Onboard equipment installation position simulation correction method for cloud total water content measurement

Abstract

The application relates to the field of aviation detection technology and discloses a simulation correction method for the installation position of an airborne device for measuring the total water content of clouds, which comprises the following steps: acquiring device mass distribution and attitude data, calculating an initial error vector and a reference, iteratively correcting the error vector to obtain optimized coordinates, extracting a stability coefficient according to the optimized coordinates, performing deviation correction to obtain a position reference value and a deviation correction coefficient, processing interference according to the reference value and a fusion environment adaptation index to obtain a dynamic balance parameter, verifying and adjusting the parameter to obtain a final verification result, generating an azimuth configuration according to the verification result and the correction coefficient, and finally performing long-term monitoring according to the configuration and an environment index. The method can reduce error accumulation in high-altitude cloud layer detection, ensure the azimuth dynamic balance of the device and the reliability of data, and improve the overall installation precision and adaptability.

Description

Technical Field

[0001] This invention relates to the field of aerial detection technology, and in particular to a simulation correction method for the installation position of airborne equipment used for measuring total cloud water content. Background Technology

[0002] Currently, in the field of aerial surveying, researching how to accurately measure the total water content in clouds is of paramount importance for weather forecasting, flight safety, and climate research. The quality of this task directly affects the operational efficiency and environmental adaptability of aircraft. The industry commonly employs simulation technology for calibration. This calibration method, which integrates digital models and physical entities, can be considered an application of Cyber-Physical Systems (CPS). Its core lies in using high-precision simulation to guide the actual configuration of physical equipment to meet the challenges of dynamic environments.

[0003] In one existing technology, the installation position is determined based on the ideal state of the equipment during ground calibration, or solely by relying on a simplified CAD model. This involves measuring the equipment's geometry and calibrating its ideal center of gravity in a laboratory environment. These static parameters are then input into a simulation model, and finally, combined with data from the aircraft in an ideal attitude (e.g., stable straight flight), a fixed installation orientation compensation value is calculated for installation position correction. However, this method has significant shortcomings. Its core flaw lies in its reliance on static data, neglecting the dynamic impact of equipment mass and center of gravity characteristics on installation orientation during complex flight. For example, uneven mass distribution (i.e., the deviation between the calibrated and actual center of gravity) can interfere with the aircraft's balance. When the aircraft performs maneuvers such as turns or climbs, this imbalance generates additional yaw or pitch moments, causing the equipment to fail to stably point towards the intended detection area, ultimately resulting in significant deviations in the acquired data.

[0004] In summary, existing technologies suffer from inaccurate equipment installation position calibration. Summary of the Invention

[0005] This invention provides a simulation correction method for the installation position of airborne equipment used for measuring total cloud water content, in order to solve the problem of inaccurate equipment installation position correction.

[0006] Firstly, to address the aforementioned technical problems, this invention provides a simulation correction method for the installation position of airborne equipment used for measuring total cloud water content, comprising:

[0007] Acquire mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. Based on the initial installation position error vector and the preliminary installation orientation reference, iterative correction is performed to obtain the optimized installation position coordinate data; Based on the optimized installation position coordinate data, the azimuth stability coefficient and error distribution range are extracted. Based on the azimuth stability coefficient and the error distribution range, deviation correction and position calibration are performed to obtain the adjusted position reference value. Based on the adjusted position reference value, combined with the preset environmental adaptation index and preset observation update weight, interference data processing is performed to obtain dynamic equilibrium parameters. Based on the dynamic equilibrium parameters, parameter verification and adjustment are performed to obtain the final verification result; Based on the final verification results, a directional configuration sequence is generated to obtain the equipment installation directional configuration. Based on the equipment installation orientation configuration and the environmental adaptability index, long-term stability monitoring is performed to obtain long-term stability data of the equipment installation orientation.

[0008] Preferably, the step of acquiring the mass distribution data, flight attitude data, and coordinate transformation parameters of the airborne equipment, and calculating the initial installation error to obtain the initial installation position error vector and the preliminary installation orientation reference includes: Retrieve mass distribution data, flight attitude data, and coordinate transformation parameters of airborne equipment from a pre-defined database; Based on the quality distribution data, data verification and standardization processes are performed to obtain standardized quality distribution information; Based on the standardized mass distribution information and the flight attitude data, coordinate transformation and vector synthesis are performed using the coordinate transformation parameters to obtain the initial installation position error vector. Based on the initial installation position error vector, spatial projection and benchmark definition are performed to obtain the preliminary installation orientation benchmark.

[0009] Preferably, the step of iteratively correcting the initial installation position error vector and the preliminary installation orientation reference to obtain optimized installation position coordinate data includes: Based on the initial installation position error vector, combined with the preset dynamic noise factor and the preset state transition matrix, state estimation and recursive correction are performed to obtain the corrected error vector. Based on the corrected error vector and the initial installation orientation reference, coordinate synthesis and updating are performed to obtain optimized installation position coordinate data.

[0010] Preferably, the step of extracting the azimuth stability coefficient and error distribution range based on the optimized installation position coordinate data, and performing deviation correction and position calibration based on the azimuth stability coefficient and the error distribution range to obtain the adjusted position reference value includes: Based on the optimized installation location coordinate data, data analysis is performed to obtain the orientation stability coefficient and error distribution range; When the orientation stability coefficient is lower than the preset stability threshold, the correction coefficient is calculated based on the error distribution range to obtain the deviation correction coefficient. Based on the deviation correction coefficient and the optimized installation position coordinate data, a weighted correction is performed to obtain the adjusted position reference value.

[0011] Preferably, the step of processing interference data based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, to obtain dynamic equilibrium parameters includes: Based on the adjusted location reference value and the preset environmental adaptability index, environmental adaptability adjustment is performed to obtain environmentally adapted location data; Based on the environmental adaptation location data and the preset observation update weights, data fusion is performed to obtain a weighted fusion result; Based on the weighted fusion result, filtering and smoothing processes are performed to obtain dynamic balance parameters.

[0012] Preferably, the step of verifying and adjusting the parameters based on the dynamic equilibrium parameters to obtain the final verification result includes: Based on the dynamic balance parameters, a consistency check is performed to obtain the check conclusion; If the verification result is unsuccessful, then the parameters are iteratively optimized based on the verification result to obtain the updated dynamic balance parameters. Based on the updated dynamic balance parameters, a consistency check is performed again until the check result is passed. The updated dynamic balance parameters that pass the check are then taken as the final verification result.

[0013] Preferably, the step of generating a directional configuration sequence based on the final verification result to obtain the equipment installation directional configuration includes: Based on the final verification results, combined with the deviation correction coefficient and the preset fusion iteration step size, serialization calculation is performed to generate a complete orientation adjustment sequence; Based on the complete orientation adjustment sequence, configuration encapsulation is performed to obtain the device installation orientation configuration.

[0014] Preferably, the step of performing long-term stability monitoring based on the equipment installation orientation configuration and the environmental adaptability index to obtain long-term stability data for the equipment installation orientation includes: Based on the installation orientation of the equipment, a monitoring baseline is set to obtain a long-term monitoring baseline; Based on the long-term monitoring benchmark and the environmental adaptability index, a stability assessment model is constructed to obtain the stability assessment model. Based on the stability assessment model, the real-time collected equipment attitude data is periodically sampled and trend analyzed to obtain long-term stability data of the equipment installation orientation.

[0015] Secondly, the present invention provides an airborne equipment installation position simulation correction system for measuring total cloud water content, comprising: The error calculation module is used to acquire mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. The iterative correction module is used to perform iterative correction based on the initial installation position error vector and the preliminary installation orientation reference to obtain optimized installation position coordinate data. The position calibration module is used to extract the azimuth stability coefficient and error distribution range based on the optimized installation position coordinate data, and to perform deviation correction and position calibration based on the azimuth stability coefficient and the error distribution range to obtain the adjusted position reference value. The interference processing module is used to process interference data based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, to obtain dynamic balance parameters. The verification and adjustment module is used to verify and adjust the parameters based on the dynamic balance parameters to obtain the final verification result. The configuration generation module is used to generate a directional configuration sequence based on the final verification result to obtain the equipment installation directional configuration. The long-term monitoring module is used to perform long-term stability monitoring based on the equipment installation orientation configuration and the environmental adaptability index, and to obtain long-term stability data of the equipment installation orientation.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention obtains the airborne equipment mass distribution matrix from a preset database, integrates flight attitude information and coordinate system transformation factors, and calculates the initial installation position error vector. Then, based on the preliminary installation orientation reference, combined with dynamic noise factors and state transition matrices, filtering techniques are used to iteratively correct the position error vector, ultimately obtaining optimized installation position coordinate data. This method accurately quantifies the actual initial error introduced by attitude changes by integrating real flight attitude, and actively suppresses the interference of dynamic noise by using iterative filtering correction, thereby achieving convergence from high error vectors to high-precision coordinates, effectively solving the problem of large initial position errors and low accuracy caused by flight attitude changes and dynamic noise in the prior art.

[0017] (2) This invention extracts the azimuth stability coefficient of the optimized coordinate data. If the coefficient is lower than a preset threshold, a deviation correction coefficient is calculated for the error distribution range to determine the adjusted position reference value. Then, based on the adjusted position reference value, the environmental adaptability index and observation update weight are integrated to process the interference data under specific meteorological conditions, ultimately obtaining the dynamic balance parameters of the equipment installation azimuth. This method quantifies and corrects the volatility of the data itself through the stability coefficient, and combines it with the environmental index to enable the reference value to actively adapt to external meteorological conditions and eliminate interference, thereby realizing the transformation from static reference to dynamic balance. This solves the problem of low equipment stability under complex meteorological interference, ensuring the dynamic balance and environmental adaptability of the equipment azimuth.

[0018] (3) This invention utilizes dynamic equilibrium parameters, combined with data consistency index and reliability assessment factors to construct a data quality assessment process, and generates a complete azimuth adjustment sequence based on the verification results. Simultaneously, by combining the error covariance matrix and environmental adaptation index, it continuously monitors minute attitude shifts during high-altitude cloud detection, ultimately obtaining long-term stability data for the equipment's installation azimuth. This method ensures the reliability of dynamic parameters through data consistency verification, thereby generating an executable configuration sequence and constructing a closed-loop monitoring process that adapts to environmental changes and continuously tracks minute shifts. This solves the problem of difficult long-term monitoring in high-altitude scenarios, achieving precise azimuth configuration and improving long-term stability. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of the airborne equipment installation position simulation correction method for measuring total cloud water content provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the airborne equipment installation position simulation correction system for measuring total cloud water content provided in the second embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 The first embodiment of the present invention provides a simulation correction method for the installation position of airborne equipment for measuring total cloud water content, comprising the following steps: S11: Obtain mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. S12, Based on the initial installation position error vector and the preliminary installation orientation reference, perform iterative correction to obtain optimized installation position coordinate data; S13. Based on the optimized installation position coordinate data, extract the azimuth stability coefficient and error distribution range, and perform deviation correction and position calibration based on the azimuth stability coefficient and error distribution range to obtain the adjusted position reference value. S14. Based on the adjusted position reference value, combined with the preset environmental adaptation index and the preset observation update weight, the interference data is processed to obtain the dynamic balance parameters. S15, Based on the dynamic balance parameters, perform parameter verification and adjustment to obtain the final verification result; S16, Based on the final verification result, generate the orientation configuration sequence to obtain the equipment installation orientation configuration; S17. Based on the equipment installation orientation configuration and the environmental adaptability index, long-term stability monitoring is performed to obtain long-term stability data of the equipment installation orientation.

[0022] In step S11, the mass distribution data, flight attitude data, and coordinate transformation parameters of the airborne equipment are acquired, and the initial installation error is calculated to obtain the initial installation position error vector and the preliminary installation orientation reference, including: Retrieve mass distribution data, flight attitude data, and coordinate transformation parameters of airborne equipment from a pre-defined database; Based on the quality distribution data, data verification and standardization processes are performed to obtain standardized quality distribution information; Based on the standardized mass distribution information and the flight attitude data, coordinate transformation and vector synthesis are performed using the coordinate transformation parameters to obtain the initial installation position error vector. Based on the initial installation position error vector, spatial projection and benchmark definition are performed to obtain the preliminary installation orientation benchmark.

[0023] First, mass distribution data, flight attitude data, and coordinate transformation parameters of the airborne equipment are obtained from a pre-set database. This pre-set database is an embedded database pre-built and stored in an airborne computer or ground control center. Its structure includes equipment model identifiers, a mass distribution matrix, flight attitude time series, and a coordinate transformation parameter table. It is constructed based on the design blueprint parameters of the airborne equipment and ground test calibration data, and is updated synchronously after changes to the equipment hardware or periodic calibrations.

[0024] It should be noted that the mass distribution data is exported from the 3D CAD model provided by the equipment manufacturer or obtained through laboratory mass characteristic tests, specifically including the mass moments and rotational inertia matrices of the equipment in three axes; the flight attitude data comes from the airborne inertial navigation system, including pitch angle, roll angle, yaw angle and its angular velocity; the coordinate transformation parameters are the transformation matrix from the equipment coordinate system to the aircraft body coordinate system, obtained through ground calibration measurements, including rotation matrix and translation vector.

[0025] Next, based on the quality distribution data, data verification and standardization are performed. This process includes format verification and integrity checks on the matrix data, based on a preset data integrity threshold. This threshold is set according to the physical constraints of device quality (e.g., non-negative quality) and the data format requirements of subsequent calculation modules. A common method for setting this threshold is to define a set of Boolean rules (e.g., data items are not empty and the value is greater than zero). For example, it checks whether each element in the matrix is ​​positive and has no missing values. If the detection result does not meet the threshold (e.g., negative or empty values ​​exist), the data needs to be reacquired.

[0026] After verification, standardization is performed. For example, the matrix data is normalized to the range of 0 to 1 using the min-max normalization method to obtain standardized mass distribution information. Then, based on the standardized mass distribution information and the flight attitude data, coordinate transformation and vector synthesis are performed using the coordinate transformation parameters. Specifically, the standardized mass distribution information (representing the equipment's center of gravity) is fused with the flight attitude data (e.g., yaw angle 5 degrees, pitch angle 3 degrees), and the corresponding factors in the coordinate transformation factor table are called to calculate the actual pointing of the equipment in the current attitude and the ideal installation pointing determined based on the orientation reference definition rules. The deviation components of the two on the x, y, and z axes are obtained through vector subtraction and synthesized into the error vector of the initial installation position (e.g., x, y, and z axis errors are 0.2, 0.15, and 0.1 degrees, respectively), to obtain the preliminary error distribution result.

[0027] Subsequently, dynamic adjustment parameters (such as the rate of change of angular velocity) from the flight attitude information are acquired, and it is determined whether they exceed a preset dynamic adjustment threshold. This threshold is set based on the aircraft's flight dynamics characteristics and the upper limit of the equipment's attitude tracking response rate. A common method for setting this threshold is to determine an upper limit (e.g., 2 degrees per second) by analyzing historical flight data or referring to the maximum permissible angular velocity in the equipment specifications. Specifically, angular velocity change rate data from a large number of historical flight missions (e.g., more than 100 flight logs containing typical maneuvers such as climb, descent, and turns) are collected. These data are statistically processed to form a data distribution, and the 95th percentile of this distribution is calculated. The 95th percentile is chosen because this threshold can statistically effectively filter out the vast majority (95%) of normal flight fluctuations, while ensuring sensitivity to significant dynamic adjustments requiring correction (i.e., the first 5% of dramatic changes). Choosing a threshold that is too small may lead to overly frequent corrections; choosing a threshold that is too large may miss common maneuvers that still require correction. Alternatively, in the absence of sufficient historical data, the upper limit of this value can be determined by referring to the maximum permissible angular velocity in the equipment specification.

[0028] If the limit is exceeded, the secondary correction is performed. The specific process is as follows: First, calculate the difference between the dynamic adjustment parameter (e.g., angular velocity change rate of 3 degrees per second) and the dynamic adjustment threshold (e.g., 2 degrees per second) to obtain the excess value (3-2=1 degree per second); then, calculate an error correction amount. The calculation formula is as follows: .

[0029] in, This is the error correction amount. The error component to be corrected. This is the preset correction sensitivity coefficient, in seconds (its setting is based on the desired dynamic response characteristics of the correction algorithm; the general setting method is to iteratively adjust it through simulation testing under typical dynamic adjustment parameter inputs). Choose a value (e.g., from 0.1 to 1.0) that will allow the corrected error to converge fastest without overshooting or oscillation, such as 0.75. The value exceeding the limit (e.g., 1 degree per second). Finally, the error correction amount is subtracted from the preliminary error distribution result to obtain the adjusted installation position error data, which is the initial installation position error vector.

[0030] Finally, based on the initial installation position error vector (i.e., the adjusted installation position error data), spatial projection and reference definition are performed. The orientation reference definition rule refers to defining the relative pointing relationship between the equipment's coordinate system and the aircraft's coordinate system under ideal installation conditions (such as when the aircraft is flying horizontally). For example, it defines that the equipment's observation axis (Z-axis) should be parallel to the aircraft's Z-axis (zenith direction). This rule is set based on the observation geometry requirements of the detection mission (downward, forward, or side-view) and the physical installation limitations of the fuselage. This process involves selecting a reference plane defined by the orientation reference definition rule (e.g., parallel Z-axis) (e.g., the XY plane perpendicular to the Z-axis), and projecting the adjusted installation position error data (three-dimensional vector) onto this reference plane through matrix transformation to obtain a two-dimensional error projection point.

[0031] Subsequently, the Euclidean distance from the projection point to the origin of the plane (i.e., the ideal reference point) is calculated, and this distance is compared with a preset orientation reference condition threshold (set based on the final system accuracy requirements for total cloud water content measurement; the general setting method is to distribute the total allowable measurement error of the system to each sub-stage (such as sensor noise, algorithm error, installation error, etc.), and this threshold is the maximum allowable error limit allocated to the initial installation stage, such as a radius of 0.1 degrees). If the condition is met (e.g., 0.0707 < 0.1), the initial installation orientation reference of the equipment is determined and output. If the condition is not met, it is determined that the initial installation error is too large, an error message is returned to the system, the subsequent process is terminated, and physical recalibration is required.

[0032] In step S12, based on the initial installation position error vector and the preliminary installation orientation reference, iterative correction is performed to obtain optimized installation position coordinate data, including: Based on the initial installation position error vector, combined with the preset dynamic noise factor and the preset state transition matrix, state estimation and recursive correction are performed to obtain the corrected error vector. Based on the corrected error vector and the initial installation orientation reference, coordinate synthesis and updating are performed to obtain optimized installation position coordinate data.

[0033] First, based on the initial installation position error vector (output of S101), state estimation and recursive correction are performed in conjunction with a preset dynamic noise factor and a preset state transition matrix. Specifically, this process involves retrieving the dynamic noise factor data from a pre-established repository. This repository is a database used to store historical environmental interference and sensor noise characteristics, storing noise values ​​(e.g., 3.2, 2.8, 3.5 degrees) in time-series format. The repository is constructed by performing statistical characteristic analysis (e.g., power spectral density estimation) on sensor data (e.g., gyroscope, accelerometer) collected during historical flight missions to extract noise distribution parameters. It is updated by incorporating newly collected data into a statistical sample after each new flight mission, periodically updating the noise parameters. The dynamic noise factor represents time-series data (e.g., 3.2, 2.8, 3.5 degrees) representing the noise from device sensor measurements and environmental vibration interference.

[0034] After acquisition, the noise factor data undergoes format verification and range comparison. The format verification process involves checking whether each value in the data sequence is of the expected numerical type (e.g., floating-point) and is not empty. Then, a range comparison is performed to determine if it exceeds a preset noise data threshold. This threshold is set based on the physical range of the device's sensors and the expected environmental noise level. A common method is to set a fixed static range (e.g., 0 to 5 degrees) according to the device specifications. If the data exceeds this threshold, the data point is removed or interpolated using the mean of neighboring values ​​to obtain a cleaned noise factor set. Simultaneously, the preset state transition matrix is ​​acquired. This matrix (e.g., a 2x2 matrix) characterizes the variation of the device installation position error over a continuous time step, and its construction is based on the kinematic model of the airborne equipment.

[0035] Next, a recursive filtering algorithm is used to perform the state estimation and recursive correction. This process includes the following steps: First, the error vector at the next time step is predicted based on the state transition matrix. Specifically, the initial installation position error vector is used as the state at the previous time step. Using the state transition matrix F, calculate the prior estimate for the current time step. The specific formula is as follows: .

[0036] The next step is to calculate the measurement update gain coefficient. The calculation process for this coefficient involves comprehensively considering the process uncertainty (prior estimate covariance) introduced by the state transition matrix F. The measurement uncertainty (measurement noise covariance R) represented by the cleaned noise factor set is used to solve for an optimal weight through covariance calculation. This weight is the gain coefficient. .

[0037] Next, the prior estimate is corrected. This process involves first obtaining a new position error observation. (From the sensor), and calculate its difference from the prior estimate. The difference between them is used to obtain the predicted residual of the model. (Its definition) Then, use the gain coefficient. For the residual Weight the results and compare them with prior estimates. Adding them together yields the corrected (posterior) error vector. The specific formula is as follows: .

[0038] This process involves multiple iterations of correction, and after each iteration, it is determined whether the corrected error value (such as the L2 norm of the vector) is lower than a preset error convergence threshold. (This threshold is set based on the system's final requirement for installation accuracy, which is stricter than the initial installation reference conditions. The general setting method is to set it as a specific proportion of the azimuth reference condition threshold in S101 (such as 90%), or an absolute value determined by simulation experiments that can ensure stable convergence of the algorithm. If it is lower than this threshold or the number of iterations exceeds the maximum number of iterations (such as 50), the loop is stopped, and the corrected error vector is obtained.)

[0039] Finally, based on the corrected error vector With the aforementioned preliminary installation orientation reference (The output of S11) is used for coordinate synthesis and updating. Specifically, this process involves adjusting the corrected error vector according to the reference calibration rules defined in S101. With the aforementioned preliminary installation orientation reference Spatial mapping and coordinate transformation are performed. For example, if the rule is defined as vector addition in the body coordinate system, then the calculation process for coordinate synthesis and updating is as follows: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] Compensation to the On (representing the ideal installation coordinates), a set of optimized installation position coordinate information is calculated. This information is the optimized installation location coordinate data. The calculation formula is: .

[0040] In step S13, based on the optimized installation position coordinate data, the azimuth stability coefficient and error distribution range are extracted. Based on the azimuth stability coefficient and the error distribution range, deviation correction and position calibration are performed to obtain the adjusted position reference value, including: Based on the optimized installation location coordinate data, data analysis is performed to obtain the orientation stability coefficient and error distribution range; When the orientation stability coefficient is lower than the preset stability threshold, the correction coefficient is calculated based on the error distribution range to obtain the deviation correction coefficient. Based on the deviation correction coefficient and the optimized installation position coordinate data, a weighted correction is performed to obtain the adjusted position reference value.

[0041] First, based on the optimized installation position coordinate data (output of S102), data parsing is performed, specifically including calculating the error distribution range and the azimuth stability coefficient. The error distribution range is calculated by iterating through the time series coordinates, finding the maximum and minimum values ​​for each axis, and the difference between the two is the distribution range for that axis. The azimuth stability coefficient is calculated by first calculating the average value of the time series coordinates. Then, based on the average value... and each coordinate value in the time series Calculate its standard deviation The calculation formula is as follows: .

[0042] Finally, to prevent the data from becoming completely stable ( A division by zero error occurs when the integer equals 0, introducing a local minimum value when calculating the reciprocal. (For example, Then, the formula for calculating the azimuth stability coefficient S is: This coefficient is used to quantify its stability. The smaller the value, the larger the value of S, indicating greater stability.

[0043] Next, when the azimuth stability coefficient is lower than a preset stability threshold, a correction coefficient is calculated based on the error distribution range to obtain the deviation correction coefficient. This process involves comparing the azimuth stability coefficient with the preset stability threshold, which is set based on the minimum acceptable stability level to ensure the accuracy of total cloud water content measurement. The general setting method is as follows: first, collect multiple sets of azimuth stability coefficients S and their corresponding measurement data quality indicators (such as signal-to-noise ratio SNR) from historical data; then, calculate the Pearson correlation coefficient between the S sequence and the SNR sequence to verify a strong correlation between the two (e.g., S and SNR); finally, determine a critical coefficient value (e.g., 0.7) through regression analysis. When S is lower than this value, SNR begins to decrease significantly, so this value is selected as the stability threshold.

[0044] If the azimuth stability coefficient is lower than the threshold, then the iterative calculation of the deviation correction coefficient is initiated. The specific process of this iterative calculation is as follows: the error distribution range is used as input, and an iteration step size is set. and an initial correction coefficient The initial correction coefficient is set to provide an iteration starting point close to the optimal solution to accelerate convergence. A common method is to select an empirical value slightly below 1.0 (e.g., 0.85) through historical simulation data analysis. The iteration step size is set to balance the iteration convergence speed and final accuracy. A common method is to select a sufficiently small positive value (e.g., 0.01), determined through simulation testing to ensure the iteration process is stable and does not diverge.

[0045] In the i-th iteration, use the current correction coefficients. (in =0.85) and the error distribution range To perform a multiplication operation, first calculate a temporary corrected error range. That is, the temporary corrected error range is obtained by multiplying the original error distribution range by the current correction factor; then, the corrected error is re-evaluated, i.e., the error is judged. Whether the convergence condition is met, wherein the convergence condition is the error range of the current iteration. Error range compared to the previous iteration absolute difference Whether it is less than a preset convergence threshold. The convergence threshold is set based on the balance between the algorithm's computational efficiency and convergence accuracy. A common method is to select a sufficiently small positive value (e.g., 0.005) to ensure the stability of the correction result. If If the value is less than the threshold, stop the iteration and output the current value. As the deviation correction factor (e.g., 0.92). If If it is not less than this threshold, then according to the iteration step size Update coefficients (e.g.) Then proceed to the next iteration. If the orientation stability coefficient is not lower than the threshold, the deviation correction coefficient is set to a neutral value (e.g., 1.0), indicating that no correction is needed.

[0046] Finally, a weighted correction is performed based on the deviation correction coefficient and the optimized installation position coordinate data to obtain the adjusted position reference value. Specifically, this process involves multiplying each component of the coordinate data with the deviation correction coefficient to obtain the corrected coordinates. The calculation formula can be expressed as: .

[0047] in, These are the adjusted coordinate components. For the optimized coordinate components, The deviation correction factor is (e.g., 0.92).

[0048] Subsequently, it is determined whether the corrected result conforms to the corrected rules. These rules are set to ensure that the corrected value is within the physically permissible range and the error converges. The general setting method is to define a maximum permissible error limit (e.g., whether the corrected coordinate error is controlled within 0.1 degrees). If it conforms, the result is determined as the adjusted position reference value. If it does not conform (e.g., the corrected coordinate error is still greater than 0.1 degrees), the optimized installation position coordinate data (i.e., the output of S102) is retained as the adjusted position reference value, and the deviation correction coefficient is reset to 1.0, indicating that this calibration is invalid.

[0049] In step S14, based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, interference data processing is performed to obtain dynamic equilibrium parameters, including: Based on the adjusted location reference value and the preset environmental adaptability index, environmental adaptability adjustment is performed to obtain environmentally adapted location data; Based on the environmental adaptation location data and the preset observation update weights, data fusion is performed to obtain a weighted fusion result; Based on the weighted fusion result, filtering and smoothing processes are performed to obtain dynamic balance parameters.

[0050] First, based on the adjusted position reference value (output of S103) and the preset environmental adaptability index, environmental adaptability adjustments are made to obtain environmentally suitable position data. The environmental adaptability index is a comprehensive parameter (exemplarily 0.8) generated based on external meteorological conditions such as temperature and humidity. Its setting is based on the degree of influence of the actual environment on the equipment installation position. A common setting method is to establish a functional relationship model between environmental factors and position deviation through regression analysis of experimental data. The functional relationship model is constructed by recording real-time readings of multiple environmental factors (e.g., X1 = temperature, X2 = humidity) and the actual position deviation Y measured by a high-precision reference device under these conditions during controlled experiments (or historical flights). Multiple linear regression is used to fit these historical data (X1, X2, and Y) to establish a functional relationship model, specifically in the form of: .

[0051] The model obtains the current real-time external weather conditions (e.g., X1 = current temperature, X2 = current humidity), substitutes them into the formula with the determined coefficients, and the calculated result is the expected position deviation. ,in, , and These coefficients are obtained through regression analysis.

[0052] Subsequently, With a maximum permissible deviation (Should The environmental adaptability index is obtained by comparing it with the total system error budget and then calculating it through normalization. The calculation formula is as follows: .

[0053] The index This is the final output of the model in this step.

[0054] The specific calculation process for the environmental adaptability adjustment involves multiplying the adjusted location reference value by the environmental adaptability index to obtain the environmentally adapted location data. The calculation formula is as follows: .

[0055] in, To adapt location data to the environment, This is the adjusted position reference value. This is the environmental adaptability index.

[0056] Next, based on the environmental adaptation location data (such as...) The data (x = {x: 0.76544}) is fused with a preset observation update weight to obtain a weighted fusion result. The observation update weight is a weight value assigned based on the reliability of the data source (for example, the weight of a high-precision sensor). Weight of ordinary equipment The data fusion is based on the calibration accuracy and real-time stability of each sensor. The general setting method involves setting initial weights according to the accuracy specifications in the sensor datasheet and periodically adjusting them based on actual performance data during operation. The specific calculation process for data fusion is as follows: acquiring a new frame of real-time observation data from airborne equipment (such as the high-precision sensor or ordinary equipment). (e.g., {x:0.750}), and the environment-adapted location data. With this real-time observation data The weighted average is calculated based on the respective observation update weights to obtain the weighted fusion result. Specifically, .

[0057] Finally, based on the weighted fusion results... The data is filtered and smoothed to obtain dynamic equilibrium parameters. This process includes detecting and processing interference data under specific meteorological conditions (such as strong winds). Specifically, the process involves calculating the deviation between the weighted fusion result and the environmentally adapted location data, and comparing it with an interference data threshold. The interference data threshold is set based on the maximum acceptable data fluctuation range of the system under specific meteorological conditions (such as strong winds). A common method for setting this threshold is to analyze historical flight data under these specific conditions and select the 99th percentile of the deviation value as the threshold. If the deviation value does not exceed the threshold, it is determined that there is no interference, and the weighted fusion result is processed. The dynamic balance parameter is output. If the deviation value exceeds the threshold (e.g., a deviation value of 0.4 degrees is detected), the data is determined to be interference data and discarded. The undisturbed environmental adaptation location data is then used instead. The output is the dynamic equilibrium parameter.

[0058] In step S15, parameter verification and adjustment are performed based on the dynamic balance parameters to obtain the final verification result, including: Based on the dynamic balance parameters, a consistency check is performed to obtain the check conclusion; If the verification result is unsuccessful, then the parameters are iteratively optimized based on the verification result to obtain the updated dynamic balance parameters. Based on the updated dynamic balance parameters, a consistency check is performed again until the check result is passed. The updated dynamic balance parameters that pass the check are then taken as the final verification result.

[0059] First, based on the output of the dynamic equilibrium parameter (S104), it is assigned a value during the first iteration. This involves performing a consistency check. This process involves retrieving a historical baseline equilibrium parameter from the historical parameter statistics database. The method for constructing the historical parameter statistical database is as follows: storing the dynamic equilibrium parameters (coordinate values) output in step S104 of each successful flight mission or simulation, and statistically averaging them (e.g., calculating the mean) to obtain the... .

[0060] Then, based on the dynamic equilibrium parameters With the The deviation is used to calculate the data consistency index. Specifically, the index The calculation formula is: in, To avoid in The smallest positive number added to prevent a division-by-zero error when the value is zero (for example, it can generally be set to 0). ).

[0061] Subsequently, The data consistency threshold is compared with a data consistency threshold. This threshold is set based on the system's minimum acceptable data reliability and historical data distribution. A common method is to select the 95th percentile of the data consistency index calculated from historical data as the threshold, such as 0.9. After comparison, a verification conclusion is obtained. If the data consistency threshold is greater than or equal to the data consistency threshold, the verification result is "passed"; if... If the result is less than the data consistency threshold, the verification conclusion is "failed".

[0062] Next, the verification conclusion is evaluated. If the verification conclusion is "passed," subsequent optimization steps are skipped, and the dynamic balance parameter is directly used as the final verification result. If the verification conclusion is "failed," parameter iterative optimization is initiated based on the verification conclusion. This process involves using the dynamic balance parameter as an initial value and iteratively correcting it according to a threshold comparison matrix. This threshold comparison matrix is ​​a pre-defined lookup table, the structure of which is based on the data consistency index. (e.g., 0.739) is used as the query key, with an adjustment factor for correction. As a value (such as) =0.95); the lookup table is designed to ensure rapid convergence of the correction process. The construction of this lookup table was tested through offline simulation. Experiment with different values ​​within different intervals (e.g., [0,0.5], [0.5,0.75], [0.75,0.9]). Coefficients, and select those that enable Fastest convergence to the data consistency threshold The value is used as the "value" for that range. An example of a key-value pair is shown below: like If it is located in the interval [0.8, 0.9) (close to passing through), then =0.98 (fine-tuning); like Located in the interval [0.7, 0.8) (e.g.) =0.739), then =0.95 (moderate adjustment); like If it is located in the interval [0.0, 0.7) (with a large deviation), then =0.90 (strong correction).

[0063] This matrix is ​​preset and usually remains static, and is only recalibrated and updated using the above simulation method when there are significant changes in the system model or convergence objective.

[0064] In one iteration, the result is obtained according to this rule (i.e., by querying the lookup table). =0.9, and the updated dynamic equilibrium parameters were calculated. The calculation formula is as follows: .

[0065] Finally, the updated dynamic balance parameters are... value assigned to Then, it returns to execute the first step (consistency check). This process is repeated cyclically, using the new... Recalculate If the verification result is still "failed", then continue the iterative calculation. Until the result calculated in a certain iteration. If the data consistency threshold is greater than or equal to the data consistency threshold (i.e., the validation result changes to "pass"), then the iteration stops. Finally, the data that has passed the validation is processed. The value is used as the final verification result.

[0066] In step S16, based on the final verification result, a directional configuration sequence is generated to obtain the equipment installation directional configuration, including: Based on the final verification results, combined with the deviation correction coefficient and the preset fusion iteration step size, serialization calculation is performed to generate a complete orientation adjustment sequence; Based on the complete orientation adjustment sequence, configuration encapsulation is performed to obtain the device installation orientation configuration.

[0067] First, it should be noted that the final verification result is the output of S105, that is, the result that passes the verification. Value, the deviation correction coefficient ( The fusion iteration step size is obtained from the calculation results of step S103. It is a preset parameter (exemplarily 0.01 degrees), which is set based on the adjustment accuracy required for equipment installation in high-altitude scenarios and the minimum stepping capability of the hardware actuator. The general setting method is to select the minimum stepping value that makes the adjustment process the fastest through simulation testing, while meeting the final accuracy requirements.

[0068] Next, based on the deviation correction coefficient, the fusion iteration step size, and the final verification result, and in conjunction with preset environmental adaptation parameters, a serialization calculation is performed to generate a complete orientation adjustment sequence. The environmental adaptation parameters... This is a weighted value related to factors such as high-altitude wind speed and air pressure. Its setting is based on the specific constraints of different high-altitude environments on the installation configuration. The general setting method is to classify high-altitude scenarios according to key environmental factors (such as different altitudes and different cloud types), and then, for each scenario (e.g., "Scenario A: Altitude > 10km, Wind Speed ​​> 15m / s"), calculate the optimal scaling ratio between the historical final verification result (S105 output) and the final successful installation configuration value (S106 output) under that scenario. This calculated ratio (or a conservative value based on this ratio, such as 0.7) is pre-calibrated as the scaling factor for that scenario. And construct a lookup table (e.g., Key="Scenario A", Value=" =0.7). During execution in S106, the table is queried based on the current scenario type to obtain the corresponding... The specific process of the serialization calculation is as follows: first, by using the final verification result... Deviation correction coefficient Environmental adaptation parameters After weighting, the final target azimuth configuration value is calculated. The calculation formula is as follows: .

[0069] Then, starting from 0, the fusion iteration step size is used. The increment is used, increasing sequentially until the target azimuth configuration value is reached or closest to is achieved. This generates a numerical sequence containing multiple adjustment steps (e.g., [0.00, 0.01, 0.02, ..., 0.48, 0.4893]), which is the complete azimuth adjustment sequence.

[0070] Finally, based on the complete orientation adjustment sequence, configuration encapsulation is performed to obtain the equipment installation orientation configuration. This process involves converting the numerical complete orientation adjustment sequence into an executable instruction set or configuration file for the airborne equipment installation system, and validating the sequence according to high-altitude scene output constraints. These high-altitude scene output constraints are the maximum allowable adjustment rate or maximum single-step adjustment angle of the equipment hardware (such as a servo motor), set based on the equipment specifications (for example, a constraint of less than 0.015 degrees per step). The specific verification process involves traversing the complete orientation adjustment sequence and calculating the step difference between every two adjacent data points in the sequence. If all step differences meet the constraint (i.e., all are less than 0.015 degrees), the sequence is directly encapsulated as the final equipment installation orientation configuration.

[0071] If a step difference (e.g., [0.70, 0.72] in the sequence results in a step difference of 0.02) does not conform to the constraint (i.e., 0.02 > 0.015), a readjustment process is initiated. Between the steps that do not conform to the constraint ([0.70, 0.72]), one or more intermediate points are inserted according to the constraint (0.015) (e.g., inserting 0.71), generating a new sequence segment ([0.70, 0.71, 0.72]) such that all step differences (all 0.01) in the new sequence conform to the constraint. After replacing the original sequence segment with the new sequence segment, the sequence is then encapsulated.

[0072] In step S17, long-term stability monitoring is performed based on the equipment installation orientation configuration and the environmental adaptability index to obtain long-term stability data for the equipment installation orientation, including: Based on the installation orientation of the equipment, a monitoring baseline is set to obtain a long-term monitoring baseline; Based on the long-term monitoring benchmark and the environmental adaptability index, a stability assessment model is constructed to obtain the stability assessment model. Based on the stability assessment model, the real-time collected equipment attitude data is periodically sampled and trend analyzed to obtain long-term stability data of the equipment installation orientation.

[0073] First, based on the device installation orientation configuration (S106), the monitoring baseline is set. This process involves setting the device's installation orientation. The value is determined as the ideal attitude reference value during the monitoring process, which is the long-term monitoring reference.

[0074] Next, based on the aforementioned long-term monitoring benchmark ( ) and the environmental adaptability index ( (Originally obtained from S104, e.g., 0.8), a stability assessment model is constructed. This process involves first setting a basic stability threshold. The threshold is set based on the highest accuracy requirement for attitude stability in the total cloud water content measurement task (i.e., the maximum permissible error under ideal conditions). The general method for setting this threshold is to allocate a fixed error limit to this stage based on the decomposition of the total system error budget (e.g., ...). =0.02 degrees). Then, a stability assessment model is constructed, which is based on the condition that the environmental quality is poor ( When the monitoring threshold is low, the system's inherent measurement noise and short-term fluctuations will increase. Keep If the threshold is too strict, the system may misinterpret "noise" as "true offset," thus frequently triggering unnecessary "dynamic adjustment" processes (i.e., "jitter"). Therefore, the monitoring threshold... Must be based on Dynamic relaxation ensures that only attitude deviations with significant amplitude (i.e., exceeding the current ambient noise level) are considered "out-of-limit" events requiring correction. The specific expression for this model is a threshold used to calculate the dynamic attitude deviation. Functions: The specific process of this dynamic weighted adjustment is to... Substituting into the function, the current environment is calculated. .Should This is the output of the stability assessment model.

[0075] Finally, based on the stability assessment model (i.e. and The system performs periodic sampling and trend analysis on the real-time acquired equipment attitude data. The real-time acquired equipment attitude data ( The data is derived from continuous readings from airborne attitude sensors (such as gyroscopes). The specific process of periodic sampling involves acquiring data according to a preset data acquisition frequency (this frequency is set based on monitoring accuracy requirements, such as once per hour). After obtaining it, compare it with the long-term monitoring benchmark. By comparison, the real-time attitude offset is calculated. .

[0076] Then, the The dynamic attitude offset threshold output by the model Perform a comparison. If... Then the The value is recorded as a qualified data point. If the stated... Exceeding This triggers a dynamic adjustment process. Specifically, this process involves first adjusting the gain based on a preset correction ratio. (Should The setting is based on the stability requirements of the control loop. The general setting method is to input a step "over-limit" signal (e.g., ...) into the model through simulation testing. (degree), and tested a series of Values ​​(e.g., from 0.1 to 1.0). If If the value is too high (e.g., 0.9), the system correction will produce "overshoot" or "oscillation"; if... If the value is too low (e.g., 0.1), the system correction will be too slow. Choose the value that allows the system to "respond the fastest" without "producing significant overshoot". Value (e.g.) =0.5) is used as the preset coefficient. Then, a reverse correction instruction is calculated. The specific calculation process is as follows: .

[0077] Should The servo motor sent to the installation mechanism performs a correction, and simultaneously records the event (e.g., "0.005 degrees exceeding the limit"). The specific process of trend analysis involves collecting all qualified data points recorded within a complete monitoring cycle (e.g., 100 samples). The values ​​are used to form a time series; then, linear regression analysis is performed on the time series to calculate the slope of the series. The statistical evaluation process involves assessing stability based on preset stability evaluation indicators. Total number of over-limit events An evaluation is conducted. The preset basis for the stability evaluation index is the minimum requirements of the scientific task for the final data quality and the early warning requirements for the equipment status. The general setting method is to define a set of threshold rules, for example, (1) the over-limit ratio threshold. Its setting is based on the minimum percentage requirement for valid data in scientific tasks (e.g., 98%). (2) Trend slope threshold (like The threshold value (degrees / hour) is set based on the maximum total drift allowed between two calibration cycles. The general setting method is to divide the maximum total drift (e.g., 0.1 degrees) by the total duration of the calibration cycle (e.g., 10,000 hours) to obtain the threshold value.

[0078] Statistical evaluation is the calculation of the percentage of long-term stability. The calculation formula is as follows: .

[0079] in The total number of samples, and Compared with the over-limit threshold, and The absolute value is compared with the trend slope threshold. The evaluation results (such as " These data are combined into a single report, which contains long-term stability data for the installation orientation of the equipment.

[0080] In summary, this invention calculates the initial error vector, iteratively corrects the coordinate data using filtering techniques, integrates environmental indices and stability coefficients for dynamic balancing and correction, constructs a data quality assessment process to generate a complete orientation adjustment sequence, and finally continuously monitors minute attitude deviations. This achieves precise orientation configuration and improved long-term stability, thereby enhancing overall installation accuracy and adaptability.

[0081] Reference Figure 2 The second embodiment of the present invention provides an airborne equipment installation position simulation correction system for measuring total cloud water content, comprising: The error calculation module is used to acquire mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. The iterative correction module is used to perform iterative correction based on the initial installation position error vector and the preliminary installation orientation reference to obtain optimized installation position coordinate data. The position calibration module is used to extract the azimuth stability coefficient and error distribution range based on the optimized installation position coordinate data, and to perform deviation correction and position calibration based on the azimuth stability coefficient and the error distribution range to obtain the adjusted position reference value. The interference processing module is used to process interference data based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, to obtain dynamic balance parameters. The verification and adjustment module is used to verify and adjust the parameters based on the dynamic balance parameters to obtain the final verification result. The configuration generation module is used to generate a directional configuration sequence based on the final verification result to obtain the equipment installation directional configuration. The long-term monitoring module is used to perform long-term stability monitoring based on the equipment installation orientation configuration and the environmental adaptability index, and to obtain long-term stability data of the equipment installation orientation.

[0082] It should be noted that the airborne equipment installation position simulation correction system for measuring total cloud water content provided in this embodiment of the invention is used to execute all the process steps of the airborne equipment installation position simulation correction method for measuring total cloud water content in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0083] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a simulation calibration program for the installation position of airborne equipment for measuring total cloud water content. When the processor executes the computer program, it implements the steps in the various embodiments of the simulation calibration method for the installation position of airborne equipment for measuring total cloud water content described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above system embodiments, such as the position calibration module.

[0084] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0085] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0086] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0087] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0088] If the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or system capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0089] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0090] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A simulation correction method for the installation position of airborne equipment used for measuring total cloud water content, characterized in that, include: Acquire mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. Based on the initial installation position error vector and the preliminary installation orientation reference, iterative correction is performed to obtain the optimized installation position coordinate data; Based on the optimized installation position coordinate data, the azimuth stability coefficient and error distribution range are extracted. Based on the azimuth stability coefficient and the error distribution range, deviation correction and position calibration are performed to obtain the adjusted position reference value. Based on the adjusted position reference value, combined with the preset environmental adaptation index and preset observation update weight, interference data processing is performed to obtain dynamic equilibrium parameters. Based on the dynamic equilibrium parameters, parameter verification and adjustment are performed to obtain the final verification result; Based on the final verification results, a directional configuration sequence is generated to obtain the equipment installation directional configuration. Based on the equipment installation orientation configuration and the environmental adaptability index, long-term stability monitoring is performed to obtain long-term stability data of the equipment installation orientation.

2. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The process of acquiring mass distribution data, flight attitude data, and coordinate transformation parameters of the airborne equipment, calculating initial installation error, and obtaining the initial installation position error vector and preliminary installation orientation reference includes: Retrieve mass distribution data, flight attitude data, and coordinate transformation parameters of airborne equipment from a pre-defined database; Based on the quality distribution data, data verification and standardization processes are performed to obtain standardized quality distribution information; Based on the standardized mass distribution information and the flight attitude data, coordinate transformation and vector synthesis are performed using the coordinate transformation parameters to obtain the initial installation position error vector. Based on the initial installation position error vector, spatial projection and benchmark definition are performed to obtain the preliminary installation orientation benchmark.

3. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The step of iteratively correcting the initial installation position error vector and the preliminary installation orientation reference to obtain optimized installation position coordinate data includes: Based on the initial installation position error vector, combined with the preset dynamic noise factor and the preset state transition matrix, state estimation and recursive correction are performed to obtain the corrected error vector. Based on the corrected error vector and the initial installation orientation reference, coordinate synthesis and updating are performed to obtain optimized installation position coordinate data.

4. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The process involves extracting the azimuth stability coefficient and error distribution range from the optimized installation position coordinate data, and then performing deviation correction and position calibration based on the azimuth stability coefficient and error distribution range to obtain the adjusted position reference value, including: Based on the optimized installation location coordinate data, data analysis is performed to obtain the orientation stability coefficient and error distribution range; When the orientation stability coefficient is lower than the preset stability threshold, the correction coefficient is calculated based on the error distribution range to obtain the deviation correction coefficient. Based on the deviation correction coefficient and the optimized installation position coordinate data, a weighted correction is performed to obtain the adjusted position reference value.

5. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The step involves processing the interference data based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, to obtain dynamic equilibrium parameters, including: Based on the adjusted location reference value and the preset environmental adaptability index, environmental adaptability adjustment is performed to obtain environmentally adapted location data; Based on the environmental adaptation location data and the preset observation update weights, data fusion is performed to obtain a weighted fusion result; Based on the weighted fusion result, filtering and smoothing processes are performed to obtain dynamic balance parameters.

6. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The step of verifying and adjusting parameters based on the dynamic equilibrium parameters to obtain the final verification result includes: Based on the dynamic balance parameters, a consistency check is performed to obtain the check conclusion; If the verification result is unsuccessful, then the parameters are iteratively optimized based on the verification result to obtain the updated dynamic balance parameters. Based on the updated dynamic balance parameters, a consistency check is performed again until the check result is passed. The updated dynamic balance parameters that pass the check are then taken as the final verification result.

7. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 4, characterized in that, The step of generating a directional configuration sequence based on the final verification result to obtain the equipment installation directional configuration includes: Based on the final verification results, combined with the deviation correction coefficient and the preset fusion iteration step size, serialization calculation is performed to generate a complete orientation adjustment sequence; Based on the complete orientation adjustment sequence, configuration encapsulation is performed to obtain the device installation orientation configuration.

8. The airborne equipment installation position simulation correction method for measuring total cloud water content according to claim 1, characterized in that, The step of conducting long-term stability monitoring based on the equipment installation orientation configuration and the environmental adaptability index to obtain long-term stability data for the equipment installation orientation includes: Based on the installation orientation of the equipment, a monitoring baseline is set to obtain a long-term monitoring baseline; Based on the long-term monitoring benchmark and the environmental adaptability index, a stability assessment model is constructed to obtain the stability assessment model. Based on the stability assessment model, the real-time collected equipment attitude data is periodically sampled and trend analyzed to obtain long-term stability data of the equipment installation orientation.

9. A simulation and correction system for the installation position of airborne equipment used for measuring total cloud water content, characterized in that, include: The error calculation module is used to acquire mass distribution data, flight attitude data and coordinate transformation parameters of airborne equipment, perform initial installation error calculation, and obtain the initial installation position error vector and preliminary installation orientation reference. The iterative correction module is used to perform iterative correction based on the initial installation position error vector and the preliminary installation orientation reference to obtain optimized installation position coordinate data. The position calibration module is used to extract the azimuth stability coefficient and error distribution range based on the optimized installation position coordinate data, and to perform deviation correction and position calibration based on the azimuth stability coefficient and the error distribution range to obtain the adjusted position reference value. The interference processing module is used to process interference data based on the adjusted position reference value, combined with a preset environmental adaptation index and a preset observation update weight, to obtain dynamic balance parameters. The verification and adjustment module is used to verify and adjust the parameters based on the dynamic balance parameters to obtain the final verification result. The configuration generation module is used to generate a directional configuration sequence based on the final verification result to obtain the equipment installation directional configuration. The long-term monitoring module is used to perform long-term stability monitoring based on the equipment installation orientation configuration and the environmental adaptability index, and to obtain long-term stability data of the equipment installation orientation.

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