Method and device for mobile fusion of millimeter wave cloud radar and background field data on unmanned surface vehicle and medium

Abstract

The application discloses an unmanned ship offshore millimeter wave cloud radar and background field data mobile fusion method, device and medium, and relates to the technical field of meteorological detection. The method comprises the following steps: acquiring a navigation route of an unmanned ship and radar detection parameters; controlling the unmanned ship to autonomously navigate according to the set route based on the navigation route of the unmanned ship and the radar detection parameters; wherein the unmanned ship is loaded with a millimeter wave cloud radar system; acquiring millimeter wave cloud radar data of the unmanned ship in the navigation process; wherein the millimeter wave cloud radar data is collected by the millimeter wave cloud radar system based on the radar detection parameters; performing cloud amount inversion on the millimeter wave cloud radar data based on a reflectivity and cloud amount formula to obtain cloud amount data in the navigation route process; and interpolating the cloud amount data into three-dimensional cloud amount data of background field data to obtain fusion data. The application can realize accurate fusion of radar data and background field data, thereby obtaining more comprehensive and accurate cloud layer information.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorological detection, more particularly, to a method and device for mobile fusion of millimeter wave cloud radar and background field data on an unmanned ship at sea, and a medium. BACKGROUND

[0002] Currently, satellites and millimeter wave cloud radars are commonly used to observe cloud amount, cloud shape, cloud height, etc.

[0003] Satellite observation technology:

[0004] Cloud amount observation: Satellites capture images of the Earth's surface and atmosphere using onboard multispectral or high-resolution cameras. These images can show the distribution and coverage of cloud layers. By analyzing the pixels in the images, it is possible to determine which areas are covered by cloud layers, and thus calculate the cloud amount. This is usually achieved by comparing the reflectivity or brightness of cloud layers with surrounding cloud-free areas.

[0005] Cloud height observation: Satellites use active or passive sensors such as lidar (Light Detection and Ranging) or microwave radiometers to measure the height of cloud layers. Lidar determines the height of cloud layers by emitting laser pulses towards them and measuring the time of return, while microwave radiometers estimate the height of cloud layers by analyzing the microwave radiation emitted by them. Lidar uses the speed of light and the time difference to calculate the height of cloud layers. Microwave radiometers estimate the height of cloud layers based on the relationship between the intensity and frequency of microwave radiation emitted by cloud layers and parameters such as temperature, humidity, and height.

[0006] Cloud shape observation: It mainly relies on high-resolution visible and infrared remote sensing images. These images can capture the details and textures of cloud layers, helping researchers identify different cloud shapes.(a) Cirrus: Cirrus clouds usually present a fine, filamentous structure with high albedo (i.e., the ability to reflect sunlight). In visible light images, they usually appear white or light gray. Cirrus clouds are usually located at high altitudes and have little impact on the weather, but can serve as an indicator of other weather systems.(b) Cumulus: Cumulus clouds are common low clouds that present a fluffy, clumpy structure. In visible light images, they usually appear white or gray. Cumulus clouds are usually associated with fair weather, but sometimes can develop into cumulonimbus clouds.(c) Stratus: Stratus clouds are uniformly distributed low clouds that usually cover the entire sky. In visible light images, they appear gray or light white. Stratus clouds are usually associated with stable weather conditions and can bring continuous precipitation.(d) Cumulonimbus: Cumulonimbus clouds are tall, towering clouds that are usually accompanied by heavy precipitation, thunderstorms, and lightning. In visible light images, cumulonimbus clouds appear as thick white or gray cloud masses. Cumulonimbus clouds usually have complex structures, including towering cloud towers and rain shafts at the bottom.

[0007] Millimeter wave cloud radar observation technology:

[0008] Millimeter wave cloud radar is an active microwave atmospheric remote sensing device that uses the backscattering echo produced by the interaction between electromagnetic waves in the millimeter wave frequency band (35 GHz, 75 GHz) and particles such as clouds, rain, and fog in the atmosphere to detect meteorological information of the atmosphere. Millimeter wave cloud radar has the following characteristics and advantages in observing cloud cover, cloud height, and cloud shape:

[0009] Cloud cover observation: Millimeter wave cloud radar can detect the backscattering echo of cloud layers, and by analyzing the intensity and time series changes of the echo, the coverage and cloud cover of the cloud layer can be determined. This observation method is not limited by day and night and can be performed all day long.

[0010] Cloud height observation: Millimeter wave cloud radar calculates the height of the cloud layer by measuring the time delay of the echo. Since the propagation speed of millimeter waves in the atmosphere is close to the speed of light, the height information of the cloud layer can be obtained by accurately measuring the time delay.

[0011] Cloud shape observation: Millimeter wave cloud radar can infer the shape and structure of the cloud layer by analyzing the intensity, shape, and vertical structure of the echo. For example, by analyzing the intensity and vertical structure of the echo, different types of clouds such as cirrus, cumulus, and stratus can be distinguished.

[0012] Problems in the prior art:

[0013] (1) Satellite cloud observation can obtain comprehensive cloud cover, cloud shape, and cloud height information, but this method is only suitable for high cloud observation because it is from the sky downward, which is not conducive to the observation of low clouds. In addition, due to the relatively low time resolution (hours) and spatial resolution (1-4 kilometers) of satellite observation data, small cloud clusters are often missed.

[0014] (2) Millimeter wave cloud radar transmits electromagnetic waves with a wavelength of 3 mm from bottom to top, with a data update rate of 1 minute and a distance resolution of 30 meters, which can quickly and accurately measure the cloud base height and cloud top height of low clouds. By mounting it on an unmanned boat and navigating it, it can achieve point observation of small cloud clusters. However, because millimeter wave cloud radar obtains radial echoes one by one, it cannot cover the entire sky and cannot obtain comprehensive cloud cover and cloud shape information. Summary of the invention

[0015] To solve the above technical problems, the present application provides an unmanned boat sea millimeter wave cloud radar and background field data mobile fusion method, device and medium, which combines the high temporal and spatial resolution PPI scanning data of millimeter wave cloud radar and global background field analysis data (GFS data, ERA5 data, etc.), and fuses the two types of data to obtain more detailed and comprehensive cloud layer information.

[0016] In a first aspect, the present application provides a method for mobile fusion of unmanned surface vehicle offshore millimeter wave cloud radar and background field data, the method comprising:

[0017] obtaining a navigation route of the unmanned surface vehicle and radar detection parameters;

[0018] converting a radar coordinate system based on the unmanned surface vehicle into a geodetic coordinate system;

[0019] integrating the cloud amount data of the previous time and the current geodetic coordinate system millimeter wave cloud radar data to set the navigation route of the unmanned surface vehicle for autonomous navigation; wherein the unmanned surface vehicle is equipped with a millimeter wave cloud radar system;

[0020] obtaining millimeter wave cloud radar data such as reflectivity factor, velocity, spectral width, etc. during the navigation of the unmanned surface vehicle; wherein the millimeter wave cloud radar data is collected by the millimeter wave cloud radar system based on the radar detection parameters;

[0021] based on the reflectivity factor and the cloud amount formula, the millimeter wave cloud radar data is cloud amount inversion, and the cloud amount data during the navigation route is obtained;

[0022] the cloud amount data is interpolated into the three-dimensional cloud amount data of the background field data to obtain the fusion data.

[0023] Further, the wavelength of the millimeter wave cloud radar system is 3mm.

[0024] Further, the cloud amount, cloud base height and cloud top height are included.

[0025] Further, in the feature fusion sharing layer, the cloud amount data is interpolated into the three-dimensional cloud amount data of the background field data to obtain the fusion data by the following formula:

[0026]

[0027] wherein, w g represents the weight coefficient of the radar data sampling point;

[0028] R O ,θ O ,Φ o represent the spherical coordinates of the interpolated radar data point P, respectively the radial distance, the azimuth angle and the elevation angle of the data point;

[0029] R g ,θ g ,Φ g represent the spherical coordinates of the original radar data point, wherein g = 1, 2, …, N;

[0030] 2 represents the smoothing parameter;

[0031] f g representing reflectivity values of the original radar data points;

[0032] f o representing reflectivity values of the interpolated data points;

[0033] N represents the number of distance bins within the reflectivity value impact area of R g , θ g , Φ g .

[0034] Further, the background field data includes EC reanalysis data or GFS global forecast field data.

[0035] Further, after the cloud amount data is interpolated into the three-dimensional cloud amount data of the background field data to obtain the fusion data, the method further comprises:

[0036] According to the fusion data, secondary meteorological products such as cloud amount, cloud base height, cloud top height, and cloud shape are generated.

[0037] In a second aspect, the present application provides a unmanned surface vehicle offshore millimeter wave cloud radar and background field data mobile fusion device, the device comprises:

[0038] The unmanned surface vehicle navigation posture and route parameter acquisition unit is configured to acquire the navigation route of the unmanned surface vehicle and radar detection parameters;

[0039] The unmanned surface vehicle control unit is configured to convert the radar coordinate system based on the unmanned surface vehicle into the geodetic coordinate system; and set the unmanned surface vehicle navigation route based on the fusion cloud amount data of the previous time and the current geodetic coordinate system millimeter wave cloud radar data for autonomous navigation; wherein the unmanned surface vehicle is equipped with a millimeter wave cloud radar system;

[0040] The millimeter wave cloud radar parameter data acquisition unit is configured to acquire millimeter wave cloud radar data of the unmanned surface vehicle during navigation; wherein the millimeter wave cloud radar data is collected based on the radar detection parameters by the millimeter wave cloud radar system;

[0041] The cloud parameter inversion unit is configured to perform cloud amount inversion on the millimeter wave cloud radar data based on a reflectivity and cloud amount formula to obtain cloud amount data during the navigation route;

[0042] The millimeter wave cloud radar and background field data fusion unit is configured to interpolate the cloud amount data into the three-dimensional cloud amount data of the background field data to obtain fusion data.

[0043] Further, the millimeter wave cloud radar and background field data fusion unit is further configured to:

[0044] Further, the millimeter wave cloud radar and background field data fusion unit is further configured to:

[0045] In the formula, w g represents the weight coefficient of the radar data sampling point;

[0046] R O , theta O , Phi o represent the spherical coordinates of the interpolated radar data point P, respectively the radial distance, the azimuth angle and the elevation angle of the data point;

[0047] R g , theta g , Phi g represent the spherical coordinates of the original radar data point, wherein g = 1, 2, …, N;

[0048] 2 represents the smoothing parameter;

[0049] f g represents the reflectivity value of the original radar data point;

[0050] f o represents the reflectivity value of the interpolated data point;

[0051] N represents the number of distance bins in the reflectivity value influence area of R g , theta g , Phi g .

[0052] In a third aspect, the present application provides a readable storage medium, the readable storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the method as described above.

[0053] The present application has at least the following beneficial effects:

[0054] The present application has wide application prospects and market demand, and can provide important support for meteorology, ocean environment and other fields; specifically, it can be widely applied in meteorological observation, ocean environment research, ship navigation safety and other fields. With the rapid development of global climate change and ocean economy, the demand for marine weather and environmental information will continue to grow. Therefore, the present application has broad market prospects and great commercial value. At the same time, the technology can also provide decision support for relevant departments, and help the rational development and utilization of marine resources. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 A flow chart of a unmanned surface vehicle offshore millimeter wave cloud radar and background field data mobile fusion method according to an embodiment of the present application is shown.

[0056] Figure 2A diagram showing the principle of interpolating cloud data into a three-dimensional cloud data of a background field according to an embodiment of the present application is shown.

[0057] Figure 3 A structural diagram of a unmanned surface vehicle offshore millimeter wave cloud radar and background field data mobile fusion device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] To enable those skilled in the art to better understand the technical solutions of the present application, the present application will be described in detail below in conjunction with the drawings and specific embodiments. The embodiments of the present application will be further described in detail below in conjunction with the drawings and specific embodiments, but not as a limitation on the present application. The order in which each step is described herein as an example should not be considered as a limitation, and those skilled in the art should know that the order can be adjusted, as long as the logic between them is not destroyed and the whole process cannot be realized.

[0059] With the continuous development of meteorology and ocean science, the monitoring and data analysis of cloud layers become more and more important. As a kind of high-precision, high-resolution detection equipment, millimeter wave cloud radar has a wide application prospect in the field of cloud layer detection and meteorological analysis. However, due to the complexity of cloud environment and the diversity of radar data, how to realize the accurate fusion of radar data and background field data, so as to obtain more comprehensive and accurate cloud information, is the key problem faced by current technology.

[0060] To realize the accurate fusion of radar data and background field data, so as to obtain more comprehensive and accurate cloud information, the present application provides a kind of unmanned surface vehicle offshore millimeter wave cloud radar and background field data mobile fusion method, as shown in the figure, the method comprises steps S10 to S50, which are described in detail as follows. Figure 1

[0061] S10, obtaining the navigation parameters and radar detection parameters of the unmanned surface vehicle.

[0062] In this embodiment, the unmanned surface vehicle determines the location of the cloud that needs to be focused on according to the global background field data and the onboard millimeter wave cloud radar detection parameters, sets the navigation route, and generates the navigation parameters of the unmanned surface vehicle such as speed, roll angle, pitch angle, heading angle, current longitude and current latitude.

[0063] (1) Determine the cloud that needs to be focused on

[0064] ​Different types of clouds have different precipitation intensities and properties due to differences in their cloud height, cloud thickness, and cloud temperature. For example, cumulonimbus clouds are mixed clouds with ice and water coexisting, and have large cloud thickness and water content, as well as strong updrafts and downdrafts, so they can produce heavy rain, snow, and even hail. Stratocumulus clouds, on the other hand, can only produce light drizzle, and stratocumulus clouds can produce light rain, snow, and graupel.

[0065] Higher clouds are usually associated with more intense convective activity, which can lead to more intense precipitation. For example, convective clouds (such as cumulonimbus clouds) usually form at higher altitudes and are associated with intense convective activity, which can lead to heavy precipitation. Lower clouds (such as stratocumulus and stratocumulus clouds) can cause light to moderate precipitation.

[0066] When cloud coverage increases, the number of cloud droplets or ice crystals also increases, increasing the likelihood of precipitation. Conversely, when cloud coverage decreases, the likelihood of precipitation also decreases.

[0067] Therefore, the method needs to focus on cumulonimbus or cumulus clouds that are high and thick.

[0068] (2) Detection parameters of millimeter wave cloud radar

[0069] a. Millimeter wave radar reflectivity factor

[0070] Cloud and precipitation particles often do not satisfy the Rayleigh approximation, and the millimeter wave radar reflectivity η is written as follows:

[0071]

[0072] where Ze is defined as the equivalent radar reflectivity factor. The radar reflectivity factor Z is a characteristic of the cloud and precipitation particle spectrum distribution and has nothing to do with the radar wavelength. The equivalent reflectivity factor involves the scattering characteristics of cloud and precipitation particles, and different frequency bands of electromagnetic waves encounter cloud and precipitation particles, and because they may not satisfy the Rayleigh scattering condition, the measured equivalent reflectivity factor has certain differences.

[0073] The radar equivalent reflectivity factor Ze calculated according to the meteorological radar equation can be expressed as follows:

[0074] Z e =CR 2 P r

[0075] where R represents the distance between the antenna and the target (unit: m), C is the radar constant calculated using radar parameters, and Pr is the power value of the target reflectivity returned by the antenna.

[0076] b. Millimeter wave radar radial velocity and spectral width

[0077] The meteorological echo signal is s(t), and the correlation function R(T) of the successive pulse echo signals in the same effective irradiation volume is:

[0078]

[0079] Where T is the successive pulse time interval.

[0080] Since the correlation function and the power spectral density of the signal are Fourier transforms of each other, the echo signal power spectral density function S(f) can be obtained as:

[0081]

[0082] The echo power P r is the integral of the power spectral density function over the entire frequency:

[0083]

[0084] The average Doppler frequency is the weighted average of the Doppler frequency f with the power spectral density function S(f) as the weight:

[0085]

[0086] The spectral variance is defined as:

[0087]

[0088] According to the relationship between Doppler velocity and frequency, the average Doppler velocity and the velocity spectrum width can be obtained as:

[0089]

[0090] (3) Inversion of cloud amount, cloud height and cloud thickness according to the detection parameters of the millimeter wave cloud radar

[0091] This method is based on the existing research basis to judge that the reflectivity factor of the millimeter wave cloud radar is greater than -20 dBz as cloud, and then the points with reflectivity factor greater than this threshold are judged one by one according to the azimuth and distance library, and the count is N cloud , the number of PPI data points is N dot , the cloud amount of the connected domain can be calculated as N cloud / N cloud ; then, according to the method of judging from bottom to top for each distance library, find the average lowest point (cloud base height) and the average highest point (cloud top height) that meet ≥-20 dBz in height, and the difference between the two is the cloud thickness; after meeting the above conditions, if more than 80% of the reflectivity factor in the cloud is greater than -5 dBz, it is judged as cumulonimbus or cumulus.

[0092] S20: converting the radar coordinates based on the unmanned boat into geodetic coordinates.

[0093] Since the global background field data is based on geodetic coordinates, it is necessary to convert the PPI data of the millimeter-wave cloud radar into geodetic coordinates in combination with the attitude parameters of the unmanned boat before spatially interpolating the millimeter-wave radar data into the background field data.

[0094] During the navigation of the unmanned boat, the attitude information of the inertial navigation is obtained: roll angle, pitch angle, heading angle, and the coordinate transformation model between the radar coordinate system and the geographic coordinate system is established. Figure 2 As shown in FIG, the coordinate transformation sequence from the radar coordinate system to the geographic coordinate system is performed in the order of roll angle γ-pitch angle β-heading angle α.

[0095] The transformation matrix T of the roll angle γ for:

[0096]

[0097] The conversion matrix Tα of the heading angle is:

[0098]

[0099] The transformation matrix T of the pitch angle β for:

[0100]

[0101] The transformation matrix Tradar from the geodetic coordinate system to the shipborne millimeter-wave cloud radar coordinate system is:

[0102] Tradar=T α T γ T β

[0103] The transformation matrix from the radar coordinate system to the geographic coordinate system is:

[0104] T Geo =(T radar ) T

[0105] S30: Combining the fused cloud cover data of the previous time and the millimeter-wave cloud radar data of the current geodetic coordinate system, the unmanned boat is set to navigate autonomously.

[0106] The navigation route of the unmanned boat is set with cumulonimbus and cumulus clouds as the target. The method for judging cumulonimbus and cumulus clouds can be found in S10(1)-(3).

[0107] An unmanned surface vehicle (USV) is an unmanned waterborne platform that integrates advanced navigation, autonomous driving, sensor, and communication technologies. Its core lies in autonomous navigation capability, which usually includes:

[0108] Intelligent navigation: Through integrated GPS, sonar, LiDAR, and other sensors, the USV can accurately obtain its own position and surrounding environment information, achieving autonomous navigation.

[0109] Path planning: The USV can autonomously plan and adjust the navigation path according to the preset task or real-time situation, avoiding obstacles to ensure safe navigation.

[0110] Collision avoidance system: By monitoring surrounding ships and obstacles in real time, the USV can autonomously make avoidance decisions to ensure safe navigation.

[0111] Based on the autonomous navigation capability of the USV platform, according to the last time's integrated cloud amount data and the current data of the millimeter wave cloud radar, the positions of cumulonimbus and cumulus clouds are comprehensively judged, and the USV carrying the millimeter wave cloud radar is sent to the target cloud cluster position to carry out fixed-point scanning detection.

[0112] S40, obtaining millimeter wave cloud radar data of the USV during navigation; wherein the millimeter wave cloud radar data is obtained by the millimeter wave cloud radar system based on radar detection parameters.

[0113] In this embodiment, the high-resolution detection technology of the millimeter wave cloud radar is used to improve the accuracy of obtaining information about the sea cloud, i.e., to obtain millimeter wave cloud radar data with higher accuracy.

[0114] The working principle of the millimeter wave cloud radar is to emit electromagnetic beams to the sky, then receive the reflected echo signals of the cloud layer, and realize high-resolution detection of the cloud layer. Because its wavelength is short (3mm), it can more accurately obtain information about the structure, shape, and water content of the sea cloud. In addition, millimeter waves have high penetration ability and small scattering, and can penetrate the cloud layer and detect the structure, cloud particle phase, water content, and other parameters inside the cloud layer.

[0115] The original background field data usually refers to EC reanalysis data, GFS global forecast field data, etc. The resolution of these data is only 0.25°X0.25°, i.e., resolution ≥ 25 kilometers. In this embodiment, the wavelength of the millimeter wave cloud radar is 3mm, the detection distance is 15 kilometers, and the distance resolution is 30 meters. By using the interpolation algorithm to fuse the millimeter wave cloud radar data, the cloud amount data resolution of the background field can be improved to 500 meters. Similarly, the cloud base height and cloud top height resolutions of high clouds, middle clouds, and low clouds are also improved by 500 meters.

[0116] S50, based on the reflectivity and cloud amount formula, the millimeter wave cloud radar data is used to quantitatively retrieve the cloud parameters, and the cloud parameter data in the navigation route process is obtained, such as cloud amount, cloud base height, cloud top height, cloud thickness, etc. The detailed retrieval method is S10(3).

[0117] S60, the cloud amount data is interpolated into the three-dimensional cloud amount data of the background field data to obtain the fusion data.

[0118] In this embodiment, a method of interpolation in polar coordinates is used, as shown in Figure 2 The calculation formula is:

[0119]

[0120] The cloud amount value of the interpolation point is obtained according to equation (2), and the corresponding weight coefficient is obtained through equation (1).

[0121] Wherein:

[0122] w g : weight coefficient of radar data sampling point;

[0123] R O , θ O , Φ o : spherical coordinates of interpolation radar data point P, which respectively refer to the radial distance, azimuth angle and elevation angle of the data point;

[0124] R g , θ g , Φ g : spherical coordinates of original radar data point, wherein g = 1, 2, …, N, N = 18;

[0125] 2 : by adjusting it, different smoothing effects can be obtained;

[0126] f g : reflectivity value of original radar data point;

[0127] f o : reflectivity value of interpolation data point;

[0128] N: the number of distance banks in the reflectivity value influence area of grid point R g , θ g , Φ g .

[0129] In another embodiment, the fusion data, i.e. the fusion three-dimensional cloud analysis result, can be used to generate secondary meteorological products such as cloud amount, cloud base height, cloud top height and cloud shape according to the fusion three-dimensional cloud analysis result.

[0130] The unmanned ship sea millimeter wave cloud radar and background field data mobile fusion device provided by the embodiment of the application is shown in the figure, and the device comprises: Figure 3

[0131] The unmanned ship navigation posture and route parameter acquisition unit 301 is configured to acquire the navigation route of the unmanned ship and radar detection parameters;

[0132] The unmanned ship control unit 302 is configured to convert the radar coordinate system based on the unmanned ship into a geodetic coordinate system; and set the unmanned ship navigation route based on the integrated cloud amount data of the previous time and the current geodetic coordinate system millimeter wave cloud radar data to perform autonomous navigation; wherein the unmanned ship is equipped with a millimeter wave cloud radar system;

[0133] The millimeter wave cloud radar parameter data acquisition unit 303 is configured to acquire millimeter wave cloud radar data of the unmanned ship during navigation; wherein the millimeter wave cloud radar data is collected based on the radar detection parameters by the millimeter wave cloud radar system;

[0134] The cloud parameter inversion unit 304 is configured to perform cloud amount inversion on the millimeter wave cloud radar data based on a reflectivity and cloud amount formula to obtain cloud amount data during the navigation route;

[0135] The millimeter wave cloud radar and background field data fusion unit 305 is configured to interpolate the cloud amount data into three-dimensional cloud amount data of the background field data to obtain fusion data.

[0136] In some embodiments, the millimeter wave cloud radar and background field data fusion unit is further configured to:

[0137]

[0138] In the formula, w g represents the weight coefficient of the radar data sampling point;

[0139] R O ,θ O ,Φ o represent the spherical coordinates of the interpolated radar data point P, respectively the radial distance, the azimuth angle and the elevation angle of the data point;

[0140] R g ,θ g ,Φ g represent the spherical coordinates of the original radar data point, wherein g = 1, 2, …, N;

[0141] 2 represents a smoothing parameter;

[0142] f g represents the reflectivity value of the original radar data point; ​

[0143] f o representing reflectance values of the interpolated data points;

[0144] N represents R g ,θ g ,Φ g The reflectance values of the interpolated data points affect the number of distance bins within the reflectance value impact zone.

[0145] It should be noted that the various apparatus structures described in the embodiments belong to the same technical concept as the methods described previously, and achieve the same technical effects through the same principles, and thus will not be described again here.

[0146] The embodiment of the application further provides a readable storage medium, the readable storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0147] In addition, although the exemplary embodiments have been described herein, the scope of their range includes any and all embodiments based on the present application having equivalent elements, modifications, omissions, combinations (for example, solutions cross various embodiments), adaptations, or alterations. The elements in the claims are to be interpreted broadly based on the language adopted in the claims, and are not limited to the examples described in the specification or during the implementation of the application, and the examples are to be interpreted as non-exclusive. Therefore, the specification and examples are intended to be considered only as examples, and the true range and spirit are indicated by the following claims and the full range of their equivalents.

[0148] The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with each other. Other embodiments can be used as would be apparent to one of ordinary skill in the art reading the foregoing description. Additionally, in the foregoing detailed description, various features can be grouped together in one or more embodiments for the purpose of streamlining the disclosure. This should not be interpreted as a requirement that the features so grouped are in any way related, but rather merely as a convenience to the reader. Rather, the subject matter of the application can be less than all of the features of a particular embodiment. As such, the following claims, as examples or embodiments, are hereby incorporated into the detailed description, wherein each claim independently represents an embodiment of the application, and the present application can be claimed as such in any combination of claims. The scope of the application should be determined with reference to the appended claims and the full range of equivalents to which such claims are entitled.

Claims

1. A method for maneuvering fusion of millimeter-wave cloud radar and background field data of an unmanned boat at sea, characterized in that: The method comprises: Obtain the navigation route and radar detection parameters of the unmanned boat; Convert the radar coordinate system based on the unmanned boat into the geodetic coordinate system; The unmanned boat is equipped with a millimeter-wave cloud radar system and a navigation route is set based on the fused cloud cover data of the previous time and the millimeter-wave cloud radar data of the current geodetic coordinate system to perform autonomous navigation. Acquiring millimeter-wave cloud radar data of the unmanned boat during navigation; wherein the millimeter-wave cloud radar data is collected by the millimeter-wave cloud radar system based on the radar detection parameters; Based on the reflectivity factor and cloud cover formula, the cloud cover data of millimeter wave cloud radar data is inverted to obtain the cloud cover data during the navigation route; The cloud cover data is interpolated into the three-dimensional cloud cover data of the background field data to obtain fused data.

2. The method for maneuvering fusion of unmanned boat marine millimeter wave cloud radar and background field data according to claim 1 is characterized in that: The wavelength of the millimeter wave cloud radar system is 3mm.

3. The method for maneuvering fusion of unmanned boat marine millimeter wave cloud radar and background field data according to claim 1 is characterized in that: The cloud cover data includes cloud cover, cloud base height and cloud top height.

4. The method for maneuvering fusion of unmanned boat marine millimeter wave cloud radar and background field data according to claim 3 is characterized in that: In the feature fusion sharing layer, the cloud cover data is interpolated into the three-dimensional cloud cover data of the background field data using the following formula to obtain the fused data: （1） （2） Where w g Represents the weight coefficient of the radar data sampling point; R o , θ o , Φ o represents the spherical coordinates of the interpolated radar data point P, which are the radial distance, azimuth, and elevation angle of the data point respectively; R g , θ g , Φ g represents the spherical coordinates of the original radar data points, where g =1,2,…, N ; 2 represents the smoothing parameter; f g Represents the reflectivity value of the original radar data point; f o Represents the reflectivity value of the interpolated data point; N express R g , θ g , Φ g The reflectivity value affects the number of distance bins in the area.

5. The method for maneuvering fusion of unmanned boat marine millimeter wave cloud radar and background field data according to claim 1 is characterized in that: The background field data include EC reanalysis data and / or GFS global forecast field data.

6. The method for maneuvering fusion of unmanned boat marine millimeter wave cloud radar and background field data according to claim 1 is characterized in that: After interpolating the cloud cover data into the three-dimensional cloud cover data of the background field data to obtain fused data, the method further includes: Secondary meteorological products are generated based on the fused data. The secondary meteorological products include cloud amount, cloud base height, cloud top height, and cloud shape.

7. A mobile fusion device for unmanned boat marine millimeter wave cloud radar and background field data, characterized in that: The device comprises: The unmanned boat navigation attitude and route parameter acquisition unit is configured to obtain the navigation route and radar detection parameters of the unmanned boat; The unmanned boat control unit is configured to convert the radar coordinate system based on the unmanned boat into a geodetic coordinate system; and to set a navigation route for the unmanned boat for autonomous navigation by combining the fused cloud cover data of the previous time and the millimeter-wave cloud radar data of the current geodetic coordinate system; wherein the unmanned boat is equipped with a millimeter-wave cloud radar system; a millimeter-wave cloud radar parameter data acquisition unit, configured to acquire millimeter-wave cloud radar data of the unmanned boat during navigation; wherein the millimeter-wave cloud radar data is acquired by the millimeter-wave cloud radar system based on the radar detection parameters; A cloud parameter inversion unit is configured to perform cloud cover inversion on millimeter-wave cloud radar data based on a reflectivity and cloud cover formula to obtain cloud cover data during the navigation route; The millimeter wave cloud radar and background field data fusion unit is configured to interpolate the cloud cover data into the three-dimensional cloud cover data of the background field data to obtain fused data.

8. The mobile fusion device for unmanned boat marine millimeter wave cloud radar and background field data according to claim 7 is characterized in that: The millimeter wave cloud radar and background field data fusion unit is further configured as follows: （1） （2） Where w g Represents the weight coefficient of the radar data sampling point; R o , θ o , Φ o represents the spherical coordinates of the interpolated radar data point P, which are the radial distance, azimuth, and elevation angle of the data point respectively; R g , θ g , Φ g represents the spherical coordinates of the original radar data points, where g =1,2,…, N ; 2 represents the smoothing parameter; f g Represents the reflectivity value of the original radar data point; f o Represents the reflectivity value of the interpolated data point; N express R g , θ g , Φ g The reflectivity value affects the number of distance bins in the area.

9. A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the method for maneuvering fusion of unmanned boat marine millimeter-wave cloud radar and background field data according to any one of claims 1 to 6.

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