Self-adaptive cooperative calibration method and system for differential reflectivity of whole system of networking X-band radar and storage medium

Abstract

The invention relates to the field of meteorological observation, and provides a networking X-band radar full-system differential reflectivity self-adaptive cooperative calibration method and system and a storage medium, and the method comprises the steps: collecting radar base data, and transmitting the radar base data to a data processing center; preprocessing the radar-based data, performing deviation detection on differential reflectivity data of the preprocessed radar-based data, and generating a calibration instruction based on a deviation detection result; based on the calibration instruction, automatically scheduling the radar to be calibrated to execute zenith calibration VPCAL mode scanning; in the scanning process, synchronously collecting VPCAL scanning data and transmitting the VPCAL scanning data to a data processing center; based on VPCAL scanning data, quantitative deviation is calculated and issued to a calibration execution unit; and carrying out automatic parameter adjustment on the X-band radar of which the quantitative deviation exceeds a threshold value, and sending a deviation warning to a user. According to the invention, the monitoring precision, the calibration precision and the quantitative rainfall estimation capability of the weather radar can be improved, and forecasting and early warning services and refined meteorological services are provided.

Description

Technical Field

[0001] The present invention relates to the field of meteorological observation, and in particular to a method, system and storage medium for adaptively co-calibrating differential reflectivity of a networked X-band radar system. Background Art

[0002] Dual-polarization weather radar is an important device for meteorological departments to monitor and forecast disastrous weather. It can capture the development and changes of severe convective weather such as thunderstorms, typhoons, and rainstorms in real time, providing strong support for meteorological warning services. Compared with traditional weather radars, dual-polarization radars can simultaneously transmit and receive two vertical electromagnetic waves, thereby obtaining more atmospheric information, such as precipitation type, shape and density of precipitation particles, etc. This information plays a vital role in improving the accuracy of precipitation estimation and hydrometeor classification, as well as identifying the characteristics of severe convective weather.

[0003] Differential reflectivity (ZDR) is an important parameter of dual-polarization radar. It reflects the ratio of reflectivity in the horizontal and vertical directions and is closely related to the shape and orientation of precipitation particles. Accurate estimation of differential reflectivity is crucial for dual-polarization radar applications because it is directly related to the accuracy and reliability of radar data. However, due to the influence of the radar system itself or the external environment, the differential reflectivity often produces certain deviations, which affects the data quality and application effect. Therefore, calibrating the differential reflectivity of dual-polarization weather radar is an important means to improve the accuracy and stability of radar data.

[0004] With the continuous maturity and widespread application of dual-polarization weather radar technology, the requirements for radar data quality and data stability are becoming higher and higher. As a type of dual-polarization radar, X-band radar has high resolution and wide coverage, so it plays an important role in meteorological observation and forecasting. However, X-band radar also faces some technical difficulties in the calibration of differential reflectivity.

[0005] During long-term operation, the dual-linear polarization radar will produce systematic deviations and random errors due to factors such as device aging and environmental changes, which will affect the measurement accuracy of differential reflectivity.

[0006] X-band radar is difficult to calibrate under complex weather conditions (such as heavy rain, strong winds, hail, etc.), and requires a more precise calibration method.

[0007] In addition, with the expansion of radar networks and the surge in data volume, how to efficiently and accurately process and analyze the consistency of multi-radar differential reflectivity has become an urgent problem to be solved. Traditional methods are difficult to meet the requirements of real-time, accuracy and precision, and it is urgent to introduce more advanced algorithms and models to improve the effectiveness of networked radars for forecasting and warning applications.

[0008] In this context, the performance and application of weather radars need to be further innovated and optimized to meet the special needs of refined meteorological service support, major event support, large cities and densely populated areas. Refined meteorological services require meteorological data to have high temporal and spatial resolution and accurate forecasting capabilities to support refined services in agriculture, transportation and other fields. Major event support relies on weather radars to monitor weather changes in real time and provide timely warnings and decision-making support for event organizers. For large cities and densely populated areas, the potential impact of meteorological disasters is more serious, and higher requirements are placed on the accuracy and timeliness of forecasts and warnings.

[0009] The patent with application number CN202410007027.7 provides a weather radar calibration method and device based on a metal ball. The method includes: based on the antenna pattern of the weather radar, after determining the length of the suspension rope, controlling the drone to suspend the metal ball to fly outside the far field of the radar antenna; scanning the metal ball based on a preset scanning strategy, and controlling the weather radar to align the metal ball for directional scanning after determining the reference pitch angle and reference azimuth angle corresponding to the weather radar according to the scanning results; controlling the drone to suspend the metal ball to continuously move the position so that the metal ball is in the middle position of the radar beam and the distance library, and obtaining the calibration data of the metal ball; based on the metal ball calibration data, technical parameters and preset radar formulas, calculating the theoretical values ​​of the reflectivity factor, differential reflectivity factor and correlation coefficient of the weather radar, and inverting the beam width and pulse width of the radar antenna; using the deviation between the measured value and the theoretical value obtained by radar observation of the metal ball, and the inverted beam width and pulse width, determine the radar calibration coefficient of the weather radar, and realize the calibration of the weather radar.

[0010] The patent with application number CN202211609704.X discloses a real-time correction method and device for the ZDR deviation of a dual-polarization weather radar. The method includes real-time acquisition of radar observation data from a signal processor of the dual-polarization weather radar, the radar observation data including a differential reflectivity factor; real-time acquisition of the internal temperature and external temperature of the dual-polarization weather radar; calculation of the deviation according to the radar observation data, the internal temperature and the external temperature, generation of a correction ZDR deviation instruction according to the deviation, and sending the correction ZDR deviation instruction to the dual-polarization weather radar, so that the dual-polarization weather radar calculates the corrected differential reflectivity factor according to the differential reflectivity factor and the deviation in the radar observation data. The present invention can correct the ZDR in real time, and in the subsequent use process, it can be screened by analyzing the radar observation data, so as to continuously supplement samples to update, and ensure the accuracy and representativeness of the correction; improve the data quality of the dual-polarization radar, and has broad application prospects.

[0011] However, the above prior art has the following disadvantages: (1) Limited scope of application: Due to the safety of calibration methods and business operation requirements, metal ball calibration is usually carried out under clear atmospheric conditions and cannot be implemented under certain conditions (such as airspace restrictions, precipitation weather, high wind speed, etc.). In addition, networked X-band radars are generally built in densely populated metropolitan areas. The limited airspace makes it impossible for some radars to carry out metal ball calibration.

[0012] (2) Poor positioning accuracy: The metal ball is usually suspended under the drone in the form of a soft connection, which makes it easy for the metal ball to move under the influence of external factors such as wind. This position movement will directly affect the accuracy of calibration, because the radar needs to accurately measure the position of the metal ball for calibration. In addition, during the flight of the drone, its posture and position may be affected by various factors such as airflow and wind speed, resulting in unstable position of the metal ball, which further affects the accuracy of the calibration results.

[0013] (3) Automatic calibration is not possible: The current metal ball calibration method usually requires manual participation, such as selecting the release location, controlling the flight trajectory of the drone, and adjusting the position of the metal ball according to weather conditions. This manual operation not only increases the workload and cost, but may also lead to errors and uncertainties in the calibration process. The metal ball calibration method has obvious deficiencies in automation, which limits its application in scenarios that require efficient and fast calibration.

[0014] In addition, the patent with application number CN201911065590.5 discloses a differential reflectivity ZDR calibration method, including: first obtaining the complex I / Q signals of the horizontal channel and the vertical channel corresponding to each distance library on each radial direction; then, according to the complex I / Q signals of the horizontal channel and the vertical channel corresponding to each distance library and the horizontal channel radial noise, the ZDR related data corresponding to each distance library is calculated accordingly; then, according to the bragg scattering characteristics and the ZDR related data corresponding to each distance library, the target distance library is screened out from all distance libraries to form a target distance library set; then, it is determined whether the target distance library set meets the ZDR deviation evaluation standard. If it meets the ZDR deviation, the ZDR deviation used to calibrate the ZDR is determined according to the ZDR related data corresponding to each target distance library in the target distance library set. When the ZDR is calibrated by this method, the normal business performed by the radar does not need to be stopped, and the real-time update of the ZDR deviation can also be achieved.

[0015] The patent with application number CN202410322646.5 discloses a method, device, equipment and storage medium for consistency assessment between weather radars, which are applied to the field of meteorological observation technology. The method includes: obtaining the base data of adjacent weather radars at the specified volume scan elevation angle at the same volume scan start time; determining the observation overlap area of ​​adjacent weather radars based on the obtained base data; screening the overlapping points in the observation overlap area using preset screening rules; and performing consistency assessment on adjacent weather radars based on the overlapping points retained after screening. In this way, the accuracy of consistency assessment of adjacent weather radars can be effectively improved.

[0016] However, the above prior art has the following disadvantages: (1) Limited calibration accuracy: Since this method relies on the accuracy and reliability of radar echo data, as well as the rationality and applicability of the preset conditions, its calibration accuracy is subject to certain limitations.

[0017] (2) Affected by precipitation characteristics: Although the ZDR value of light rain echo is between 0.25 and 0.35 dB and is independent of the shape and orientation of precipitation particles, the characteristics of precipitation particles in different types of light rain (such as stratiform cloud precipitation and convective cloud precipitation) may be different, which may have a certain impact on the calibration accuracy. In addition, its accuracy may be significantly affected by environmental conditions. For example, under extreme weather conditions (such as strong winds, heavy rain, heavy snow, etc.), the characteristics of radar echoes may change significantly, thereby affecting the selection of target differential reflectivity and the calculation of deviations.

[0018] (3) Data quality and screening criteria: This method relies on accurately selecting the target differential reflectivity from the radar echo, which requires the radar data to have high quality. However, in practical applications, the selection of radar data at low elevation angles may be interfered by various factors, such as electromagnetic interference, clutter, noise, etc., which may affect the accuracy and reliability of the data. In addition, the preset conditions for screening the target differential reflectivity (such as range, pitch angle, altitude, signal-to-noise ratio, etc.) may need to be adjusted according to the specific situation to adapt to different weather and radar performance.

[0019] (4) Data dependence and stability and reliability: The light rain method requires the accumulation of a large amount of data, and changes with the precipitation environment and radar system, which may cause the deviation of the differential reflectivity to change. Therefore, this method needs to be recalibrated and verified regularly or irregularly to ensure its long-term stability and reliability. Moreover, its calibration deviation is an approximate value and cannot be equated with its "quantitative" deviation result. Summary of the invention

[0020] One of the purposes of the present invention is to provide a networked X-band radar full-system differential reflectivity adaptive collaborative calibration method, which aims to improve the monitoring precision, calibration accuracy and quantitative precipitation estimation capability of weather radar through technical innovation and algorithm optimization, and optimize the data processing process to meet the refined and efficient needs of meteorological services under the background of global climate change and urbanization development. By building a more complete weather radar system, it provides solid technical support for short-term nowcasting and warning services, refined meteorological service support, major event support, and meteorological disaster prevention in large cities and densely populated areas.

[0021] An embodiment of the present invention provides a method for adaptive collaborative calibration of differential reflectivity of a networked X-band radar system, comprising: The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; The data processing center preprocesses the radar base data and performs deviation detection on the preprocessed radar differential reflectivity data. Based on the deviation detection results, it generates calibration instructions and sends them to the calibration execution unit. The calibration execution unit automatically schedules the radar to be calibrated to perform the zenith calibration VPCAL scan based on the calibration instruction; During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the deviation threshold and issues a deviation prompt to the user center.

[0022] Optionally, the radar base data includes at least: reflectivity factor, radial velocity, spectrum width, differential reflectivity; The pre-processing step at least includes: a data cleaning step and a quality control step; The data cleaning step includes at least: detection and elimination based on data time, data structure and integrity, and rationality of data content; The quality control step includes at least: removing noise and outliers based on radial continuity and filtering.

[0023] Optionally, the data processing center is based on a deviation detection result, wherein the deviation detection result at least includes: The differential reflectivity data in the pre-processed radar base data with a reflectivity factor of 22-26 dBZ above 1 km and below the melting layer is statistically averaged, and the average value is used as the deviation detection result; The execution conditions for generating calibration instructions include: The deviation detection result indicates that an X-band radar with a deviation exceeding the deviation threshold has been identified, the vertical echo above the radar is greater than 20dBZ and there is no echo intensity exceeding 35dBZ in the surrounding 10KM area (the above thresholds are adjustable).

[0024] Optionally, the calibration instruction includes at least: calibration time, calibration mode and radar numbers involved in the calibration.

[0025] Optionally, each X-band radar, radar monitoring unit, data processing center and calibration execution unit communicate with each other through a communication network.

[0026] Optionally, the calibration mode includes: zenith calibration VPCAL scan.

[0027] Optionally, the VPCAL scan mode uses the CDX waveform.

[0028] Optionally, also include: S7, obtaining multi-source monitoring information; wherein the multi-source monitoring information at least includes: operating status information of the radar monitoring unit, the data processing center, and the calibration execution unit; S8. Build a digital model based on multi-source monitoring information; S9. Assist management personnel to monitor and manage radar collaborative calibration based on digital models; Wherein, the S9, assistant management personnel, performs radar collaborative calibration monitoring and management based on the digital model, including: S91. Identify events to be managed in the digital model; S92, when the manager starts to view the occurrence area of ​​the event to be managed in the digital model, continuously track the operation history of the manager's operation in the occurrence area and the landing point trajectory formed by the movement of the manager's sight in the digital model; S93, when active characterization of the region occurs or management opportunities are passively characterized by operation history, a virtual projection area is determined in the digital model based on the landing point trajectory; S94, Capture value information from digital models based on management opportunity value information capture template; S95. Project the value information onto the virtual projection area.

[0029] An embodiment of the present invention provides a networked X-band radar full-system differential reflectivity adaptive collaborative calibration system, comprising: a radar monitoring unit, a data processing center and a calibration execution unit; The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; The data processing center preprocesses the base data and performs deviation detection on the preprocessed radar differential reflectivity data. Based on the deviation detection results, it generates calibration instructions and sends them to the calibration execution unit. The calibration execution unit automatically schedules the radar to be calibrated to perform the zenith calibration VPCAL scan based on the calibration instruction; During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the deviation threshold and issues a deviation prompt to the user center.

[0030] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of any of the above-mentioned methods for adaptive collaborative calibration of differential reflectivity of the entire system of a networked X-band radar are implemented.

[0031] A computer device provided in an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the adaptive collaborative calibration method for differential reflectivity of the entire system of a networked X-band radar are implemented as described in any one of the above items.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 Schematic diagram of a method for adaptive collaborative calibration of differential reflectivity of a networked X-band radar system in an embodiment of the present invention; Figure 2 It is a schematic diagram of a networked X-band radar full system differential reflectivity adaptive collaborative calibration system in an embodiment of the present invention; Figure 3 is a calibration flow chart in an embodiment of the present invention; Figure 4Schematic diagram of the adaptive collaborative calibration radar scheduling process in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0036] This application aims to solve the following technical problems: (1) Improve the calibration accuracy of polarization radar differential reflectivity: When the weather radar is used to identify specified weather conditions (such as light rain and no wind), and the radar antenna is pointed to the zenith, theoretically, the components of the raindrops falling in the horizontal and vertical polarization directions should be approximately the same, and the high elevation angles are not affected by factors such as ground clutter. Therefore, the theoretical value of the differential reflectivity factor (ZDR) should be 0 dB. The actual statistical value obtained can be used to correct the theoretical deviation of the entire system link.

[0037] (2) Improving the scope of application of calibration: Overcoming the limitations of the metal ball calibration method on environmental conditions (such as airspace restrictions and precipitation systems), using suitable rainfall weather conditions, and through an automated and intelligent collaborative calibration strategy, ensuring accurate calibration of the deviation radar under different precipitation conditions and uninterrupted volume scanning of other radars.

[0038] (3) Improving positioning and data matching accuracy: By achieving the self-rotation of the antenna at the top (elevation angle of 90°), the pitch error is reduced, thereby improving the antenna pointing accuracy and the quantitative deviation calibration accuracy.

[0039] (4) Automatic calibration: Through intelligent algorithms and radar automatic scheduling, the data processing process is optimized, and the zenith calibration mode of the deviation radar is automatically realized (the offline mode is improved to online real-time operation), reducing the dependence on manual operation and realizing the automation and intelligence of the calibration process. This can not only significantly reduce the workload and cost, but also reduce the errors and uncertainties introduced by human factors.

[0040] (5) Reduce dependence on data sources: Data consistency assessment based on the light rain method requires a large amount of data to improve stability. This method automatically realizes intelligent calibration of radar deviation by estimating the deviation radar, reduces excessive dependence on data sources, and improves tolerance to problems such as missing data and poor quality.

[0041] The embodiment of the present invention provides a method for adaptive collaborative calibration of differential reflectivity of a networked X-band radar system. Figure 1 As shown, including: S1. The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; S2. The data processing center preprocesses the radar differential reflectivity data, performs deviation detection on the preprocessed radar differential reflectivity data, and then generates a calibration instruction based on the deviation detection result, and then sends it to the calibration execution unit; S3, the calibration execution unit automatically schedules the radar to be calibrated to perform zenith calibration VPCAL scanning based on the calibration instruction; S4. During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; S5. The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; S6. The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the deviation threshold and sends a deviation prompt to the user.

[0042] The preprocessing steps at least include: data cleaning and quality control.

[0043] Based on the deviation detection results, the data processing center generates the execution conditions of the calibration instructions, including: The deviation detection result indicates that an X-band radar with a deviation exceeding the deviation threshold has been identified, the vertical echo strength above the radar is greater than 20dBZ and the echo strength of the surrounding 10KM echo does not exceed 35dBZ (the above thresholds are adjustable).

[0044] The calibration instruction at least includes: calibration time, calibration mode and the number of the radar involved in the calibration.

[0045] Each X-band radar, radar monitoring unit, data processing center and calibration execution unit communicate with each other through a communication network.

[0046] The calibration mode includes: zenith calibration VPCAL scan.

[0047] The VPCAL scanning mode uses the CDXX waveform.

[0048] The specific working principle and beneficial effects of the above technical solution are: The following specific technical solutions are provided: 1.1 System Architecture: The system architecture proposed in the present invention includes a radar monitoring unit, a data processing center, a calibration execution unit and a communication network. The radar monitoring unit is responsible for real-time monitoring of the operating status and basic data of each X-band radar; the data processing center is responsible for data analysis, deviation detection and calibration instruction generation; the calibration execution unit is responsible for sending zenith calibration VPCAL scanning tasks to each radar; and the communication network is responsible for data transmission and instruction delivery between each unit.

[0049] 1.2 Implementation steps: Real-time monitoring and data collection: The radar monitoring unit continuously collects basic data from each X-band radar and transmits it to the data processing center in real time.

[0050] The data processing center pre-processes the collected data, including data cleaning and quality control. Specifically, it collects radar-based data, which includes parameter data such as reflectivity factor, radial velocity, spectral width, and differential reflectivity. During data cleaning, detection and elimination are performed based on data time, data structure and integrity, and the rationality of data content. For example, when detecting and eliminating based on data time, it detects whether the data is historical data. If so, it is eliminated. During quality control, noise and outliers are removed by performing radial continuity and filtering on the parameters in the radar-based data.

[0051] Deviation detection: The data processing center uses an algorithm to detect deviations in the preprocessed data and identify radars with large deviations. Specifically, the algorithm workflow is as follows: After quality control of the radar-based data preprocessing, the historical average temperature, sounding or radar melting layer data are used to determine the radar differential reflectivity factor conditions. The differential reflectivity data from 1km to 500 meters below the melting layer at an elevation angle of 2-5° is selected. For each library (the library is an indicator of the radar's distance resolution, for example: the detection distance is 75km, and the distance resolution is 75 meters, then there are 1000 libraries in the radial direction of an azimuth with the radar as the center, that is, 75 meters * 1000 libraries = 75KM of detection distance), the search stops when it encounters data greater than 35dBZ, and then the lightning rod azimuth ±15° azimuth is eliminated. The data of angles are averaged; if the average value is greater than 0.2dB, it is considered as a deviation radar; when detecting deviation, the echo intensity in the vertical sky and within the vicinity of 10km is considered. When a certain radar is detected to have a large deviation, the deviation radar is greater than 20dBZ and there is no echo greater than 35dBZ within 10KM, the calibration process is triggered; generally, the ZDR of dual-polarization radars requires an accuracy of 0.2dB, and the phase state of meteorological targets can be distinguished by polarization parameters. Therefore, if the statistical value that meets the condition is greater than 0.2dB, it is considered that the radar deviation is large and needs to be calibrated to see its true deviation; the differential reflectivity adopts the VPCAL mode, and calibration can only be performed when the echo above the vertical sky of the monitoring radar has a value. The limit of 10km to the radar center is to avoid calibration distortion caused by weather changes and waste of radar calibration scheduling opportunities; Calibration instruction generation and distribution: The data processing center generates calibration instructions based on the deviation detection results and sends them to the calibration execution unit and the radar to be calibrated through the communication network; the deviation detection results include: the differential reflectivity statistics of each radar calculated by the above algorithm.

[0052] The calibration command includes calibration time, calibration mode (zenith VPCAL scan), IP address of the radar involved in the calibration and other information.

[0053] Zenith VPCAL scanning and data calculation: The calibration execution unit automatically schedules the radar to be calibrated to perform zenith VPCAL scanning according to the calibration instructions. During the scanning process, each radar collects data synchronously and transmits it to the data processing center through the communication network. The data processing center uses advanced algorithms to process the collected VPCAL scanning data, calculates and gives quantitative deviations; the weather radar collects data while running the scan, and the collected data needs to be sent to the radar monitoring unit of the server to realize real-time monitoring of the data for subsequent processing; based on the radar data preprocessing, the differential reflectivity of the reflectivity factor of 22-26dBZ above 1km and below the melt layer is statistically averaged, and the value obtained is the quantitative deviation value (theoretically it should be 0); Feedback and adjustment of calibration results: The data processing center feeds back the calibration results to the radar monitoring unit and the calibration execution unit; the calibration results are the quantitative deviation values ​​mentioned above. According to the calibration results, the parameters of the radar with large deviations are automatically adjusted and deviation prompts are sent to the user to ensure that the user understands the changes and the accuracy and consistency of the radar data; the calibration execution unit modifies the configuration file of the radar through commands to perform automatic parameter adjustments, and sends deviation prompts to the user of the monitoring unit to remind the user that the deviation adjustment has been made, so that the user can confirm and query the subsequent information.

[0054] like Figure 2 As shown, the connection relationship between the radar monitoring unit, the data processing elevation unit, the calibration execution unit and the communication network is demonstrated.

[0055] like Figure 3 As shown in the figure, the whole process from real-time monitoring to calibration result feedback is described, including data collection, deviation detection, calibration instruction generation, zenith VPCAL scanning, data calculation and calibration result feedback. Figure 3In step 1, the sounding data is the temperature value that can be directly obtained by using the sounding balloon, and the 0°C height in the sky (the height where the air temperature is 0°C) can be found; and the radar can also identify the height of the melting layer through the base data. Generally, a few hundred meters below 0° is the melting layer, and 500 meters below the melting layer is the area for taking the data. Step 2 is actually to find the potential deviation radar without destroying the normal volume scanning of the radar. The radar is generally affected by ground clutter, so a slightly higher elevation angle is selected, but there is a deviation between different elevation angles, so a constraint of 2.5-4.5 is imposed, and the subsequent threshold is a constraint on the theoretical differential reflectivity. Step 3 Since the radar is blocked and the low-level elevation angle data is incomplete, the maximum value method is used to realize the radar puzzle, so as to find the valid value according to the puzzle. In step 4, the radar scheduling has a scheduling time, and the selected valid value is actually to reduce the invalid scheduling VPCAL scanning of the radar (conventional radar uses volume scanning mode and does not use VPCAL scanning). In step 5, the key parameters are obtained through the status file mainly to eliminate radar failures; whether the radar is scheduled is to exclude the radar from performing other tasks. Step 6 is to schedule the radar to execute the VPCAL mode. The three groups are executed to prevent singular values ​​and average the data. Step 7 is to obtain the calibration value.

[0056] like Figure 4 As shown in the figure, a schematic diagram of the VPCAL scan performed by the radar to be calibrated is shown, including information such as the scanning direction, scanning angle and scanning range. The differential reflectivity is scanned using the zenith VPCAL scan, that is, the antenna elevation angle is 90°, and then the azimuth is rotated 360° to scan the sky, which is equivalent to "looking up and seeing that the raindrops are all round", so its theoretical deviation is 0.

[0057] This application provides real-time monitoring and intelligent triggering mechanisms: The present invention proposes a real-time monitoring of radar operating status (radar status and whether it is scheduled), which can automatically detect potential deviations in radar data or identify weather processes suitable for calibration (such as low echo intensity areas). This is the prerequisite for achieving adaptive calibration and ensures that the calibration process is started at the best time. The design of the intelligent trigger mechanism is the key. It combines data analysis and pattern recognition technology to accurately determine when to start calibration, avoiding unnecessary waste of resources and misoperation. It is of great significance to improve the timeliness and accuracy of calibration.

[0058] This application provides quantitative deviation calculation and evaluation: Based on the data collected by the zenith VPCAL scan, a quantitative deviation calculation and evaluation method was invented. This method uses advanced algorithms and data processing technology to accurately analyze the scan data and calculate the differential reflectivity deviation of the calibrated radar.

[0059] The evaluation process not only considers the size of the deviation, but also factors such as the distribution and change trend of the deviation, providing a reliable basis for subsequent calibration adjustments.

[0060] This application provides adaptive calibration and feedback adjustment: The invention realizes adaptive calibration of the entire networked X-band radar system. When a deviation is detected, the system can automatically schedule the relevant radars to perform calibration tasks, automatically adjust parameters based on the quantitative deviation calculation results, and send deviation prompts to the user.

[0061] The feedback adjustment mechanism ensures closed-loop control of the calibration process and improves the accuracy and stability of the calibration. At the same time, the system can also record the calibration process and results to provide data support for subsequent data analysis and system optimization.

[0062] This application has achieved the following beneficial effects: (1) Improving the scope of calibration application: The adaptive collaborative calibration method overcomes the problem that the traditional calibration method is limited by environmental conditions, and realizes the calibration of radar under rainfall weather processes.

[0063] (2) Improve positioning accuracy: The zenith VPCAL scanning technology is used to reduce the impact of external factors on calibration accuracy. The accuracy of the calibration results is improved through precise positioning of the radar antenna control. The radar volume scanning process generally uses a combination of several low elevation angles, rotating and lifting at the same time. However, the zenith calibration VPCAL mode sets the elevation angle to 90° and then rotates 360°. The antenna pitch is not involved during the rotation process.

[0064] (3) Realize automated calibration: The entire calibration process does not require human intervention, which realizes the automation and intelligence of the calibration process, reducing workload and cost.

[0065] (4) Reduce dependence on data sources: Automatic calibration under suitable weather conditions or potential deviations does not require the accumulation of large amounts of data, reducing dependence on data sources. Zenith VPCAL calibration collects data after the antenna is elevated 90° and then rotated 360° in azimuth. It is an accurate system ZDR calibration method. After deviation detection, the radar is directly scheduled to execute one or more VPCALs under suitable conditions. The default value obtained is the system full-link differential reflectivity deviation.

[0066] (5) Improving data consistency: Through real-time monitoring and calibration adjustment, the consistency and accuracy of the differential reflectivity data of the entire networked X-band radar system are ensured.

[0067] (6) Automatic calibration: The zenith calibration VPCAL mode is a type of weather radar scanning mode that can be configured by the user. In the configuration table, set a single-layer PPI scan and set the scan elevation to 90°. However, the zenith calibration VPCAL is generally an offline operation. It is necessary to select the VPCAL mode in the radar control RCW software and configure it to 90° in the PPI Elevation. The control center can send control instructions to the deviation radar using the SNMP protocol. Scheduling realizes the automatic operation of VPCAL. The radar volume scan base data is realized by combining multiple groups of PPI (plane scans) with different elevations (Elevation). When using VPCAL, set a single-layer plane (PPI) scan with a 90° elevation (ELevation).

[0068] In one embodiment, the networked X-band radar full system differential reflectivity adaptive collaborative calibration method further includes: S7, obtaining multi-source monitoring information; wherein the multi-source monitoring information at least includes: operating status information of the radar monitoring unit, the data processing center, and the calibration execution unit; In S71, the operation status information may include whether the radar equipment monitored by the radar monitoring unit is operating normally, signal reception quality, transmission status, data acquisition frequency, etc., as well as the system load, processing speed, and processing item queue status of the data processing execution, and may also include the progress of the calibration execution task, the reception and execution status of the calibration instruction, etc.; S8. Build a digital model based on multi-source monitoring information; In S8, when building a digital model, the multi-source monitoring information is visualized, and the multi-source monitoring information can be viewed comprehensively and globally in the digital model; S9. Assist management personnel to monitor and manage radar collaborative calibration based on digital models; In S9, managers can understand the overall operating status of the radar monitoring unit, data processing center and calibration execution unit through digital models, and then make management decisions based on the content they have viewed, and monitor and manage the radar collaborative calibration work based on the results of the management decisions; managers do not need to call up each operating status of the radar monitoring unit, data processing center and calibration execution unit to make management decisions, which improves the decision-making efficiency of managers and the stability of radar collaborative calibration work, and is also more user-friendly; Wherein, the S9, assistant management personnel, performs radar collaborative calibration monitoring and management based on the digital model, including: S91. Identify events to be managed in the digital model; In S91, the events to be managed may be system hardware failure, system software failure, radar collaborative calibration obstacle problem, system performance optimization event, unreasonable system work resource allocation event, etc. The digital model reflects the overall operation status of the radar monitoring unit, data processing center and calibration execution unit. Based on the overall operation status reflected, the events to be managed can be determined to realize automatic identification of the events to be managed. S92, when the manager starts to view the occurrence area of ​​the event to be managed in the digital model, continuously track the operation history of the manager's operation in the occurrence area and the landing point trajectory formed by the movement of the manager's sight in the digital model; In S92, the event to be managed will have an occurrence area in the digital model, and the occurrence area is the model area reflecting the event to be managed, on which the displayed contents can be combined to form the event to be managed; when the manager views the occurrence area, the operation history of the manager operating the occurrence area is tracked, including at least: the operation type and operation time generated when operating the occurrence area, and the landing point track formed by the movement of the manager's sight in the digital model. When the manager views the occurrence area, his sight will generate a landing point in the digital model, and when the sight moves, the landing point will also move, thereby forming a landing point track; S93, when active characterization of the region occurs or management opportunities are passively characterized by operation history, a virtual projection area is determined in the digital model based on the landing point trajectory; In S93, the active characterization management opportunity of the occurrence area refers to that the newly generated content in the occurrence area represents a time when a radar collaborative calibration monitoring management decision needs to be made. For example, if the newly generated content in the occurrence area is that a more serious fault has occurred in the system hardware, then a radar collaborative calibration monitoring management decision needs to be made urgently. The active characterization management opportunity is that the system hardware fault needs to be dealt with immediately. The passive characterization management opportunity of the operation history refers to the manager operating the occurrence area and representing that he wants to make a management decision. For example, if the manager checks the system hardware status and the list of responsible personnel for the system hardware in succession, it means that the manager wants to dispatch the hardware responsible personnel to deal with the hardware. Then the passive characterization management opportunity is that the dispatcher handles the hardware problem. The virtual projection area is an area where auxiliary content that assists the manager in performing radar collaborative calibration monitoring management needs to be displayed. The virtual projection area is reasonably determined based on the trajectory of the manager's line of sight. S94, Capture value information from digital models based on management opportunity value information capture template; In S94, the value information capture template is an execution template for the system to capture value information of auxiliary value to the management personnel that needs to be displayed to the management personnel at the management opportunity. Based on the template, the value information is captured in the digital model. For example, if the management opportunity is for the dispatcher to handle the hardware problem, the corresponding captured value information is the historical processing experience information of each person who can handle the hardware problem, etc.; S95, projecting the value information to the virtual projection area; In S95, after projecting the value information to the virtual projection area, the management personnel can view the value information in the virtual projection area, so as to get assistance, and make corresponding decisions on radar collaborative calibration monitoring management after getting assistance, so as to improve their work efficiency and the efficiency of management decision-making, and at the same time, it is more humane and intelligent; Wherein, the step S93, determining a virtual projection area in a digital model based on the landing point trajectory, includes: S931, determining the track interval corresponding to the total length of the landing point track from the track interval table; In S931, the track interval table contains track intervals corresponding to different track total lengths. The larger the track total length, the larger the corresponding track interval. S932, determining a target landing point at every track interval on the landing point track to obtain multiple target landing points; In S932, the target landing points on the landing point trajectory are spaced apart from each other by a trajectory interval; S933, based on the landing point evaluation system, determining an evaluation value according to the movement attributes of each target landing point; wherein the movement attributes at least include: the landing point stay time, the average stay time of other landing points within the front and rear preset trajectory lengths, and the landing point movement speed; the preset trajectory length is less than the trajectory interval; In S933, the landing point evaluation system has evaluation values ​​corresponding to different movement attributes. The higher the degree to which the movement attribute reflects the need of the manager to check the model position content of the target landing point, the greater the corresponding evaluation value; the landing point stay time is the time when the sight landing point stops moving, that is, the landing point movement speed is the instantaneous speed when the sight landing point moves to the target landing point, and the preset track length range can be the track range within 6 cm before and after the target landing point. There will be other landing points within the preset track length range before and after, and the other landing points will also have a stay time. The stay time of each other landing point is averaged to obtain the average stay time; in order to avoid the reasonable interval of the target landing points, the preset track length is set to be less than the track interval; S934, based on the constraints of the location to be constructed, three locations to be constructed are determined from the target locations; In S934, under the constraints of the to-be-built landing point determination constraint, three target landing points are determined from the target landing points as three to-be-built landing points; S935, constructing a virtual projection area based on the three points to be constructed; wherein the virtual projection area is an area on the plane where the three points to be constructed coexist under preset size constraints, and the center position of the virtual projection area is the center of the triangle formed by the connection lines of the three points to be constructed; In S935, the preset size is the length and width, the center position of the virtual projection area is determined, the length and width are determined, and the virtual projection area can be constructed from the plane where the three landing points to be constructed coexist; The constraints for determining the point of architecture to be constructed include: Constraint 1: The evaluation values ​​of the three locations to be constructed belong to the last N of the target locations; N is the rounded-down value of the product of the total number of target locations and the preset proportional coefficient; as well as, Constraint 2: The three points to be constructed are not collinear; as well as, Constraint three: the trajectory similarity between the trajectory segment involved in the three landing points to be constructed on the landing point trajectory and the larger of the other two trajectory segments exceeds a preset similarity threshold.

[0069] In constraint one, ensuring that the evaluation values ​​of each of the to-be-built landing points are in the top N of the target landing points can make the model location where the to-be-built landing points are located less likely to be viewed by the management personnel, so that when constructing the virtual projection area, it is close to the current viewing area of ​​the management personnel and avoids affecting the current viewing status of the management personnel; the preset proportionality coefficient can be 0.6, and setting N to the downward integer value of the product of the total number of target landing points and the preset proportionality coefficient can reasonably determine N; in constraint two, ensuring that the three to-be-built landing points are not collinear, so that the three to-be-built landing points can determine the only collinear in the plane; in constraint three, the trajectory segment involved in the landing point trajectory of the three landing points to be constructed refers to the longest line segment among the three line segments formed by taking the two landing points of the three landing points to be constructed as the endpoints of the line segments; the remaining two trajectory segments are connected with the trajectory segments involved in the landing point trajectory of the three landing points to be constructed to form a landing point trajectory, ensuring that the trajectory similarity between the trajectory segment involved in the landing point trajectory of the three landing points to be constructed and the larger of the remaining two trajectory segments exceeds the preset similarity threshold, so that the similarity between the involved trajectory segment and the larger of the remaining two trajectory segments is high, so that the management personnel can view the virtual projection area at an appropriate angle.

[0070] When the above three constraints work together, they can quickly and accurately determine the three points to be constructed that are suitable for constructing the virtual projection area, which greatly improves the accuracy and rationality of the determination of the virtual projection area.

[0071] An embodiment of the present invention provides a networked X-band radar full-system differential reflectivity adaptive collaborative calibration system, comprising: a radar monitoring unit, a data processing center and a calibration execution unit; The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; The data processing center preprocesses the base data and performs deviation detection on the preprocessed radar differential reflectivity. Based on the deviation detection results, it generates calibration instructions and sends them to the calibration execution unit. The calibration execution unit automatically schedules the radar to be calibrated to perform zenith VPCAL scanning based on the calibration instruction; During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the deviation threshold and sends a deviation prompt to the user.

[0072] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the steps of any of the above-mentioned methods for adaptive collaborative calibration of differential reflectivity of the entire system of a networked X-band radar are implemented.

[0073] A computer device provided in an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the adaptive collaborative calibration method for differential reflectivity of the entire system of a networked X-band radar are implemented as described in any one of the above items.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A networked X-band radar full system differential reflectivity adaptive collaborative calibration method, characterized in that: include: The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; The data processing center preprocesses the radar-based data and performs deviation detection on the differential reflectivity data of the preprocessed radar-based data. Based on the deviation detection results, the center generates calibration instructions and sends them to the calibration execution unit. The calibration execution unit automatically schedules the radar to be calibrated to perform the zenith calibration VPCAL scan based on the calibration instruction; During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the threshold and issues a deviation warning to the user.

2. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: The radar basic data at least includes: reflectivity factor, radial velocity, spectrum width, and differential reflectivity; The pre-processing step at least includes: a data cleaning step and a quality control step; The data cleaning step includes at least: detection and elimination based on data time, data structure and integrity, and rationality of data content; The quality control step includes at least: removing noise and outliers based on radial continuity and filtering.

3. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: The deviation detection result at least includes: The differential reflectivity data in the pre-processed radar base data with a reflectivity factor of 22-26 dBZ above 1 km and below the melting layer is statistically averaged, and the average value is used as the deviation detection result; Based on the deviation detection results, the data processing center generates the execution conditions of the calibration instructions, including: The deviation detection result indicates that an X-band radar whose deviation exceeds a deviation threshold is identified and the vertical echo above the radar is not less than 20dBZ and the echo intensity within the surrounding 10KM does not exceed 35dBZ; the threshold is adjustable.

4. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: The calibration instruction at least includes: calibration time, calibration mode and the number of the radar involved in the calibration.

5. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: Each X-band radar, radar monitoring unit, data processing center and calibration execution unit communicate with each other through a communication network.

6. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system as claimed in claim 4, characterized in that: The calibration mode includes: zenith calibration VPCAL scan.

7. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: The VPCAL scanning mode uses the CDX waveform.

8. The adaptive collaborative calibration method for differential reflectivity of the entire networked X-band radar system according to claim 1, characterized in that: Also includes: S7, obtaining multi-source monitoring information; wherein the multi-source monitoring information at least includes: operating status information of the radar monitoring unit, the data processing center, and the calibration execution unit; S8. Build a digital model based on multi-source monitoring information; S9. Assist management personnel to monitor and manage radar collaborative calibration based on digital models; Wherein, the S9, assistant management personnel, performs radar collaborative calibration monitoring and management based on the digital model, including: S91. Identify events to be managed in the digital model; S92, when the manager starts to view the occurrence area of ​​the event to be managed in the digital model, continuously track the operation history of the manager's operation in the occurrence area and the landing point trajectory formed by the movement of the manager's sight in the digital model; S93, when active characterization of the region occurs or management opportunities are passively characterized by operation history, a virtual projection area is determined in the digital model based on the landing point trajectory; S94, Capture value information from digital models based on management opportunity value information capture template; S95. Project the value information onto the virtual projection area.

9. Networked X-band radar full system differential reflectivity adaptive collaborative calibration system, characterized by: include: Radar monitoring unit, data processing center and calibration execution unit; The radar monitoring unit continuously collects radar-based data from each X-band radar and transmits it to the data processing center in real time; The data processing center preprocesses the radar base data and performs deviation detection on the preprocessed radar differential reflectivity data. Based on the deviation detection results, it generates calibration instructions and sends them to the calibration execution unit. The calibration execution unit automatically schedules the radar to be calibrated to perform the zenith calibration VPCAL scan based on the calibration instruction; During the scanning process, the radar monitoring unit synchronously collects VPCAL scanning data and transmits it to the data processing center; The data processing center calculates the quantitative deviation based on the VPCAL scanning data and sends it to the calibration execution unit; The calibration execution unit automatically adjusts the parameters of the X-band radar whose quantitative deviation exceeds the deviation threshold and issues a deviation prompt to the user.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for adaptive collaborative calibration of differential reflectivity of the entire system of a networked X-band radar according to any one of claims 1 to 7 is implemented.

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