Meteorological detection system and method based on phased array radar point cloud

Abstract

The invention discloses a meteorological detection system and method based on phased array radar point cloud, and the system comprises a data collection module which is used for obtaining high-density three-dimensional echo point cloud data generated by a phased array radar; the spatial reconstruction module is used for performing spatial reconstruction and dynamic registration on the meteorological echo field of the target area in combination with the time sequence beam scanning information; the feature extraction module is used for identifying local abnormal structures of the echo intensity, the radial speed and the phase features by using a point cloud feature extraction and clustering algorithm; the fusion discrimination module is used for carrying out correlation analysis on the point cloud features of different levels through a depth feature fusion model; the early warning output module is connected to the fusion judgment module and is used for outputting refined meteorological element distribution and dangerous weather early warning results; the system has adaptive feature extraction and anomaly recognition capabilities, and can maintain high recognition precision in a complex terrain and a multi-target superposition environment, thereby improving the monitoring sensitivity and early warning timeliness of local strong weather.

Description

Technical Field

[0001] This invention relates to the field of meteorological detection technology, specifically to a meteorological detection system and method based on phased array radar point clouds. Background Technology

[0002] Traditional mechanically scanned weather radars, employing fixed-angle and periodic rotation scanning methods, suffer from long scan cycles, limited spatial resolution, and delayed echo updates, making it difficult to achieve refined detection in rapidly changing weather systems. For example, in monitoring microbursts in airport areas or identifying localized severe convection in mountainous regions, traditional radars often fail to capture localized strong echo changes occurring within short timescales, leading to delays in hazardous weather identification and insufficient early warning timeliness. Phased array radars offer advantages such as rapid beam scanning and multi-beam parallel observation, enabling high-frequency volume scanning of target areas. However, current phased array radar meteorological applications primarily rely on two-dimensional reflectivity image processing, lacking the ability to spatially reconstruct and analyze the features of raw point cloud-level echo data. This prevents the full exploitation of the correlation features between high-dimensional information such as echo intensity, phase, and Doppler velocity. Consequently, echo identification accuracy decreases in scenarios with complex cloud structures, boundary layer disturbances, or multiple targets, resulting in biases in the quantitative inversion of meteorological elements. Summary of the Invention

[0003] The purpose of this invention is to propose a meteorological detection system based on phased array radar point clouds to solve the problems in the existing technology where the accuracy of echo identification decreases and the quantitative inversion of meteorological elements is biased in scenarios with complex cloud structures, boundary layer disturbances, or multiple targets superimposed.

[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is: a meteorological detection system based on phased array radar point clouds, comprising: The data acquisition module is used to acquire high-density three-dimensional echo point cloud data generated by the phased array radar; A spatial reconstruction module, connected to the data acquisition module, is used to perform spatial reconstruction and dynamic registration of the meteorological echo field of the target area by combining time-series beam scanning information. A feature extraction module, connected to the spatial reconstruction module, is used to identify local anomalous structures of echo intensity, radial velocity, and phase characteristics using point cloud feature extraction and clustering algorithms. A fusion discrimination module, which is connected to the feature extraction module, is used to perform correlation analysis on point cloud features at different levels through a deep feature fusion model; The early warning output module is connected to the fusion discrimination module and is used to output refined meteorological element distribution and hazardous weather early warning results.

[0005] Furthermore, the spatial reconstruction module is used to perform spatial coordinate transformation and noise filtering on the point cloud features of each time frame, and to construct a voxelized mesh model from the noise-filtered point cloud features.

[0006] Furthermore, the fusion discrimination module performs fusion analysis on the spatial texture, velocity gradient, and phase difference features in the point cloud features based on a deep neural network.

[0007] Furthermore, the early warning output module supports multi-format data output, including visualized images, structured data files, and standardized early warning protocol messages, adapting to the receiving needs of different terminals.

[0008] Furthermore, it also includes a fault self-diagnosis module, which is used to monitor the operating status of each module in real time. When a data transmission interruption or processing abnormality is detected, it automatically triggers the redundancy backup mechanism and issues a fault alarm.

[0009] To achieve the aforementioned objectives, another technical solution adopted by this invention is: a meteorological detection method based on phased array radar point clouds, applied to a meteorological detection system based on phased array radar point clouds, comprising the following steps: S1, acquire high-density three-dimensional echo point cloud data generated by phased array radar; S2, based on the analysis of time-series beam scanning information from three-dimensional echo point cloud data, spatial reconstruction and dynamic registration of the meteorological echo field of the target area are performed; S3 utilizes point cloud feature extraction and clustering algorithms to identify local anomalous structures in terms of echo intensity, radial velocity, and phase characteristics; S4. By using a deep feature fusion model to perform correlation analysis on point cloud features at different levels, potential strong convective cores, micro-downbursts, or wind shear regions are determined based on the analysis results, and the determination results are obtained. S5 outputs refined meteorological element distribution and hazardous weather warning results based on the judgment results.

[0010] Furthermore, the identification of local abnormal structures in step S3 also includes calculating the spatial distribution entropy value of the echo point cloud. When the entropy value exceeds a preset threshold, it is determined to be an irregular disturbance area.

[0011] Furthermore, in step S4, the deep feature fusion model employs an attention mechanism to assign different weights to features such as echo intensity anomalies, velocity gradient abrupt changes, and phase difference anomalies for correlation analysis.

[0012] Furthermore, the output hazardous weather warning results include a classification identifier, which is divided into 3-5 risk levels based on the echo intensity, velocity gradient, and impact range of the strong convection core.

[0013] Due to the application of the above technical solution, the present invention has the following advantages compared with the prior art: This invention introduces three-dimensional point cloud data from phased array radar into the meteorological detection process, overcoming the limitations of traditional two-dimensional echo images, which suffer from low resolution and slow update frequency. Through point cloud spatial reconstruction and feature fusion analysis, it achieves three-dimensional identification and dynamic tracking of meteorological targets, and can capture the rapid evolution characteristics of strong convection or microbursts within a second-level timescale. At the same time, the system has adaptive feature extraction and anomaly identification capabilities, and can maintain high identification accuracy in complex terrain and multi-target superposition environments, thereby significantly improving the monitoring sensitivity and early warning timeliness of local severe weather.

[0014] Figure 1 A block diagram of a meteorological detection system based on phased array radar point clouds provided in an embodiment of the present invention is shown; Figure 2 The flowchart of the meteorological detection method based on phased array radar point cloud provided by the embodiment of the present invention is shown. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. 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 comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] like Figure 1 As shown, this embodiment of the invention provides a meteorological detection system based on phased array radar point clouds, comprising: The data acquisition module is used to acquire high-density three-dimensional echo point cloud data generated by the phased array radar; The spatial reconstruction module is connected to the data acquisition module and is used to perform spatial reconstruction and dynamic registration of the meteorological echo field of the target area by combining time-series beam scanning information. The feature extraction module, connected to the spatial reconstruction module, is used to identify local anomalous structures of echo intensity, radial velocity, and phase characteristics using point cloud feature extraction and clustering algorithms. The fusion and discrimination module is connected to the feature extraction module and is used to perform correlation analysis on point cloud features at different levels through a deep feature fusion model. The early warning output module is connected to the fusion discrimination module and is used to output refined meteorological element distribution and hazardous weather warning results.

[0019] According to an embodiment of the present invention, the spatial reconstruction module is used to perform spatial coordinate transformation and noise filtering on the point cloud features of each time frame, and to construct a voxelized mesh model from the noise-filtered point cloud features.

[0020] According to an embodiment of the present invention, the fusion discrimination module performs fusion analysis on the spatial texture, velocity gradient and phase difference features in the point cloud features based on a deep neural network.

[0021] According to an embodiment of the present invention, the early warning output module supports multi-format data output, including visualized images, structured data files and standardized early warning protocol messages, to adapt to the receiving needs of different terminals.

[0022] According to an embodiment of the present invention, a fault self-diagnosis module is also included, which is used to monitor the operating status of each module in real time, and automatically trigger a redundancy backup mechanism and issue a fault alarm when a data transmission interruption or processing abnormality is detected.

[0023] According to an embodiment of the present invention, a data storage module is also included, which is used to perform structured storage of historical point cloud data, feature information and early warning results, and to support retrospective analysis of subsequent meteorological processes.

[0024] According to an embodiment of the present invention, the feature extraction module is further used to calculate the local curvature of the echo point cloud, and when the curvature value exceeds a preset threshold, it is determined to be a cloud boundary layer disturbance region.

[0025] like Figure 2 As shown, to achieve the above-mentioned objective, another technical solution adopted by this invention is: a meteorological detection method based on phased array radar point clouds, applied to a meteorological detection system based on phased array radar point clouds, comprising the following steps: S1, acquire high-density three-dimensional echo point cloud data generated by phased array radar; S2, based on the analysis of time-series beam scanning information from three-dimensional echo point cloud data, spatial reconstruction and dynamic registration of the meteorological echo field of the target area are performed; S3 utilizes point cloud feature extraction and clustering algorithms to identify local anomalous structures in terms of echo intensity, radial velocity, and phase characteristics; S4. By using a deep feature fusion model to perform correlation analysis on point cloud features at different levels, potential strong convective cores, micro-downbursts, or wind shear regions are determined based on the analysis results, and the determination results are obtained. S5 outputs refined meteorological element distribution and hazardous weather warning results based on the judgment results.

[0026] According to an embodiment of the present invention, the identification of local abnormal structures in step S3 further includes calculating the spatial distribution entropy value of the echo point cloud, and when the entropy value exceeds a preset threshold, it is determined to be an irregular disturbance region.

[0027] According to an embodiment of the present invention, in step S4, the deep feature fusion model adopts an attention mechanism to assign different weights to features such as echo intensity anomalies, velocity gradient abrupt changes, and phase difference anomalies for correlation analysis.

[0028] According to an embodiment of the present invention, the output hazardous weather warning result includes a classification identifier, which is divided into 3-5 risk levels based on the echo intensity, velocity gradient and impact range of the strong convection core.

[0029] In summary, this invention introduces three-dimensional point cloud data from phased array radar into the meteorological detection process, overcoming the limitations of traditional two-dimensional echo images, which suffer from low resolution and slow update frequency. Through point cloud spatial reconstruction and feature fusion analysis, it achieves three-dimensional identification and dynamic tracking of meteorological targets, enabling the capture of rapid evolution characteristics of strong convection or microbursts within a second-level timescale. Simultaneously, the system possesses adaptive feature extraction and anomaly recognition capabilities, maintaining high recognition accuracy in complex terrain and multi-target overlay environments, thereby significantly improving the monitoring sensitivity and early warning timeliness of local severe weather.

[0030] Those skilled in the art will understand that, for ease of explanation, the example is provided with one memory and one processor. In actual terminals or servers, multiple processors and memories may exist. Memory can also be referred to as storage medium or storage device, etc., and the embodiments of this application do not limit this.

[0031] It should be understood that in the embodiments of this application, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may also be a general-purpose microprocessor, graphics processing unit (GPU), or one or more integrated circuits to execute relevant programs to achieve the functions required by the embodiments of this application.

[0032] The processor can also be an integrated circuit chip with signal processing capabilities. In implementation, each step of this application can be completed through integrated logic circuits in the processor hardware or instructions in software form. The aforementioned processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the functions required by the units included in the methods, devices, and storage media of the embodiments of this application.

[0033] It should also be understood that the memory mentioned in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache.

[0034] By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0035] The memory can also be a Compact Disc Read-Only Memory (CD-ROM) or other optical disc storage, optical disk storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory can exist independently and be connected to the processor via a bus. The memory can also be integrated with the processor. The memory can store programs, and when the program stored in the memory is executed by the processor, the processor performs the various steps of the method determined in the above embodiments of this application.

[0036] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory (storage module) is integrated into the processor. It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0037] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0038] In implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or by a combination of hardware and software modules within the processor. The software modules can reside in mature storage media in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. Since this storage medium is located in memory, the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method; to avoid repetition, these will not be described in detail here.

[0039] Those skilled in the art will recognize that the various illustrative logical blocks (ILBs) and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0040] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer-programmed program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a processor, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a computer network, or other programmable device.

[0041] This embodiment also provides a computer-readable storage medium storing a computer program that causes a computer to execute in order to implement the above-described method for monitoring the operating status data of mining cars used in underground transportation.

[0042] It should be noted that computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic) or wireless (e.g., infrared, wireless, microwave, etc.) means, or from one website, computer, server, or data center to a mobile phone processor via a wired means. A computer-readable storage medium can be any usable medium that a computer can access, or a data storage device such as a server or data center that integrates one or more usable media. Usable media can be magnetic media (e.g., floppy disks, hard disks), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives), etc.

[0043] Finally, it should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A meteorological detection system based on phased array radar point clouds, characterized in that, include: The data acquisition module is used to acquire high-density three-dimensional echo point cloud data generated by the phased array radar; A spatial reconstruction module, connected to the data acquisition module, is used to perform spatial reconstruction and dynamic registration of the meteorological echo field of the target area by combining time-series beam scanning information. A feature extraction module, connected to the spatial reconstruction module, is used to identify local anomalous structures of echo intensity, radial velocity, and phase characteristics using point cloud feature extraction and clustering algorithms. A fusion discrimination module, which is connected to the feature extraction module, is used to perform correlation analysis on point cloud features at different levels through a deep feature fusion model; The early warning output module is connected to the fusion discrimination module and is used to output refined meteorological element distribution and hazardous weather early warning results.

2. The meteorological detection system based on phased array radar point clouds as described in claim 1, characterized in that, The spatial reconstruction module is used to perform spatial coordinate transformation and noise filtering on the point cloud features of each time frame, and to construct a voxelized mesh model from the noise-filtered point cloud features.

3. The meteorological detection system based on phased array radar point clouds as described in claim 2, characterized in that, The fusion discrimination module performs fusion analysis on the spatial texture, velocity gradient and phase difference features in the point cloud features based on a deep neural network.

4. The meteorological detection system based on phased array radar point clouds as described in claim 3, characterized in that, The early warning output module supports multi-format data output, including visualized images, structured data files, and standardized early warning protocol messages, adapting to the receiving needs of different terminals.

5. The meteorological detection system based on phased array radar point clouds as described in claim 1, characterized in that, It also includes a fault self-diagnosis module, which is used to monitor the operating status of each module in real time. When a data transmission interruption or processing abnormality is detected, it automatically triggers the redundancy backup mechanism and issues a fault alarm.

6. A meteorological detection method based on phased array radar point clouds, applied to the meteorological detection system based on phased array radar point clouds as described in any one of claims 1-5, characterized in that, Includes the following steps: S1, acquire high-density three-dimensional echo point cloud data generated by phased array radar; S2, based on the analysis of time-series beam scanning information from three-dimensional echo point cloud data, spatial reconstruction and dynamic registration of the meteorological echo field of the target area are performed; S3 utilizes point cloud feature extraction and clustering algorithms to identify local anomalous structures in terms of echo intensity, radial velocity, and phase characteristics; S4. By using a deep feature fusion model to perform correlation analysis on point cloud features at different levels, potential strong convective cores, micro-downbursts, or wind shear regions are determined based on the analysis results, and the determination results are obtained. S5 outputs refined meteorological element distribution and hazardous weather warning results based on the judgment results.

7. The meteorological detection method based on phased array radar point clouds as described in claim 6, characterized in that, The identification of local abnormal structures in step S3 also includes calculating the spatial distribution entropy value of the echo point cloud. When the entropy value exceeds a preset threshold, it is determined to be an irregular disturbance area.

8. The meteorological detection method based on phased array radar point clouds as described in claim 7, characterized in that, In step S4, the deep feature fusion model employs an attention mechanism to assign different weights to features such as echo intensity anomalies, velocity gradient abrupt changes, and phase difference anomalies for correlation analysis.

9. The meteorological detection method based on phased array radar point clouds as described in claim 8, characterized in that, The output hazardous weather warning results include a classification label, which is divided into 3-5 risk levels based on the echo intensity, velocity gradient and impact range of the strong convection core.

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