Balloon-borne environmental exploration multi-source data quality control processing method and system

Abstract

The present application relates to the technical field of data processing, and is a sounding instrument multi-source data quality control processing method and system for high-altitude environment, specifically comprising: obtaining space-time asynchronous error, sensor physical conflict and navigation data anomaly based on sounding instrument original data; analyzing the data quality index of the sounding instrument based on space-time asynchronous error, sensor physical conflict and navigation data anomaly; obtaining environmental adaptability correction coefficient based on the atmospheric environment characteristics of the height layer where the sounding instrument is located and historical sounding data; analyzing dynamic measurement error based on the flight attitude and motion state data of the sounding instrument; predicting the reliability weight of the sounding instrument in multi-source data fusion based on the data quality index, environmental adaptability correction coefficient and dynamic measurement error of the sounding instrument, and judging whether the sounding instrument data meets the high-altitude detection quality standard, the present application considers the high-altitude special environment and the dynamic characteristics of the sounding instrument, predicts the fusion reliability of multi-source data under extreme conditions, and improves the quality control precision of high-altitude detection data.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of data processing, and is a sounding instrument multi-source data quality control processing method and system for high-altitude environments. BACKGROUND

[0002] The sounding instrument performs atmospheric detection in high-altitude environments such as the troposphere and the stratosphere, and the multi-source data such as temperature, humidity, air pressure and wind field collected by the sounding instrument is directly related to the accuracy of weather forecasting and climate research. The traditional data quality control relies on fixed threshold inspection, and has the problems of poor adaptability and high misjudgment rate. However, the addition of multi-source data fusion technologies such as space-time registration and physical consistency inspection can significantly improve the accuracy and reliability of data quality control, reduce data distortion in extreme environments, and thus guarantee the quality of high-altitude detection data. However, the existing technology for sounding instrument data quality control is mainly to use a single threshold to judge and automatically detect the out-of-limit abnormality of data such as temperature range and humidity range, and lacks the combination of sounding instrument data quality index, environmental adaptability correction coefficient and dynamic measurement error to jointly evaluate the reliability weight of the sounding instrument in multi-source data fusion, resulting in that the sounding instrument data quality does not match the actual situation in extreme environments, thereby affecting the accuracy of meteorological analysis. In summary, how to improve the precision of sounding instrument multi-source data quality control is a problem to be solved by those skilled in the art. SUMMARY

[0003] The application considers the high-altitude special environment and the dynamic characteristics of the sounding instrument, predicts the fusion reliability of multi-source data under extreme conditions, and proposes a sounding instrument multi-source data quality control processing method and system for high-altitude environments in order to improve the quality control precision of high-altitude detection data.

[0004] In order to achieve the above purpose, the technical scheme of the sounding instrument multi-source data quality control processing method for high-altitude environments of the application comprises the following steps:

[0005] S1: collecting original data of the sounding instrument, obtaining time-space asynchronous error, sensor physical conflict and navigation data anomaly based on the original data of the sounding instrument, and analyzing the data quality index of the sounding instrument based on the time-space asynchronous error, the sensor physical conflict and the navigation data anomaly;

[0006] S2: collecting atmospheric environmental characteristics and historical sounding data of the height layer where the sounding instrument is located, and obtaining an environmental adaptability correction coefficient based on the atmospheric environmental characteristics and the historical sounding data of the height layer where the sounding instrument is located;

[0007] S3: collecting flight attitude and motion state data of the sounding instrument, and analyzing dynamic measurement error based on the flight attitude and motion state data of the sounding instrument;

[0008] S4: Based on the data quality index of the radiosonde, the environmental adaptability correction coefficient and the dynamic measurement error, the reliability weight of the radiosonde in multi-source data fusion is predicted;

[0009] S5: Based on the reliability weight of the radiosonde in multi-source data fusion, the radiosonde data meeting the high-altitude detection quality standard is judged and screened.

[0010] Preferably, S1 comprises:

[0011] Collecting the original data of the radiosonde to be processed;

[0012] Based on the original data of the radiosonde to be processed, the space-time asynchronous error, the sensor physical conflict and the navigation data anomaly are obtained, the space-time asynchronous error includes Beidou GPS timestamp deviation and sensor acquisition delay, the sensor physical conflict includes temperature pressure high contradiction and water vapor saturation anomaly, and the navigation data anomaly includes height jump anomaly and horizontal drift overrun;

[0013] A radiosonde data quality evaluation model is constructed, the space-time asynchronous error, the sensor physical conflict and the navigation data anomaly of each sampling point of the radiosonde are input, and the data quality index of the sampling point of the radiosonde is output.

[0014] Preferably, S2 comprises:

[0015] Collecting the atmospheric environment characteristics and historical radiosonde data of the height layer where the radiosonde is located, the atmospheric environment characteristics include jet intensity, turbulence index and temperature vertical gradient, and the historical radiosonde data include sensor failure height and physical contradiction frequency of historical detection in the same region;

[0016] Based on the atmospheric environment characteristics and historical radiosonde data of the height layer where the radiosonde is located, the environmental adaptability correction coefficient is obtained.

[0017] Preferably, S3 comprises:

[0018] The flight attitude and motion state data of the radiosonde are collected using the IMU inertial measurement unit, the flight attitude data includes pitch angle, roll angle and yaw angle, and the motion state data includes ascending rate, swing amplitude and rotation angular velocity;

[0019] Collecting the structure parameters of the radiosonde, the structure parameters of the radiosonde include sensor response delay, protective cover air permeability coefficient and antenna phase center offset;

[0020] Based on the flight attitude data, motion state data and structure parameters of the radiosonde, the dynamic measurement error of each sampling point is obtained.

[0021] Preferably, S4 comprises:

[0022] The quality index influence value is obtained by multiplying the data quality index of the sonde by a quality index influence weight, the environmental adaptability influence value is obtained by multiplying the environmental adaptability correction coefficient by an environmental adaptability influence weight, the dynamic error influence value is obtained by multiplying the dynamic measurement error by a dynamic error influence weight, and the reliability weight of the sonde at the time of multi-source data fusion is obtained by summing the quality index influence value, the environmental adaptability influence value and the dynamic error influence value of each sampling point of the sonde.

[0023] Preferably, S5 comprises:

[0024] A reliability threshold is obtained, if the reliability weight of the sonde at the time of multi-source data fusion is greater than or equal to the reliability threshold, it is judged that the sonde data meets the high-altitude detection quality standard, and if the reliability weight of the sonde at the time of multi-source data fusion is less than the reliability threshold, it is judged that the sonde data does not meet the high-altitude detection quality standard.

[0025] In addition, the sonde multi-source data quality control processing system for high-altitude environment comprises the following modules:

[0026] The data quality evaluation module, the environmental adaptability analysis module, the dynamic error analysis module, the reliability weight prediction module and the quality standard judgment module;

[0027] The data quality evaluation module is used to collect the original data of the sonde, obtain the spatio-temporal asynchronous error, the sensor physical conflict and the navigation data anomaly based on the original data of the sonde, and analyze the data quality index of the sonde based on the spatio-temporal asynchronous error, the sensor physical conflict and the navigation data anomaly;

[0028] The environmental adaptability analysis module is used to collect the atmospheric environment characteristics and the historical sounding data of the height layer where the sonde is located, and obtain the environmental adaptability correction coefficient based on the atmospheric environment characteristics and the historical sounding data of the height layer where the sonde is located;

[0029] The dynamic error analysis module is used to collect the flight attitude and motion state data of the sonde, and analyze the dynamic measurement error based on the flight attitude and motion state data of the sonde;

[0030] The reliability weight prediction module is used to predict the reliability weight of the sonde at the time of multi-source data fusion based on the data quality index of the sonde, the environmental adaptability correction coefficient and the dynamic measurement error;

[0031] The quality standard judgment module is used to judge whether the sonde data meets the high-altitude detection quality standard based on the reliability weight of the sonde at the time of multi-source data fusion.

[0032] A storage medium, wherein instructions are stored in the storage medium, when a computer reads the instructions, the computer executes the sonde multi-source data quality control processing method for high-altitude environment.

[0033] 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.

[0034] Compared with the prior art, the technical effects of the present invention are as follows:

[0035] 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

[0036] 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:

[0037] 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.

[0038] 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

[0039] 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.

[0040] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details set forth in this description. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present application.

[0041] It should also be noted that, as used in the specification and in the claims, the article "a", "an", or "the" is intended to mean that there are one or more of the features or elements. As used in this specification and the claims, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from the context, the designation "X employs A or B" means that X employs A or B or both A and B. In addition, the articles "a", "an", and "the" are intended to mean that there are one or more (for example, one) of the features or elements, unless otherwise indicated or unless it would be clear from the context.

[0042] Embodiment One:

[0043] As shown in Figure 1 , the sounding instrument multi-source data quality control processing method for high-altitude environment of the embodiment of the present application, as shown in Figure 1 , includes the following specific steps:

[0044] S1: Collecting sounding instrument original data, obtaining space-time asynchronous error, sensor physical conflict and navigation data anomaly based on the sounding instrument original data, and analyzing the data quality index of the sounding instrument based on the space-time asynchronous error, sensor physical conflict and navigation data anomaly;

[0045] Exemplarily, in the present embodiment, S1 includes:

[0046] Deploy distributed data collection nodes to continuously receive multi-source heterogeneous data streams uploaded by the sounding instrument. When the data stream transmission stability reaches the preset standard, start the data integrity verification program to collect the original observation data of the sounding instrument to be processed. All collected data will be stored in a special space-time database, and the Kalman filter and sliding window average algorithm are used for noise suppression, and the time reference difference between multi-source data is eliminated through precise clock synchronization and interpolation alignment technology.

[0047] Based on the preprocessed sounding instrument data, the system performs anomaly detection from three dimensions, including: time sequence consistency dimension, that is, detecting the timestamp deviation and collection delay between Beidou GPS positioning data and sensor collected data; physical rationality dimension, that is, identifying temperature-pressure height relationship contradiction and water vapor saturation anomaly; motion trajectory dimension, that is, finding height jump anomaly and horizontal drift overrun phenomenon.

[0048] In specific implementation, the system presets multiple levels of abnormal threshold values: the timestamp deviation threshold value is ≥ 50 ms, the sensor collection delay threshold value is ≥ 20 ms, the temperature-pressure-high contradiction threshold value is the difference between the navigation height and the pressure-reversed height is ≥ 15 m, the water vapor saturation abnormal threshold value is that the relative humidity value exceeds the reasonable range of [0%, 100%], the height jump abnormal threshold value is the vertical speed is ≥ 5 m / s, and the horizontal drift abnormal threshold value is the horizontal motion rate is ≥ 50 m / s;

[0049] Using the time series analysis method, the Beidou GPS positioning data is accurately aligned with the collection timestamp of the temperature, humidity and pressure sensor, the timestamp difference is detected, the sampling points with timestamp difference exceeding the threshold value are marked, the atmospheric static equilibrium equation and the state equation are used, the theoretical height is calculated based on the temperature and the pressure, the navigation height is compared, the height difference exceeding the threshold value is recorded, the saturated water vapor pressure formula is used, the maximum water vapor content is calculated based on the temperature, the actual water vapor content is compared, the physically unreliable saturated state is detected, the difference analysis method is used, the height change rate of adjacent sampling points is detected, the height jump exceeding the threshold value is recorded, the trajectory smoothing algorithm is used, the rationality of the horizontal displacement of the sonde is detected, and the abnormal drift exceeding the threshold value is recorded.

[0050] A sonde data quality evaluation model based on machine learning is constructed, a training sample library covering multiple working conditions is constructed, including normal observation data and typical abnormal cases, and the occurrence height, duration and severity of each type of abnormality are marked in detail. The sample data is deeply cleaned and feature engineered, including abnormal value processing, dimension normalization and Z-score standardization.

[0051] Sonde data quality features are extracted from the preprocessed data, including: timestamp continuity and sampling interval stability for representing time series features; temperature-pressure-high relationship consistency and water vapor saturation rationality for representing physical features; trajectory smoothness and speed mutation frequency for representing kinematic features.

[0052] An integrated learning framework is adopted, with random forest as the main model and gradient boosting decision tree as the auxiliary model for collaborative training. The model hyperparameters are optimized through grid search and cross-validation. Finally, the accuracy, recall rate and F1 score are used as the model performance evaluation indicators on the test set. The sonde samples to be evaluated are input into the trained model to obtain the prediction results of the data quality index. The time-space asynchronous error of each sampling point of the sonde, the sensor physical conflict and the navigation data abnormality are input, and the data quality index of the sonde sampling point is output. By constructing an accurate and efficient sonde data quality evaluation model and inputting the timestamp deviation, collection delay, temperature-pressure-high contradiction, water vapor saturation abnormality, height jump and horizontal drift parameters, the data quality index of the sonde is output, realizing the accurate quantitative evaluation of the sonde data quality and providing a strong guarantee for the reliability of the high-altitude detection data.

[0053] S2: Collect the atmospheric environment characteristics of the height layer where the radiosonde is located and the historical radiosonde data, and obtain the environmental adaptability correction coefficient based on the atmospheric environment characteristics of the height layer where the radiosonde is located and the historical radiosonde data;

[0054] Exemplarily, in the embodiment, S2 includes:

[0055] Collect the atmospheric environment characteristics of the height layer where the radiosonde is located and the historical radiosonde data, the atmospheric environment characteristics including jet intensity, turbulence index and temperature vertical gradient, the jet intensity affecting the swing amplitude of the radiosonde and the space-time registration error, the turbulence index affecting the instantaneous fluctuation of the sensor measurement, and the temperature vertical gradient affecting the applicability of the temperature-pressure height relationship, the historical radiosonde data including the sensor failure height and the physical contradiction frequency of the historical detection in the same region, the historical radiosonde data being obtained through statistical analysis of the historical database, the sensor failure height indicating the reliability change of different sensors in extreme environment, and the physical contradiction frequency reflecting the difficulty of data quality control of a specific height layer;

[0056] Optionally, the atmospheric environment characteristics and the historical radiosonde data jointly affect the quality of the radiosonde data, and the key influencing factors of the quality control of the key height layer are shown in the following table:

[0057] High level Main data anomaly Key impact factor Tropopause (10-15 km) Temperature inversion, wind shear error Jet intensity, temperature gradient, historical contradiction frequency Lower stratosphere (20-25 km) Ozone heating anomaly, sensor response delay Temperature vertical gradient, turbulence index, sensor failure height Stratosphere top (30-35 km) High temperature and pressure contradiction, time and space registration error Jet intensity, historical contradiction frequency, temperature gradient Lower mesosphere (50-60 km) Sensor failure, navigation data drift Synthesis of all environmental factors

[0058] Based on the atmospheric environment characteristics of the height layer where the radiosonde is located and the historical radiosonde data, the common influence of the atmospheric environment and the historical data on the radiosonde data can be more accurately predicted to predict the difficulty and reliability of the data quality control in different height layers.

[0059] Exemplarily, in the embodiment, the difficulty of the stratosphere top data quality control can be obtained by a stratosphere top quality control difficulty coefficient calculation formula, and the stratosphere top quality control difficulty coefficient calculation formula is:

[0060] ;

[0061] Wherein, is the jet intensity, is the standard jet intensity, and the value of the embodiment is 60 m / s, is the temperature vertical gradient, is the standard temperature vertical gradient, and the value of the embodiment is 0.2 K / 100 m, is the historical physical contradiction frequency, is the standard contradiction frequency, and the value of the embodiment is 5 times / 100 m, is the turbulence index, is the standard turbulence index, and the value of the embodiment is 0.15, is the influence weight of the jet intensity on the quality control difficulty, a weight of the temperature gradient on the quality control difficulty, a weight of the turbulence index on the quality control difficulty,

[0062] Exemplarily, in the embodiment, an acquisition strategy of the environmental adaptation correction coefficient is also provided, and specifically as follows:

[0063]

[0064] wherein, a stratosphere top quality control difficulty coefficient, an environmental adaptation coefficient of the radiosonde, exemplarily, when the radiosonde is a new generation digital radiosonde, the environmental adaptation coefficient is 0.85±0.05, when the radiosonde is a traditional analog radiosonde, the environmental adaptation coefficient is 0.65±0.08, and when the radiosonde is a special scientific research radiosonde, the environmental adaptation coefficient is 0.92±0.03.

[0065] S3: collecting flight attitude and motion state data of the radiosonde, and analyzing dynamic measurement errors based on the flight attitude and motion state data of the radiosonde;

[0066] In the embodiment, S3 includes:

[0067] The flight attitude and motion state data of the radiosonde are collected by using an IMU (Inertial Measurement Unit), the flight attitude data includes a pitch angle, a roll angle and a yaw angle, and the motion state data includes an ascending rate, a swing amplitude and a rotation angular velocity, the state changes of the radiosonde in completing basic motions such as ascending, swinging and rotating are collected, the spatial displacement amount of the radiosonde in completing the basic motions is acquired, for example, the offset amount of the sensor position in swinging, the IMU data is fused with Beidou GPS data, a 0.1 m precision three-dimensional motion trajectory model is established, and motion sensitive periods such as violent swinging, rapid rotation and jet crossing are marked;

[0068] Radiosonde structure parameters are collected, the radiosonde structure parameters include sensor response delay, protective cover wind permeability coefficient and antenna phase center offset, the sensor response delay can be acquired by laboratory calibration, the antenna phase center offset can be measured by installing the antenna on a turntable, the phase center changes under different attitudes are measured, the phase center offset data under different angles are recorded by changing the pitch and roll angles of the radiosonde, an angle-offset curve is drawn to acquire, and the protective cover wind permeability coefficient can be determined by using a wind tunnel experiment to test the relationship between the wind speed and the pressure difference inside and outside the protective cover.

[0069] ​​The dynamic measurement error of each sampling point is obtained based on the flight attitude data, motion state data and structure parameters of the sonde, the measurement error of each sensor of the sonde under different motion states is accurately analyzed, the influence degree of the motion of the sonde on the data quality is judged, the possible dynamic error is quickly located, the weak link of quality control of the motion of the sonde is identified, and potential data distortion is predicted.

[0070] For example, in the embodiment, the temperature sensor dynamic error is obtained through a temperature sensor dynamic error calculation formula, and the temperature sensor dynamic error calculation formula is as follows:

[0071] ;

[0072] wherein, is a temperature sensor response time constant, is a temperature change rate, is a protective cover dynamic heating coefficient, is an ascending rate, is a swing angle;

[0073] For example, in the embodiment, the air pressure sensor dynamic error is obtained through an air pressure sensor dynamic error calculation formula, and the air pressure sensor dynamic error calculation formula is as follows:

[0074] ;

[0075] wherein, is air density, is a protective cover air permeability coefficient;

[0076] For example, in the embodiment, the wind vector calculation dynamic error is obtained through a wind vector calculation dynamic error calculation formula, and the wind vector calculation dynamic error calculation formula is as follows:

[0077] ;

[0078] wherein, and is a horizontal displacement rate, is an antenna phase center offset angle.

[0079] S4: predicting the reliability weight of the sonde in multi-source data fusion based on the data quality index of the sonde, the environmental adaptability correction coefficient and the dynamic measurement error;

[0080] Exemplarily, in the embodiment, S4 comprises: obtaining a quality index influence value based on the quality index of the radiosonde multiplied by a quality index influence weight, obtaining an environmental adaptability influence value based on the environmental adaptability correction coefficient multiplied by an environmental adaptability influence weight, obtaining a dynamic error influence value based on the dynamic measurement error multiplied by a dynamic error influence weight, and obtaining the credibility weight of each sampling point of the radiosonde in the multi-source data fusion based on the sum of the quality index influence value, the environmental adaptability influence value and the dynamic error influence value of each sampling point of the radiosonde.

[0081] It should be further noted that, in the embodiment, the specific method for determining the influence weights and the credibility threshold is as follows: first, complete data sets from multiple historical radiosonde experiments are systematically collected, including original observation data collected by the radiosonde, atmospheric environmental parameters (such as jet intensity, turbulence index, etc.) corresponding to the height layer, flight attitude records (such as pitch angle, swing amplitude, etc.) of the radiosonde itself, and structural characteristic parameters (such as sensor response delay, protective cover air permeability coefficient, etc.) of the radiosonde. After completing the complete data quality control process provided by the embodiment, further in-depth analysis is performed on these data 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 by the embodiment, and the credibility weight estimation value of each sampling point of the radiosonde in the multi-source data fusion stage is obtained through calculation of each step. Finally, the credibility data obtained based on actual observation and the credibility weight estimation value calculated by the embodiment are jointly imported into the MATLAB calculation platform, and the built-in data fitting and optimization tools are used for iterative comparison and parameter optimization, so as to screen out the optimal weight coefficients and threshold parameters that can make the overall judgment accuracy reach a peak.

[0082] S5: judging and screening the radiosonde data meeting the high-altitude detection quality standard based on the credibility weight of the radiosonde in the multi-source data fusion.

[0083] Exemplarily, in the embodiment, S5 comprises: obtaining the credibility threshold, if the credibility weight of the radiosonde in the multi-source data fusion is greater than or equal to the credibility threshold, it is judged that the radiosonde data meets the high-altitude detection quality standard, if the credibility weight of the radiosonde in the multi-source data fusion is less than the credibility threshold, it is judged that the radiosonde data does not meet the high-altitude detection quality standard, and re-collection processing is performed.

[0084] The embodiment improves the quality control precision of the high-altitude detection data by considering the special high-altitude environment and the dynamic characteristics of the radiosonde, predicting the fusion credibility of the multi-source data under extreme conditions.

[0085] Embodiment two:

[0086] As Figure 2As shown, the high-altitude environment-oriented sonde multi-source data quality control processing system of the embodiment of the application comprises the following modules: Figure 2 As shown, the high-altitude environment-oriented sonde multi-source data quality control processing system of the embodiment of the application comprises the following modules:

[0087] The data quality evaluation module, the environmental adaptability analysis module, the dynamic error analysis module, the credibility weight prediction module and the quality standard judgment module;

[0088] The data quality evaluation module is configured to collect sonde raw data, obtain time-space asynchronous errors, sensor physical conflicts and navigation data abnormalities based on the sonde raw data, and analyze a data quality index of the sonde based on the time-space asynchronous errors, the sensor physical conflicts and the navigation data abnormalities.

[0089] The environmental adaptability analysis module is configured to collect atmospheric environment characteristics and historical sounding data of a height layer where the sonde is located, and obtain an environmental adaptability correction coefficient based on the atmospheric environment characteristics and the historical sounding data of the height layer where the sonde is located.

[0090] The dynamic error analysis module is configured to collect flight attitude and motion state data of the sonde, and analyze dynamic measurement errors based on the flight attitude and the motion state data of the sonde.

[0091] The credibility weight prediction module is configured to predict a credibility weight of the sonde in multi-source data fusion based on the data quality index, the environmental adaptability correction coefficient and the dynamic measurement errors of the sonde.

[0092] The quality standard judgment module is configured to judge whether the data of the sonde meets a high-altitude sounding quality standard based on the credibility weight of the sonde in multi-source data fusion.

[0093] Embodiment three

[0094] The embodiment provides an electronic device, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0095] The processor executes the above-mentioned high-altitude environment-oriented sonde multi-source data quality control processing method by calling the computer program stored in the memory.

[0096] The electronic device can have a large difference due to different configurations or performances, and can include one or more processors (Central Processing Units, CPUs) and one or more memories, wherein the memory stores at least one computer program, the computer program is loaded and executed by the processor to implement the sounding instrument multi-source data quality control processing method for high-altitude environment provided by the above method embodiment. The electronic device can also include other components for implementing device functions, for example, the electronic device can also have a wired or wireless network interface and an input and output interface, etc., so as to input and output data. This embodiment will not be described here.

[0097] Embodiment four:

[0098] The embodiment provides a computer readable storage medium, which stores an erasable computer program.

[0099] When the computer program runs on the computer device, the computer device executes the above-mentioned sounding instrument multi-source data quality control processing method for high-altitude environment.

[0100] For example, the computer readable storage medium can be a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a read-only compact disc (Compact Disc Read-Only Memory, CD-ROM), a magnetic tape, a floppy disk and an optical data storage device, etc.

[0101] It should be understood that in various embodiments of the present application, the size of the sequence number of each process does not mean the order of execution, the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0102] It should be understood that according to A, B is determined, which means that B is determined only according to A, but also B can be determined according to A and / or other information.

[0103] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments can be implemented in the form of a computer program product. The 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, the processes or functions according to the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0104] Those skilled in the art can clearly understand that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0106] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are merely schematic, for example, the division of units is only one, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0107] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed to multiple network units. Part or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.

[0108] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0109] In the description of the present specification, the description referring to the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0110] The basic principles and main features of the present application and the advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only illustrative of the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for multi-source data quality control processing of a sounding instrument for high-altitude environment, characterized in that, The method comprises: S1: collecting raw data of the sonde, obtaining space-time asynchronous errors, sensor physical conflicts and navigation data anomalies based on the raw data of the sonde, and analyzing data quality indexes of the sonde based on the space-time asynchronous errors, the sensor physical conflicts and the navigation data anomalies, wherein the space-time asynchronous errors comprise Beidou GPS timestamp deviation and sensor acquisition delay, the sensor physical conflicts comprise temperature-pressure height back-calculation contradiction and water vapor saturation anomaly, and the navigation data anomalies comprise height jump anomaly and horizontal drift overrun; S2: collecting atmospheric environment characteristics and historical sounding data of the height layer where the sonde is located, and obtaining an environment adaptability correction coefficient based on the atmospheric environment characteristics and the historical sounding data; S3: collecting flight attitude and motion state data of the sonde, and analyzing dynamic measurement errors based on the flight attitude and motion state data of the sonde; S4: obtaining a credibility weight of the sonde in multi-source data fusion by weighted summation based on the data quality indexes, the environment adaptability correction coefficient and the dynamic measurement errors of the sonde; S5: judging and screening sonde data meeting the high-altitude sounding quality standard by comparing the credibility weight of the sonde in multi-source data fusion with a credibility threshold.

2. The method of claim 1, wherein, S1 comprises: collecting raw data of a to-be-processed sonde; obtaining space-time asynchronous errors, sensor physical conflicts and navigation data anomalies based on the raw data of the to-be-processed sonde, specifically comprising: using a time series analysis method, accurately aligning Beidou GPS positioning data with temperature, humidity and pressure sensor acquisition timestamps, detecting timestamp differences, marking sampling points with timestamp differences exceeding a threshold, using atmospheric static equilibrium equations and state equations, calculating theoretical height based on temperature and pressure, comparing with navigation height, recording height differences exceeding a threshold, using a saturated water vapor pressure formula, calculating maximum water vapor content based on temperature, comparing with actual water vapor content, detecting physically unreliable saturated state, using a difference analysis method, detecting height change rate of adjacent sampling points, recording height jumps exceeding a threshold, using a trajectory smoothing algorithm, detecting rationality of horizontal displacement of the sonde, recording abnormal drift exceeding a threshold; constructing a sonde data quality evaluation model, inputting space-time asynchronous errors, sensor physical conflicts and navigation data anomalies of each sampling point of the sonde, and outputting a data quality index of the sampling point of the sonde, wherein the sonde data quality evaluation model adopts an ensemble learning framework, takes a random forest as a main model, is supplemented with gradient boosting decision trees for collaborative training, optimizes model hyperparameters through grid search and cross-validation, finally takes accuracy, recall rate and F1 score as model performance evaluation indexes on a test set, inputs a to-be-evaluated sonde sample into the trained model, and obtains a prediction result of the data quality index.

3. The method of claim 2, wherein S2 comprises: collecting atmospheric environment characteristics and historical sounding data of the height layer where the sonde is located, the atmospheric environment characteristics comprising jet intensity, turbulence index and temperature vertical gradient, and the historical sounding data comprising sensor failure height and physical contradiction frequency of historical detection in the same region; The environmental adaptability correction coefficient is obtained based on atmospheric environment characteristics of a height layer where the sonde is located and historical sounding data.

4. The method of claim 3, wherein S3 The method comprises the following steps: collecting flight attitude and motion state data of the sonde by using an IMU (inertial measurement unit), wherein the flight attitude data comprises a pitch angle, a roll angle and a yaw angle, and the motion state data comprises an ascending rate, a swing amplitude and a rotation angular velocity; collecting sonde structure parameters, wherein the sonde structure parameters comprise sensor response delay, protective cover air permeability coefficient and antenna phase center offset; obtaining dynamic measurement error of each sampling point based on the flight attitude data, the motion state data and the sonde structure parameters of the sonde.

5. The method of claim 4, wherein S4 The method comprises the following steps: multiplying the data quality index of the sonde by a quality index influence weight to obtain a quality index influence value, multiplying the environmental adaptability correction coefficient by an environmental adaptability influence weight to obtain an environmental adaptability influence value, multiplying the dynamic measurement error by a dynamic error influence weight to obtain a dynamic error influence value, and obtaining a credibility weight of each sampling point of the sonde in multi-source data fusion based on a sum of the quality index influence value, the environmental adaptability influence value and the dynamic error influence value of each sampling point of the sonde.

6. The method of claim 5, wherein S5 The method comprises the following steps: obtaining a credibility threshold value, and if the credibility weight of the sonde in multi-source data fusion is greater than or equal to the credibility threshold value, it is judged that the sonde data meets the high-altitude sounding quality standard, and if the credibility weight of the sonde in multi-source data fusion is less than the credibility threshold value, it is judged that the sonde data does not meet the high-altitude sounding quality standard.

7. The sounding balloon multi-source data quality control processing system for high-altitude environment, used to realize the sounding balloon multi-source data quality control processing method for high-altitude environment according to any one of claims 1-6, characterized in that, The system comprises the following modules: a data quality evaluation 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 evaluation module is configured to collect original data of the sonde, obtain time-space asynchronous error, sensor physical conflict and navigation data anomaly based on the original data of the sonde, and analyze the data quality index of the sonde based on the time-space asynchronous error, the sensor physical conflict and the navigation data anomaly; the environmental adaptability analysis module is configured to collect atmospheric environment characteristics of a height layer where the sonde is located and historical sounding data, and obtain the environmental adaptability correction coefficient based on the atmospheric environment characteristics of the height layer where the sonde is located and the historical sounding data; the dynamic error analysis module is configured to collect flight attitude and motion state data of the sonde, and analyze the dynamic measurement error based on the flight attitude and the motion state data of the sonde; the credibility weight prediction module is configured to predict the credibility weight of the sonde in multi-source data fusion based on the data quality index of the sonde, the environmental adaptability correction coefficient and the dynamic measurement error; the quality standard judgment module is configured to judge whether the sonde data meets the high-altitude sounding quality standard based on the credibility weight of the sonde in multi-source data fusion.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the high-altitude environment-oriented sonde multi-source data quality control processing method of any one of claims 1-6.

9. An electronic device, comprising: The computer program product comprises the following elements: a memory configured to store instructions; a processor configured to execute the instructions to cause the device to perform operations for implementing the high-altitude environment-oriented sonde multi-source data quality control processing method of any one of claims 1-6.

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