High-altitude environment-oriented sonde multi-source data quality control processing method and system

Abstract

The invention relates to the technical field of data processing, in particular to a sonde multi-source data quality control processing method and system for a high-altitude environment, and the method specifically comprises the steps: obtaining a space-time asynchronous error, a sensor physical conflict and navigation data abnormity based on the original data of a sonde; analyzing a data quality index of the sonde based on the space-time asynchronous error, the sensor physical conflict and the navigation data anomaly; obtaining an environmental adaptability correction coefficient based on the atmospheric environment characteristics of the height layer where the sonde is located and historical sounding data; analyzing a dynamic measurement error based on the flight attitude and motion state data of the sonde; and the credibility weight of the sonde during multi-source data fusion is predicted based on the data quality index, the environmental adaptability correction coefficient and the dynamic measurement error of the sonde, and whether the data of the sonde meet the high-altitude detection quality standard is judged. The fusion credibility of the multi-source data under the extreme condition is predicted, and the quality control precision of the high-altitude detection data is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and is a method and system for quality control processing of multi-source data from radiosondes for high-altitude environments. Background Technology

[0002] Radiosondes conduct atmospheric sounding in the troposphere, stratosphere, and other upper-level environments. The multi-source data they collect, such as temperature, humidity, air pressure, and wind fields, directly affect the accuracy of weather forecasts and climate research. Traditional data quality control relies on fixed threshold checks, which suffers from poor adaptability and a high false positive rate. However, incorporating multi-source data fusion techniques, such as spatiotemporal registration and physical consistency checks, can significantly improve the accuracy and reliability of data quality control, reduce data distortion in extreme environments, and thus ensure the quality of upper-level sounding data. However, current radiosonde data quality control technologies mostly rely on single threshold judgments to automatically detect out-of-limit anomalies in data, such as temperature and humidity ranges. They lack a comprehensive evaluation that combines the radiosonde's data quality index, environmental adaptability correction coefficient, and dynamic measurement error to assess the reliability weight of the radiosonde during multi-source data fusion. This leads to discrepancies between radiosonde data quality and actual conditions in extreme environments, thus affecting the accuracy of meteorological analysis. Therefore, improving the accuracy of multi-source data quality control for radiosondes is a problem that needs to be solved by those skilled in the art. Summary of the Invention

[0003] This invention takes into account the special environment of high altitude and the dynamic characteristics of radiosondes, and predicts the reliability of multi-source data fusion under extreme conditions. In order to improve the quality control accuracy of high-altitude sounding data, a quality control processing method and system for multi-source radiosonde data oriented towards the high-altitude environment is proposed.

[0004] To achieve the above objectives, the technical solution of the radiosonde multi-source data quality control processing method for high-altitude environments of the present invention includes the following steps: S1: Collect raw data from the radiosonde, obtain spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly based on the raw data from the radiosonde, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly; S2: Collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. S3: Collect data on the flight attitude and motion status of the radiosonde, and analyze dynamic measurement errors based on the data on the flight attitude and motion status of the radiosonde; S4: Based on the data quality index, environmental adaptability correction coefficient and dynamic measurement error of the radiosonde, the credibility weight of the radiosonde in multi-source data fusion is predicted; S5: Based on the credibility weight of the radiosonde during multi-source data fusion, determine and filter radiosonde data that meets the quality standards for high-altitude sounding.

[0005] Preferably, S1 includes: Collect raw data from the radiosonde to be processed; Based on the raw data of the radiosonde to be processed, spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies are obtained. The spatiotemporal asynchronous errors include BeiDou-GPS timestamp deviation and sensor acquisition delay. The sensor physical conflicts include temperature and pressure high-inverse calculation contradictions and water vapor saturation anomalies. The navigation data anomalies include altitude jump anomalies and horizontal drift exceeding limits. A radiosonde data quality assessment model is constructed, which takes into account the spatiotemporal asynchronous error, sensor physical conflict, and navigation data anomaly of each sampling point of the radiosonde, and outputs the data quality index of the sampling point of the radiosonde.

[0006] Preferably, S2 includes: The atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde are collected. The atmospheric environmental characteristics include jet intensity, turbulence index and vertical temperature gradient. The historical radiosonde data includes the sensor failure altitude and the frequency of physical inconsistencies in the same area. Environmental adaptability correction coefficients are obtained based on the atmospheric environmental characteristics at the altitude of the radiosonde and historical radiosonde data.

[0007] Preferably, S3 includes: The flight attitude and motion data of the radiosonde are collected using an IMU (Inertial Measurement Unit). The flight attitude data includes pitch angle, roll angle, and yaw angle, and the motion data includes rate of climb, yaw amplitude, and rotational angular velocity. Collect the structural parameters of the radiosonde, which include sensor response delay, protective cover ventilation coefficient, and antenna phase center offset; The dynamic measurement error of each sampling point is obtained based on the flight attitude data, motion state data, and structural parameters of the radiosonde.

[0008] Preferably, S4 includes: The influence value of the quality index is obtained by multiplying the data quality index of the radiosonde by the influence weight of the quality index. The influence value of the environmental adaptability is obtained by multiplying the environmental adaptability correction coefficient by the influence weight of the environmental adaptability. The influence value of the dynamic error is obtained by multiplying the dynamic measurement error by the influence weight of the dynamic error. The credibility weight of each sampling point of the radiosonde in the multi-source data fusion is obtained by summing the influence value of the quality index, the influence value of the environmental adaptability, and the influence value of the dynamic error of each sampling point of the radiosonde.

[0009] Preferably, S5 includes: A credibility threshold is obtained. If the credibility weight of the radiosonde in the multi-source data fusion is greater than or equal to the credibility threshold, the radiosonde data is judged to meet the high-altitude sounding quality standard. If the credibility weight of the radiosonde in the multi-source data fusion is less than the credibility threshold, the radiosonde data is judged to not meet the high-altitude sounding quality standard.

[0010] In addition, the multi-source data quality control and processing system for radiosondes in the high-altitude environment of this invention includes the following modules: The module includes a data quality assessment module, an environmental adaptability analysis module, a dynamic error analysis module, a credibility weight prediction module, and a quality standard judgment module. The data quality assessment module is used to collect raw data from the radiosonde, obtain spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies based on the raw data, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies. The environmental adaptability analysis module is used to collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and to obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. The dynamic error analysis module is used to collect data on the flight attitude and motion state of the radiosonde, and to analyze the dynamic measurement error based on the data on the flight attitude and motion state of the radiosonde. The credibility weight prediction module is used to predict the credibility weight of the radiosonde during multi-source data fusion based on the radiosonde's data quality index, environmental adaptability correction coefficient, and dynamic measurement error. The quality standard judgment module is used to determine whether the radiosonde data meets the quality standards for high-altitude sounding based on the credibility weight of the radiosonde during multi-source data fusion.

[0011] A storage medium storing instructions, which, when read by a computer, cause the computer to execute the aforementioned multi-source data quality control processing method for high-altitude environments.

[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for multi-source data quality control processing of a radiosonde for high-altitude environments.

[0013] Compared with the prior art, the technical effects of the present invention are as follows: This invention collects raw data from a radiosonde, and based on this raw data, it identifies spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies. It then analyzes the radiosonde's data quality index based on these factors. The invention also collects atmospheric environmental characteristics and historical radiosonde data at the radiosonde's altitude, and obtains an environmental adaptability correction coefficient based on these data. Furthermore, it collects the radiosonde's flight attitude and motion state data, analyzes dynamic measurement errors based on this data, and predicts the radiosonde's reliability weight during multi-source data fusion based on the data quality index, environmental adaptability correction coefficient, and dynamic measurement error. Finally, it determines whether the radiosonde data meets the high-altitude sounding quality standards based on this reliability weight. This invention considers the special high-altitude environment and the dynamic characteristics of the radiosonde, predicting the reliability of multi-source data fusion under extreme conditions, thus improving the quality control accuracy of high-altitude sounding data. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the multi-source data quality control processing method for radiosondes in high-altitude environments according to the present invention. Figure 2 This is a schematic diagram of the structure of the multi-source data quality control and processing system for radiosondes designed for high-altitude environments according to the present invention. Detailed Implementation

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0018] Example 1: like Figure 1 As shown in the embodiment of the present invention, a multi-source data quality control processing method for radiosondes in high-altitude environments is as follows: Figure 1 As shown, the specific steps include the following: S1: Collect raw data from the radiosonde, obtain spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly based on the raw data from the radiosonde, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly; For example, in this embodiment, S1 includes: Distributed data acquisition nodes are deployed to continuously receive multi-source heterogeneous data streams uploaded by the radiosonde. Once the data stream transmission stability reaches a preset standard, a data integrity verification program is initiated to acquire the raw observation data from the radiosonde. All acquired data is stored in a dedicated spatiotemporal database, and noise suppression is performed using Kalman filtering and sliding window averaging algorithms. Simultaneously, precise clock synchronization and interpolation alignment techniques are used to eliminate time reference differences between multi-source data.

[0019] Based on the preprocessed radiosonde data, the system performs anomaly detection from three dimensions: temporal consistency, which detects the timestamp deviation and acquisition delay between BeiDou GPS positioning data and sensor-collected data; physical rationality, which identifies contradictions in the relationship between temperature, pressure and altitude and anomalies in water vapor saturation; and motion trajectory, which detects anomalous height jumps and excessive horizontal drift.

[0020] In specific implementation, the system presets multiple levels of abnormal thresholds: timestamp deviation threshold is ≥50ms, sensor acquisition delay threshold is ≥20ms, temperature and pressure high back-calculation contradiction threshold is the difference between navigation altitude and air pressure back-calculated altitude is ≥15m, water vapor saturation abnormal threshold is the relative humidity value exceeding the reasonable range of [0%, 100%], altitude jump abnormal threshold is the vertical speed ≥5m / s, horizontal drift abnormal threshold is the horizontal movement speed ≥50m / s; Using time series analysis, the BeiDou GPS positioning data is precisely aligned with the acquisition timestamps of temperature, humidity and pressure sensors to detect timestamp differences. Sampling points with timestamp differences exceeding a threshold are marked. The theoretical altitude is calculated based on temperature and air pressure using atmospheric static equilibrium equations and state equations, and compared with the navigation altitude. Altitude differences exceeding a threshold are recorded. The maximum water vapor content is calculated based on temperature using the saturated water vapor pressure formula, and compared with the actual water vapor content to detect physically unreliable saturation states. The altitude change rate between adjacent sampling points is detected using differential analysis methods, and altitude jumps exceeding a threshold are recorded. The trajectory smoothing algorithm is used to detect the rationality of the radiosonde's horizontal displacement and record abnormal drifts exceeding a threshold. A machine learning-based data quality assessment model for radiosondes was constructed, and a training sample library covering various operating conditions was built, including normal observation data and typical anomaly cases, with detailed annotations of the occurrence altitude, duration, and severity of various anomalies. Deep cleaning and feature engineering were performed on the sample data, including outlier handling, dimensional normalization, and Z-score standardization. The radiosonde data quality features are extracted from the preprocessed data, including: timestamp continuity and sampling interval stability for time series features; consistency of temperature-pressure-high relationship and rationality of water vapor saturation for physical features; and trajectory smoothness and velocity change frequency for kinematic features. An ensemble learning framework was adopted, using a random forest as the main model and gradient boosting decision trees for collaborative training. Model hyperparameters were optimized through grid search and cross-validation. Precision, recall, and F1 score were used as performance evaluation metrics on the test set. The radiosonde samples to be evaluated were input into the trained model to obtain the predicted data quality index. The model was designed to take into account the spatiotemporal asynchronous error, sensor physical conflicts, and navigation data anomalies at each radiosonde sampling point, and output the data quality index for each sampling point. By constructing an accurate and efficient radiosonde data quality evaluation model and inputting parameters such as timestamp deviation, acquisition delay, temperature and pressure inconsistencies, water vapor saturation anomalies, altitude jumps, and horizontal drift, the model outputs the radiosonde data quality index, achieving precise quantitative evaluation of radiosonde data quality and providing strong support for the reliability of upper-air sounding data.

[0021] S2: Collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. For example, in this embodiment, S2 includes: The atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde are collected. The atmospheric environmental characteristics include jet intensity, turbulence index, and vertical temperature gradient. Jet intensity affects the radiosonde's oscillation amplitude and spatiotemporal registration error, turbulence index affects the instantaneous fluctuation of sensor measurements, and vertical temperature gradient affects the applicability of temperature-pressure-elevation relationships. The historical radiosonde data includes the sensor failure altitude and physical inconsistency frequency in the same area. The historical radiosonde data is obtained through statistical analysis of the historical database. The sensor failure altitude indicates the reliability changes of different sensors under extreme environments, and the frequency of physical inconsistencies reflects the difficulty of data quality control at a specific altitude. Optionally, atmospheric environmental characteristics and historical radiosonde data jointly affect the quality of radiosonde data. The key factors affecting quality control at critical altitudes are shown in the table below: Height layer Major data anomalies Key influencing factors Tropopause (10-15 km) Temperature inversion and wind shear error Jet intensity, temperature gradient, frequency of historical conflicts Lower stratosphere (20-25 km) Ozone heating anomaly, sensor response delay Temperature vertical gradient, turbulence index, sensor failure height Stratosphere top (30-35km) High temperature and pressure discrepancies, misalignment of spatiotemporal registration jet intensity, frequency of historical conflicts, temperature gradient Lower part of the intermediate layer (50-60km) Sensors fail completely, navigation data drift The combined impact of all environmental factors Based on the atmospheric environmental characteristics at the altitude of the radiosonde and the environmental adaptability correction coefficient for historical radiosonde data acquisition, analyzing the combined impact of atmospheric environment and historical data on radiosonde data can more accurately predict the difficulty and reliability of data quality control at different altitudes.

[0022] For example, in this embodiment, the difficulty of stratospheric data quality control can be obtained through a formula for calculating the stratospheric quality control difficulty coefficient. The formula for calculating the stratospheric quality control difficulty coefficient is as follows: ; in, For the intensity of the rapid flow, For the standard jet flow intensity, this embodiment uses a value of 60 m / s. For the vertical temperature gradient, For the standard temperature vertical gradient, this embodiment uses a value of 0.2K / 100m. For the frequency of historical physical contradictions To determine the standard frequency of contradictions, this embodiment uses a value of 5 times per 100m. The turbulence index, The standard turbulence index is 0.15 in this embodiment. The weighting of the impact of jet flow intensity on the difficulty of quality control. The weighting of the temperature gradient on the difficulty of quality control. The weighting of the turbulence index on the difficulty of quality control. ; For example, in this embodiment, a strategy for obtaining the environmental adaptability correction coefficient is also provided, as follows: ; in, The difficulty coefficient for quality control at the stratosphere. The environmental adaptability coefficient of the radiosonde is 0.85±0.05 for a new generation digital radiosonde, 0.65±0.08 for a traditional analog radiosonde, and 0.92±0.03 for a special scientific research radiosonde.

[0023] S3: Collect data on the flight attitude and motion status of the radiosonde, and analyze dynamic measurement errors based on the data on the flight attitude and motion status of the radiosonde; In this embodiment, S3 includes: The flight attitude and motion state data of the radiosonde are collected using an IMU (Inertial Measurement Unit). The flight attitude data includes pitch angle, roll angle, and yaw angle, and the motion state data includes rate of ascent, oscillation amplitude, and rotational angular velocity. The state changes of the radiosonde during basic motions such as ascent, oscillation, and rotation are collected, and the spatial displacement of the radiosonde during these basic motions is obtained, such as the offset of the sensor position during oscillation. The IMU data is fused with BeiDou and GPS data to establish a three-dimensional motion trajectory model with an accuracy of 0.1m, and motion-sensitive periods such as violent oscillation, rapid rotation, and crossing rapids are marked. The structural parameters of the radiosonde are collected. These parameters include sensor response delay, protective cover ventilation coefficient, and antenna phase center offset. Sensor response delay can be obtained through laboratory calibration. Antenna phase center offset can be obtained by mounting the antenna on a turntable and measuring the phase center change under different attitudes. By changing the pitch and roll angles of the radiosonde, the phase center offset data at different angles is recorded and an angle-offset curve is plotted. The protective cover ventilation coefficient can be determined by testing the relationship between wind speed and the pressure difference inside and outside the protective cover in a wind tunnel experiment. Based on the flight attitude data, motion state data, and structural parameters of the radiosonde, the dynamic measurement error of each sampling point is obtained. By accurately analyzing the measurement errors of each sensor under different motion states of the radiosonde, the degree of influence of the radiosonde's motion on the data quality can be determined, thereby quickly locating potential dynamic errors, identifying weak links in the quality control of the radiosonde's motion, and facilitating the prediction of potential data distortion.

[0024] For example, in this embodiment, the dynamic error of the temperature sensor is obtained through the temperature sensor dynamic error calculation formula, which is: ; in, The response time constant of the temperature sensor. For the rate of temperature change, The dynamic heating coefficient of the protective cover. For the rate of increase, For swing angle; For example, in this embodiment, the dynamic error of the barometric pressure sensor is obtained through the barometric pressure sensor dynamic error calculation formula, which is: ; in, air density, The ventilation coefficient of the protective cover; For example, in this embodiment, the dynamic error of wind vector calculation is obtained through the dynamic error calculation formula for wind vector calculation. The dynamic error calculation formula for wind vector calculation is as follows: ; in, and The horizontal displacement rate, This is the antenna phase center offset angle.

[0025] S4: Based on the data quality index, environmental adaptability correction coefficient and dynamic measurement error of the radiosonde, the credibility weight of the radiosonde in multi-source data fusion is predicted; For example, in this embodiment, S4 includes: obtaining the quality index influence value by multiplying the data quality index of the radiosonde by the quality index influence weight; obtaining the environmental adaptability influence value by multiplying the environmental adaptability correction coefficient by the environmental adaptability influence weight; obtaining the dynamic error influence value by multiplying the dynamic measurement error by the dynamic error influence weight; and obtaining the credibility weight of each sampling point of the radiosonde in multi-source data fusion based on the sum of the quality index influence value, environmental adaptability influence value, and dynamic error influence value of each sampling point of the radiosonde.

[0026] It should also be noted that the specific method for determining the influence weights and credibility thresholds in this embodiment is as follows: First, a complete dataset from multiple historical radiosonde experiments is systematically collected, including raw observation data collected by the radiosonde, atmospheric environmental parameters at the corresponding altitude (such as jet stream intensity, turbulence index, etc.), the radiosonde's own flight attitude records (such as pitch angle, oscillation amplitude, etc.), and the radiosonde's structural characteristic parameters (such as sensor response delay, protective cover ventilation coefficient, etc.). After completing the complete data quality control process provided in this embodiment, these data are further analyzed in depth to evaluate the actual credibility level exhibited by each sampling point in a real application scenario. Subsequently, all the above data are input into the entire processing chain constructed in this embodiment, and the credibility weight estimate of each sampling point of the radiosonde in the multi-source data fusion stage is obtained through calculations at each step. Finally, the credibility data obtained based on actual observations and the credibility weight estimate calculated in this embodiment are jointly imported into the MATLAB computing platform, and its built-in data fitting and optimization tools are used for iterative comparison and parameter optimization to select the optimal set of weight coefficients and threshold parameters that enable the overall judgment accuracy to reach its peak.

[0027] S5: Based on the credibility weight of the radiosonde during multi-source data fusion, determine and filter radiosonde data that meets the quality standards for high-altitude sounding.

[0028] For example, in this embodiment, S5 includes: obtaining a confidence threshold; if the confidence weight of the radiosonde in multi-source data fusion is greater than or equal to the confidence threshold, then the radiosonde data is determined to meet the high-altitude detection quality standard; if the confidence weight of the radiosonde in multi-source data fusion is less than the confidence threshold, then the radiosonde data is determined to not meet the high-altitude detection quality standard, and re-acquisition processing is performed.

[0029] The implementation plan improves the quality control accuracy of high-altitude sounding data by considering the special environment of high altitude and the dynamic characteristics of the radiosonde and predicting the fusion reliability of multi-source data under extreme conditions.

[0030] Example 2: like Figure 2 As shown in the figure, the multi-source data quality control and processing system for radiosondes in high-altitude environments according to an embodiment of the present invention, such as Figure 2 As shown, it includes the following modules: The module includes a data quality assessment module, an environmental adaptability analysis module, a dynamic error analysis module, a credibility weight prediction module, and a quality standard judgment module. The data quality assessment module is used to collect raw data from the radiosonde, obtain spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies based on the raw data, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies. The environmental adaptability analysis module is used to collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and to obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. The dynamic error analysis module is used to collect data on the flight attitude and motion state of the radiosonde, and to analyze the dynamic measurement error based on the data on the flight attitude and motion state of the radiosonde. The credibility weight prediction module is used to predict the credibility weight of the radiosonde during multi-source data fusion based on the radiosonde's data quality index, environmental adaptability correction coefficient, and dynamic measurement error. The quality standard judgment module is used to determine whether the radiosonde data meets the quality standards for high-altitude sounding based on the credibility weight of the radiosonde during multi-source data fusion.

[0031] Example 3: This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the aforementioned multi-source data quality control processing method for high-altitude environments by calling computer programs stored in memory.

[0032] The electronic device can vary considerably depending on its configuration and performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the multi-source data quality control processing method for high-altitude environments provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0033] Example 4: This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When the computer program runs on the computer device, it causes the computer device to perform the above-mentioned multi-source data quality control processing method for radiosondes in high-altitude environments.

[0034] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0035] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0036] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0037] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0038] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0039] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0040] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0041] The units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0042] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0043] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for quality control processing of multi-source data from radiosondes in high-altitude environments, characterized in that, The method includes: S1: Collect raw data from the radiosonde, obtain spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly based on the raw data from the radiosonde, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous error, sensor physical conflict and navigation data anomaly; S2: Collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. S3: Collect data on the flight attitude and motion status of the radiosonde, and analyze dynamic measurement errors based on the data on the flight attitude and motion status of the radiosonde; S4: Based on the data quality index, environmental adaptability correction coefficient and dynamic measurement error of the radiosonde, the credibility weight of the radiosonde in multi-source data fusion is predicted; S5: Based on the credibility weight of the radiosonde during multi-source data fusion, determine and filter radiosonde data that meets the quality standards for high-altitude sounding.

2. The method for quality control processing of multi-source data from a radiosonde for high-altitude environments according to claim 1, characterized in that, S1 includes: Collect raw data from the radiosonde to be processed; Based on the raw data of the radiosonde to be processed, spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies are obtained. The spatiotemporal asynchronous errors include BeiDou-GPS timestamp deviation and sensor acquisition delay. The sensor physical conflicts include temperature and pressure high-inverse calculation contradictions and water vapor saturation anomalies. The navigation data anomalies include altitude jump anomalies and horizontal drift exceeding limits. A radiosonde data quality assessment model is constructed, which takes into account the spatiotemporal asynchronous error, sensor physical conflict, and navigation data anomaly of each sampling point of the radiosonde, and outputs the data quality index of the sampling point of the radiosonde.

3. The method for quality control processing of multi-source data from a radiosonde for high-altitude environments according to claim 2, characterized in that, S2 include: The atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde are collected. The atmospheric environmental characteristics include jet intensity, turbulence index and vertical temperature gradient. The historical radiosonde data includes the sensor failure altitude and the frequency of physical inconsistencies in the same area. Environmental adaptability correction coefficients are obtained based on the atmospheric environmental characteristics at the altitude of the radiosonde and historical radiosonde data.

4. The method for quality control processing of multi-source data from a radiosonde for high-altitude environments according to claim 3, characterized in that, S3 include: The flight attitude and motion data of the radiosonde are collected using an IMU (Inertial Measurement Unit). The flight attitude data includes pitch angle, roll angle, and yaw angle, and the motion data includes rate of climb, yaw amplitude, and rotational angular velocity. Collect the structural parameters of the radiosonde, which include sensor response delay, protective cover ventilation coefficient, and antenna phase center offset; The dynamic measurement error of each sampling point is obtained based on the flight attitude data, motion state data, and structural parameters of the radiosonde.

5. The method for quality control processing of multi-source data from a radiosonde for high-altitude environments according to claim 4, characterized in that, S4 include: The influence value of the quality index is obtained by multiplying the data quality index of the radiosonde by the influence weight of the quality index. The influence value of the environmental adaptability is obtained by multiplying the environmental adaptability correction coefficient by the influence weight of the environmental adaptability. The influence value of the dynamic error is obtained by multiplying the dynamic measurement error by the influence weight of the dynamic error. The credibility weight of each sampling point of the radiosonde in the multi-source data fusion is obtained by summing the influence value of the quality index, the influence value of the environmental adaptability, and the influence value of the dynamic error of each sampling point of the radiosonde.

6. The method for quality control processing of multi-source data from a radiosonde for high-altitude environments according to claim 5, characterized in that, S5 include: A credibility threshold is obtained. If the credibility weight of the radiosonde in the multi-source data fusion is greater than or equal to the credibility threshold, the radiosonde data is judged to meet the high-altitude sounding quality standard. If the credibility weight of the radiosonde in the multi-source data fusion is less than the credibility threshold, the radiosonde data is judged to not meet the high-altitude sounding quality standard.

7. A radiosonde multi-source data quality control processing system for high-altitude environments, used to implement the radiosonde multi-source data quality control processing method for high-altitude environments as described in any one of claims 1-6, characterized in that, The system includes the following modules: The module includes a data quality assessment module, an environmental adaptability analysis module, a dynamic error analysis module, a credibility weight prediction module, and a quality standard judgment module. The data quality assessment module is used to collect raw data from the radiosonde, obtain spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies based on the raw data, and analyze the data quality index of the radiosonde based on spatiotemporal asynchronous errors, sensor physical conflicts, and navigation data anomalies. The environmental adaptability analysis module is used to collect atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde, and to obtain environmental adaptability correction coefficients based on the atmospheric environmental characteristics and historical radiosonde data at the altitude of the radiosonde. The dynamic error analysis module is used to collect data on the flight attitude and motion state of the radiosonde, and to analyze the dynamic measurement error based on the data on the flight attitude and motion state of the radiosonde. The credibility weight prediction module is used to predict the credibility weight of the radiosonde during multi-source data fusion based on the radiosonde's data quality index, environmental adaptability correction coefficient, and dynamic measurement error. The quality standard judgment module is used to determine whether the radiosonde data meets the quality standards for high-altitude sounding based on the credibility weight of the radiosonde during multi-source data fusion.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-source data quality control processing method for radiosondes in high-altitude environments as described in any one of claims 1-6.

9. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the radiosonde multi-source data quality control processing method for high-altitude environments as described in any one of claims 1-6.

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