An Adaptive Cooperative Calibration Method and System for a Networked X-Band Radar

Abstract

The present invention provides a method and system for adaptive cooperative calibration of a networked X-band radar, which relates to the technical field of radar calibration. The method includes: detecting a deviation radar in the networked X-band radar; scheduling the deviation radar to perform multiple sets of opposite RHI scans with a standard radar in the networked X-band radar to obtain scan results; and calibrating the deviation radar based on the scan results. The method and system for adaptive cooperative calibration of the networked X-band radar of the present invention utilize the mutual cooperation between the standard radar and the deviation radar in the network to automatically identify and correct measurement deviations, enhance the data fusion and cooperative working capabilities between multiple radars, and improve the overall detection accuracy of the radar.

Description

Technical Field

[0001] The present invention relates to the technical field of radar calibration, and particularly to a method and system for adaptive cooperative calibration of a networked X-band radar. Background Art

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

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

[0004] Under the dual background of the intensification of global climate change and the rapid development of urbanization, the frequent occurrence of extreme weather events has posed unprecedented challenges to meteorological monitoring, forecasting, and early warning systems. Weather radars, as key tools for disaster weather monitoring and early warning, have become increasingly important. Especially in short-term nowcasting and early warning services, their accuracy and timeliness are directly related to the efficiency and effectiveness of disaster prevention.

[0005] Currently, although significant progress has been made in weather radar systems, they still face multiple challenges. In areas prone to severe convective weather, due to reasons such as urban construction, complex terrain, insufficient radar networking, or performance limitations, there are monitoring blind spots, which affect the timely capture and accurate assessment of disaster weather. To solve this problem, the "low-altitude blind spot filling" strategy has emerged. New devices such as X-band radars are widely used to fill low-altitude monitoring blind spots due to their advantages of high spatial resolution and short detection cycle, providing more refined data support for short-term nowcasting.

[0006] However, the widespread application of low-altitude blind spot filling devices such as X-band radars has also brought new technical problems. High-frequency operation makes the radar more sensitive to the electromagnetic environment and is easily affected by factors such as ground clutter and electromagnetic interference, leading to the complication of calibration and calibration problems and inaccurate calibration of echo intensity. At the same time, when quantitatively estimating precipitation, although high resolution is an advantage, its attenuation characteristics also exacerbate problems such as precipitation type identification and raindrop size distribution estimation, making the precipitation estimation results uncertain.

[0007] In addition, with the expansion of the radar network and the explosion of 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 there is an urgent need to introduce more advanced methods to improve the effectiveness of networked radars for forecast and early 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 guarantee, major event guarantee, large cities, and densely populated areas. Refined meteorological services require meteorological data to have high spatio-temporal resolution and accurate forecasting capabilities to support refined services in fields such as agriculture and transportation. Major event guarantee relies on weather radars to monitor weather changes in real time and provide timely early 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 forecast and early warning.

[0009] In the existing technology, such as a weather radar calibration method and device based on metal balls in patent application No. CN202410007027.7, a weather radar calibration method and device based on metal balls are provided. The method includes: after determining the suspension rope length based on the antenna pattern of the weather radar, controlling the unmanned aerial vehicle (UAV) to suspend the metal ball and fly it to the far field of the radar antenna; scanning the metal ball based on a preset scanning strategy, and after determining the reference elevation angle and reference azimuth angle corresponding to the weather radar according to the scanning results, controlling the weather radar to align with the metal ball for directional scanning; controlling the UAV to suspend the metal ball and continuously move its position so that the metal ball is in the middle position of the radar beam and range bin to obtain metal ball calibration data; calculating the theoretical values of the reflectivity factor, differential reflectivity factor, and correlation coefficient of the weather radar based on the metal ball calibration data, technical parameters, and a preset radar formula, and inversely obtaining the beam width and pulse width of the radar antenna; using the deviation between the measured value and the theoretical value obtained by the radar observing the metal ball, as well as the inversely obtained beam width and pulse width, to determine the radar calibration coefficient of the weather radar and achieve the calibration of the weather radar.

[0010] Authorized Publication Number CN 105866751 B Metal sphere calibration method for X-band solid-state dual-polarization weather radar A metal sphere calibration method for X-band solid-state dual-polarization weather radar is provided, including: selecting a test point respectively within the wide pulse detection range and the narrow pulse detection range of the radar; placing a metal sphere at each test point; for each metal sphere, calculating the corresponding radar observation elevation angle, azimuth angle and the number of the bin where it is located by using its GPS information and the radar longitude, latitude and altitude information; setting a calibration observation mode to obtain observation data, the calibration observation mode including the horizontal change range of the radar observation azimuth, the change range and the step angle of the radar observation elevation angle, and the observation data including the radar reflectivity factor observation value and the differential reflectivity observation value; using the difference between the radar reflectivity factor observation value and the theoretical value of the radar reflectivity factor or the difference between the differential reflectivity observation value and the theoretical value of the differential reflectivity as the measurement error of the radar, and using this measurement error to correct the detection result of the radar within the corresponding detection range.

[0011] However, the above-mentioned prior art has the following deficiencies:

[0012] Limited application scope: Limited by the safety of the calibration means and the requirements of business operation, the metal sphere calibration is usually carried out under clear sky atmospheric conditions and cannot be carried out under specific conditions (such as airspace restrictions, precipitation weather, large wind speed, etc.). And networked radars are generally built in areas with dense radars, and the airspace restrictions make it impossible for some radars to carry out metal sphere calibration work.

[0013] Poor positioning accuracy: The metal sphere is usually suspended under the unmanned aerial vehicle in the form of a soft connection, which makes the metal sphere prone to position movement under the influence of external factors such as wind. This position movement will directly affect the accuracy of the calibration, because the radar needs to accurately measure the position of the metal sphere for calibration. And during the flight of the unmanned aerial vehicle, its attitude and position may be affected by various factors such as air flow and wind speed, resulting in unstable position of the metal sphere and further affecting the accuracy of the calibration result.

[0014] Unable to achieve automatic calibration: The current metal sphere calibration method usually requires manual participation, such as selecting the release position, controlling the flight trajectory of the unmanned aerial vehicle, adjusting the position of the metal sphere according to the weather conditions, etc. This kind of manual operation not only increases the workload and cost, but also may lead to errors and uncertainties during the calibration process. The metal sphere calibration method has obvious deficiencies in terms of automation, which limits its application in scenarios that require efficient and rapid calibration.

[0015] In other existing technologies, the authorized announcement number CN115097459B discloses an interactive calibration method and system for the reflectivity factors of S and X-band networked weather radars, including: Step 1, constructing datasets of S and X-band networked weather radar reflectivity factors with close time proximity and consistent spatial resolution; Step 2, extracting the observation overlapping area based on basic information such as radar position and detection range; Step 3, traversing the observation overlapping area and interactively matching the reflectivity factors of S and X-band networked weather radars; Step 4, taking the difference, grading, and marking the point-to-point grid data pairs, and generating an interactive calibration product graphically. It can give the detection differences of S and X-band networked weather radars for the same meteorological target, realize the interactive verification between weather radars of different bands (S and X) within the weather radar network, provide favorable support for the meteorological department to dynamically and quickly monitor and evaluate the data quality of collaborative detection by networked weather radars and the calibration effect of single radar data, and serve the current blind area supplementation construction and collaborative observation work of the weather radar network.

[0016] The application number 202410322646.5, a method, device, equipment, and storage medium for evaluating the consistency between weather radars, is applied to the field of meteorological observation technology. The provided method includes: obtaining the basic data of adjacent weather radars at a specified elevation angle of the same volume scan start time; determining the observation overlapping area of adjacent weather radars according to the obtained basic data; screening the overlapping points in the observation overlapping area by using a preset screening rule; and evaluating the consistency of adjacent weather radars according to the overlapping points retained after screening. In this way, the accuracy of evaluating the consistency of adjacent weather radars can be effectively improved.

[0017] However, the above existing technologies have the following deficiencies:

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

[0019] Errors caused by band differences: There are differences in detection principles, detection ranges, and detection accuracies between S-band and X-band weather radars. These differences may cause additional errors during the interactive calibration process and affect the accuracy of the calibration results.

[0020] Computational complexity and difficulty in "quantification": The observation results of weather radars are affected by various environmental factors. Constructing a dataset of weather radar reflectivity factors with close time proximity and consistent spatial resolution requires complex processing procedures, including data preprocessing, quality control, spatio-temporal matching, etc. These steps are not only time-consuming and laborious, but the preset screening rules may be somewhat subjective when screening overlapping points, and different screening rules may lead to different evaluation results, introducing new interpolation and matching errors, and unable to give accurate "quantification" 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 an adaptive collaborative calibration method for a networked X-band radar to solve the above deficiencies in the prior art.

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

[0024] Detecting a deviation radar in the networked X-band radar;

[0025] Scheduling the deviation radar to perform multiple sets of opposite RHI scans with a standard radar in the networked X-band radar to obtain scan results;

[0026] Calibrating the deviation radar based on the scan results.

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

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

[0029] Preprocessing the collected reflectivity factor data;

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

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

[0032] Performing data cleaning processing 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 sets of opposite RHI scans with a standard radar in the networked X-band radar, it further includes:

[0035] When it is detected that any radar deviation in the networking X-band radar exceeds the deviation threshold and there is no echo exceeding 35 dBZ in the adjacent radial bin of the relative azimuth, the deviated radar is then scheduled to perform multiple sets of opposite RHI scans with the standard radar in the networking X-band radar.

[0036] Optionally, the scheduling of the deviated radar to perform multiple sets of opposite RHI scans with the standard radar in the networking X-band radar and obtaining the scan results includes:

[0037] During the scanning process of scheduling the deviated radar to perform multiple sets of opposite RHI scans with the standard radar in the networking X-band radar, the deviated radar and the standard radar in the networking X-band radar synchronously collect data to obtain the scan results.

[0038] Optionally, the above calibration of the deviated radar based on the scan results includes:

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

[0040] Optionally, the calibration of the deviated radar based on the scan results includes:

[0041] Attempt to determine the first calibration strategy corresponding to the scan results from the calibration strategy library;

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

[0043] Calibrate the deviated radar based on the second calibration strategy.

[0044] Optionally, the assisting the expert group in making an online decision on the second calibration strategy based on the scan results includes:

[0045] Match a view networked template for the scan results;

[0046] Based on the view networked template, perform view networking processing on the scan results to obtain a result view network;

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

[0048] Match an auxiliary logic sequence for the scan results;

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

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

[0051] Determine the target area from the result view network jointly viewed by the expert group; among them, the similarity between the stay characteristics of the local trajectories of the line-of-sight trajectories generated by more than the proportion of the number of people in the expert group when viewing the result view network in the recent preset time passing through the target area and the standard stay characteristics exceeds the similarity threshold;

[0052] Determine the first target logic related to the target area from the auxiliary logic sequence;

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

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

[0055] Execute the 1st to the jth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the sequence order to obtain the first execution situation; where j is the position threshold;

[0056] Based on the first execution situation, determine the execution values of the (j + 1)th to the Nth second target logics in the valid auxiliary logic sequence respectively; where N is the total number of second target logics in the valid auxiliary logic sequence;

[0057] Execute the (j + 1)th to the Nth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the execution values from large to small to obtain the second execution situation;

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

[0059] Receive the second calibration strategy of the online decision input by the expert group;

[0060] Among them, the local continuous logic sequence truncation constraint includes:

[0061] Constraint 1: The number of sequence items in the local continuous logic 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 pairwise adjacent in the local continuous logic sequence.

[0064] Optionally, the determination steps of the position threshold are as follows:

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

[0066] ,

[0067] where, is the action weight of the second target logic, is the action value of the second target logic on the other second target logics in the effective auxiliary logic sequence;

[0068] Set a preliminary value and calculate the delimiting tool value:

[0069] ,

[0070] where, is the delimiting tool value, is the action weight of the th second target logic in the effective auxiliary logic sequence;

[0071] The position threshold takes the value of the preliminary value when calculating the largest delimiting tool value not exceeding the delimiting tool value threshold.

[0072] Optionally, the determining the execution values of the (j + 1)-th to N-th second target logics in the effective auxiliary logic sequence based on the first execution situation includes:

[0073] Calculate the execution value through the following formula:

[0074] ,

[0075] where, is the execution value of the second target logic, is the first logic execution weight of the second target logic, is the second logic execution weight of the first execution situation on the second target logic, and are preset weight values.

[0076] Optionally, the selecting the second target logic from the effective auxiliary logic sequence as the new latest executed auxiliary logic based on the second execution situation includes:

[0077] Based on the characterization template, perform characterization processing on the second execution situation and the second target logics in the effective auxiliary logic sequence to obtain a feature set;

[0078] Determine the selection rule corresponding to the feature set from the selection rule library;

[0079] Based on the selection rule, select the second target logic from the valid auxiliary logic sequences as the new latest executed auxiliary logic.

[0080] Embodiments of the present invention provide a networking X-band radar adaptive collaborative calibration system, including:

[0081] A detection module, configured to detect a deviation radar in the networking X-band radar;

[0082] A scheduling module, configured to schedule the deviation radar and a standard radar in the networking X-band radar to perform multiple groups of opposite RHI scans and obtain scan results;

[0083] A calibration module, configured to calibrate the deviation radar based on the scan results.

[0084] Optionally, the calibration module calibrates the deviation radar based on the scan results, including:

[0085] Attempt to determine a first calibration strategy corresponding to the scan results from a calibration strategy library;

[0086] 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 a second calibration strategy based on the scan results;

[0087] Calibrate the deviation radar based on the second calibration strategy.

[0088] Optionally, the calibration module assisting the expert group in making an online decision on a second calibration strategy based on the scan results includes:

[0089] Match a view networked template for the scan results;

[0090] Based on the view networked template, perform view networking processing on the scan results to obtain a result view network;

[0091] Guide the expert group to collaboratively view the result view network;

[0092] Match an auxiliary logic sequence for the scan results;

[0093] When the expert group collaboratively views the result view network, assist the expert group in making an online decision on a second calibration strategy based on the auxiliary logic sequence.

[0094] The present invention has achieved the following beneficial effects:

[0095] Improve the calibration applicable range: Overcome the limitations of the metal ball calibration method by environmental conditions (such as airspace restrictions, precipitation weather, high wind speed) and the non-full system link problem under the conventional in-aircraft calibration method, and realize the calibration work under rainfall weather conditions. Through the automated and intelligent collaborative calibration strategy, ensure accurate calibration of the deviation radar under different precipitation conditions.

[0096] Improve positioning and data matching accuracy: Improve the longitude of the relative pointing positioning of the antenna through the automatic scheduling controlled by the relative azimuth antennas of 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 calibration accuracy of the quantitative deviation.

[0097] Achieve automatic calibration: Through intelligent algorithms, optimize the data processing flow, automatically realize the automatic calibration of the deviation radar, reduce the dependence on manual operations, and realize 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.

[0098] Reduce the dependence on data sources: The original data consistency assessment requires a large amount of data to improve stability. This method automatically realizes the intelligent calibration of radar deviation by predicting the deviation radar, reduces the excessive dependence on data sources, and improves the tolerance for problems such as data loss and poor quality.

[0099] Reduce the errors caused by differences in different frequency bands: In response to the differences between S-band and X-band weather radars, develop a special calibration algorithm applicable to X-band radars to reduce the impact of frequency band differences on the cross-check results. Through precise calibration and error correction, ensure the accuracy of the quantitative deviation of X-band radar data.

[0100] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the drawings.

[0101] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0102] The 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 to the present invention. In the drawings:

[0103] Figure 1 It is a schematic diagram of an adaptive cooperative calibration method for a networked X-band radar in an embodiment of the present invention;

[0104] Figure 2 It is a schematic diagram of the structural relationship between multiple working units in an embodiment of the present invention;

[0105] Figure 3 It is a flowchart of an adaptive cooperative calibration method for a networked X-band radar in an embodiment of the present invention;

[0106] Figure 4 Schematic diagram of collaborative RHI scan scheduling in the embodiment of the present invention;

[0107] Figure 5 Schematic diagram of a networking X-band radar adaptive collaborative calibration system in the embodiment of the present invention. Specific embodiments

[0108] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0109] An embodiment of the present invention provides a networking X-band radar adaptive collaborative calibration method, as Figure 1 shown, including:

[0110] S1. Detect the deviation radar in the networking X-band radar;

[0111] In S1, the deviation radar refers to a radar in the network whose measurement result has a certain deviation from the actual target, and this deviation may be caused by hardware failure, environmental change or long-term use; when detecting the deviation radar, each radar in the network can be monitored, the detection data of different radars for the same target can be compared, and the measurement results of multiple radars can be used for data fusion to identify the radar that is inconsistent with the measurement result 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, then this radar may have a deviation;

[0112] S2. Schedule the deviation radar to perform multiple groups of opposite RHI scans with the standard radar in the networking X-band radar to obtain scan results;

[0113] In S2, RHI scan (Range Height Indicator) is a radar scan method, mainly used to obtain the vertical distribution information of the target. Different from the traditional horizontal scan, RHI scan can provide the position and height information of the target in the vertical direction; through multiple groups of opposite RHI scans, that is, the deviation radar and the standard radar perform relative scans, the system can obtain different observation results of the radar for the same target to form scan results, which at least include: information such as the distance, angle, and speed of the target; for example, during the scheduling process, the deviation radar and the standard radar may scan the same target multiple times, and by comparing the scan results of the two, the measurement error of the reflectivity factor of the deviation radar can be identified and used as the scan result;

[0114] S3. Calibrate the deviation radar based on the scan results.

[0115] In S3, the scanning result reflects the deviation of the deviation radar. The deviation can be eliminated by automatically compensating for errors or prompting experts to make decisions for radar parameter calibration or adjustment. Technicians can preset calibration methods corresponding to different scanning results in advance to calibrate the deviation radar.

[0116] 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 measurement deviations, enhance the data fusion and collaborative working capabilities between multiple radars, and improve the overall detection accuracy of the radar.

[0117] In one embodiment, as Figures 2 to 4 shown, the deviation radar in the detection networked X-band radar includes:

[0118] Collect the reflectivity factor data of each X-band radar in the networked X-band radar;

[0119] Preprocess the collected reflectivity factor data;

[0120] Based on the deviation detection algorithm, determine the deviation radar from the networked X-band radar according to the preprocessed reflectivity factor data.

[0121] The preprocessing of the collected reflectivity factor data includes:

[0122] Perform data cleaning processing on the collected reflectivity factor data;

[0123] And perform quality control processing on the collected reflectivity factor data.

[0124] Before scheduling the deviation radar to perform multiple sets of opposite RHI scans with the standard radar in the networked X-band radar, it further includes:

[0125] 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 35 dBZ in the relative azimuth adjacent radial bin, then schedule the deviation radar to perform multiple sets of opposite RHI scans with the standard radar in the networked X-band radar.

[0126] The scheduling of the deviation radar to perform multiple sets of opposite RHI scans with the standard radar in the networked X-band radar to obtain the scanning result includes:

[0127] During the scanning process of scheduling the deviation radar to perform multiple sets of opposite RHI scans with the standard radar in the networked X-band radar, the deviation radar and the standard radar in the networked X-band radar synchronously collect data to obtain the scanning result.

[0128] The above calibration of the deviation radar based on the scanning result includes:

[0129] Based on the deviation calibration algorithm, the deviation radar is calibrated according to the scanning results.

[0130] First, by collecting the reflectivity factor data of each radar in the X-band radar, the intensity of the reflected signal observed by the radar is obtained.

[0131] Preprocess these data, including data cleaning to remove noise and outliers, ensure the quality and validity of the data, and at the same time perform quality control to ensure the credibility of the data.

[0132] Using the preprocessed reflectivity factor data, based on the deviation detection algorithm, determine the deviation radar. The deviation radar refers to a radar with a large difference in reflectivity factor data compared to the standard radar. The deviation detection algorithm can be set in advance by technicians according to actual needs.

[0133] The deviation detection process will judge whether there is a deviation by comparing the outputs of each radar, especially in the case where the relative azimuths are close.

[0134] When the deviation of a certain radar is detected to exceed the preset deviation threshold, and there is no strong echo (greater than 35 dBZ) within the azimuth range where the radar is located, then multiple sets of opposite RHI scans can be initiated between the deviation radar and the standard radar.

[0135] RHI scan is to perform a vertical cross-section scan through the radar to collect echo information from different height angles, so that the reflectivity factor information of the common overlapping area for relatively calibrating the two radars can be obtained.

[0136] During the scanning process, the deviation radar and the standard radar collect data synchronously to obtain the scanning results.

[0137] According to the scanning results, use the deviation calibration algorithm to calibrate the deviation radar, 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 technicians according to actual needs.

[0138] The embodiments of the present invention have achieved the following beneficial effects:

[0139] Improve the calibration applicable range: Overcome the limitations of the metal sphere calibration method by environmental conditions (such as airspace restrictions, precipitation weather, high wind speed) and the non-full-system link problem under the conventional in-aircraft calibration method, and realize the calibration work under rainfall weather conditions. Through the collaborative calibration strategy of automation and intelligence, ensure accurate calibration of the deviation radar under different precipitation conditions.

[0140] Improve positioning and data matching accuracy: The longitude of the relative pointing positioning of the antenna is improved through the automatic scheduling controlled by the relative azimuth antennas of two radars; By re-optimizing the design of the RHI scanning mode to increase the number of samples, thereby improving the data quality mechanism for matching the common coverage area and enhancing the calibration accuracy of the quantitative deviation.

[0141] Achieve automatic calibration: Through intelligent algorithms, optimize the data processing flow, automatically achieve the automatic calibration of the deviation radar, reduce the dependence on manual operations, and realize 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.

[0142] Reduce the dependence on data sources: The original data consistency assessment requires a large amount of data to improve stability. This method automatically realizes the intelligent calibration of radar deviation through predicting the deviation radar, reduces the excessive dependence on data sources, and improves the tolerance for problems such as data loss and poor quality.

[0143] Reduce the errors caused by differences in different bands: For the differences between S-band and X-band weather radars, develop a dedicated calibration algorithm applicable to X-band radars to reduce the impact of band differences on the cross-check results. Through precise calibration and error correction, ensure the accuracy of the quantitative deviation of X-band radar data.

[0144] In one embodiment, S3, based on the scanning results, calibrate the deviation radar, including:

[0145] S31. Try to determine the first calibration strategy corresponding to the scanning results from the calibration strategy library;

[0146] In S31, there are first calibration strategies corresponding to different scanning results in the calibration strategy library. The first calibration strategy indicates how to calibrate the deviation radar when the scanning results appear, and can be set in advance by technicians;

[0147] 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;

[0148] In S32, when the attempt is successful, it means there is a corresponding first calibration strategy, then directly calibrate the deviation radar based on the first calibration strategy; otherwise, when the attempt fails, it means there is no corresponding first calibration strategy, assist the expert group in making an online decision on the second calibration strategy based on the scanning results. The expert group includes multiple technicians specializing in the field of radar calibration;

[0149] S33. Calibrate the deviation radar based on the second calibration strategy.

[0150] In S33, after online decision-making of the second calibration strategy, the deviation radar is calibrated based on the second calibration strategy.

[0151] In the embodiments of the present invention, when calibrating the deviation radar based on the scan results, the system is given priority to independently attempt to determine the first calibration strategy. When the attempt fails, the expert group is assisted to make an online decision on the second calibration strategy based on the scan results, improving the adaptability of calibration resource allocation and greatly enhancing the efficiency of calibrating the deviation radar.

[0152] In one embodiment, in S32, assisting the expert group to make an online decision on the second calibration strategy based on the scan results includes:

[0153] S321: Match a view network template for the scan results;

[0154] In S321, the view network template is a template for the system to perform view network processing on the scan results. There are visual interfaces corresponding to different types of data in the view network template. When matching the view network template for the scan results, the corresponding view network template is matched according to the type of data in the scan results. When using the view network template, different types of data in the scan results are mapped to the corresponding visual interfaces in the view network template, and the multiple visual interfaces after data mapping form a result view network;

[0155] S322: Perform view network processing on the scan results based on the view network template to obtain a result view network;

[0156] In S322, after matching the view network template, perform view network processing on the scan results based on it to obtain a result view network;

[0157] S323: Guide the expert group to view the result view network collaboratively;

[0158] In S323, when guiding the expert group, information prompting them to view the result view network collaboratively can be pushed to each person in the expert group; by collaboratively viewing the result view network, the expert group can decide how to calibrate the deviation radar, that is, make a decision on the second calibration strategy;

[0159] S324: Match an auxiliary logic sequence for the scan results;

[0160] In S324, the auxiliary logic sequence contains the auxiliary logic indicating the operations that the system needs to perform in sequence when the expert group is in the auxiliary state. When matching the auxiliary logic sequence with the scan results, the matching can be performed according to different data types of the scan results. For example, if the scan results contain data of type A and data of type B, and it is necessary for the auxiliary expert group to understand the data of type A first in order to understand the data of type B, then the order of the auxiliary logic in the matched auxiliary logic sequence is to first assist the expert group in understanding the data of type A and then assist the expert group in understanding the data of type B.

[0161] S325. When the expert group collaboratively views the result view network, based on the auxiliary logic sequence, assist the expert group in making an online decision on the second calibration strategy.

[0162] In S325, on the result view network where the expert group collaboratively views, based on the matched auxiliary logic sequence, assist the expert group in making an online decision on the second calibration strategy.

[0163] In an embodiment of the present invention, when assisting the expert group in making an online decision on the second calibration strategy based on the scan results, first, the scan results are processed into a result view network through view networking and the expert group is guided to view it, which is convenient for the expert group to intuitively and conveniently understand the reflection of the scan results, etc., improving the humanization; second, when the expert group collaboratively views the result view network, based on the auxiliary logic sequence, assist the expert group in making an online decision on the second calibration strategy, improving the accuracy and efficiency of the system in assisting the expert group in making an online decision on the second calibration strategy based on the scan results, and improving the intelligence.

[0164] In one embodiment, in S325, based on the auxiliary logic sequence, assisting the expert group in making an online decision on the second calibration strategy includes:

[0165] S3251. Determine the target area from the result view network collaboratively viewed by the expert group; wherein, the similarity between the stay feature of the local trajectory of the line-of-sight trajectory of more than the proportion of the number of people in the expert group passing through the target area during the recent preset time when viewing the result view network and the standard stay feature exceeds the similarity threshold.

[0166] 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;

[0167] S3252, determining a first target logic related to the target area from the auxiliary logic sequence;

[0168] 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;

[0169] 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;

[0170] In S3253, under the constraint of the local continuous logic sequence interception constraint, the local continuous logic sequence is targetedly and accurately intercepted from the auxiliary logic sequence according to the first target logic;

[0171] 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;

[0172] In S3254, if the local continuous logic sequence is unique, the local continuous logic sequence can be directly used as the valid auxiliary logic sequence; otherwise, the longest local continuous logic sequence is used as the valid auxiliary logic sequence, where the longest means the sequence with the largest number of sequence items in the sequence, so as to improve the richness of continuous valid assistance as much as possible, thereby enhancing the assistance effect;

[0173] S3255. Execute the 1st to jth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the sequence order, and obtain the first execution situation; where j is the position threshold;

[0174] In S3255, the position threshold can divide the second target logics in the valid auxiliary logic sequence into two different execution orders. Execute the 1st to jth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the sequence order, that is, the second target logic with a more forward arrangement is executed first. When executing in sequence, obtain the first execution situation, and the first execution situation at least includes: the behavior information of each person in the expert group after each execution of the 1st to jth second target logics, such as: continue to view the content type in the result view network, the communication information between people, etc.;

[0175] S3256. Based on the first execution situation, determine the execution values of the (j + 1)th to Nth second target logics in the valid auxiliary logic sequence respectively; where N is the total number of second target logics in the valid auxiliary logic sequence;

[0176] In S3256, based on the first execution situation, the subsequent execution of the (j + 1)th to Nth second target logics can be further accurately arranged, and the execution values are determined in sequence. The execution value represents the degree of priority of the second target logic to be executed;

[0177] S3257. Execute the (j + 1)th to Nth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the execution values from large to small, and obtain the second execution situation;

[0178] In S3257, execute the (j + 1)th to Nth second target logics in the valid auxiliary logic sequence for the expert group in sequence according to the execution values from large to small. Correspondingly, the second execution situation at least includes: the behavior information of each person in the expert group after each execution of the (j + 1)th to Nth second target logics;

[0179] S3258. Based on the second execution situation, select the second target logic from the valid auxiliary logic sequence as the new latest executed auxiliary logic;

[0180] In S3258, based on the second execution scenario, it is possible to further precisely arrange which second target logic needs to be used as the new latest executed auxiliary logic to enter the local continuous logic sequence interception constraint, so as to constrain the interception of subsequent local continuous logic sequences;

[0181] S3259. Receive the second calibration strategy of the online decision input by the expert group;

[0182] 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 system assistance, so as to make a fast and precise decision on the second calibration strategy. Finally, input the second calibration strategy of its online decision;

[0183] Among them, the local continuous logic sequence interception constraint includes:

[0184] Constraint 1. The number of sequence items in the local continuous logic sequence does not exceed the number threshold; and,

[0185] 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,

[0186] Constraint 3. The first target logic is unique or pairwise adjacent in the local continuous logic sequence.

[0187] In Constraint 1, the number threshold is a number representing that the number of sequence items is not too large, such as: 5; through the constraint of Constraint 1, the number of sequence items in the intercepted local continuous logic sequence will not be too large, so as not to provide overly long continuous effective assistance to the expert group; in Constraint 2, the latest executed auxiliary logic is preferentially determined based on the previous selection from the effective auxiliary logic sequence. When there is no previous selection, the auxiliary logic at the head of the auxiliary logic sequence can be defaulted as the latest executed auxiliary logic; using the latest executed auxiliary logic as the sequence head of the local continuous logic sequence and the first target logic as the sequence tail of the local continuous logic sequence, all the auxiliary logics in the intercepted local continuous logic sequence are suitable for providing continuous effective assistance to the expert group; in Constraint 3, ensuring that the first target logic is unique or pairwise adjacent in the local continuous logic sequence, so that no other auxiliary logics between two first target logics can enter the local continuous logic sequence, can further improve the accuracy and suitability of the local continuous logic sequence interception.

[0188] Embodiments of the present invention can quickly determine a target area suitable as a basis for intercepting a local continuous logic sequence by analyzing the line-of-sight trajectory and residence characteristics of the expert group in real time, combining with standard residence characteristics and a similarity threshold, and trigger corresponding interception, which improves the working efficiency of the system and reduces the working resources of the system. By accurately intercepting a local continuous logic sequence from the auxiliary logic sequence and dynamically adjusting the execution order of subsequent target logics according to the execution results of the local logic sequence, the assistance obtained by the expert during the decision-making process is not only one-time, but is dynamically optimized according to his real-time behavior feedback, which improves the continuity and pertinence of the auxiliary decision-making. 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 performing interception constraints through local continuous logic sequence interception constraints, excessive information-assisted intervention is avoided, ensuring that the expert group obtains appropriate and effective information, reducing the cognitive load of the expert group during decision-making, and also avoiding delays or misjudgments in the decision-making process caused by excessive irrelevant information.

[0189] In one embodiment, the determination step of the position threshold is as follows:

[0190] Calculate the action weight of each second target logic in the effective auxiliary logic sequence:

[0191] ,

[0192] where, is the action weight of the second target logic, is the action value of the second target logic on the other th second target logics in the effective auxiliary logic sequence;

[0193] Set a preliminary value , and calculate the delimiting tool value:

[0194] ,

[0195] where, is the delimiting tool value, is the action weight of the th second target logic in the effective auxiliary logic sequence; among them ;

[0196] The position threshold takes the value of the preliminary value when the calculated delimiting tool value that does not exceed the delimiting tool value threshold is the largest.

[0197] In the above technical solution, the second target logic on the other The action value of a second target logic refers to the degree of assistance that the second target logic can provide to help the personnel in the expert group accept the execution of the second target logic, which can be preset by technicians in advance. For example, if the two second target logics are to assist the expert group in understanding type A data first and then type B data, and it is necessary to assist the expert group in understanding type A data first to facilitate their understanding of type B data, then the action value of the previous second target logic for the latter second target logic is a relatively large 8; the position threshold is used as a preliminary value The defined tool value obtained by substituting into the calculation formula of the defined tool value is the maximum value not exceeding the defined tool value threshold; the defined tool value threshold can be, for example, 80; the greater the action weight, the higher the comprehensive action degree of the second target logic; by reasonably setting the defined tool value threshold, under its limitation, the first execution situations obtained successively in order in the 1st to jth second target logics in the effective assistance logic sequence are more suitable for further accurately arranging how to execute the (j + 1)th to Nth second target logics, and it is also applicable to avoid over-assisting each user in the expert group, causing their complete dependence, and greatly improving the flexibility of the system.

[0198] In one embodiment, determining the execution values of the (j + 1)th to Nth second target logics in the effective assistance logic sequence based on the first execution situation includes:

[0199] Calculating the execution value through the following formula:

[0200] ,

[0201] where, is the execution value of the second target logic, is the first logic execution weight of the second target logic, is the second logic execution weight of the first execution situation for the second target logic, and are preset weight values.

[0202] In the above technical solution, the first logical execution weight of the second target logic represents the importance of the execution of the second target logic itself, which can be preset by technicians in advance; the second logical execution weight of the first execution situation for the second target logic refers to the degree to which the first execution situation reflects that the second target logic needs to be executed. For example, if the two second target logics are to assist the expert group in understanding type A data first and then type B data, and it is necessary to assist the expert group in understanding type A data first to facilitate their understanding of type B data, and the first execution situation obtained after the execution of the previous second target logic is that the expert group has already viewed and understood type B data by themselves, then the latter second target logic does not need to be executed, and the corresponding second logical execution weight is 0; by combining the first logical execution weight and the second logical execution weight, weight values are respectively assigned to calculate the execution value, which improves the suitability for accurately arranging how to execute the (j + 1)-th to N-th second target logics subsequently.

[0203] In one embodiment, the S3258, based on the second execution situation, selects a second target logic from the effective auxiliary logic sequence as the new latest executed auxiliary logic, including:

[0204] S32581. Based on the characterization template, perform characterization processing on the second execution situation and the second target logic in the effective auxiliary logic sequence to obtain a feature set;

[0205] In S32581, the characterization template is a template for the system to perform data characterization processing; the features in the feature set at least include: the content type in the result view network to continue viewing, the communication semantics between personnel, etc.;

[0206] S32582. Determine the selection rule corresponding to the feature set from the selection rule library;

[0207] In S32582, the feature set reflects the acceptance situation of the expert group for different second target logics. Therefore, based on it, the selection rule can be determined. For example, if the feature set reflects that the acceptance degree of the expert group for multiple second target logics is relatively low, then select the foremost second target logic among the multiple second target logics as the new latest executed auxiliary logic, and subsequently re-assist the expert group based on it and the subsequent auxiliary logics to further improve the acceptance degree; different selection rules corresponding to different feature sets are preset in the selection rule library;

[0208] S32583. Based on the selection rule, select a second target logic from the effective auxiliary logic sequence as the new latest executed auxiliary logic.

[0209] Embodiments of the present invention introduce a feature set and a selection rule library, determine a selection rule corresponding to the feature set from the selection rule library, and based on the selection rule, select a second target logic from the valid auxiliary logic sequences as the new latest executed auxiliary logic, improving the accuracy and efficiency of the selection of the latest executed auxiliary logic.

[0210] Embodiments of the present invention provide a networking X-band radar adaptive collaborative calibration system, as Figure 5 shown, including:

[0211] A detection module 1 for detecting a deviation radar in the networking X-band radar;

[0212] A scheduling module 2 for scheduling the deviation radar and a standard radar in the networking X-band radar to perform multiple groups of opposite RHI scans and obtain scan results;

[0213] A calibration module 3 for calibrating the deviation radar based on the scan results.

[0214] The calibration module calibrates the deviation radar based on the scan results, including:

[0215] Attempt to determine a first calibration strategy corresponding to the scan results from the calibration strategy library;

[0216] 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 a second calibration strategy based on the scan results;

[0217] Calibrate the deviation radar based on the second calibration strategy.

[0218] The calibration module assisting the expert group in making an online decision on a second calibration strategy based on the scan results includes:

[0219] Match a view networked template for the scan results;

[0220] Based on the view networked template, perform view networking processing on the scan results to obtain a result view network;

[0221] Guide the expert group to collaboratively view the result view network;

[0222] Match an auxiliary logic sequence for the scan results;

[0223] When the expert group collaboratively views the result view network, assist the expert group in making an online decision on a second calibration strategy based on the auxiliary logic sequence.

[0224] Obviously, those skilled in the art can make various modifications and variations 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 equivalent technologies, 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; Attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library; When the attempt is successful, calibrating the deviation radar based on the first calibration strategy; Otherwise, assist the expert group to make an online decision on the second calibration strategy based on the scanning results; Based on the second calibration strategy, the deviation radar is calibrated. 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; Identify target areas from a network of result views that are collaboratively reviewed by a team of experts; 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.

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 pre-processed reflectivity factor data. The target area meets the following conditions: 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 generated by the expert group exceeding the proportion of the number of people in the expert group within the recent preset time and the standard stay feature exceeds the similarity threshold, 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.

3. The networked X-band radar adaptive collaborative calibration method according to claim 2, 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, the following steps are also included: 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. 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; A calibration module, attempting to determine a first calibration strategy corresponding to the scan result from a calibration strategy library; When the attempt is successful, calibrating the deviation radar based on the first calibration strategy; Otherwise, assist the expert group to make an online decision on the second calibration strategy based on the scanning results; Based on the second calibration strategy, the deviation radar is calibrated. 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; Identify target areas from a network of result views that are collaboratively reviewed by a team of experts; 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.

8. The networked X-band radar adaptive collaborative calibration system according to claim 7, characterized in that: The target area meets the following conditions: 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 generated by the expert group exceeding the proportion of the number of people in the expert group within the recent preset time and the standard stay feature exceeds the similarity threshold, 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.

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