Meteorological data processing method, meteorological data processing device and electronic equipment

Abstract

The invention provides a meteorological data processing method and device, electronic equipment and a non-instantaneous computer readable storage medium, and is applied to meteorological sensing equipment, the processing method comprises the following steps: constructing a unified data model, the data model comprises equipment information, positioning information, attitude information, a time sampling window and / or meteorological observation data; and generating a meteorological data message based on the data model. According to the embodiment of the invention, the meteorological data and the meteorological sensing equipment parameters are constructed into the unified data model, so that consistent reporting, automatic decoding and standard access of heterogeneous data of multiple equipment are realized, the development cost of newly added equipment is reduced, and the problem of non-unified space-time definition of traditional equipment is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of low-altitude flight management, in particular to a meteorological data processing method and device, electronic equipment and a non-transitory computer readable storage medium. BACKGROUND

[0002] Current meteorological perception systems play a key role in low-altitude airspace safety, meteorological warning and environmental perception tasks. However, in actual application, different types of meteorological perception devices (such as radars, lidars, weather stations, etc.) generally have different data processing methods, large differences in data formats, confusion in reference system definition, poor spatio-temporal consistency, etc., resulting in the need to develop independent data acquisition and upload channels for each device, and some data often need to be converted manually or semi-automatically, significantly increasing data processing delay and increasing the risk of access errors. Especially when the device model or parameters change, the server needs to be developed again, which seriously affects the system scalability, operation and maintenance efficiency and data fusion quality. SUMMARY

[0003] The present application proposes a meteorological data processing method and device, electronic equipment and a non-transitory computer readable storage medium to solve the problem that heterogeneous detection data collected by different types of meteorological perception devices do not have a unified standard digital spatio-temporal representation.

[0004] According to an aspect of the present application, a meteorological data processing method is proposed, applied to a meteorological perception device, the processing method comprising:

[0005] constructing a unified data model, wherein the data model includes device information, positioning information, attitude information, time sampling window and / or meteorological observation data;

[0006] generating a meteorological data packet based on the data model.

[0007] According to some embodiments, the meteorological perception device includes at least one of a meteorological radar, a cloud radar, a wind profile radar, a lidar, an acoustic radar, a sounding radar, an electromagnetic lightning detector, a ceilometer, a visibility meter, a full-sky imager, an atmospheric electric field meter, a weather station,

[0008] constructing a unified data model, comprising:

[0009] adding device manufacturer information, device category and operation information in the device information to assist the backend in understanding the device state and perception range semantically.

[0010] According to some embodiments, constructing a unified data model comprises:

[0011] Based on the preset spatiotemporal reference definition, the raw observation data of different devices are standardized and described.

[0012] The spatiotemporal reference definitions include WGS84, CGCS2000, PZ90 coordinate systems and MSL, HAE, and AGL elevation systems.

[0013] According to some implementation examples, a unified data model is constructed, including:

[0014] The attitude information is defined in a standardized manner, which includes Euler angle system attitude information and antenna pointing angle system attitude information.

[0015] According to some implementation examples, a unified data model is constructed, including:

[0016] Set a time sampling window to limit the sampling time range of meteorological observation data.

[0017] According to some embodiments, the meteorological observation data includes temperature, humidity, pressure, wind speed and direction, visibility, precipitation, radiation, cloud cover, pollutants, and / or electric field.

[0018] According to some implementation examples, a unified data model is constructed, including:

[0019] The meteorological observation data is processed using either instantaneous observation data type or moving average observation data type.

[0020] According to some implementation examples, a unified data model is constructed, including:

[0021] A matrix-type data index is added to the meteorological observation data to improve the transmission and parsing efficiency of complex matrix-type data. The matrix-type data index includes radar echo data in NetCDF or GeoTIFF format.

[0022] According to some embodiments, constructing a unified data model also includes:

[0023] A matrix file is generated using the matrix-type data index, and the location information of the matrix file is indicated by a link in the data model.

[0024] According to some embodiments, the data model further includes supplementary description information for meteorological sensing equipment and supplementary meteorological description, wherein the supplementary description information for meteorological sensing equipment is used to supplement the equipment parameters of the meteorological sensing equipment, and the supplementary meteorological description is used to supplement the meteorological data parameters.

[0025] According to one aspect of this application, a meteorological data processing apparatus is provided for use in meteorological sensing equipment, the processing apparatus comprising:

[0026] A data model building unit is used to build a unified data model, wherein the data model includes device information, positioning information, attitude information, time sampling window and / or meteorological observation data;

[0027] The meteorological data report generation unit is used to generate meteorological data reports based on the data model.

[0028] According to one aspect of this application, an electronic device is provided, characterized in that it includes: a processor; a memory for storing a computer program; and when the computer program is executed by the processor, causing the processor to implement the processing method as described in any of the preceding embodiments.

[0029] According to one aspect of this application, a non-transitory computer-readable storage medium is provided, on which computer-readable instructions are stored, which, when executed by a processor, cause the processor to perform the processing method as described in any of the preceding embodiments.

[0030] According to the example embodiments of this application, by constructing meteorological data and meteorological sensing device parameters into a unified data model, consistent reporting, automatic decoding and standardized access of heterogeneous data from multiple devices are achieved, reducing the development cost of new devices and solving the problem of inconsistent spatiotemporal definitions of traditional devices. Attached Figure Description

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

[0032] Figure 1 A flowchart of a meteorological data processing method according to an example embodiment of this application is shown.

[0033] Figure 2 A block diagram of a meteorological data processing apparatus according to an example embodiment of this application is shown.

[0034] Figure 3 An electronic device is shown according to an exemplary embodiment of this application. Detailed Implementation

[0035] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0036] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of these specific details, or other methods, components, materials, apparatus, or operations may be employed. In these cases, well-known structures, methods, apparatuses, implementations, materials, or operations will not be shown or described in detail.

[0037] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0038] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0039] The specific embodiments according to this application will now be described in detail with reference to the accompanying drawings.

[0040] Figure 1 A flowchart illustrating a meteorological data processing method according to an example embodiment of this application is shown. According to the embodiment of this application, Figure 1 The processing method shown is applied to meteorological sensing equipment, and Figure 1 The method shown includes steps S101 and S103. The following will use... Figure 1 For example, a method for processing meteorological data according to an example embodiment of this application.

[0041] like Figure 1 As shown, in step S101, a unified data model is constructed, wherein the data model includes equipment information, positioning information, attitude information, time sampling window and / or meteorological observation data.

[0042] According to embodiments of this application, the meteorological sensing equipment includes at least one of the following: meteorological radar, cloud radar, wind profiler radar, lidar, acoustic radar, radiosonde radar, electromagnetic lightning detector, cloud height meter, visibility meter, all-sky imager, atmospheric electric field meter, and meteorological station.

[0043] In step S101, a unified data model is constructed, including adding equipment manufacturer information, equipment category and operation information to the equipment information to assist the backend in semantic understanding of equipment status and perception range.

[0044] In order to unify the spatiotemporal definition of meteorological sensing equipment and support the standardized description and unified backend conversion of raw observation data from different equipment, when constructing a unified data model, it is necessary to standardize the description of raw observation data from different equipment according to the preset spatiotemporal reference definition.

[0045] In specific embodiments, the spatiotemporal reference definition includes the WGS84, CGCS2000, and PZ90 coordinate systems, as well as the MSL, HAE, and AGL elevation systems. Specifically, WGGSS8844 corresponds to the WGS84 ellipsoid, CCGGCCSS22000000 corresponds to the CGCS2000 ellipsoid, and PPZZ9900 corresponds to the PZ90 ellipsoid. HAE is the ellipsoidal height / geodetic height; elevations directly obtained from GNSS are typically HAE, with the reference ellipsoid corresponding to the coordinate system identified by CRS as the benchmark. MSL is the altitude or average altitude, and the reference surface for MSL (approximately replaced by the geoid) is based on a geoid model. In some embodiments, MSL can be obtained by transforming the geoid height using an Earth gravity field model, and AGL is the altitude above the ground. This is applicable to ground obstacle avoidance and safe takeoff and landing for traditional civil aircraft during takeoff and landing phases and stable flight phases.

[0046] According to an embodiment of this application, the location information includes the device location data of the meteorological sensing device.

[0047] In a specific embodiment, the device location refers to the 3D spatial point where the meteorological sensing device is situated, generally referring to the device reference point when describing the position of the sensing target relative to the device.

[0048] In other embodiments, when constructing a unified data model, it is also necessary to standardize the definition of attitude information.

[0049] In a specific embodiment, the attitude information includes Euler angle system attitude information and antenna pointing angle system attitude information.

[0050] In some embodiments, the attitude of a sensing device is described using Euler angle system attitude information, such as a camera or other device that can flexibly adjust its attitude around three axes; the attitude of a meteorological device is described using antenna pointing angle system attitude information, such as a radar system or other device that adjusts its attitude around a fixed point.

[0051] For example, when attitude information is represented using Euler angles, the fixed point of the sensing device (usually the same as the device's positioning point) is taken as the origin, "northeast-east" is taken as the world coordinate system, and "right-front-up" is taken as the carrier coordinate system. The roll angle is the rotation angle of the meteorological sensing device around the world X-axis, with clockwise rotation being positive. The pitch angle is the rotation angle of the meteorological sensing device around the world Y-axis, with clockwise rotation being positive. The yaw angle is the rotation angle of the meteorological sensing device around the world Z-axis, with clockwise rotation being positive.

[0052] For example, when attitude information is represented by the antenna pointing angle system, the polar coordinate system with the location of the sensing device as the origin is used as the reference system. The azimuth angle is the rotation angle of the meteorological sensing device's field of view on the horizontal plane, with due north as the reference (corresponding to an azimuth angle of 0). The counterclockwise rotation of the line of sight at the center of the field of view is positive. The elevation angle is the elevation angle of the meteorological sensing device's field of view relative to the horizontal plane, with the azimuth angle of the horizontal plane as the reference (corresponding to an elevation angle of 0). The line of sight at the center of the field of view is positive when it is away from the ground (i.e., "looking up"), and negative when it is "looking down". The twist angle is the angle of twist of the sensing device's field of view around its own line of sight at the center of the field of view, with the counterclockwise direction being positive.

[0053] According to an embodiment of this application, when constructing a unified data model, it is necessary to set a time sampling window to limit the sampling time range of meteorological observation data.

[0054] According to an embodiment of this application, the time sampling window includes the sensing sampling time, the observation start time, and the sensing sampling location.

[0055] In some embodiments, the sensing sampling time is used to record the sampling end time when the sensing device completes one observation sampling process, that is, the time point at which the observation result represented by the data record is finally generated. This timestamp is applicable to all single-point sampling sensors (such as thermometers, hygrometers, radar echoes, image frames, etc.), and also to each frame and each record in continuous observation data. For non-continuous sampling devices, the sensing sampling time is the end time of each observation cycle; for streaming or real-time sampling devices, the sensing sampling time is the generation time of this data record.

[0056] In other embodiments, the observation start time is used to record the start time of the current observation sampling period, that is, the starting point at which the sensing device initiates the acquisition operation of this observation data. The observation start time is suitable for observation data with sampling period or time window characteristics (such as sliding precipitation, wind field matrix, etc.).

[0057] In specific embodiments, when the observation data is a statistical, integrated, or continuous sampling result of a certain time period (such as 5-minute sliding precipitation or radar scan data), the observation start time is used to mark the beginning of that time period; for multi-frame synthesis observations (such as image sequence fusion or radar multi-beam synthesis), the observation start time indicates the starting moment of the data acquisition behavior.

[0058] In some embodiments, the sensing sampling location is used to precisely define the physical location of the current observation data. The sensing sampling location represents the coordinates of the three-dimensional sampling point corresponding to the observation itself, rather than the geometric center or casing location of the sensing device. For any spatially dependent observation data, the actual geographic location of its sampling point must be clearly indicated through the sensing sampling location.

[0059] In specific embodiments, for contact sensors (such as thermometers, hygrometers, anemometers, rain gauges, etc.), if the device integrates multiple sensors and they are distributed at a considerable distance in space (greater than 1 meter from the device's center point), the sampling location for each type of observation should correspond to the absolute coordinates of its physical installation point, and the device's center point should not be used interchangeably. For non-contact telemetry devices (such as radar, laser, imaging sensors, etc.), this location should be calculated using parameters such as sensor attitude, pitch angle, azimuth angle, and distance measurement, representing the measurement point or focal point of the measurement beam / wavelength in space. For volumetric or area scanning observations, a representative spatial centroid or main measurement point location must be used for labeling.

[0060] According to embodiments of this application, meteorological observation data include temperature, humidity, pressure, wind speed and direction, visibility, precipitation, radiation, cloud cover, pollutants, and / or electric fields.

[0061] In a specific embodiment, when constructing a unified data model, the meteorological observation data is processed using either instantaneous observation data type or moving average observation data type.

[0062] In a specific embodiment, meteorological observation data includes instantaneous air temperature, real-time temperature, instantaneous relative humidity, real-time relative humidity, instantaneous surface air pressure, real-time surface air pressure, instantaneous wind speed, instantaneous wind direction, real-time wind speed, real-time wind direction, instantaneous visibility, real-time visibility, precipitation type, precipitation amounts of multiple different types, total cloud cover, cloud cover rate, thick cloud cover, thin cloud cover, cloud base height, cloud top height, sunrise time, sunset time, solar azimuth angle, solar altitude angle, overall illumination intensity, sky brightness, total radiation, shortwave radiation, longwave radiation, ultraviolet index, dew point temperature, aerosol optical thickness, and electric field intensity.

[0063] The instantaneous air temperature is recorded at the sampling time (sampleTime), representing the instantaneous air temperature information acquired by the sensing device. Real-time temperature represents the average air temperature acquired by the sensing device within a 2-minute time window [sampleTime–60s, sampleTime] corresponding to the sampling time (sampleTime). Instantaneous relative humidity is recorded at the sampling time (sampleTime), representing the percentage of water vapor content in the air to saturated water vapor content, indicating the degree of air humidity. Real-time relative humidity represents the average humidity acquired by the sensing device within a 1-minute time window [sampleTime–60s, sampleTime], representing the percentage of water vapor content in the air to saturated water vapor content, indicating the degree of air humidity. Instantaneous ground pressure is recorded at the sampling time (sampleTime), representing the atmospheric pressure value at the station's altitude measured by the sensing device. Real-time ground pressure represents the atmospheric pressure value at the station's altitude measured by the sensing device within a 1-minute time window [sampleTime–60s, sampleTime]. Instantaneous wind speed represents the raw instantaneous wind speed value measured by the sensing device within the minimum observation sampling period (e.g., 1 second) at the sampling time corresponding to sampleTime. It reflects the instantaneous change in the wind field at that point in time. Instantaneous wind direction represents the current wind direction angle value measured by the sensing device at the sampleTime field, i.e., the direction of airflow at that moment. It is obtained based on a single sampling period (e.g., 1 second) without time smoothing. Real-time wind speed represents the vector average value (scalar method) of the wind speed component within a 2-minute time window [sampleTime–120s, sampleTime] sliding forward from the sampling time corresponding to sampleTime. It reflects the representativeness of the current wind field state. Real-time wind direction represents the vector average value (i.e., average wind direction) of the wind direction data within a 2-minute time window [sampleTime–120s, sampleTime] preceding the sampling time corresponding to sampleTime. Instantaneous visibility records the visibility measured by a visibility sensor (usually an automatic scattering or transmission type) under the current meteorological conditions at the sampling time sampleTime. Real-time visibility represents the visibility measured by a visibility sensor (usually an autoscattering or transmission type) within a forward 1-minute time window [sampleTime – 60s, sampleTime]. Precipitation type identifies the current precipitation type, such as rain, snow, freezing rain, etc. Total cloud cover represents the proportion of pixels identified as clouds out of all recognizable sky pixels within the entire sky cone (hemispherical image), reflecting the overall degree of visible cloud cover in the current sky.Cloud cover percentage represents the percentage of cloud obscuration calculated using hemispherical projection correction methods (such as visual solid angle weighting) after comprehensively considering the geometric projection differences of the entire sky image. It emphasizes the three-dimensional distribution and area impact of clouds within the actual sky field of view. Thick cloud cover represents the percentage of pixels in the entire sky image that, after image classification algorithms (such as deep learning models), will be classified as thick clouds (e.g., cumulus, stratocumulus, cumulonimbus, nimbostratus). This field emphasizes the optical thickness or visible opacity of clouds, reflecting their energy and radiation shielding characteristics. Thin cloud cover represents the percentage of pixels identified as thin clouds (e.g., cirrus, translucent stratus, and upper-level haze). It emphasizes the low optical thickness and translucency of the cloud layer. This field can be summed with thickCloudCover to exceed totalCloudCover, revealing the cloud layer stacking structure. Cloud base height represents the vertical distance from the lowest visible cloud base directly above the observation point to the ground observation point. This height corresponds to the boundary between clear air and the cloud mass below the cloud layer. Cloud top height represents the vertical distance from the highest visible peak of a cloud directly above the observation point to the ground-based observation point. This height reflects the position of the cloud structure's top, and is used as a reference for the cloud top regardless of whether the cloud is isolated or layered. Sunrise time represents the moment when the upper edge of the sun first touches the horizon at the observation point's local time. This moment is calculated based on the geometric zenith angle of the sun's center being 90.833° (considering atmospheric refraction of approximately 0.566° and the sun's radius of approximately 0.266°). Sunset time represents the moment when the upper edge of the sun last sets below the horizon at the observation point's local time. It also uses the moment when the sun's center sinks to the geometric zenith angle of 90.833°, combined with atmospheric refraction and sun size corrections to reflect the actual visible sunset.

[0064] The solar azimuth angle represents the position of the sun on a horizontal plane relative to the local geographic true north. Measured clockwise, this angle is defined as the angle required for the observer to rotate clockwise from true north to the direction of the sun's projection, ranging from 0° to 90° (east), 180° (south), 270° (west), and 360° (north). The solar altitude angle represents the angle of elevation of the sun relative to the horizontal plane at the observation location, defined as the angle between the center of the sun and the horizon. Total illuminance represents the total luminous intensity received per unit horizontal surface (including direct sunlight, diffused skylight, and reflected light from the ground), typically measured using a photoelectric sensor or illuminance meter placed flat on the ground. This value is calculated by measuring the luminous flux received per unit area and, according to the International Commission on Illumination (CIE) definition, weighting it according to the human eye's spectral response, and converting it to lux (1 lm / m²). 2The value represents the intensity of light received per unit horizontal surface on the ground from only diffused light from the sky (excluding direct sunlight). This value reflects the ambient lighting conditions of the pure "diffuse sky field" when unaffected by direct sunlight, and is particularly suitable for analyzing cloud thickness, cloud transmittance, and atmospheric attenuation effects. Total radiation represents the sum of all electromagnetic radiation energy flux received per unit area of ​​the ground per unit time, including all bands of solar shortwave radiation and longwave radiation emitted by the atmosphere and the Earth's surface. Shortwave radiation represents the portion of shortwave electromagnetic radiation received per unit area of ​​the ground from direct or diffused solar radiation, typically including visible light, ultraviolet light, and near-infrared light in the wavelength range of approximately 0.3 μm to 5 μm. Shortwave reflection and absorption determine the surface energy absorption rate under clear / cloudy / dark conditions. Longwave radiation represents the thermal radiation received per unit area of ​​the Earth's surface from the atmosphere, clouds, and the Earth itself. It primarily falls within the wavelength range of 4μm to 100μm and is classified as thermal infrared radiation related to the temperature of Earth objects and the atmosphere. Longwave emission and re-emission regulate the rate of cooling and temperature stability at night. The ultraviolet index measures the raw physical value of the intensity of solar ultraviolet (UVB and UVA) radiation. With the increasing use of carbon fiber structures in drones, ultraviolet radiation significantly accelerates the aging of these fibers; this parameter serves as a reference. Dew point temperature represents the temperature threshold at which air must be cooled to reach full saturation (100% relative humidity) and begin to condense (water droplets or frost) under current atmospheric pressure and water vapor content. Aerosol optical thickness represents the attenuation capacity of suspended particles in the atmosphere to solar or surface radiation; it is the result of integrating the aerosol attenuation coefficient over the path length along a vertical path. Electric field strength reflects the change in potential difference per unit distance on the Earth's surface or in the atmosphere, particularly used to monitor the degree of charge accumulation in thunderstorms or strong convective clouds. The background voltage in clear weather is approximately ±100 V / m, but in thunderstorm cloud areas it can suddenly increase to several thousand V / m or even more than ten kV / m. For example, it can reach 6 kV / m in thunderstorm generation areas, and in extreme cases, it can exceed 20 kV / m. Ground-sensed electric field strength data directly reflects the likelihood of lightning strikes in a region and can serve as one of the key triggering conditions for drones to be prohibited from entering certain areas or to make an emergency return.

[0065] According to an embodiment of this application, when constructing a unified data model, a matrix-type data index is added to the meteorological observation data to provide address index information for reporting meteorological matrix data files (wind field, cloud structure, meteorological radar echoes). The matrix-type data index includes radar echo data in NetCDF or GeoTIFF format.

[0066] In a specific embodiment, constructing a unified data model further includes: generating matrix files using a matrix-type data index, and indicating the location information of the matrix files in the data model via links. When processing meteorological data in the backend, it is determined whether the matrix files need to be retrieved based on business requirements.

[0067] In some embodiments, the constructed data model also includes supplementary descriptive information of meteorological sensing devices and supplementary meteorological descriptions, thereby improving the compatibility of the processing method and adapting to data access from new or uncommon meteorological sensing devices in the future, as well as seamless compatibility with new sensing devices and future expansion.

[0068] In specific embodiments, meteorological supplementary descriptions are used to supplement meteorological data parameters and can correspond to different types of meteorological sensing devices. By supporting the reporting of supplementary meteorological parameters, the availability and richness of meteorological data can be improved.

[0069] In other embodiments, the meteorological sensing device supplementary description information is used to supplement the device parameters of the meteorological sensing device, which can support other unknown devices.

[0070] In some embodiments, attribute parameters of other location sensing devices can be determined using attribute parameters of known devices.

[0071] For example, for radar and vision fusion sensing devices, the parameter attributes of radar sensing devices and vision sensing devices can be spliced ​​or merged as the parameter attributes of the fusion sensing device, thereby solving the problem that the technical solution for identifying low-altitude target flying objects needs to be modified every time the demand increases.

[0072] For example, by using meteorological sensing equipment to supplement the description information, the type definition of the meteorological sensing equipment and the reporting of operating parameters, such as operating mode (e.g., radar scan mode, number of altitude layers, etc.), can facilitate the backend to perform function identification, resource scheduling and dynamic sensing model invocation.

[0073] In step S103, a meteorological data report is generated based on the data model.

[0074] According to an embodiment of this application, in step S105, a sensing target data message is generated based on a data model according to a preset message standard.

[0075] according to Figure 1 The illustrated embodiment constructs a unified data model from meteorological data and meteorological sensing device parameters, enabling consistent reporting, automatic decoding, and standardized access of heterogeneous data from multiple devices. This reduces the development cost of new devices and solves the problem of inconsistent spatiotemporal definitions in traditional devices.

[0076] According to embodiments of this application, reporting data collected by meteorological sensing devices via matrix file index addresses not only reduces the difficulty of backend data parsing but also avoids the situation in traditional methods where the transmitted matrix direction is reversed and it is impossible to determine whether it is reversed. By supporting two mainstream scientific raster formats, NetCDF and GeoTIFF, it provides efficient indexing and parsing paths for different technical approaches and different matrix data types (cloud images, wind fields, echo images, etc.).

[0077] The above description primarily focuses on the methodological aspects of the embodiments of this application. Those skilled in the art should readily recognize that, based on the operations or steps described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Those skilled in the art can implement the described functionality in different ways for each specific operation or method, and such implementations should not be considered beyond the scope of this application.

[0078] The apparatus embodiments of this application are described below. For details not described in the apparatus embodiments of this application, please refer to the method embodiments of this application.

[0079] Figure 2 A block diagram of a meteorological data processing apparatus according to an example embodiment of this application is shown, such as... Figure 2 The processing apparatus shown includes a data model building unit 201 and a meteorological data message generation unit 203. According to an embodiment of this application, Figure 2 The processing device shown is used in meteorological sensing equipment.

[0080] exist Figure 2 In the processing device shown, the data model construction unit 201 is used to construct a unified data model, wherein the data model includes device information, positioning information, attitude information, time sampling window and / or meteorological observation data; the meteorological data message generation unit 203 is used to generate meteorological data messages based on the data model.

[0081] Figure 3 An electronic device according to an exemplary embodiment of this application is shown. Reference is made below. Figure 3 To describe an electronic device 200 according to this embodiment of the present application. Figure 3 The electronic device 200 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0082] like Figure 3As shown, the electronic device 200 is presented in the form of a general-purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one storage unit 220, a bus 230 connecting different system components (including storage unit 220 and processing unit 210), a display unit 240, etc.

[0083] The storage unit stores program code that can be executed by the processing unit 210, causing the processing unit 210 to perform the methods described in this specification according to various exemplary embodiments of this application. For example, the processing unit 210 can perform the methods described above.

[0084] Storage unit 220 may include readable media in the form of volatile storage units, such as random access memory (RAM) 2201 and / or cache memory 2202, and may further include read-only memory (ROM) 2203.

[0085] Storage unit 220 may also include a program / utility 2204 having a set (at least one) program module 2205, such program module 2205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0086] Bus 230 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0087] Electronic device 200 can also communicate with one or more external devices 300 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 200, and / or with any device that enables electronic device 200 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 250. Furthermore, electronic device 200 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 260. Network adapter 260 can communicate with other modules of electronic device 200 via bus 230. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0088] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. The technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this application.

[0089] Software products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example,, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0090] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0091] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0092] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the aforementioned functions.

[0093] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0094] According to an embodiment of this application, a computer program is proposed, including a computer program or instructions, which, when executed by a processor, can perform the methods described above.

[0095] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

[0096] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0097] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, and on the specific implementation methods and application scope of this application, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for processing meteorological data, characterized in that, The processing method, applied to meteorological sensing equipment, includes: Construct a unified data model, wherein the data model includes device information, positioning information, attitude information, time sampling window and / or meteorological observation data; Meteorological data reports are generated based on the data model.

2. The processing method according to claim 1, characterized in that, The meteorological sensing equipment includes at least one of the following: meteorological radar, cloud radar, wind profiler radar, lidar, acoustic radar, radiosonde radar, electromagnetic lightning detector, cloud height meter, visibility meter, all-sky imager, atmospheric electric field meter, and meteorological station. Constructing a unified data model includes: The device information includes device manufacturer information, device category, and operational information to assist the backend in semantically understanding the device status and sensing range.

3. The processing method according to claim 1, characterized in that, Constructing a unified data model includes: Based on the preset spatiotemporal reference definition, the raw observation data of different devices are standardized and described. The spatiotemporal reference definitions include WGS84, CGCS2000, PZ90 coordinate systems and MSL, HAE, and AGL elevation systems.

4. The processing method according to claim 1, characterized in that, Constructing a unified data model includes: The attitude information is defined in a standardized manner, which includes Euler angle system attitude information and antenna pointing angle system attitude information.

5. The processing method according to claim 1, characterized in that, Constructing a unified data model includes: Set a time sampling window to limit the sampling time range of meteorological observation data.

6. The processing method according to claim 1, characterized in that, The meteorological observation data includes temperature, humidity, pressure, wind speed and direction, visibility, precipitation, radiation, cloud cover, pollutants, and / or electric fields.

7. The processing method according to claim 1, characterized in that, Constructing a unified data model includes: The meteorological observation data is processed using either instantaneous observation data type or moving average observation data type.

8. A meteorological data processing device, characterized in that, The processing device, applied to meteorological sensing equipment, includes: A data model building unit is used to build a unified data model, wherein the data model includes device information, positioning information, attitude information, time sampling window and / or meteorological observation data; The meteorological data report generation unit is used to generate meteorological data reports based on the data model.

9. An electronic device, characterized in that, include: processor; Memory, used to store computer programs; When the computer program is executed by the processor, the processor performs the processing method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor, cause the processor to perform the processing method as described in any one of claims 1-7.

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