Self-adaptive cooperative calibration method and system for networking X-band radar

Abstract

The invention provides a networking X-band radar adaptive cooperative calibration method and system, and relates to the technical field of radar calibration, and the method comprises the steps: detecting a deviation radar in a networking X-band radar; scheduling the deviation radar and a standard radar in the networking X-band radar to perform multi-group opposite RHI scanning to obtain a scanning result; and based on the scanning result, calibrating the deviation radar. According to the networking X-band radar self-adaptive cooperative calibration method and system, the measurement deviation is automatically identified and corrected through mutual cooperation between the standard radar and the deviation radar in networking, the data fusion and cooperative work capacity among multiple radars is enhanced, and the overall detection precision of the radars is improved.

Description

Technical Field

[0001] The present invention relates to the field of radar calibration technology, and in particular to a method and system for adaptive collaborative calibration of networked X-band radars. Background Art

[0002] With the rapid development of radar technology, especially in the fields of military defense, aviation, and meteorological monitoring, X-band radars are widely used due to their high resolution and strong detection capabilities. However, with the expansion of radar systems and the development of networking technology, how to ensure that multiple radars work together and ensure their detection accuracy and consistency has become an important technical challenge.

[0003] In a radar network, each radar usually has different hardware characteristics, working environment and sensor errors, which leads to certain performance differences in the network. In particular, factors such as radar hardware failure, environmental changes, and performance degradation after long-term operation will cause some radars to have measurement errors, affecting the overall detection accuracy. In order to solve these problems, radar accuracy calibration is particularly important.

[0004] Against the backdrop of global climate change and rapid urbanization, the frequent occurrence of extreme weather events has posed unprecedented challenges to meteorological monitoring, forecasting and early warning systems. Weather radar, as a key tool for monitoring and early warning of disastrous weather, has become increasingly important, especially in short-term forecasting and early warning services, where its accuracy and timeliness are directly related to the efficiency and effectiveness of disaster prevention.

[0005] Although significant progress has been made in weather radar systems, they still face multiple challenges. In areas prone to severe convective weather, there are monitoring blind spots due to urban buildings, complex terrain, insufficient radar networks or performance limitations, which affect the timely capture and accurate assessment of disastrous weather. To solve this problem, the "low-altitude blind spot" strategy came into being. New equipment such as X-band radars are widely used to fill low-altitude monitoring blind spots due to their high spatial resolution and short detection cycle, providing more detailed data support for short-term forecasting.

[0006] However, the widespread use of low-altitude blind spot equipment such as X-band radar has also brought new technical challenges. High-frequency operation makes radar more sensitive to the electromagnetic environment and susceptible to factors such as ground clutter and electromagnetic interference, which complicates the calibration and calibration issues and inaccurate echo intensity calibration. At the same time, when quantitatively estimating precipitation, although high resolution is an advantage, its attenuation characteristics also exacerbate the difficulties of precipitation type identification and raindrop spectrum distribution estimation, making the precipitation estimation results uncertain.

[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 reflectivity factors has become an urgent problem to be solved. Traditional methods are difficult to meet the requirements of real-time, accuracy and precision, and more advanced methods are urgently needed 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] In the prior art, for example, the application number CN202410007027.7 (A weather radar calibration method and device based on metal balls) provides a weather radar calibration method and device based on metal balls. 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 after determining the reference pitch angle and reference azimuth angle corresponding to the weather radar according to the scanning results, controlling the weather radar to align the metal ball for directional scanning; 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 calibration data of the metal ball, technical parameters and preset radar formulas, calculating and obtaining 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, determining the radar calibration coefficient of the weather radar, and realizing the calibration of the weather radar.

[0010] Authorization publication number CN 105866751 Metal ball calibration method for BX-band solid-state dual-polarization weather radar A metal ball calibration method for an X-band solid-state dual-polarization weather radar is provided, comprising: selecting a test point in the radar wide pulse detection range and the narrow pulse detection range respectively; placing a metal ball at each test point; for each metal ball, using its GPS information and the radar latitude, longitude and altitude information to calculate the corresponding radar observation elevation angle, azimuth angle and library number; setting a calibration observation mode to obtain observation data, the calibration observation mode including the horizontal variation range of the radar observation azimuth, and the variation range and step angle of the radar observation elevation angle, the observation data including the radar reflectivity factor observation value and the differential reflectivity observation value; taking the difference between the radar reflectivity factor observation value and the radar reflectivity factor theoretical value or the difference between the differential reflectivity observation value and the differential reflectivity theoretical value as the radar measurement error, and using the measurement error to correct the detection result of the radar within the corresponding detection range.

[0011] However, the above prior art has the following shortcomings:

[0012] 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 carried out under certain conditions (such as airspace restrictions, precipitation weather, high wind speed, etc.). In addition, networked radars are generally built in radar-intensive areas, and airspace restrictions make it impossible for some radars to carry out metal ball calibration.

[0013] 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.

[0014] Automatic calibration is not possible: Current metal ball calibration methods usually require manual intervention, 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.

[0015] In other prior arts, the authorization announcement number CN115097459B discloses an interactive verification method and system for the reflectivity factors of S and X-band networked weather radars, including step 1, constructing a data set of reflectivity factors of S and X-band networked weather radars with close time and consistent spatial resolution; step 2, extracting the observation overlap area based on basic information such as radar position and detection range; step 3, traversing the observation overlap area, interactively matching the reflectivity factors of S and X-band networked weather radars; step 4, subtracting, grading and marking the point-to-point grid data, and generating interactive verification products in an image. It can give the difference in detection of the same meteorological target by S and X-band networked weather radars, realize interactive verification between weather radars of different bands (S and X) in the weather radar network, provide favorable support for the meteorological department to dynamically and quickly monitor and evaluate the quality of networked weather radar collaborative detection data and the calibration effect of single radar data, and serve the current weather radar network blind spot filling construction and collaborative observation work.

[0016] Application No. 202410322646.5 A method, device, equipment and storage medium for consistency assessment between weather radars, applied in the field of meteorological observation technology. The method provided includes: obtaining base data of adjacent weather radars at a 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.

[0017] However, the above prior art has the following deficiencies:

[0018] Strong dependence on data sources: In order to achieve "spatial matching", long-term sequence accumulation is required to achieve consistency assessment; both methods rely on the basic data of adjacent (comparison) weather radars at the same time and the same body scanning elevation. If there are problems with radar data acquisition (such as missing data, poor data quality, etc.), it will directly affect the accuracy of consistency assessment.

[0019] Errors caused by band differences: S-band and X-band weather radars differ in detection principles, detection ranges, and detection accuracy. These differences may cause additional errors during the interactive verification process, affecting the accuracy of the verification results.

[0020] Computational complexity and difficulty in "quantification": Weather radar observations are affected by a variety of environmental factors. Constructing a weather radar reflectivity factor dataset with similar time and consistent spatial resolution requires a complex processing flow, including data preprocessing, quality control, and time-space matching. These steps are not only time-consuming and labor-intensive, but the preset screening rules may be subjective when screening overlapping points. Different screening rules may lead to different evaluation results and introduce new interpolation and matching errors, making it impossible to give accurate "quantified" deviation results, thus affecting the reliability of consistency evaluation.

[0021] Therefore, a solution is urgently needed. Summary of the invention

[0022] One of the purposes of the present invention is to provide a networked X-band radar adaptive collaborative calibration method to address the deficiencies in the above-mentioned prior art.

[0023] An embodiment of the present invention provides a networked X-band radar adaptive collaborative calibration method, comprising:

[0024] Detect deviation radar in networked X-band radars;

[0025] The deviation radar is dispatched to perform multiple groups of RHI scans in opposite directions with the standard radar in the networked X-band radar to obtain the scanning results;

[0026] Based on the scan results, the deviation radar is calibrated.

[0027] Optionally, the detecting a deviation radar in the networked X-band radar includes:

[0028] Collect reflectivity factor data of each X-band radar in the networked X-band radar;

[0029] Preprocessing the collected reflectivity factor data;

[0030] Based on the deviation detection algorithm, the deviation radar is determined from the networked X-band radar according to the preprocessed reflectivity factor data.

[0031] Optionally, the preprocessing of the collected reflectivity factor data includes:

[0032] Perform data cleaning on the collected reflectivity factor data;

[0033] and, performing quality control processing on the collected reflectivity factor data.

[0034] Optionally, before scheduling the deviation radar to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar, the method further includes:

[0035] When it is detected that the deviation of any radar in the networked X-band radar exceeds the deviation threshold and there is no echo exceeding 35dBZ in the radial library near the relative azimuth, the deviation radar is dispatched to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar.

[0036] Optionally, the scheduling deviation radar performs multiple groups of opposite RHI scans with the standard radar in the networked X-band radar to obtain scanning results, including:

[0037] In the process of scheduling the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans, the deviation radar and the standard radar in the networked X-band radar synchronously collect data to obtain the scanning result.

[0038] Optionally, the above-mentioned calibration of the deviation radar based on the scanning result includes:

[0039] Based on the deviation calibration algorithm, the deviation radar is calibrated according to the scanning results.

[0040] Optionally, calibrating the deviation radar based on the scanning result includes:

[0041] Attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library;

[0042] When the attempt is successful, the deviation radar is calibrated based on the first calibration strategy; otherwise, the expert group is assisted in making an online decision on the second calibration strategy based on the scanning results;

[0043] Based on the second calibration strategy, the deviation radar is calibrated.

[0044] Optionally, the assisting expert group to make an online decision on the second calibration strategy based on the scanning result includes:

[0045] Matching view network templates for scan results;

[0046] Based on the view networking template, the scanning results are processed into a view networking to obtain a result view network;

[0047] Guide the expert group to collaboratively review the result view network;

[0048] Match auxiliary logic sequence for scan results;

[0049] When the expert group collaboratively reviews the result view network, based on the auxiliary logic sequence, the auxiliary expert group makes an online decision on the second calibration strategy.

[0050] Optionally, the method of assisting the expert group in making online decision on the second calibration strategy based on the auxiliary logic sequence includes:

[0051] The target area is determined from the result view network collaboratively viewed by the expert group; wherein the similarity between the stay feature of the local track passing through the target area in the sight track generated by the result view network viewed by more than a proportion of the people in the expert group within the recent preset time and the standard stay feature exceeds a similarity threshold;

[0052] Determining a first target logic associated with the target area from the auxiliary logic sequence;

[0053] Based on the local continuous logic sequence interception constraint, according to the first target logic, intercept the local continuous logic sequence from the auxiliary logic sequence;

[0054] If the local continuous logic sequence is unique, the local continuous logic sequence is used as a valid auxiliary logic sequence; otherwise, the longest local continuous logic sequence is used as a valid auxiliary logic sequence;

[0055] Execute the first to jth second target logics in the effective auxiliary logic sequence on the expert group in sequence order to obtain a first execution status; wherein j is a position threshold;

[0056] Based on the first execution situation, determining execution values ​​of the j+1th to Nth second target logics in the valid auxiliary logic sequence; wherein N is the total number of second target logics in the valid auxiliary logic sequence;

[0057] Execute the j+1th to Nth second target logics in the effective auxiliary logic sequence on the expert group in order according to the execution values ​​from large to small, and obtain the second execution status;

[0058] Based on the second execution situation, selecting a second target logic from the valid auxiliary logic sequence as a new latest executed auxiliary logic;

[0059] a second calibration strategy for online decision making that receives input from a panel of experts;

[0060] The local continuous logic sequence interception constraints include:

[0061] Constraint 1: The number of sequence items in the local continuous logical sequence does not exceed the number threshold; and

[0062] Constraint 2: the sequence head of the local continuous logic sequence is the latest executed auxiliary logic in the auxiliary logic sequence, and the sequence tail of the local continuous logic sequence is the first target logic; and,

[0063] Constraint 3: The first target logic is unique or adjacent to each other in the local continuous logic sequence.

[0064] Optionally, the steps for determining the position threshold are as follows:

[0065] Calculate the contribution weight of each second target logic in the effective auxiliary logic sequence: ,

[0066] in, is the action weight of the second target logic, The second target logic is valid for the other first The action value of the second target logic;

[0067] Set reserve value , calculate the delineation tool value: ,

[0068] in, To define the tool value, The first The weight of the second goal logic;

[0069] The position threshold is a reserve value when the maximum demarcation tool value that does not exceed the demarcation tool value threshold is calculated. The value of .

[0070] Optionally, determining the execution values ​​of the j+1th to Nth second target logics in the valid auxiliary logic sequence based on the first execution situation includes:

[0071] The execution value is calculated using the following formula: ,

[0072] in, is the execution value of the second target logic, The first logic execution weight for the second target logic, a second logic execution weight for the second target logic for the first execution case, and is the preset weight value.

[0073] Optionally, the selecting, based on the second execution situation, the second target logic from the valid auxiliary logic sequence as the new latest executed auxiliary logic includes:

[0074] Based on the characterization template, characterize the second execution situation and the second target logic in the effective auxiliary logic sequence to obtain a feature set;

[0075] Determine a selection rule corresponding to the feature set from a selection rule library;

[0076] Based on the selection rule, the second target logic is selected from the valid auxiliary logic sequence as the new latest executed auxiliary logic.

[0077] An embodiment of the present invention provides a networked X-band radar adaptive collaborative calibration system, comprising:

[0078] A detection module, used to detect deviation radars in networked X-band radars;

[0079] A scheduling module is used to schedule the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans to obtain the scanning results;

[0080] The calibration module is used to calibrate the deviation radar based on the scanning results.

[0081] Optionally, the calibration module calibrates the deviation radar based on the scanning result, including:

[0082] Attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library;

[0083] When the attempt is successful, the deviation radar is calibrated based on the first calibration strategy; otherwise, the expert group is assisted in making an online decision on the second calibration strategy based on the scanning results;

[0084] Based on the second calibration strategy, the deviation radar is calibrated.

[0085] Optionally, the calibration module assists the expert group in making an online decision on a second calibration strategy based on the scanning result, including:

[0086] Matching view network templates for scan results;

[0087] Based on the view networking template, the scanning results are processed into a view networking to obtain a result view network;

[0088] Guide the expert group to collaboratively review the result view network;

[0089] Match auxiliary logic sequence for scan results;

[0090] When the expert group collaboratively reviews the result view network, based on the auxiliary logic sequence, the auxiliary expert group makes an online decision on the second calibration strategy.

[0091] The present invention has achieved the following beneficial effects:

[0092] Improve the scope of calibration application: overcome the limitations of the metal ball calibration method due to environmental conditions (such as airspace restrictions, precipitation weather, high wind speed) and the problem of non-full system link under the conventional in-machine calibration method, and realize calibration work under rainy weather conditions. Through automated and intelligent collaborative calibration strategies, ensure accurate calibration of deviation radar under different precipitation conditions.

[0093] Improve positioning and data matching accuracy: Improve the longitude of the relative pointing positioning of the antennas through automatic scheduling of the relative azimuth antenna control of the two radars; increase the number of samples by re-optimizing the design of the RHI scanning mode, thereby improving the data quality mechanism for matching the common coverage area and improving the accuracy of quantitative deviation calibration.

[0094] Automatic calibration: Through intelligent algorithms, data processing flow is optimized to automatically calibrate the deviation radar, reduce the reliance on manual operations, and realize automation and intelligence of the calibration process. This can not only significantly reduce workload and cost, but also reduce errors and uncertainties introduced by human factors.

[0095] Reduce dependence on data sources: The original data consistency assessment 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 for problems such as missing data and poor quality.

[0096] Reduce the errors caused by differences between different bands: In view of the differences between S-band and X-band weather radars, a special calibration algorithm for X-band radar is developed to reduce the impact of band differences on the results of cross-calibration. Through precise calibration and error correction, the accuracy of the quantitative deviation of X-band radar data is ensured.

[0097] 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.

[0098] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] 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:

[0100] Figure 1 Schematic diagram of an adaptive collaborative calibration method for networked X-band radars in an embodiment of the present invention;

[0101] Figure 2 A schematic diagram of the structural relationship between multiple working units in an embodiment of the present invention;

[0102] Figure 3 It is a flow chart of a method for adaptive collaborative calibration of networked X-band radars in an embodiment of the present invention;

[0103] Figure 4Schematic diagram of collaborative RHI scanning scheduling in an embodiment of the present invention;

[0104] Figure 5 Schematic diagram of a networked X-band radar adaptive collaborative calibration system in an embodiment of the present invention. DETAILED DESCRIPTION

[0105] 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.

[0106] An embodiment of the present invention provides a networked X-band radar adaptive collaborative calibration method, such as Figure 1 As shown, including:

[0107] S1, detecting deviation radar in networked X-band radar;

[0108] In S1, a deviation radar refers to a radar in the network whose measurement results deviate from the actual target to a certain extent. The deviation may be caused by hardware failure, environmental changes or long-term use. When detecting a deviation radar, each radar in the network can be monitored, and the detection data of the same target by different radars can be compared. The measurement results of multiple radars can be used for data fusion to identify the radar whose measurement results are inconsistent with those of the standard radar. For example, if the position of the target detected by a certain radar deviates too much from the measurement data of other radars, the radar may be deviated.

[0109] S2, dispatching the deviation radar to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar to obtain the scanning results;

[0110] In S2, RHI scanning (Range Height Indicator) is a radar scanning method, which is mainly used to obtain the vertical distribution information of the target. Different from the traditional horizontal scanning, RHI scanning can provide the position and height information of the target in the vertical direction. Through multiple sets of opposite RHI scanning, that is, the deviation radar and the standard radar perform relative scanning, the system can obtain different observation results of the radar on the same target and form a scanning result, which at least includes: the distance, angle, speed and other information of the target. For example, during the scheduling process, the deviation radar and the standard radar may scan the same target multiple times. By comparing the scanning results of the two, the reflectivity factor measurement error of the deviation radar can be identified and used as the scanning result.

[0111] S3. Calibrate the deviation radar based on the scanning results.

[0112] In S3, the scanning results reflect the deviation of the deviation radar, which can be eliminated by automatically compensating for the error or prompting experts to calibrate or adjust the radar parameters. The technicians can pre-set the calibration method corresponding to different scanning results to calibrate the deviation radar.

[0113] The embodiments of the present invention utilize the mutual cooperation between the standard radar and the deviation radar in the network to automatically identify and correct the measurement deviation, thereby enhancing the data fusion and collaborative working capabilities between multiple radars and improving the overall detection accuracy of the radar.

[0114] In one embodiment, Figures 2 to 4 As shown, the deviation radar in the detection network X-band radar includes:

[0115] Collect reflectivity factor data of each X-band radar in the networked X-band radar;

[0116] Preprocessing the collected reflectivity factor data;

[0117] Based on the deviation detection algorithm, the deviation radar is determined from the networked X-band radar according to the preprocessed reflectivity factor data.

[0118] The preprocessing of the collected reflectivity factor data includes:

[0119] Perform data cleaning on the collected reflectivity factor data;

[0120] and, performing quality control processing on the collected reflectivity factor data.

[0121] Before the deviation radar is scheduled to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar, it also includes:

[0122] When it is detected that the deviation of any radar in the networked X-band radar exceeds the deviation threshold and there is no echo exceeding 35dBZ in the radial library near the relative azimuth, the deviation radar is dispatched to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar.

[0123] The scheduling deviation radar performs multiple groups of opposite RHI scans with the standard radar in the networked X-band radar to obtain scanning results, including:

[0124] In the process of scheduling the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans, the deviation radar and the standard radar in the networked X-band radar synchronously collect data to obtain the scanning result.

[0125] Based on the scanning results, the deviation radar is calibrated, including:

[0126] Based on the deviation calibration algorithm, the deviation radar is calibrated according to the scanning results.

[0127] Firstly, by collecting the reflectivity factor data of each radar in the X-band radar, the reflected signal intensity observed by the radar is obtained.

[0128] The data are preprocessed, including data cleaning, removing noise and outliers, ensuring the quality and validity of the data, and performing quality control to ensure the credibility of the data.

[0129] Using the pre-processed reflectivity factor data, the deviation radar is determined based on the deviation detection algorithm. A deviation radar is a radar with a large difference in reflectivity factor data compared to a standard radar. The deviation detection algorithm can be set in advance by the technician according to actual needs.

[0130] The deviation detection process compares the outputs of the various radars to determine if a deviation has occurred, especially when the relative bearings are close.

[0131] When it is detected that the deviation of a certain radar exceeds the preset deviation threshold, and there is no strong echo (greater than 35dBZ) within the azimuth range of the radar, the deviation radar and the standard radar can be started to perform multiple sets of opposite RHI scans.

[0132] RHI scanning is to perform vertical section scanning through the radar to collect echo information from different height angles, so as to obtain the reflectivity factor information of the common overlapping area of ​​​​the two radars for relative calibration.

[0133] During the scanning process, the deviation radar and the standard radar collect data synchronously to obtain the scanning results.

[0134] According to the scanning results, the deviation radar is calibrated using the deviation calibration algorithm to adjust its measurement parameters to reduce the deviation and ensure its consistency with the standard radar data. The deviation calibration algorithm can also be set in advance by the technician according to actual needs.

[0135] The embodiments of the present invention achieve the following beneficial effects:

[0136] Improve the scope of calibration application: overcome the limitations of the metal ball calibration method due to environmental conditions (such as airspace restrictions, precipitation weather, high wind speed) and the problem of non-full system link under the conventional in-machine calibration method, and realize calibration work under rainy weather conditions. Through automated and intelligent collaborative calibration strategies, ensure accurate calibration of deviation radar under different precipitation conditions.

[0137] Improve positioning and data matching accuracy: Improve the longitude of the relative pointing positioning of the antennas through automatic scheduling of the relative azimuth antenna control of the two radars; increase the number of samples by re-optimizing the design of the RHI scanning mode, thereby improving the data quality mechanism for matching the common coverage area and improving the accuracy of quantitative deviation calibration.

[0138] Automatic calibration: Through intelligent algorithms, data processing flow is optimized to automatically calibrate the deviation radar, reduce the reliance on manual operations, and realize automation and intelligence of the calibration process. This can not only significantly reduce workload and cost, but also reduce errors and uncertainties introduced by human factors.

[0139] Reduce dependence on data sources: The original data consistency assessment 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 for problems such as missing data and poor quality.

[0140] Reduce the errors caused by differences between different bands: In view of the differences between S-band and X-band weather radars, a special calibration algorithm for X-band radar is developed to reduce the impact of band differences on the results of cross-calibration. Through precise calibration and error correction, the accuracy of the quantitative deviation of X-band radar data is ensured.

[0141] In one embodiment, the step S3, calibrating the deviation radar based on the scanning result, includes:

[0142] S31, attempting to determine a first calibration strategy corresponding to the scanning result from a calibration strategy library;

[0143] In S31, the calibration strategy library has first calibration strategies corresponding to different scanning results. The first calibration strategy indicates how to calibrate the deviation radar when a scanning result occurs, and can be set in advance by a technician.

[0144] S32. When the attempt is successful, calibrate the deviation radar based on the first calibration strategy; otherwise, assist the expert group in making an online decision on the second calibration strategy based on the scanning results;

[0145] In S32, when the attempt is successful, it means that there is a corresponding first calibration strategy, and the deviation radar is calibrated directly based on the first calibration strategy; otherwise, when the attempt fails, it means that there is no corresponding first calibration strategy, and the expert group is assisted to make an online decision on the second calibration strategy based on the scanning result, and the expert group includes a plurality of technicians specializing in the field of radar calibration;

[0146] S33. Calibrate the deviation radar based on the second calibration strategy.

[0147] In S33, after the second calibration strategy is determined online, the deviation radar is calibrated based on the second calibration strategy.

[0148] When calibrating the deviation radar based on the scanning results, the embodiment of the present invention gives priority to letting the system try to determine the first calibration strategy on its own. When the attempt fails, it assists the expert group to make an online decision on the second calibration strategy based on the scanning results, thereby improving the adaptability of calibration resource allocation and greatly improving the efficiency of calibrating the deviation radar.

[0149] In one embodiment, in S32, assisting the expert group to make an online decision on the second calibration strategy based on the scanning result includes:

[0150] S321, matching a view networking template for the scan result;

[0151] In S321, the view networking template is a template for the system to perform view networking processing on the scan results. The view networking template has visualization interfaces corresponding to different types of data. When matching the view networking template for the scan results, the corresponding view network template is matched according to the type of data in the scan results. When the view networking template is used, different types of data in the scan results are mapped to corresponding visualization interfaces in the view networking template. After the data mapping is completed, multiple visualization interfaces form a result view network.

[0152] S322, performing view networking processing on the scan results based on the view networking template to obtain a result view network;

[0153] In S322, after the view networking template is matched, the scan result is subjected to view networking processing based on the template to obtain a result view network;

[0154] S323, guiding the expert group to collaboratively view the result view network;

[0155] In S323, when guiding the expert group, information prompting each member of the expert group to collaboratively view the result view network may be pushed to each member of the expert group; by collaboratively viewing the result view network, the expert group may decide how to calibrate the deviation radar, that is, make a decision on the second calibration strategy;

[0156] S324, matching an auxiliary logic sequence for the scan result;

[0157] In S324, the auxiliary logic sequence includes auxiliary logics that the instruction system needs to execute in sequence when assisting the expert group. When matching the auxiliary logic sequence for the scan result, matching can be performed according to different data types of the scan result. For example, if the scan result includes type A data and type B data, and the expert group needs to be assisted to understand type A data before it can understand type B data, then the sequential auxiliary logic in the matched auxiliary logic sequence is to assist the expert group to understand type A data first and then assist the expert group to understand type B data.

[0158] S325. When the expert group collaboratively views the result view network, the auxiliary expert group makes an online decision on the second calibration strategy based on the auxiliary logic sequence.

[0159] In S325, on the expert group collaborative review result view network, based on the matching auxiliary logic sequence, the auxiliary expert group makes an online decision on the second calibration strategy.

[0160] When assisting the expert group to make an online decision on the second calibration strategy based on the scanning results, the embodiment of the present invention first processes the scanning results into a result view network, and guides the expert group to view it, so that the expert group can intuitively and conveniently understand the reflection of the scanning results, etc., thereby improving humanization; secondly, when the expert group collaboratively views the result view network, based on the auxiliary logical sequence, the expert group is assisted in making an online decision on the second calibration strategy, thereby improving the accuracy and efficiency of the system in assisting the expert group to make an online decision on the second calibration strategy based on the scanning results, thereby improving intelligence.

[0161] In one embodiment, in S325, based on the auxiliary logic sequence, assisting the expert group to make an online decision on the second calibration strategy includes:

[0162] S3251, determining a target area from the result view network viewed collaboratively by the expert group; wherein the similarity between the stay feature of the local track passing through the target area in the sight track generated by the result view network viewed by more than a proportion of the people in the expert group within a recent preset time and the standard stay feature exceeds a similarity threshold;

[0163] In S3251, the number ratio is the ratio of the number of representatives that accounts for a large proportion, such as: 3 / 5; the preset time is the time that represents a long viewing time, such as: 300 seconds; when a person views the result view network, his or her sight will have a sight point on the terminal interface that displays the result view network. When the sight moves, the sight point will also move. The trajectory formed by the movement of the sight point is the sight trajectory; the local trajectory passing through the target area refers to the local trajectory segment corresponding to the local trajectory segment after entering the target area on the sight trajectory, and its stay characteristics include at least: the length of time the sight point represents the movement of the local trajectory, the type of data passed by the local trajectory in the target area, the order of passing through data of different data types, etc.; the standard stay characteristics are the representative personnel's heavy The characteristics of the target area of ​​interest are as follows, such as: the moving time of the sight point represented by the local trajectory is 40 seconds, and there are 5 types of data that the local trajectory passes through in the target area; the similarity threshold is a threshold representing a large similarity, such as: 75%; if more than the proportion of people in the expert group view the sight trajectory generated by the result view network passing through the target area within the recent preset time, and the similarity between the stay characteristics and the standard stay characteristics exceeds the similarity threshold, it means that the target area is paid attention to by many people in the expert group at the current stage and the sight stay of these people represents that they may have similar decision-making thinking about the target area. At this time, the expert group can be assisted based on the target area and the auxiliary logic sequence can be triggered;

[0164] S3252, determining a first target logic related to the target area from the auxiliary logic sequence;

[0165] In S3252, being related to the target area means that the execution of the first target logic depends on the target area, for example, if the first target logic is to assist the expert group in understanding a certain type of data, and the data is displayed in the target area, then it is related;

[0166] S3253, based on the local continuous logic sequence interception constraint, according to the first target logic, intercept the local continuous logic sequence from the auxiliary logic sequence;

[0167] In S3253, under the constraint of the local continuous logic sequence interception constraint, the local continuous logic sequence is intercepted from the auxiliary logic sequence in a targeted and accurate manner according to the first target logic;

[0168] S3254. If the local continuous logical sequence is unique, the local continuous logical sequence is used as a valid auxiliary logical sequence; otherwise, the longest local continuous logical sequence is used as a valid auxiliary logical sequence;

[0169] In S3254, if the local continuous logic sequence is unique, the local continuous logic sequence can be directly used as the effective auxiliary logic sequence; otherwise, the longest local continuous logic sequence is used as the effective auxiliary logic sequence, and the longest refers to the sequence with the largest number of sequence items in the sequence, so that the richness of continuous effective assistance can be increased as much as possible, thereby improving the auxiliary effect;

[0170] S3255, executing the first to jth second target logics in the effective auxiliary logic sequence on the expert group in sequence order to obtain a first execution status; wherein j is a position threshold;

[0171] In S3255, the position threshold can divide the second target logic in the effective auxiliary logic sequence into two different sequential execution modes, and execute the first to j-th second target logic in the effective auxiliary logic sequence to the expert group in sequence according to the sequence sequence, that is, the second target logic that is arranged earlier has a higher priority to be executed. When executing in sequence, the first execution situation is obtained, and the first execution situation at least includes: the behavior information of each person in the expert group after each execution of the first to j-th second target logic, such as: continue to view the content type in the result view network, the communication information between people, etc.;

[0172] S3256: Based on the first execution situation, determine the execution values ​​of the j+1th to Nth second target logics in the valid auxiliary logic sequence; wherein N is the total number of second target logics in the valid auxiliary logic sequence;

[0173] In S3256, based on the first execution situation, the subsequent execution of the j+1th to Nth second target logics can be further accurately arranged, and the execution values ​​are determined in sequence, where the execution values ​​represent the degree to which the second target logic needs to be executed first;

[0174] S3257, executing the j+1th to Nth second target logics in the effective auxiliary logic sequence on the expert group in order from large to small according to the execution values, and obtaining a second execution status;

[0175] In S3257, the j+1th to Nth second target logics in the effective auxiliary logic sequence are executed on the expert group in order according to the execution values ​​from large to small. Accordingly, the second execution situation at least includes: the behavior information of each person in the expert group after each execution of the j+1th to Nth second target logics;

[0176] S3258. Based on the second execution situation, select a second target logic from the valid auxiliary logic sequence as a new latest executed auxiliary logic;

[0177] In S3258, based on the second execution situation, it can be further precisely arranged which second target logic needs to be used as the new latest executed auxiliary logic to enter the local continuous logic sequence interception constraint to constrain the subsequent local continuous logic sequence interception;

[0178] S3259, receiving a second calibration strategy for online decision-making input by the expert group;

[0179] In S3259, each time the corresponding second target logic is executed after each interception of the local continuous logic sequence, the expert group will continuously receive assistance from the system, thereby making a fast and accurate decision on the second calibration strategy, and finally, inputting the second calibration strategy for its online decision;

[0180] The local continuous logic sequence interception constraints include:

[0181] Constraint 1: The number of sequence items in the local continuous logical sequence does not exceed the number threshold; and

[0182] Constraint 2: the sequence head of the local continuous logic sequence is the latest executed auxiliary logic in the auxiliary logic sequence, and the sequence tail of the local continuous logic sequence is the first target logic; and,

[0183] Constraint 3: The first target logic is unique or adjacent to each other in the local continuous logic sequence.

[0184] In constraint one, the number threshold is a number that represents a relatively large number of sequence items, such as 5. Through the constraint of constraint one, the number of sequence items in the intercepted local continuous logic sequence will not be too large, so that the expert group will not be provided with continuous and effective assistance for too long. In constraint two, the latest executed auxiliary logic is preferentially determined based on the previous selection from the effective auxiliary logic sequence. When it has not been selected, the auxiliary logic at the head of the auxiliary logic sequence can be defaulted as the latest executed auxiliary logic. The latest executed auxiliary logic is used as the sequence head of the local continuous logic sequence, and the first target logic is used as the sequence tail of the local continuous logic sequence. In this way, all the auxiliary logics in the intercepted local continuous logic sequence are suitable for continuous and effective assistance to the expert group. In constraint three, it is ensured that the first target logic is unique or adjacent to each other in the local continuous logic sequence, so that no other auxiliary logics between the first target logics will enter the local continuous logic sequence, which can further improve the accuracy and suitability of the local continuous logic sequence interception.

[0185] The embodiment of the present invention can quickly determine the target area suitable as the basis for intercepting the local continuous logic sequence by performing real-time analysis on the expert group's sight trajectory and stay features, combined with the standard stay features and the similarity threshold, and trigger the corresponding interception, thereby improving the system's work efficiency and reducing the system's work resources. By accurately intercepting the local continuous logic sequence from the auxiliary logic sequence, and dynamically adjusting the execution order of the subsequent target logic according to the execution result of the local logic sequence, the assistance obtained by the expert in the decision-making process is not only one-time, but is dynamically optimized according to its real-time behavior feedback, thereby improving the continuity and pertinence of the auxiliary decision. By setting the execution value, the priority of different auxiliary logics can be adjusted during the execution process, so that the auxiliary logic can maximize the decision-making efficiency of the expert group during execution, avoid unnecessary interference, and improve the fluency of the decision-making process. By intercepting the constraints through the local continuous logic sequence interception constraints, the auxiliary intervention of too much information is avoided, ensuring that the expert group obtains appropriate and effective information, reducing the cognitive load of the expert group during decision-making, and avoiding delays or misjudgments in the decision-making process due to too much irrelevant information.

[0186] In one embodiment, the steps for determining the position threshold are as follows:

[0187] Calculate the contribution weight of each second target logic in the effective auxiliary logic sequence: ,

[0188] in, is the action weight of the second target logic, The second target logic is valid for the other first The action value of the second target logic;

[0189] Set reserve value , calculate the delineation tool value: ,

[0190] in, To define the tool value, The first The weight of the second target logic; ;

[0191] The position threshold is a reserve value when the maximum demarcation tool value that does not exceed the demarcation tool value threshold is calculated. The value of .

[0192] In the above technical solution, the second target logic is effective for the other first The effect value of the second goal logic refers to the second goal logic that can help the experts in the expert group accept the first The degree of effect of the execution of the second target logic can be preset in advance by the technical staff. For example, the two second target logics are to first assist the expert group to understand the A type data and then assist the expert group to understand the B type data. The expert group needs to first assist the expert group to understand the A type data in order to facilitate their understanding of the B type data. The effect value of the first second target logic on the second second target logic is the larger one 8; the position threshold is used as a reserve value The demarcation tool value obtained after substituting into the calculation formula of the demarcation tool value is the maximum value not exceeding the demarcation tool value threshold; the demarcation tool value threshold can be, for example, 80; the greater the effect weight, the higher the comprehensive effect degree of the second target logic; by reasonably setting the demarcation tool value threshold, under its restriction, the first execution situation obtained in the 1st to jth second target logic in the effective auxiliary logic sequence in sequence is more suitable for further precise arrangement of how to execute the subsequent j+1th to Nth second target logics, and can also be used to avoid excessive assistance to each user in the expert group, causing their complete dependence, which greatly improves the flexibility of the system.

[0193] In one embodiment, determining the execution values ​​of the j+1th to Nth second target logics in the valid auxiliary logic sequence based on the first execution situation includes:

[0194] The execution value is calculated using the following formula: ,

[0195] in, is the execution value of the second target logic, The first logic execution weight for the second target logic, a second logic execution weight for the second target logic for the first execution case, and is the preset weight value.

[0196] In the above technical solution, the first logic execution weight of the second target logic represents the importance of the execution of the second target logic itself, which can be preset in advance by the technical personnel; the second logic execution weight of the first execution situation to the second target logic refers to the degree to which the first execution situation reflects the need to execute the second target logic, such as: the two second target logics are to first assist the expert group to understand type A data and then assist the expert group to understand type B data. The expert group needs to be assisted to understand type A data before it can understand type B data. The first execution situation obtained after the execution of the previous second target logic is that the expert group has checked and understood type B data by themselves, then the next second target logic does not need to be executed, and the corresponding second logic execution weight is 0; combined with the first logic execution weight and the second logic execution weight, weight values ​​are assigned respectively to calculate the execution value, which improves its suitability for accurately arranging how the subsequent j+1 to Nth second target logics are executed.

[0197] In one embodiment, the step S3258, based on the second execution situation, selecting the second target logic from the valid auxiliary logic sequence as the new latest executed auxiliary logic, includes:

[0198] S32581. Based on the characterization template, characterize the second execution situation and the second target logic in the valid auxiliary logic sequence to obtain a feature set;

[0199] In S32581, the characterization template is a template used by the system to perform data characterization processing; the features in the feature set include at least: the content type in the result view network, the communication semantics between people, etc.;

[0200] S32582. Determine a selection rule corresponding to the feature set from a selection rule library;

[0201] In S32582, the feature set reflects the expert group's acceptance of different second target logics. Therefore, selection rules can be determined based on it. For example, if the feature set reflects that the expert group has a low degree of acceptance of multiple second target logics, the second target logic that is the most forward among the multiple second target logics is selected as the new latest executed auxiliary logic, and the expert group is subsequently assisted based on it and subsequent auxiliary logics to further improve the acceptance. The selection rule library has preset selection rules corresponding to different feature sets.

[0202] S32583. Based on the selection rule, select the second target logic from the valid auxiliary logic sequence as the new latest executed auxiliary logic.

[0203] An embodiment of the present invention introduces a feature set and a selection rule library, determines the selection rule corresponding to the feature set from the selection rule library, and based on the selection rule, selects the second target logic from the valid auxiliary logic sequence as the new latest executed auxiliary logic, thereby improving the accuracy and efficiency of the selection of the latest executed auxiliary logic.

[0204] An embodiment of the present invention provides a networked X-band radar adaptive collaborative calibration system, such as Figure 5 As shown, including:

[0205] Detection module 1, used to detect deviation radar in networked X-band radar;

[0206] Scheduling module 2 is used to schedule the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans to obtain scanning results;

[0207] The calibration module 3 is used to calibrate the deviation radar based on the scanning results.

[0208] The calibration module calibrates the deviation radar based on the scanning result, including:

[0209] Attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library;

[0210] When the attempt is successful, the deviation radar is calibrated based on the first calibration strategy; otherwise, the expert group is assisted in making an online decision on the second calibration strategy based on the scanning results;

[0211] Based on the second calibration strategy, the deviation radar is calibrated.

[0212] The calibration module assists the expert group in making online decisions on a second calibration strategy based on the scanning results, including:

[0213] Matching view network templates for scan results;

[0214] Based on the view networking template, the scanning results are processed into a view networking to obtain a result view network;

[0215] Guide the expert group to collaboratively review the result view network;

[0216] Match auxiliary logic sequence for scan results;

[0217] When the expert group collaboratively reviews the result view network, based on the auxiliary logic sequence, the auxiliary expert group makes an online decision on the second calibration strategy.

[0218] 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 adaptive collaborative calibration method, characterized in that: include: Detect deviation radar in networked X-band radars; The deviation radar is dispatched to perform multiple groups of RHI scans in opposite directions with the standard radar in the networked X-band radar to obtain the scanning results; Based on the scan results, the deviation radar is calibrated.

2. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: The deviation radar in the detection network X-band radar includes: Collect reflectivity factor data of each X-band radar in the networked X-band radar; Preprocessing the collected reflectivity factor data; Based on the deviation detection algorithm, the deviation radar is determined from the networked X-band radar according to the preprocessed reflectivity factor data.

3. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: The preprocessing of the collected reflectivity factor data includes: Perform data cleaning on the collected reflectivity factor data; and, performing quality control processing on the collected reflectivity factor data.

4. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: Before the deviation radar is scheduled to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar, it also includes: When it is detected that the deviation of any radar in the networked X-band radar exceeds the deviation threshold and there is no echo exceeding 35dBZ in the radial library near the relative azimuth, the deviation radar is dispatched to perform multiple groups of opposite RHI scans with the standard radar in the networked X-band radar.

5. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: The scheduling deviation radar performs multiple groups of opposite RHI scans with the standard radar in the networked X-band radar to obtain scanning results, including: In the process of scheduling the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans, the deviation radar and the standard radar in the networked X-band radar synchronously collect data to obtain the scanning result.

6. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: Based on the scanning results, the deviation radar is calibrated, including: Based on the deviation calibration algorithm, the deviation radar is calibrated according to the scanning results.

7. The networked X-band radar adaptive collaborative calibration method according to claim 1, characterized in that: The method of calibrating the deviation radar based on the scanning result includes: Attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library; When the attempt is successful, the deviation radar is calibrated based on the first calibration strategy; otherwise, the expert group is assisted in making an online decision on the second calibration strategy based on the scanning results; Based on the second calibration strategy, the deviation radar is calibrated.

8. The networked X-band radar adaptive collaborative calibration method according to claim 7, characterized in that: The assisting expert group to make an online decision on the second calibration strategy based on the scanning result includes: Matching view network templates for scan results; Based on the view networking template, the scanning results are processed into a view networking to obtain a result view network; Guide the expert group to collaboratively review the result view network; Match auxiliary logic sequence for scan results; When the expert group collaboratively reviews the result view network, the expert group is assisted in making an online decision on the second calibration strategy based on the auxiliary logic sequence.

9. The networked X-band radar adaptive collaborative calibration method according to claim 8, characterized in that: The method of assisting the expert group in making online decision on the second calibration strategy based on the auxiliary logic sequence includes: The target area is determined from the result view network collaboratively viewed by the expert group; wherein the similarity between the stay feature of the local track passing through the target area in the sight track generated by the result view network viewed by more than a proportion of the people in the expert group within the recent preset time and the standard stay feature exceeds a similarity threshold; Determining a first target logic associated with the target area from the auxiliary logic sequence; Based on the local continuous logic sequence interception constraint, according to the first target logic, intercept the local continuous logic sequence from the auxiliary logic sequence; If the local continuous logic sequence is unique, the local continuous logic sequence is used as a valid auxiliary logic sequence; otherwise, the longest local continuous logic sequence is used as a valid auxiliary logic sequence; Execute the first to jth second target logics in the effective auxiliary logic sequence on the expert group in sequence order to obtain a first execution status; wherein j is a position threshold; Based on the first execution situation, determining execution values ​​of the j+1th to Nth second target logics in the valid auxiliary logic sequence; wherein N is the total number of second target logics in the valid auxiliary logic sequence; Execute the second target logics from j+1th to Nth in the effective auxiliary logic sequence on the expert group in order from large to small according to the execution values, and obtain the second execution status; Based on the second execution situation, selecting a second target logic from the valid auxiliary logic sequence as a new latest executed auxiliary logic; a second calibration strategy for online decision making that receives input from a panel of experts; The local continuous logic sequence interception constraints include: Constraint 1: The number of sequence items in the local continuous logical sequence does not exceed the number threshold; and Constraint 2: the sequence head of the local continuous logic sequence is the latest executed auxiliary logic in the auxiliary logic sequence, and the sequence tail of the local continuous logic sequence is the first target logic; and, Constraint 3: The first target logic is unique or adjacent to each other in the local continuous logic sequence.

10. A networked X-band radar adaptive collaborative calibration system, characterized in that: include: A detection module, used to detect deviation radars in networked X-band radars; A scheduling module is used to schedule the deviation radar and the standard radar in the networked X-band radar to perform multiple groups of opposite RHI scans to obtain the scanning results; The calibration module is used to calibrate the deviation radar based on the scanning results.

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