensive observation system. In the weather radar network, how to coordinate each radar in the weather radar network to observe according to the information Of an important observation Area (AOI, also called Area Of Interest, also called weighing point Area) is one Of the main research directions in the field Of radar detection. The important observation Area (AOI) may be, for example, a strong convection area when weather occurs, a set important service area, a manually selected key area, or the like.
When three-dimensional detection and cooperative detection of an important observation Area (AOI) are carried out, because the volume scanning mode of the networking weather radar influences the coverage area and the falling area of a three-dimensional space scale and influences the acquisition of space atmosphere scanning information, how to set the volume scanning mode of the networking weather radar according to the important observation Area (AOI) so as to enable a scanning strategy to be matched with the important observation Area (AOI) is a problem that cooperative detection cannot be avoided.
The radar volume scanning mode is a mode in which the radar operates according to configuration parameters such as a predetermined pulse width, a predetermined pulse repetition frequency, a predetermined number of elevation layers, and a predetermined antenna rotation rate. In a traditional radar scanning mode, an operator often sets parameter information of a radar body scanning mode through a display console, and then the display console sends a control command to change an antenna scanning mode. The prior art has the following defects: firstly, the operation level of a radar manipulator is highly dependent; secondly, when the system is manually set, the scanning center of each sector scanning is difficult to ensure to be the optimal alignment of an observation target, so that the observation precision is reduced; moreover, because the manually set fan scanning range is often far larger than the fan scanning area required by the target to be observed, the intercepted effective data rate is lower under the condition of fixed fan scanning speed, and great pressure is caused to front-end sorting data processing and computing resource maintenance.
On the other hand, the accurate analysis of the coverage capability of the radar is an important basis for applying the new generation of weather radar observation data. With the proposal of the concept of urban fine management, the research of the fine landing points of the multi-radar coverage areas on the three-dimensional lattice point field of the large city and the surrounding area is increasingly important. The method provides a model for researching the real construction of a three-dimensional reflectivity field, urban fine prediction on a surface scale and analysis of a vertical reflectivity profile under non-interpolation on a point scale, and has a guiding function on how to more scientifically deploy atmospheric vertical observation equipment and how to better apply the existing observation data.
However, in the prior art, when radar coverage information between equal-height layers is researched, radar coverage analysis between equal-height layers is often performed by interpolating to a specific height layer according to a radar beam width. In the actual forecasting service, how to realize more precise radar coverage analysis and construct a more precise and balanced stereo detection network from the application angle by paying more attention to the complete three-dimensional echo structure is also a technical problem which needs to be solved at present.
Disclosure of Invention
The invention aims to: the defects of the prior art are overcome, and the self-adaptive observation method and the system for the AOI suitable for the networking weather radar are provided. According to the self-adaptive observation method for the networking weather radar suitable for the AOI, provided by the invention, after the radar coverage information of the three-dimensional lattice field of the weather radar network is obtained by analyzing the coverage information on the equal altitude surface and the coverage information between the equal altitude layers, the identification of a strong convection center and a strong convection area is carried out based on the fusion information of the multilayer combined reflectivity information, and then a radar body scanning mode based on the ground potential and machine learning joint extrapolation is triggered or a radar body scanning mode based on the strong convection center and area identification is triggered according to the detection range of the strong convection area and the radar to be added, so that the body scanning mode of the newly added radar can realize better matching aiming at the strong convection center and the area (namely a key area), and the detection capability of the newly added radar is fully utilized. Furthermore, the scanning mode of the radar body to be added into the radar can be adjusted according to the information of the selected area set by a user or a system.
In order to achieve the above object, the present invention provides the following technical solutions:
an adaptive observation method for AOI (automatic optical inspection) applicable to a networking weather radar comprises the following steps:
constructing a weather radar mesh three-dimensional lattice point field of a networking weather radar, and analyzing coverage information on an equal-height surface and coverage information between equal-height layers to obtain radar coverage information of each lattice point in the weather radar mesh three-dimensional lattice point field;
acquiring the reflectivity value of each networking weather radar on each grid point according to the radar coverage information of each grid point, and determining the final reflectivity value of each grid point according to the reflectivity value;
acquiring layer combination reflectivity information of each layer of equal-height surface on a set spatial height, and carrying out identification on a strong convection center and a strong convection region based on fusion information of the multilayer layer combination reflectivity information;
collecting data of radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and area identification.
Further, adjusting the radar body scanning mode of the radar to be added according to the selected area information set by the user or the system, and the steps are as follows:
acquiring selection area information set by a user or a system; acquiring an extrapolated strong convection center and an extrapolated area in a radar body scanning mode based on ground potential and machine learning joint extrapolation, acquiring the identified strong convection center and the identified area in a radar body scanning mode based on strong convection center and area identification, and acquiring strong convection area boundary information corresponding to the extrapolated or identified strong convection center and the identified area;
comparing the boundary information of the strong convection region with the information of the selection region, and configuring a radar volume scanning mode based on a region with a smaller area in the boundary information of the strong convection region and the information of the selection region when the boundary of the strong convection region is matched with the selection region; when the area of the selected area is larger than that of the strong convection area, configuring a radar body scanning mode based on the strong convection area; and when the area of the selected area is smaller than that of the strong convection area, configuring a radar body scanning mode based on the selected area.
Further, collecting selection area information set by a user through a key area setting module;
the region of interest setting module is configured to: and outputting position information of areas possibly causing urban waterlogging, areas where important activities are located and/or preset important service areas under strong convection coverage to a user, collecting selection operations of the user on the areas, and taking one or more areas selected by the user as selection areas set by the user.
Further, the radar to be added is an X-band radar.
Further, when the radar body scanning mode based on the strong convection center and region identification is adopted, the planar scanning PPI form is adopted by combining the identified strong convection center and region range, or the combined scanning form of the vertical scanning RHI and the planar scanning PPI is adopted.
Further, when a radar volume scanning mode based on ground potential and machine learning joint extrapolation is adopted, the method comprises the following steps:
performing strong convection extrapolation prediction through a machine learning and time sequence deep learning model, and obtaining the position relation between the strong convection center and the region and the X-band radar after a set time period by combining a Q vector analysis method based on ground high-altitude data;
based on the quadrants of the X-band radar full-scanning area, acquiring quadrant information of the strong convection center and the area falling into the quadrants, wherein each quadrant corresponds to an angle range;
and configuring a fan scanning range of the X-band radar volume scanning mode according to the angle range corresponding to the quadrant, wherein the fan scanning range comprises a starting angle and an ending angle.
Further, when identifying the center and the area of strong convection based on the fusion information of the multilayer combined reflectivity information, a core area and a boundary area of the strong convection area are determined by adopting double-threshold screening, wherein the double thresholds comprise a high threshold and a low threshold, the high threshold can be used for determining the core area of the strong convection area, the low threshold can be used for determining the boundary area of the strong convection area, and the steps are as follows:
acquiring a high threshold, searching a core region after increasing according to a preset step length by taking the high threshold as a base number, calculating the area of the core region, and performing centroid identification on the core region with the area larger than or equal to a preset area value;
making low threshold contour boundary identification in a range which is greater than or equal to a low threshold and less than or equal to a high threshold around the centroid to determine a boundary area of the centroid, wherein an influence area of the centroid comprises a core area and a boundary area corresponding to the influence area;
judging whether the boundary lines of the influence areas of the centroids are not intersected, acquiring independent boundary ranges of the centroids when the boundary lines are not intersected, combining and identifying the centroid positions and the boundary areas of the intersected influence areas when the boundary lines are intersected, and acquiring the combined boundary ranges, so that the influence areas of the centroids are independent and do not intersect.
Further, the step of constructing the three-dimensional lattice field of the weather radar net of the networking weather radar comprises the following steps:
acquiring position data and volume scanning mode parameter data of a plurality of networking weather radars in a target area, wherein the position data comprises radar longitude and latitude and feed source altitude;
for each networking weather radar, converting the polar coordinates into a lattice field under Cartesian coordinates according to the position data and the volume scanning mode parameter data of the networking weather radar, and calculating the longitude range and the latitude range detected by each networking weather radar according to the detection radius of each networking weather radar;
and combining the grid point fields of the various networking weather radars to construct an initial weather radar grid three-dimensional grid point field, and determining the horizontal range, the horizontal resolution, the height range, the equidistant height hs and the spatial resolution hr of the three-dimensional grid point field.
Further, the horizontal range of the three-dimensional lattice point field of the initial weather radar mesh is determined by respectively comparing the longitude range and the latitude range detected by all the networking weather radars, wherein the horizontal range comprises a longitude interval range and a latitude interval range, the longitude interval range is the sum of the distance value between the radar with the largest detection longitude and the radar with the smallest detection longitude, the detection radius of the radar with the largest detection longitude and the detection radius of the radar with the smallest detection longitude, and the latitude interval range is the sum of the distance value between the radar with the largest detection latitude and the radar with the smallest detection latitude, the detection radius of the radar with the largest detection latitude and the detection radius of the radar with the smallest detection latitude;
the horizontal resolution, height range, equidistant height hs, and spatial resolution hr are set by the user or the system.
Further, the step of analyzing the coverage information on the equal altitude surface is as follows,
lattice point conversion: according to the set height range, all grid points on the equidistant height hs on the set height Z are obtained, and any grid point P in the grid points istObtaining a grid point PtCoordinate (X) oft,Yt,Zt) Wherein T =1,2, … …, T, T is the total number of lattice points, XtIs latitude, YtIs longitude, ZtIs the height; determination of grid point P by radar beam propagation and large circle geometry theorytPolar position (r) in a radar polar coordinate systemt,θt,φt) Wherein r istIs the pitch, θtIs an azimuth angle phitIs a pitch angle;
identification: for the aforementioned arbitrary lattice point PtOne by one will (r)t,θt,φt) Matching with azimuth, elevation, beam width and distance information of single radar, and determining (r)t,θt,φt) When the grid point P is in the detection rangetIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point; after all radars in the weather radar network identify the grid points according to the method, the radar coverage quantity of each grid point is obtained, and when one grid point existsOr when the grid points are not covered by the radar, the grid points are marked as blind points.
Further, the step of analyzing the coverage information between equal height layers is as follows:
based on a single radar, all grid points on the space resolution hr at the set height Z are obtained according to the set height range, and any grid point P in the grid points isjObtaining a grid point PjCoordinate (X) ofj,Yj,Zj) And coordinate conversion is carried out to obtain the corresponding polar coordinate position (r)j,θj,φj) Wherein j =1,2, … …, m, m is the total number of grid points; will (r)j,θj,φj) Matching the azimuth, elevation, beam width and distance information of the single radar, and determining (r)j,θj,φj) When the grid point P is in the detection rangejIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point;
regarding the height interval between any two adjacent equidistant heights as a layer Lq，LqRepresenting a q-th layer, wherein the value of q is an integer which is more than or equal to 1; according to the set height Z, the lattice point P on the equal distance height hstLattice point P with spatial resolution hrjMatching, and marking the grid points with the same height as repeated grid points; and acquiring all layers at a set height Z, L for each layerqCalculate the layer LqThe corresponding number of lattice points, which is equal to the number of the lattice points on the equidistant height hs corresponding to the layer and the number of the lattice points on the corresponding spatial resolution hr after the duplication is removed, the layer L is judgedqWhether the corresponding grid points are all blind points or not, and if so, the layer L is divided into the blind pointsqMarking as a blind spot, otherwise, if any grid point is a radar coverage point, the layer L is marked as a blind spotqMarking as a radar coverage point;
and after all radars in the traversing weather radar network identify the layers according to the method, acquiring the radar coverage quantity of each layer, identifying the radar coverage points when one or more radars cover the layer, and identifying the blind points when no radar covers the layer.
The invention also provides an adaptive observation system suitable for AOI (automated optical inspection) for the networking weather radar, which comprises the following components:
the three-dimensional lattice point field construction module is used for constructing a weather radar network three-dimensional lattice point field of the networking weather radar, analyzing coverage information on an equal-height surface and coverage information between equal-height layers and obtaining radar coverage information of each lattice point in the weather radar network three-dimensional lattice point field;
the area identification module is used for acquiring the reflectivity value of each networking weather radar on each grid point according to the radar coverage information of each grid point and determining the final reflectivity value of each grid point according to the reflectivity value; acquiring layer combination reflectivity information of each layer of equal-height surface on a set space height, and identifying strong convection centers and regions based on fusion information of the multilayer layer combination reflectivity information;
the cooperative observation processing module is used for acquiring data of the radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and area identification.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects as examples: after radar coverage information of a three-dimensional lattice point field of a weather radar net is obtained by analyzing coverage information on an equal-altitude surface and coverage information between equal-altitude layers, strong convection center and area identification is carried out based on fusion information of combined reflectivity information of multiple layers, and then a radar body scanning mode based on ground potential and machine learning combined extrapolation is selected to be triggered or a radar body scanning mode based on strong convection center and area identification is triggered according to the strong convection area and a detection range of a radar to be added, so that the body scanning mode of a newly added radar can be better matched with the strong convection center and the area, and the detection capability of the newly added radar is fully utilized. Furthermore, the scanning mode of the radar body to be added into the radar can be adjusted according to the selected area information (key area) set by a user or a system.
Meanwhile, when the three-dimensional lattice field is constructed, the horizontal range area of the three-dimensional lattice field can be intelligently set according to the position information, detection range and other information of each networking weather radar, and the construction precision and efficiency of the lattice field are improved. Furthermore, when grid point coverage information in a three-dimensional grid point field is identified, an equal-height interlayer coverage algorithm is provided, coverage information identification can be carried out between equal-height layers, information of grid points at equal heights and grid points at spatial resolution is considered when a coverage area is identified through layer information combination, the problem of blind area expansion caused by only considering the coverage information on the equal-height surface is avoided, and the radar simulation precision efficiency is improved.
Drawings
Fig. 1 is a schematic flowchart of an adaptive observation method for AOI applicable to a networked weather radar according to an embodiment of the present invention.
Fig. 2 is an exemplary diagram for adjusting the start-stop elevation angle of the radar volume scanning by combining the identified strong convection region and the selected region according to the embodiment of the present invention.
Fig. 3 is an exemplary diagram for adjusting the start-stop elevation angle of the radar volume scanning by combining the extrapolated strong convection area and the selected area according to the embodiment of the present invention.
Fig. 4 is a diagram illustrating a calculation example of a horizontal range of a three-dimensional lattice field according to an embodiment of the present invention.
Fig. 5 is a diagram illustrating a calculation example for obtaining overlay information on an equal-height surface and overlay information between equal-height layers according to an embodiment of the present invention.
Fig. 6 is an exemplary diagram of searching reflectivity values of all radars within an allowed time period according to an embodiment of the present invention.
Fig. 7 is a flowchart of a volume scanning mode for configuring the radar with the strong convection center and area in combination with the detection range and identification of the radar according to an embodiment of the present invention.
Fig. 8 is an exemplary diagram of different fan sweep ranges due to different radar center heights according to an embodiment of the present invention.
Fig. 9 is an exemplary diagram of elevation angles corresponding to the centroids of a plurality of strong convection areas provided by an embodiment of the present invention.
FIG. 10 is a flowchart of an exemplary radar volume sweep model using joint extrapolation based on ground potential and machine learning according to an embodiment of the present invention.
Fig. 11 is a first diagram illustrating an example of determining a fan sweep range according to quadrant information in which a strong convection area falls according to an embodiment of the present invention.
Fig. 12 is a second exemplary diagram for determining a fan sweep range according to quadrant information in which a strong convection area falls, according to an embodiment of the present invention.
Detailed Description
The following describes the adaptive observation method and system for AOI applicable to the networked weather radar according to the present invention in further detail with reference to the accompanying drawings and specific embodiments. It should be noted that technical features or combinations of technical features described in the following embodiments should not be considered as being isolated, and they may be combined with each other to achieve better technical effects. In the drawings of the embodiments described below, the same reference numerals appearing in the respective drawings denote the same features or components, and may be applied to different embodiments. Thus, once an item is defined in one drawing, it need not be further discussed in subsequent drawings.
It should be noted that the structures, proportions, sizes, and other dimensions shown in the drawings and described in the specification are only for the purpose of understanding and reading the present disclosure, and are not intended to limit the scope of the invention, which is defined by the claims, and any modifications of the structures, changes in the proportions and adjustments of the sizes and other dimensions, should be construed as falling within the scope of the invention unless the function and objectives of the invention are affected. The scope of the preferred embodiments of the present invention includes additional implementations in which functions may be executed out of order from that described or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
In addition, the blind spot and the radar coverage point referred to in the present invention may be a geometric point (without dimension, with uniqueness, such as a grid point) or a block (with dimension, with uniqueness, such as a layer with height). Similar to the prior art pixels, they are often referred to as pixels, but are actually a block.
Examples
The position and the volume scanning mode of the networking weather radar influence the coverage and the falling area of a three-dimensional space scale and influence the acquisition of space atmosphere scanning information. At present, the basic scanning modes of a radar VCP are mainly classified into a clear sky mode and a precipitation mode, and generally include 9 volume scanning modes, which are respectively: VCP32, VCP31, VCP21, VCP11, VCP12, VCP121, VCP211, VCP212 and VCP221, wherein the scanning mechanisms of VCP211, VCP221 and VCP212 correspond to VCP11, VCP21 and VCP12, respectively, except that a phase encoding algorithm is employed at two elevation angles of the lower layer to improve detection of unambiguous velocity.
For clear sky mode, there are mainly two kinds of VCP31 and VCP32, including 5 elevation angles, and the scanning time is about 10 minutes. VCP31 has a pulse width of 4.7us and a PRF of approximately 322 Hz; and the pulse width of VCP32 is 1.57us, the PRF is about 1013Hz, and the detection precision and the speed measurement range are higher. For the precipitation mode, the VCP11 and VCP21 are mainly used for detecting, tracking and analyzing the precipitation type and the characteristics of strong convection weather, and the VCP12 mode is introduced for meeting the field observation requirement in the strong convection weather at the later stage. The details of the VCPs in precipitation mode are shown in table 1.
Table 1 weather radar common volume scan mode parameter table.
Figure 83019DEST_PATH_IMAGE001
The radar volume scanning mode is a mode in which the radar operates according to configuration parameters such as a predetermined pulse width, a predetermined pulse repetition frequency, a predetermined number of elevation layers, and a predetermined antenna rotation rate. During meteorological detection, volume scanning mode information such as a sector scanning parameter (such as a sector scanning center, a sector scanning range and the like) and a pitch angle parameter (such as the number of elevation angles, the angle of elevation and the like) of a radar are required to be set through a related control console, and the control console sends a parameter setting instruction to adjust an antenna scanning mode.
According to the technical scheme provided by the invention, after the radar coverage information of the three-dimensional lattice field of the weather radar net is obtained by analyzing the coverage information on the equal altitude surface and the coverage information between the equal altitude layers, the identification of the strong convection center and the area is carried out based on the fusion information of the multilayer combined reflectivity information, and then the radar body scanning mode is configured according to the parameters of the radar to be added and the identified strong convection center and the identified area, so that the body scanning mode of the newly added radar can realize more optimal matching aiming at the strong convection center and the area (namely the key area), and the detection capability of the newly added radar is fully utilized.
Referring to fig. 1, the invention provides an adaptive observation method for AOI applicable to a networked weather radar. The method comprises the following steps:
s100, constructing a three-dimensional grid point field of the networking weather radar and analyzing coverage information: and constructing a weather radar mesh three-dimensional lattice point field of the networking weather radar, and analyzing coverage information on an equal-height surface and coverage information between equal-height layers to obtain radar coverage information of each lattice point in the weather radar mesh three-dimensional lattice point field.
And analyzing the reflectivity value of each grid point in the three-dimensional grid point field: and according to the radar coverage information of each grid point, acquiring the reflectivity value of each networking weather radar on the grid point, and determining the final reflectivity value of the grid point according to the reflectivity value.
Identification of strong convection centers and regions: and acquiring layer combination reflectivity information of each layer of equal-height surface on the set space height, and identifying the strong convection center and the strong convection area based on the fusion information of the multilayer layer combination reflectivity information.
Acquiring data of radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and area identification.
The radar to be added can be one or more, for example, a weather radar outside a preset target area, and a user can select the radar according to needs. In this embodiment, the radar to be added is preferably an X-band radar. The X-band dual-polarization short-distance network radar with the rapid adaptive scanning capability (the detection radius of the radar is 30-40km, and the average distance between the radars is 25km) can make up the defect of the low-layer detection capability of the conventional radar network. By adopting the technical scheme, when the automatically identified strong convection area or the extrapolated strong convection area falls into the X-band radar observation area, the volume scanning mode of the X-band radar can be configured based on the preset area identification and area matching algorithm to form a specific observation mode.
In this embodiment, the scanning mode of the radar body to be added to the radar may also be adjusted according to the information of the selected area set by the user or the system. At this time, after the step S400, the step S500 is performed: and acquiring a selection area, and adjusting a radar volume scanning mode after performing area matching with the extrapolated or identified strong convection area. The method comprises the following specific steps:
s501, acquiring selection area information set by a user or a system; and acquiring an extrapolated strong convection center and an extrapolated area in a radar volume scanning mode based on ground potential and machine learning joint extrapolation, acquiring the identified strong convection center and the identified area in a radar volume scanning mode based on strong convection center and area identification, and acquiring strong convection area boundary information corresponding to the extrapolated or identified strong convection center and the identified area.
And comparing the boundary information of the strong convection region with the information of the selection region, and configuring a radar volume scanning mode based on a region with a smaller area in the boundary information of the strong convection region and the information of the selection region when the boundary of the strong convection region is matched with the selection region. When the area of the selected area is larger than that of the strong convection area, configuring a radar body scanning mode based on the strong convection area; and when the area of the selected area is smaller than that of the strong convection area, configuring a radar body scanning mode based on the selected area.
The embodiment also provides a key area setting module to collect the information of the selected area set by the user, namely, the user sets the selected area, so that the user can detect the key area according to the actual detection requirement.
Optionally, the region of interest setting module may be configured to: and outputting position information of areas possibly causing urban waterlogging, areas where important activities are located and/or preset important service areas under strong convection coverage to a user, collecting selection operations of the user on the areas, and taking one or more areas selected by the user as selection areas set by the user. The scheme is particularly suitable for setting the selection area smaller than the strong convection area by a user (such as a forecaster) aiming at various key areas under the strong convection coverage, such as the area where urban waterlogging is possibly caused, the area where important activities are located, such as a major event area, a preset important service area and the like when a plurality of strong convection areas fall into the scanning range of the X-band radar, so that the body scanning strategy of the radar is matched with the selection area of the user.
By way of example and not limitation, referring to fig. 2, for example, when the automatically identified strong convection region is the whole region where the irregular shaded part is located in the figure, and the radar volume scanning mode based on the strong convection center and region identification is triggered, the volume scanning range of the X-band radar is configured to be 230 ° to 30 °; however, since the user sets a selection area, which is a square area portion in the figure, as the key area, the X-band radar volume scanning mode is configured with the square area portion as the key area, and the volume scanning range of the X-band radar is configured to be 290 ° to 325 °. For another example, referring to fig. 3, for a strong convection region (the whole region where the irregular shaded portion is located in the figure) determined by extrapolation, the volume scanning range of the X-band radar is configured to be 225 ° to 45 °, however, since the user sets a selection region, the square region portion in the figure, as the key region, the X-band radar volume scanning mode is configured with the square region portion as the key region, and the volume scanning range of the X-band radar is configured to be 290 ° to 325 °.
In the step S100 in this embodiment, the specific steps of constructing the weather radar mesh three-dimensional lattice field of the networking weather radar may be as follows:
firstly, position data and volume scanning mode parameter data of a plurality of networking weather radars in a target area are obtained, wherein the position data comprises radar longitude and latitude and feed source altitude.
And then, for each networking weather radar, converting the polar coordinates into a lattice field under Cartesian coordinates according to the position data and the volume scanning mode parameter data of the networking weather radar, and calculating the longitude range and the latitude range detected by each networking weather radar according to the detection radius of each networking weather radar.
And finally, combining the grid point fields of the various networking weather radars to construct an initial weather radar grid three-dimensional grid point field, and determining the horizontal range, the horizontal resolution, the height range, the equidistant height hs and the spatial resolution hr of the three-dimensional grid point field.
And determining the horizontal range of the three-dimensional grid point field of the initial weather radar net by respectively comparing the longitude range and the latitude range detected by all the networking weather radars.
The horizontal range includes a longitude range and a latitude range, the longitude range is a sum of a distance value between the radar with the largest detection longitude and the radar with the smallest detection longitude, a detection radius of the radar with the largest detection longitude and a detection radius of the radar with the smallest detection longitude, and the latitude range is a sum of a distance value between the radar with the largest detection latitude and the radar with the smallest detection latitude, a detection radius of the radar with the largest detection latitude and a detection radius of the radar with the smallest detection latitude.
The horizontal resolution, height range, equidistant height hs, and spatial resolution hr are set by the user or the system.
Preferably, the horizontal resolution is set to 1 km; the spatial resolution hr is set to a multiple of 250 meters, such as 250m, 500m, 750m, 1km or 2km, and may be set as desired.
Referring to fig. 4, the following describes how to determine the horizontal range of the three-dimensional lattice field in detail, taking the example that the weather radar net includes 3 radars.
The weather radar net comprises a radar A, a radar B and a radar C. The longitude and latitude of the A radar are (LonA, LatA), and the detection radius is Ra; the longitude and latitude of the radar B are (LonB, LatB), and the detection radius is Rb; the longitude and latitude of the C radar are (LonC, LatC), and the detection radius is Rc.
According to the positional relationship of the radar a, the radar B, and the radar C shown in fig. 4, the radar C is located at the maximum detection longitude (Lon) (corresponding to the rightmost side of fig. 4), and the radar a is located at the minimum detection longitude (corresponding to the leftmost side of fig. 4), and then the range of the longitude interval of the three-dimensional lattice field is the sum of the distance value between the radar C with the maximum detection longitude and the radar a with the minimum detection longitude, the detection radius of the radar C with the maximum detection longitude, and the detection radius of the radar a with the minimum detection longitude. That is, the longitude range L1 of the three-dimensional lattice field is the detection radius Ra of the a radar and the distance d between the AC radarsACAnd the detection radius Rc of the C radar, i.e. L1= Ra + dAC+Rc。
According to the position relationship among the radar a, the radar B, and the radar C shown in fig. 4, the radar B is the largest detected latitude (Lat) (corresponding to the uppermost edge of fig. 4), and the radar C is the smallest detected latitude (corresponding to the lowermost edge of fig. 4), and thus the range of the latitude interval of the three-dimensional lattice field is the sum of the distance value between the radar B with the largest detected latitude and the radar C with the smallest detected latitude, the detection radius of the radar B with the largest detected latitude, and the detection radius of the radar C with the smallest detected latitude. That is, the range of the latitude interval L2 of the three-dimensional lattice field is the distance d between the detection radii Rb and BC of the B radarBCAnd the detection radius Rc of the C radar, i.e. L1= Rb + dBC+Rc。
In this embodiment, the radar coverage information of each grid point in the three-dimensional grid point field is analyzed by combining the coverage information on the equal-height surface and the coverage information between the equal-height layers.
Wherein, the step of analyzing the coverage information on the equal altitude surface may include an S121 lattice point converting step and an S122 identifying step.
The step of converting the lattice points of S121 is as follows: according to the set height range, all grid points on the equidistant height hs on the set height Z are obtained, and any grid point P in the grid points istObtaining a grid point PtCoordinate (X) oft,Yt,Zt) Wherein T =1,2, … …, T, T is the total number of lattice points, XtIs latitude, YtIs longitude, ZtIs the height; determination of grid point P by radar beam propagation and large circle geometry theorytPolar position (r) in a radar polar coordinate systemt,θt,φt) Wherein r istIs the pitch, θtIs an azimuth angle phitIs a pitch angle.
The step of S122 identification is specifically as follows: for the aforementioned arbitrary lattice point PtOne by one will (r)t,θt,φt) Matching with azimuth, elevation, beam width and distance information of single radar, and determining (r)t,θt,φt) When the grid point P is in the detection rangetIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point; after all radars in the traversing weather radar network identify the grid points according to the method, the radar coverage quantity of each grid point is obtained, when one or more radars cover the grid point, the grid point is identified as a radar coverage point, and when no radar covers the grid point, the grid point is identified as a blind point.
Wherein, the step of analyzing the coverage information between the equal height layers may be as follows:
s131, based on a single radar, obtaining all grid points on the set height Z with the spatial resolution hr according to the set height range, and obtaining any grid point P in the grid pointsjObtaining a grid point PjCoordinate (X) ofj,Yj,Zj) And coordinate conversion is carried out to obtain the corresponding polar coordinate position (r)j,θj,φj) Wherein j =1,2, … …, mM is the total number of grid points; will (r)j,θj,φj) Matching the azimuth, elevation, beam width and distance information of the single radar, and determining (r)j,θj,φj) When the grid point P is in the detection rangejAnd identifying the grid points as radar coverage points, otherwise, identifying the grid points as blind points.
The height interval between any two adjacent equidistant heights is regarded as a layer Lq，LqRepresenting a q-th layer, wherein the value of q is an integer which is more than or equal to 1; according to the set height Z, the lattice point P on the equal distance height hstLattice point P with spatial resolution hrjMatching, and marking the grid points with the same height as repeated grid points; and acquiring all layers at a set height Z, L for each layerqCalculate the layer LqThe corresponding number of lattice points, which is equal to the number of the lattice points on the equidistant height hs corresponding to the layer and the number of the lattice points on the corresponding spatial resolution hr after the duplication is removed, the layer L is judgedqWhether the corresponding grid points are all blind points or not, and if so, the layer L is divided into the blind pointsqMarking as a blind spot, otherwise, if any grid point is a radar coverage point, the layer L is marked as a blind spotqThe markers are radar coverage points.
And after all radars in the traversing weather radar network identify the layers according to the method, acquiring the radar coverage quantity of each layer, identifying the radar coverage points when one or more radars cover the layer, and identifying the blind points when no radar covers the layer.
In this embodiment, blind spots and radar coverage points may be identified, preferably digitally. As a typical preference, the blind spot is identified by the number 0. When identifying a radar coverage point, a corresponding number k may be identified according to the number of radar coverage on the grid point, where k =1,2, … …, N is the total number of radars, and the radar coverage point includes a single radar coverage point, a double radar coverage punctuation point, … …, k radar coverage points. By way of example and not limitation, for example, N =3, then k =1,2, 3, respectively corresponding to a single radar coverage point, a double radar coverage punctuation point, and a triple radar coverage point.
The manner in which the coverage areas are identified by the overlay information on the equal-altitude surface and between the equal-altitude layers is described in detail below in conjunction with fig. 5.
The set height Z, equidistant height hs, and spatial resolution hr may be set by a user or system as desired. Taking the example shown in fig. 5, height Z is set to include equidistant heights hs1, hs2, hs3 and hs4, including spatial resolutions hr1, hr2, hr3, hr4, hr5, hr6, hr7 and hr8, hs1 includes hr1 and hr2, hs2 includes hr3 and hr4, hs3 includes hr5 and hr6, hs4 includes hr7 and hr8, i.e. equidistant heights hs =2 hr.
When the coverage information on the equal-height surface is analyzed, it is found that 5 grid points, i.e., T =5, are present on the equidistant height hs at the set height Z, corresponding to P in fig. 5i，Pi+2，Pi+4，Pi+6，Pi+8。
For an arbitrary grid point P, taking a single radar as an example, such as radar AtBy its corresponding polar position (r)t,θt,φt) And judging whether the grid points are radar coverage points of the radar A, if so, identifying the grid points as 1, otherwise, identifying the grid points as 0, namely blind points. For 5 lattice points Pi，Pi+2，Pi+4，Pi+6，Pi+8Are identified according to the above method.
Then, traversing all radars in the weather radar network, including the radar A, the radar B and the radar C, identifying the grid points according to the method, and acquiring the grid point Pi，Pi+2，Pi+4，Pi+6，Pi+8K, of radar coverage. Specifically, k may be set to an initial value of 0, and k + + may be performed every time 1 radar coverage is determined.
A grid point is identified as a radar coverage point when it has one or more radar coverage, and a value of k is identified, and a grid point is identified as a 0 when it has no radar coverage, i.e., a blind spot.
When the contour interlayer coverage information is analyzed, it is known that there are 9 grid points in the spatial resolution hr at the set height Z, i.e., m =9, corresponding to P in fig. 5i，Pi+1，Pi+2，Pi+3，Pi+4，Pi+5，Pi+6，Pi+7，Pi+8。
Taking a single radar, such as radar A, for any grid point P thereinjBy its corresponding polar position (r)j,θj,φj) And judging whether the grid points are radar coverage points of the radar A, if so, identifying the grid points as 1, otherwise, identifying the grid points as 0, namely blind points. For 9 grid points Pi，Pi+1，Pi+2，Pi+3，Pi+4，Pi+5，Pi+6，Pi+7，Pi+8Are identified according to the above method.
Regarding the height interval between any two adjacent equidistant heights as a layer Lq，LqRepresenting the q-th level, a total of 4 levels in fig. 5, i.e. q =4, corresponding to L in the figurei+1，L i+2，L i+3，L i+4. Lattice point P on the peer-to-peer distance height hstLattice point P with spatial resolution hrjMatching is performed, and grid points with the same height are marked as repeated grid points, in FIG. 5, Pi，Pi+2，Pi+4，Pi+6，Pi+8Are repeating grid points.
For all layers L at a set height Zi+1，L i+2，L i+3And L i+4For each layer LqCalculate the layer LqThe corresponding number of lattice points, which is equal to the number of the lattice points on the equidistant height hs corresponding to the layer and the number of the lattice points on the corresponding spatial resolution hr after the duplication is removed, the layer L is judgedqWhether the corresponding grid points are all blind points or not, and if so, the layer L is divided into the blind pointsqMarking as a blind spot, otherwise, only any grid point is a radar coverage point, and then, marking the layer L as a blind spotqThe markers are radar coverage points. With Li+1For example, the number of corresponding grid points is equal to the number of grid points on the equidistant height hs (i.e. P) corresponding to the layeriAnd Pi+2Wherein P isiIs a lower equidistant height grid point of the layer, Pi+2Equal distance height grid points of a layer) and a corresponding number of spatial resolution hr grid points (i.e., P)i，Pi+1And Pi+2) The sum of (a) and (b) is the value after de-duplication, i.e. 2+3-2=3, Li+1Corresponding 3 gridsThe points are respectively Pi，Pi+1，Pi+2And (5) grid points. Fault judgment Li+1Whether the corresponding 3 grid points are all blind points or not, if the 3 grid points are all blind points, the layer L is divided into three layersi+1Marking as 0, namely blind spot, otherwise, if any grid point is a radar coverage point, the layer L is markedqThe label is 1, i.e. the radar coverage point.
And by analogy, all the layers are identified according to the steps. Specifically, in FIG. 5, Li+2The corresponding 3 grid points are respectively Pi+2，Pi+3，Pi+4Lattice points, Li+3The corresponding 3 grid points are respectively Pi+4，Pi+5，Pi+6Lattice points, Li+4The corresponding 3 grid points are respectively Pi+6，Pi+7，Pi+8And (5) grid points. By way of example, and not limitation, layer L in FIG. 5i+1As radar coverage points, L i+2As radar coverage points, L i+3Is a blind spot, L i+4Is a radar coverage point.
And traversing all radars in the weather radar network, including the radar A, the radar B and the radar C, and identifying the layers according to the method to obtain the radar coverage quantity of each layer. Similarly, k can be initialized to 0 and executed every 1 radar coverage is determined. A radar coverage point is identified when a layer has one or more radar coverage and a value of k is identified, and a value of 0, i.e. a blind spot, is identified when a layer has no radar coverage. Therefore, the grid point coverage information analysis of the three-dimensional grid point field of the weather radar net is completed.
In step S200 of this embodiment, radar coverage information on each grid point can be obtained through the grid point coverage information analysis of the weather radar grid three-dimensional grid point field. When weather occurs, for any grid point PjThe coordinate thereof is (X)j,Yj,Zj) Mapping the coordinate system to a polar coordinate system of the radar to obtain a corresponding polar coordinate position (r)j,θj,φj) Wherein r isjIs the pitch, θjIs an azimuth angle phijIs a pitch angle. According to the converted detection distance, azimuth angle and pitch angle, finding out the data point with the closest distance in the radar data as the grid point in the radarReflectance values (i.e., dBZ).
Since the networking weather radar net comprises N weather radars, the lattice point PjThe coordinate conversion by N radars has N corresponding reflectivity values, which is (dBZ)1，dBZ2，dBZ3，……，dBZN). For a certain time t, the dBZ values of all the radars are searched within an allowed time period t + X, where X is a set maximum allowed time threshold, which may be set by a user or a system. Then the lattice point PjMay be set to (dBZ)1，dBZ2，dBZ3，……，dBZN) Maximum value of dBZmaxOr set to (dBZ)1，dBZ2，dBZ3，……，dBZN) Average value dBZ of all values inaverThe user can make adaptive settings as desired.
By way of example and not limitation, referring to fig. 6, a networked weather radar network, for example, includes 4 radars (i.e., N = 4), R1, R2, R3, R4, respectively, and radars R1, R2, R3, R4 arrive at the grid point in order. The time axis in fig. 6 shows the order of arrival of each radar, and at time t4 in fig. 6, 4 reflectivity values (dBZ) of the radar at the grid point can be obtained (R1, R2, R3, R4)1，dBZ2，dBZ3，dBZ4) (ii) a At time t5, 4 reflectivity values (dBZ) of the radar at the grid point may be obtained (R2, R3, R4, R1)2，dBZ3，dBZ4，dBZ1) (ii) a By analogy, at time t10, 4 reflectivity values (dBZ) of the radar at the grid point may be obtained (R3, R4, R1, R2)3，dBZ4，dBZ1，dBZ2). At any one time, the lattice point PjThe final reflectance value dBZ of (d) is the reflectance value at that moment in time (dBZ)1，dBZ2，dBZ3，dBZ4) Or (dBZ)1，dBZ2，dBZ3，dBZ4) Average of all values in (1).
In step S300 of this embodiment, when identifying the center and the region of strong convection based on the fusion information of the multi-layer combined reflectivity information, a dual-threshold screening is employed to determine the core region and the boundary region of the strong convection region. The dual threshold includes a high threshold that can be used to determine a core region of the strong convection region and a low threshold that can be used to determine a boundary region of the strong convection region.
The specific steps can be as follows: acquiring a high threshold, searching a core region after increasing according to a preset step length by taking the high threshold as a base number, calculating the area of the core region, and performing centroid identification on the core region with the area larger than or equal to a preset area value; making low threshold contour boundary identification in a range which is greater than or equal to a low threshold and less than or equal to a high threshold around the centroid to determine a boundary area of the centroid, wherein an influence area of the centroid comprises a core area and a boundary area corresponding to the influence area; judging whether the boundary lines of the influence areas of the centroids are not intersected, acquiring independent boundary ranges of the centroids when the boundary lines are not intersected, combining and identifying the centroid positions and the boundary areas of the intersected influence areas when the boundary lines are intersected, and acquiring the combined boundary ranges, so that the influence areas of the centroids are independent and do not intersect.
Referring to fig. 7, by way of example and not limitation, a high threshold of 50dBZ is used to segment the core region of a plurality of different regions of interest and a low threshold of 40dBZ is used to determine the boundary region of a strong convection region. First, using a high threshold of 50dBZ as a base, the maximum reflectance factor in the core region is gradually increased from 50dBZ to 50.5dBZ, 51dBZ, and the like, as an example of step =0.5 dBZ. Then, the area of the core region is calculated, and the area is greater than or equal to a preset area value, for example, the threshold value is set to be 5km2The core area of the core is marked with a mass center, and the area is less than 5km2The core regions of (a) are not identified (some smaller area of the core regions are discarded). Then, 40dBZ isoline boundary identification is carried out in the range that R is more than or equal to 40dBZ and less than or equal to 50dBZ around the centroid so as to determine the boundary area of the centroid. Finally, a multi-boundary disjoint decision is made, and if the low threshold contour boundary (40 dBZ) contains the core region contour (50 dBZ), the centroid position is summedAnd combining and identifying the boundary areas, thereby ensuring that the influence areas of the centroids are independent and not crossed.
Because the boundary lines do not intersect, the centroid impact regions are separate and do not cross-connect so that the regions of interest remain as separate entities.
In step S400 of this embodiment, for each X-band radar, the maximum detection distance of the X-band radar and the distance between the strong convection center and the area relative to the X-band radar are obtained, and whether the distance is greater than the maximum detection distance is determined. When the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the X-band radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, judging that the identified strong convection center and the identified region are located in the detection range of the X-band radar, and triggering a radar body scanning mode based on the strong convection center and the identified region.
When the radar body scanning mode based on the strong convection center and region identification is adopted, the planar scanning PPI form or the combined scanning form of the vertical scanning RHI and the planar scanning PPI can be adopted by combining the identified strong convection center and region range.
Take the common plane scan PPI scan strategy as an example. The scanning azimuth of each layer of the X-band radar is mainly determined according to the boundary range of a strong convection region (such as a networking storm monomer) in each layer, wherein the boundary range contains a surrounding contour line of a centroid. Referring to fig. 8, the longitude and latitude of a point on the equivalent line and the longitude and latitude of the radar center O1 on the equivalent plane are subjected to azimuth calculation, and the start-stop elevation angle at the height is calculated in the coordinate system of O1. If a plurality of continuous or discontinuous storms exist in the radar scanning range, the starting elevation angle is selected as the minimum value of the starting elevation angles of the plurality of storms, and the ending elevation angle is selected as the maximum value of the ending elevation angles of the plurality of storms. The algorithm assumes that the center of the radar is located on the same equal altitude plane with the layer of key area.
In this embodiment, considering that the radar is on the ground, an error is formed by a difference between each layer of the ground and each layer of the space, so that when a scanning strategy is set, for the same key echo area, a simulated radar sector scanning range at the center of the equal-height radar is larger than a real radar sector scanning range of the echo, thereby ensuring that the echo area is covered and scanned. The scheme can effectively avoid missing the core storm region during observation.
The azimuth of start and stop of each layer is determined, i.e. the overall boundary value of the coverage area of the storm component range is identified. In this embodiment, if the start-stop difference is too large when the start-stop coincidence of the azimuths between the layers is too small, when switching between the layers, the start of the next layer of azimuths is not reached after the elevation angle of one layer of azimuths is terminated, which may cause frequent acceleration start and deceleration braking during radar scanning, the start-stop azimuths of all the layers are integrated to form the final scanning range of all the layers.
Specifically, the integration steps of the scanning range are as follows: when the initial azimuth angle is smaller than the ending azimuth angle, the scanning range is the ending azimuth angle minus the initial azimuth angle; when the starting azimuth is greater than the ending azimuth, the scanning range is 360 minus the starting azimuth plus the ending azimuth. When the scanning range is larger than a preset value, scanning covering 360 degrees can be performed in consideration of a buffer angle area where the radar antenna stops braking. During specific operation, the preset numerical value can be set according to actual operation radar scanning angular velocity, braking stopping time, acceleration starting time and the like.
The pitching angles of all layers of the band radar are determined according to the positions of key areas (components of networking storm monomers in all layers) in all layers. Specifically, firstly, determining the number of layers of an elevation angle according to the number of layers set by networking fusion, wherein the number of layers of the elevation angle of the X-band radar is matched with the number of layers of the elevation angle; then, the elevation angle in each layer is determined according to the position of the strongest networking storm monomer component in each layer, namely, the networking storm monomer components in each layer are sequenced according to the maximum reflectivity value, the area size, the mean reflectivity value and the like of the storm component to smoothly determine the elevation angle of the layer.
Determining the elevation angle according to the elevation layer order may produce a jump in the radar pitch angle, taking into account the difference in the position of the center of the strong convective storm from the position of the radar. By way of example and not limitation, referring to fig. 9, the elevation angles corresponding to the centroids of the strong convection regions distributed over 1km, 2km, 3km and 4km are e1, e2, e3 and e4, respectively, if the elevation angles are determined according to the height layer order, elevation jump will occur on e1, e2, e3 and e4, which not only easily damages radar hardware but also disturbs subsequent data fusion. Therefore, it is preferred to set the elevation scanning strategy by reordering the elevation angles. Taking fig. 7 as an example, the elevation angle size sequence of the X-band radar body scanning is e1 < e4 < e3 < e2, and the final elevation angle scanning sequence of the X-band radar is determined to be e1, e4, e3 and e2 according to the tier elevation ordering.
In this embodiment, when the radar volume scanning mode based on the ground potential and the machine learning joint extrapolation is adopted, the method specifically includes the following steps: performing strong convection extrapolation prediction through a machine learning and time sequence deep learning model, and obtaining the position relation between the strong convection center and the region and the X-band radar after a set time period by combining a Q vector analysis method based on ground high-altitude data; based on the quadrants of the X-band radar full-scanning area, acquiring quadrant information of the strong convection center and the area falling into the quadrants, wherein each quadrant corresponds to an angle range; and configuring a fan scanning range of the X-band radar volume scanning mode according to the angle range corresponding to the quadrant, wherein the fan scanning range comprises a starting angle and an ending angle.
That is, when the identified strong convection region is not within the detection range of the X-band radar, a strong convection extrapolation based on machine learning and depth models and a Q vector analysis method based on ground high-altitude data are performed to determine whether the strong convection region is within the detection range of the X-band radar after a set time period, and a typical procedure may be as shown in fig. 10.
Further, when strong convection extrapolation prediction is carried out through a machine learning model and a time sequence deep learning model, the rainfall can be finely divided by introducing an anti-noise module and converting an original regression problem into a sequence regression problem. Taking heavy rainfall as an example, it is predicted whether the strong convection region is within the detection range of the X-band radar after 1 hour, for example. Specifically, in the Q vector analysis method based on ground high-altitude data, multiple physical diagnoses of the strong convection nascent environment can be combined and developed by using the Q vector, the second-class thermal wind helicity, the wind field divergence and other diagnostic quantities which only need one layer of calculated data based on minute-level data such as temperature, wind field and air pressure obtained by ground automatic meteorological station observation, and the internal 'characteristics' of the strong convection nascent environment can be obtained, so that the strong convection nascent development can be predicted.
In this embodiment, when dividing the full scanning area of the X-band radar, the scanning area of the X-band radar is preferably divided into 8 quadrants, see areas a, b, c, d, e, f, g, and h in fig. 11, and the corresponding angles of the quadrants are 0 ° to 45 °, 45 ° to 90 °, 90 ° to 135 °, 135 ° to 180 °, 180 ° to 225 °, 225 ° to 270 °, 270 ° to 315 °, and 315 ° to 360 °, respectively. By way of example, if the strong convection region falls in the b region, the start angle and the end angle of the radar volume scan are 45 ° to 90 °, respectively.
If the strong convection region falls into a plurality of quadrants, the starting angle and the ending angle of the radar body scanning are set according to the starting quadrant and the ending quadrant related to the falling region, and the scanning range is (starting angle and ending angle).
Specifically, if the start angle is smaller than the end angle, such as the start angle =225 °, the end angle =315 °, the fan sweep range of the X-band radar volume scanning mode is configured to be 225 ° to 315 °, i.e., (225 °, 315 °). If the start angle is larger than the end angle, such as start angle =225 ° in fig. 12, the end angle is 45 °, and the fan scan range configuration of the X-band radar volume scan pattern is 225 ° to 45 °, i.e., (225 °, 45 °). The fan sweep range of the X-band radar volume scan pattern is configured to be 0 ° to 360 °, i.e., (0 °, 360 °), at multiple start elevations if multiple regions are present.
After the scanning range is determined, the elevation angle can be scanned from 0.5 degrees, 1.0 degrees, 1.5 degrees, 2.0 degrees, 2.5 degrees, 3.0 degrees, 3.5 degrees and 4.0 degrees in a fixed elevation angle sequence based on fixed parameters set by a user or a system.
The invention further provides an adaptive observation system of AOI suitable for the networking weather radar.
The system comprises a three-dimensional lattice point field construction module, a region identification module and a collaborative observation processing module.
The three-dimensional grid point field construction module is used for constructing a weather radar grid three-dimensional grid point field of the networking weather radar, analyzing coverage information on an equal-height surface and coverage information between equal-height layers, and obtaining radar coverage information of each grid point in the weather radar grid three-dimensional grid point field.
The area identification module is used for acquiring the reflectivity value of each networking weather radar on each grid point according to the radar coverage information of each grid point and determining the final reflectivity value of each grid point according to the reflectivity value; and acquiring layer combination reflectivity information of each layer equal-height surface on the set space height, and identifying the strong convection center and the region based on the fusion information of the multilayer layer combination reflectivity information.
The cooperative observation processing module is used for acquiring data of radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and area identification.
In this embodiment, the three-dimensional lattice point field building module may include a lattice point field building unit, an equal-height surface overlay information identification unit, and an equal-height interlayer overlay information identification unit.
The lattice field construction unit is configured to perform the steps of: acquiring position data and volume scanning mode parameter data of a plurality of networking weather radars in a target area, wherein the position data comprises radar longitude and latitude and feed source altitude; for each networking weather radar, converting the polar coordinates into a lattice field under Cartesian coordinates according to the position data and the volume scanning mode parameter data of the networking weather radar, and calculating the longitude range and the latitude range detected by each networking weather radar according to the detection radius of each networking weather radar; and combining the grid point fields of the various networking weather radars to construct an initial weather radar grid three-dimensional grid point field, and determining the horizontal range, the horizontal resolution, the height range, the equidistant height hs and the spatial resolution hr of the three-dimensional grid point field.
The overlay information identification unit on the equal altitude surface is configured to perform the steps of: according to the set height range, all grid points on the equidistant height hs on the set height Z are obtained, and any grid point P in the grid points istObtaining a grid point PtCoordinate (X) oft,Yt,Zt) Wherein T =1,2, … …, T, T is the total number of lattice points, XtIs latitude, YtIs longitude, ZtIs the height; determination of grid point P by radar beam propagation and large circle geometry theorytPolar position (r) in a radar polar coordinate systemt,θt,φt) Wherein r istIs the pitch, θtIs an azimuth angle phitIs a pitch angle; for the aforementioned arbitrary lattice point PtOne by one will (r)t,θt,φt) Matching with azimuth, elevation, beam width and distance information of single radar, and determining (r)t,θt,φt) When the grid point P is in the detection rangetIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point; after all radars in the traversing weather radar network identify the grid points according to the method, the radar coverage quantity of each grid point is obtained, when one or more radars cover the grid point, the grid point is identified as a radar coverage point, and when no radar covers the grid point, the grid point is identified as a blind point.
The equal-height interlayer coverage information identification unit is configured to perform the steps of: based on a single radar, all grid points on the space resolution hr at the set height Z are obtained according to the set height range, and any grid point P in the grid points isjObtaining a grid point PjCoordinate (X) ofj,Yj,Zj) And coordinate conversion is carried out to obtain the corresponding polar coordinate position (r)j,θj,φj) Wherein j =1,2, … …, m, m is the total number of grid points; will (r)j,θj,φj) Matching with the azimuth, elevation angle, beam width and distance information of the single radar to judge(rj,θj,φj) When the grid point P is in the detection rangejIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point; regarding the height interval between any two adjacent equidistant heights as a layer Lq，LqRepresenting a q-th layer, wherein the value of q is an integer which is more than or equal to 1; according to the set height Z, the lattice point P on the equal distance height hstLattice point P with spatial resolution hrjMatching, and marking the grid points with the same height as repeated grid points; and acquiring all layers at a set height Z, L for each layerqCalculate the layer LqThe corresponding number of lattice points, which is equal to the number of the lattice points on the equidistant height hs corresponding to the layer and the number of the lattice points on the corresponding spatial resolution hr after the duplication is removed, the layer L is judgedqWhether the corresponding grid points are all blind points or not, and if so, the layer L is divided into the blind pointsqMarking as a blind spot, otherwise, if any grid point is a radar coverage point, the layer L is marked as a blind spotqMarking as a radar coverage point; and after all radars in the traversing weather radar network identify the layers according to the method, acquiring the radar coverage quantity of each layer, identifying the radar coverage points when one or more radars cover the layer, and identifying the blind points when no radar covers the layer.
For other technical features, referring to the description of the previous embodiment, each processing module may be configured to execute a corresponding information processing procedure, which is not described herein again.
In the foregoing description, the disclosure of the present invention is not intended to limit itself to these aspects. Rather, the various components may be selectively and operatively combined in any number within the intended scope of the present disclosure. In addition, terms like "comprising," "including," and "having" should be interpreted as inclusive or open-ended, rather than exclusive or closed-ended, by default, unless explicitly defined to the contrary. All technical, scientific, or other terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. Common terms found in dictionaries should not be interpreted too ideally or too realistically in the context of related art documents unless the present disclosure expressly limits them to that. Any changes and modifications of the present invention based on the above disclosure will be within the scope of the appended claims.

Claims (10)

1. An adaptive observation method of AOI (automatic optical inspection) applicable to a networking weather radar is characterized by comprising the following steps of:
constructing a weather radar mesh three-dimensional lattice point field of a networking weather radar, and analyzing coverage information on an equal-height surface and coverage information between equal-height layers to obtain radar coverage information of each lattice point in the weather radar mesh three-dimensional lattice point field;
acquiring the reflectivity value of each networking weather radar on each grid point according to the radar coverage information of each grid point, and determining the final reflectivity value of each grid point according to the reflectivity value;
acquiring layer combination reflectivity information of each layer of equal-height surface on a set spatial height, and carrying out identification on a strong convection center and a strong convection region based on fusion information of the multilayer layer combination reflectivity information;
collecting data of radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and region identification;
the step of analyzing the coverage information on the equal-height surface is as follows:
lattice point conversion: according to the set height range, all grid points on the equidistant height hs on the set height Z are obtained, and any grid point P in the grid points istObtaining a grid point PtCoordinate (X) oft,Yt,Zt) Wherein T =1,2, … …, T, T is the total number of lattice points, XtIs latitude, YtIs longitude, ZtIs the height; by radar beamsPropagation and large circle geometry theory to determine lattice point PtPolar position (r) in a radar polar coordinate systemt,θt,φt) Wherein r istIs the pitch, θtIs an azimuth angle phitIs a pitch angle;
identification: for the aforementioned arbitrary lattice point PtOne by one will (r)t,θt,φt) Matching with azimuth, elevation, beam width and distance information of single radar, and determining (r)t,θt,φt) When the grid point P is in the detection rangetIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point; after all radars in the weather radar network identify the grid points according to the method, acquiring the radar coverage quantity of each grid point, identifying the grid points as radar coverage points when one or more radars cover the grid points, and identifying the grid points as blind points when no radar covers the grid points;
the steps of analyzing the coverage information between the equal height layers are as follows:
based on a single radar, all grid points on the space resolution hr at the set height Z are obtained according to the set height range, and any grid point P in the grid points isjObtaining a grid point PjCoordinate (X) ofj,Yj,Zj) And coordinate conversion is carried out to obtain the corresponding polar coordinate position (r)j,θj,φj) Wherein j =1,2, … …, m, m is the total number of grid points; will (r)j,θj,φj) Matching the azimuth, elevation, beam width and distance information of the single radar, and determining (r)j,θj,φj) When the grid point P is in the detection rangejIdentifying as a radar coverage point, otherwise, identifying the grid point as a blind point;
regarding the height interval between any two adjacent equidistant heights as a layer Lq，LqRepresenting a q-th layer, wherein the value of q is an integer which is more than or equal to 1; according to the set height Z, the lattice point P on the equal distance height hstLattice point P with spatial resolution hrjMatching, and marking the grid points with the same height as repeated grid points; and acquiring all layers at a set height Z, for eachA layer LqCalculate the layer LqThe corresponding number of lattice points, which is equal to the number of the lattice points on the equidistant height hs corresponding to the layer and the number of the lattice points on the corresponding spatial resolution hr after the duplication is removed, the layer L is judgedqWhether the corresponding grid points are all blind points or not, and if so, the layer L is divided into the blind pointsqMarking as a blind spot, otherwise, if any grid point is a radar coverage point, the layer L is marked as a blind spotqMarking as a radar coverage point;
and after all radars in the traversing weather radar network identify the layers according to the method, acquiring the radar coverage quantity of each layer, identifying the radar coverage points when one or more radars cover the layer, and identifying the blind points when no radar covers the layer.
2. The method of claim 1, wherein: adjusting the scanning mode of the radar body to be added into the radar according to the information of the selected area set by the user or the system, and the steps are as follows,
acquiring selection area information set by a user or a system; acquiring an extrapolated strong convection center and an extrapolated area in a radar body scanning mode based on ground potential and machine learning joint extrapolation, acquiring the identified strong convection center and the identified area in a radar body scanning mode based on strong convection center and area identification, and acquiring strong convection area boundary information corresponding to the extrapolated or identified strong convection center and the identified area;
comparing the boundary information of the strong convection region with the information of the selection region, and configuring a radar volume scanning mode based on a region with a smaller area in the boundary information of the strong convection region and the information of the selection region when the boundary of the strong convection region is matched with the selection region; when the area of the selected area is larger than that of the strong convection area, configuring a radar body scanning mode based on the strong convection area; and when the area of the selected area is smaller than that of the strong convection area, configuring a radar body scanning mode based on the selected area.
3. The method of claim 2, wherein: collecting selection area information set by a user through a key area setting module;
the region of interest setting module is configured to: and outputting position information of areas possibly causing urban waterlogging, areas where important activities are located and/or preset important service areas under strong convection coverage to a user, collecting selection operations of the user on the areas, and taking one or more areas selected by the user as selection areas set by the user.
4. The method according to any one of claims 1-3, wherein: the radar to be added is an X-band radar.
5. The method of claim 4, wherein: when the radar body scanning mode based on the strong convection center and region identification is adopted, the identified strong convection center and region range are combined, and a plane scanning PPI form is adopted, or a combined scanning form of vertical scanning RHI and plane scanning PPI is adopted.
6. The method of claim 4, wherein: when a radar volume scanning mode based on ground potential and machine learning joint extrapolation is adopted, the method comprises the following steps:
performing strong convection extrapolation prediction through a machine learning and time sequence deep learning model, and obtaining the position relation between the strong convection center and the region and the X-band radar after a set time period by combining a Q vector analysis method based on ground high-altitude data;
based on the quadrants of the X-band radar full-scanning area, acquiring quadrant information of the strong convection center and the area falling into the quadrants, wherein each quadrant corresponds to an angle range;
and configuring a fan scanning range of the X-band radar volume scanning mode according to the angle range corresponding to the quadrant, wherein the fan scanning range comprises a starting angle and an ending angle.
7. The method according to claim 5 or 6, characterized in that: when carrying out the discernment to strong convection center and region based on the integration information of multilayer layer combination reflectivity information, adopt two threshold value screening in order to confirm the regional core region and the boundary region of strong convection, two threshold values include high threshold value and low threshold value, high threshold value can be used for confirming the regional core region of strong convection, low threshold value can be used for confirming the regional boundary region of strong convection, the step is as follows:
acquiring a high threshold, searching a core region after increasing according to a preset step length by taking the high threshold as a base number, calculating the area of the core region, and performing centroid identification on the core region with the area larger than or equal to a preset area value;
making low threshold contour boundary identification in a range which is greater than or equal to a low threshold and less than or equal to a high threshold around the centroid to determine a boundary area of the centroid, wherein an influence area of the centroid comprises a core area and a boundary area corresponding to the influence area;
judging whether the boundary lines of the influence areas of the centroids are not intersected, acquiring independent boundary ranges of the centroids when the boundary lines are not intersected, combining and identifying the centroid positions and the boundary areas of the intersected influence areas when the boundary lines are intersected, and acquiring the combined boundary ranges, so that the influence areas of the centroids are independent and do not intersect.
8. The method according to claim 5 or 6, characterized in that: the method for constructing the three-dimensional grid field of the weather radar net of the networking weather radar comprises the following steps:
acquiring position data and volume scanning mode parameter data of a plurality of networking weather radars in a target area, wherein the position data comprises radar longitude and latitude and feed source altitude;
for each networking weather radar, converting the polar coordinates into a lattice field under Cartesian coordinates according to the position data and the volume scanning mode parameter data of the networking weather radar, and calculating the longitude range and the latitude range detected by each networking weather radar according to the detection radius of each networking weather radar;
and combining the grid point fields of the various networking weather radars to construct an initial weather radar grid three-dimensional grid point field, and determining the horizontal range, the horizontal resolution, the height range, the equidistant height hs and the spatial resolution hr of the three-dimensional grid point field.
9. The method of claim 8, wherein: determining the horizontal range of the three-dimensional grid point field of the initial weather radar net by respectively comparing the longitude range and the latitude range detected by all the networking weather radars, wherein the horizontal range comprises a longitude interval range and a latitude interval range, the longitude interval range is the sum of the distance value between the radar with the largest detection longitude and the radar with the smallest detection longitude, the detection radius of the radar with the largest detection longitude and the detection radius of the radar with the smallest detection longitude, and the latitude interval range is the sum of the distance value between the radar with the largest detection latitude and the radar with the smallest detection latitude, the detection radius of the radar with the largest detection latitude and the detection radius of the radar with the smallest detection latitude;
the horizontal resolution, height range, equidistant height hs, and spatial resolution hr are set by the user or the system.
10. A networked weather radar compliant AOI adaptive observation system according to the method of claim 1, comprising:
the three-dimensional lattice point field construction module is used for constructing a weather radar network three-dimensional lattice point field of the networking weather radar, analyzing coverage information on an equal-height surface and coverage information between equal-height layers and obtaining radar coverage information of each lattice point in the weather radar network three-dimensional lattice point field;
the area identification module is used for acquiring the reflectivity value of each networking weather radar on each grid point according to the radar coverage information of each grid point and determining the final reflectivity value of each grid point according to the reflectivity value; acquiring layer combination reflectivity information of each layer of equal-height surface on a set space height, and carrying out identification on the strong convection center and the strong convection region based on fusion information of the multilayer layer combination reflectivity information;
the cooperative observation processing module is used for acquiring data of the radars to be added, acquiring the maximum detection distance of each radar to be added and the distance between the strong convection center and the area relative to the radar, and judging whether the distance is greater than the maximum detection distance; when the distance is larger than the maximum detection distance, judging that the identified strong convection center and the identified strong convection area are not in the detection range of the radar, and triggering a radar body scanning mode based on ground potential and machine learning joint extrapolation; otherwise, triggering a radar body scanning mode based on strong convection center and area identification.
CN202110248001.8A 2021-03-06 2021-03-06 Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar Active CN112924975B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110248001.8A CN112924975B (en) 2021-03-06 2021-03-06 Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110248001.8A CN112924975B (en) 2021-03-06 2021-03-06 Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar

Publications (2)

Publication Number Publication Date
CN112924975A CN112924975A (en) 2021-06-08
CN112924975B true CN112924975B (en) 2021-10-12

Family

ID=76171769

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110248001.8A Active CN112924975B (en) 2021-03-06 2021-03-06 Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar

Country Status (1)

Country Link
CN (1) CN112924975B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114019515B (en) * 2022-01-10 2022-06-07 湖南宜通华盛科技有限公司 Weather radar and azimuth tracking detection method and system thereof, equipment and storage medium
CN114660591A (en) * 2022-05-25 2022-06-24 成都远望科技有限责任公司 Three-dimensional networking generation method based on multiple weather radars
CN117214846B (en) * 2023-11-09 2024-01-30 成都远望科技有限责任公司 Detection method for near-ground detection blind area range of weather radar

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7808422B1 (en) * 2003-07-31 2010-10-05 Rockwell Collins, Inc. Predictive and adaptive weather radar detection system and method
CN109061650A (en) * 2018-08-10 2018-12-21 北京无线电测量研究所 Networking detection method and system based on array weather radar
CN109116358A (en) * 2018-08-09 2019-01-01 成都信息工程大学 Hail identification and occurring area forecast method based on China New Generation Weather Radar
CN110297246A (en) * 2019-07-16 2019-10-01 上海市气象信息与技术支持中心 A kind of cooperative self-adapted control method of networking X-band weather radar and system
CN111323782A (en) * 2020-04-16 2020-06-23 内蒙古工业大学 Radar synchronous monitoring method based on fuzzy logic
CN111398949A (en) * 2020-04-10 2020-07-10 江苏省气象科学研究所 Networking X-band radar-based self-adaptive collaborative scanning method
CN112417375A (en) * 2020-10-26 2021-02-26 上海市气象信息与技术支持中心 Method for calculating contribution of weather radar net three-dimensional grid point field radar and application

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7365673B2 (en) * 2004-12-30 2008-04-29 Honeywell International, Inc. Compression and transmission of weather data
US7082382B1 (en) * 2005-01-25 2006-07-25 The Weather Channel, Inc. System for producing high-resolution, real-time synthetic meteorological conditions for a specified location
US7843378B2 (en) * 2008-03-04 2010-11-30 Colorado State University Research Foundation Dynamic and adaptive radar tracking of storms (DARTS)
CA2723720C (en) * 2008-05-07 2014-12-23 Colorado State University Research Foundation Dual-polarization radar processing system using time domain method
KR101131194B1 (en) * 2011-03-25 2012-03-28 메테오르 주식회사 Radar system for estimation of local precipitation using marine x-band radar network
KR101431707B1 (en) * 2013-11-26 2014-09-22 한국건설기술연구원 method of classification and quantification using data of X band dual polarization radar
CN104569981B (en) * 2015-01-28 2017-02-22 中国科学院大气物理研究所 Synergy self-adaption observing method
CN109814175B (en) * 2019-02-14 2021-01-26 浙江省气象台 Strong convection monitoring method based on satellite and application thereof
CN110135654B (en) * 2019-05-24 2021-08-27 北京百度网讯科技有限公司 Method and apparatus for predicting strong convection weather

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7808422B1 (en) * 2003-07-31 2010-10-05 Rockwell Collins, Inc. Predictive and adaptive weather radar detection system and method
CN109116358A (en) * 2018-08-09 2019-01-01 成都信息工程大学 Hail identification and occurring area forecast method based on China New Generation Weather Radar
CN109061650A (en) * 2018-08-10 2018-12-21 北京无线电测量研究所 Networking detection method and system based on array weather radar
CN110297246A (en) * 2019-07-16 2019-10-01 上海市气象信息与技术支持中心 A kind of cooperative self-adapted control method of networking X-band weather radar and system
CN111398949A (en) * 2020-04-10 2020-07-10 江苏省气象科学研究所 Networking X-band radar-based self-adaptive collaborative scanning method
CN111323782A (en) * 2020-04-16 2020-06-23 内蒙古工业大学 Radar synchronous monitoring method based on fuzzy logic
CN112417375A (en) * 2020-10-26 2021-02-26 上海市气象信息与技术支持中心 Method for calculating contribution of weather radar net three-dimensional grid point field radar and application

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
X-band Weather Radar Network in Chengdu;Xuehua Li 等;《2019 International Conference on Meteorology Observations (ICMO)》;20200309;第1-4页 *
网络化天气雷达协同自适应观测技术的实现;李思腾 等;《气象科技》;20160831;第44卷(第4期);第517-527页 *

Also Published As

Publication number Publication date
CN112924975A (en) 2021-06-08

Similar Documents

Publication Publication Date Title
CN112924975B (en) Adaptive observation method and system for AOI (automatic optical inspection) applicable to networking weather radar
CN106950614B (en) A kind of region automatic weather station hour rainfall data method of quality control
CN112558076B (en) Volume scanning mode calculation method based on networking weather radar coverage area and application
CN105203023B (en) A kind of one-stop scaling method of vehicle-mounted three-dimensional laser scanning system placement parameter
CA2320174C (en) Method and system for estimating a subscriber&#39;s location in a wireless communication system service area
CN108174343B (en) Wireless positioning method and system for power indoor communication operation and maintenance scene
CN109996175B (en) Indoor positioning system and method
US7013235B2 (en) Method, apparatus and program for determining growth of trees
CN109725324A (en) A method of realizing that plane internal coordinate positions using laser radar
CN115047463B (en) Collaborative scanning scheduling method considering radar echo attenuation in rainfall area
CN110018450A (en) AIS is associated with calibration method with radar angular system deviation
CN110261857A (en) A kind of weather radar spatial interpolation methods
CN104597449B (en) A kind of airborne many scanning weather radar target vertical contour reconstruction methods
CN103995257B (en) The detection method of target and device
CN105717491B (en) The prediction technique and prediction meanss of Weather Radar image
KR101866505B1 (en) Optimum position of LIDAR deployment in complex terrain for wind resource assessment
CN113156418B (en) Monte Carlo simulation-based radar target tracking precision prediction method
CN112417375B (en) Method for calculating contribution of weather radar net three-dimensional grid point field radar and application
CN112731564B (en) Intelligent thunder forecasting method based on Doppler weather radar data
JP5429493B2 (en) Investment plan evaluation method for rainfall observation facilities
CN113534130B (en) Multi-station radar multi-target data association method based on sight angle
CN114137637A (en) Thunderstorm center trace ensemble probability forecasting method based on lightning and radar data
CN111366921A (en) Double-station radar cross positioning method, system and medium based on distance weighted fusion
Bingöl et al. Lidar error estimation with WAsP engineering
CN109458979A (en) A kind of Downtilt measurement method based on unmanned plane visual analysis

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant