Plasma detection data calibration method and device, medium and electronic equipment

Abstract

The invention provides a plasma detection data calibration method and device, a medium and electronic equipment. The method comprises the following steps: acquiring a downlink plasma data sequence of a satellite; performing noise separation processing on the plasma data sequence; based on a preset correlation model of plasma data and space environment parameters, according to the space environment parameters when the plasma data sequence is collected, determining target reference values corresponding to the plasma data at all moments of the plasma data sequence; and based on the plasma data of the plasma data sequence at each moment and the target reference value, performing calibration processing on the plasma data to obtain calibrated plasma data. According to the invention, high-quality conversion from original, noisy and environment-interfered observation data to calibration data is realized, and a data foundation is laid for subsequent establishment of an accurate plasma environment model and reliable space weather forecast.

Description

Technical Field

[0001] This application relates to the field of space environment monitoring technology, and more specifically, to a calibration method, apparatus, medium, and electronic equipment for plasma detection data. Background Technology

[0002] Space plasma observation is a core foundation for studying the dynamic characteristics of the Earth's magnetosphere, radiation belts, and plasmasphere, and the quality of its data directly affects the accuracy of related research.

[0003] Currently, plasma detection data processing and calibration are mainly achieved through three types of techniques: first, using filtering techniques such as low-pass filters and median filters to smooth the data and remove high-frequency noise; second, using statistical models to construct typical data behaviors and variation ranges, identifying and eliminating outliers; and third, using other plasma data to calibrate particles with the same state in the geomagnetic B / L coordinate system.

[0004] Existing technologies have significant drawbacks: erroneous data removal relies on complex statistical tests, which can easily lead to misjudgments; noise information in low-energy detection data is not effectively utilized, resulting in data waste; advanced data analysis techniques are only used for noise reduction and do not combine with other space environment data to improve the calibration process, making it difficult to address the data differences under different space locations and environmental conditions. Summary of the Invention

[0005] The purpose of this application is to provide a method, apparatus, medium, and electronic device for calibrating plasma detection data, which can solve at least one of the technical problems mentioned above. The specific solution is as follows:

[0006] According to a specific embodiment of this application, in a first aspect, this application provides a method for calibrating plasma detection data, the method comprising:

[0007] Obtain the downlink plasma counting sequence from the satellite;

[0008] Noise separation processing is performed on the plasma counting sequence;

[0009] Based on the pre-defined correlation model between plasma count and space environment parameters, the predicted values ​​of plasma counts at each moment of the plasma count sequence after noise separation are obtained.

[0010] Based on the plasma count and predicted value after noise separation at each time point of the plasma counting sequence, the plasma count after noise separation is calibrated to obtain the calibrated plasma count.

[0011] In a possible embodiment, noise separation processing is performed on the plasma counting sequence, including:

[0012] Calculate the arithmetic mean of plasma counts with energies less than a preset threshold in the plasma count;

[0013] Using the arithmetic mean as the noise reduction benchmark, noise reduction correction is performed on the counting points in the plasma counting sequence where the particle energy is greater than a preset threshold, resulting in a denoised plasma counting sequence.

[0014] In a possible embodiment, the original plasma count is calibrated based on the plasma count and predicted value at each time point of the plasma counting sequence, including:

[0015] Based on preset data instruction detection rules, the data quality status of the plasma count after noise reduction is determined.

[0016] When the data quality does not meet the preset quality conditions, the plasma counts after noise separation are calibrated based on the plasma counts and corresponding predicted values ​​at each time point of the plasma counting sequence.

[0017] In a possible embodiment, the original plasma count is calibrated based on the plasma count and predicted value at each time point of the plasma counting sequence, including:

[0018] When the plasma count at the current moment is identified as an outlier, the plasma count is weighted and fused with the corresponding predicted value, and the fused result is used as the calibrated plasma count; wherein, the weights of the weighted fusion are determined based on the uncertainty of the predicted value; or,

[0019] When the plasma count at the current moment is missing, the predicted value at that moment is used as the calibrated count.

[0020] In a possible implementation, the weights are negatively correlated with the uncertainty of the predicted values; where uncertainty is the standard deviation of the predicted values ​​output by the association model.

[0021] In a possible embodiment, the association model is a machine learning model or a mathematical statistical model; wherein, the method for constructing the association model includes:

[0022] Acquire historical datasets, which include time-aligned sequences of historical spatial environment parameters and historical plasma counts that correspond one-to-one with each time point in the historical spatial environment parameter sequence after noise separation.

[0023] Based on machine learning algorithms or statistical models, the correlation model is trained by using each parameter in the historical spatial environment parameter sequence as input features and historical plasma counts as training targets.

[0024] The trained model is fine-tuned based on a pre-set validation dataset to obtain an association model.

[0025] In a possible embodiment, the trained model is fine-tuned based on a preset validation dataset, including iteratively fine-tuning the model based on the AIC criterion or test set error.

[0026] According to a specific embodiment of this application, in a second aspect, this application also provides a calibration device for plasma detection data, the device comprising:

[0027] The acquisition unit is used to acquire the downlink plasma counting sequence from the satellite;

[0028] A noise reduction unit is used to perform noise separation processing on the plasma counting sequence;

[0029] The prediction unit is used to obtain the predicted value of the plasma count at each moment of the plasma count sequence after noise separation, based on a preset correlation model between plasma count and space environment parameters.

[0030] The calibration unit is used to calibrate the plasma counts after noise separation based on the plasma counts and predicted values ​​at each time point of the plasma counting sequence, so as to obtain the calibrated plasma counts.

[0031] According to a specific embodiment of this application, in a third aspect, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described above.

[0032] According to a specific embodiment of this application, in a fourth aspect, this application also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described above.

[0033] Compared with the prior art, the above-described solutions of this application have at least the following beneficial effects:

[0034] This application employs targeted noise separation processing on the raw counts to effectively eliminate interference signals introduced by non-plasma sources such as ultraviolet radiation and high-energy particles, thereby obtaining purer observational data that better reflects the true space plasma environment. By using a pre-established correlation model to calibrate the data, it overcomes data discrepancies caused by differences in satellite location (e.g., geomagnetic B / L coordinates) and environmental conditions (e.g., solar wind, geomagnetic activity), enabling comparison and fusion of detection data from different times and locations on a unified physical benchmark. Introducing a data-driven model into the calibration process, compared to traditional methods relying on fixed thresholds or statistical assumptions, allows for more flexible and accurate compensation for systematic errors caused by space environment disturbances, improving the automation of data processing and the accuracy of calibration results. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0036] Figure 1 A schematic flowchart of a method for calibrating plasma detection data provided in an embodiment of this application;

[0037] Figure 2 This is another flowchart illustrating the calibration method for plasma detection data provided in this embodiment of the invention.

[0038] Figure 3 A schematic diagram of plasma data noise removal for the plasma detection data calibration method provided in this embodiment of the invention;

[0039] Figure 4 Plasma proton differential energy spectrum, which is a method for calibrating plasma detection data provided in this embodiment of the invention;

[0040] Figure 5 Electron differential energy spectrum of the calibration method for plasma detection data provided in the embodiments of the present invention;

[0041] Figure 6 A schematic diagram of the distribution function of the calibration method for plasma detection data provided in this embodiment of the invention;

[0042] Figure 7 This is a schematic diagram of the structure of the plasma detection data calibration device provided in an embodiment of the present invention;

[0043] Figure 8This is a schematic diagram of the electronic device structure shown in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms "a," "the," and "the" as used in the embodiments of this application and the appended claims are also intended to include the plural forms, and "multiple" generally includes at least two unless the context clearly indicates otherwise.

[0046] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0047] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0048] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a product or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a product or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the product or device that includes that element.

[0049] Space plasma observation is fundamental to studying the dynamics of Earth's magnetosphere, radiation belts, and plasmasphere. However, the data is susceptible to external interference: solar ultraviolet radiation, after multiple reflections, enters the detector, triggering the photoelectric effect and generating spurious signals unrelated to actual plasma particles; high-energy electrons and protons from Earth's radiation belts can penetrate the detector's outer shell or generate secondary particles, increasing signal complexity. Furthermore, data from different space locations and environmental conditions exhibit variability, affecting data accuracy and the analysis of plasma dynamics characteristics.

[0050] Currently, plasma detection data processing and calibration are mainly achieved through three types of technologies:

[0051] Filtering and noise reduction: Applying low-pass filters, median filters, etc., to smooth the data and remove noise components with frequencies higher than the signal itself;

[0052] Statistical anomaly removal: Construct a statistical data model, determine typical behaviors and expected range of variation, and identify and remove outliers or erroneous data that deviate from the range;

[0053] Plasma data calibration: Using other plasma data, calibration is performed on particles with the same state in the geomagnetic B / L coordinate system.

[0054] Some solutions also employ advanced techniques such as principal component analysis (PCA) and machine learning algorithms to assist in noise reduction.

[0055] Error data removal relies on complex statistical hypothesis testing and outlier detection techniques, which carries the risk of misjudgment.

[0056] Noise information in low-energy detection data is not fully utilized, resulting in data waste;

[0057] Advanced data analysis techniques were used only for noise reduction and were not combined with other spatial environment data to improve calibration.

[0058] The calibration relies on other plasma data in the geomagnetic B / L coordinate system, but the existing data cannot cover all altitude orbits and is prone to errors due to the small number of overlapping B / L coordinate data samples.

[0059] This application utilizes the characteristic that "the lowest energy level signal is mainly composed of noise" as a background noise benchmark, subtracting this benchmark from other energy level observation data. Compared with existing filtering and statistical methods, this approach is simpler to operate, introduces less noise, and is more timely. In addition to plasma data from other sources, this application incorporates space environment data and supplements calibration information by constructing a correlation model, ensuring the accuracy of calibration data through multiple channels and solving the problems of insufficient data coverage and small sample size in existing technologies. Ultimately, it can generate unified B / L magnetic coordinates and data decoupled from environmental influences. Comparison and linear regression yield more complete data fusion results, providing multi-source detection data input for plasma environment models.

[0060] The optional embodiments of this application are described in detail below with reference to the accompanying drawings.

[0061] like Figure 1 As shown, this embodiment provides a calibration method for plasma detection data, which includes:

[0062] S202. Obtain the downlink plasma counting sequence from the satellite;

[0063] Understandably, due to the dispersed and mixed nature of the satellite downlink raw data, systematic processing is necessary. First, the binary data needs to be read according to the correct endian byte order and verified during transmission. Data that fails verification is discarded to ensure data quality. Then, the verified data is unpacked. Scientific data is stored according to its source, with data from the electron detector and ion detector stored separately for subsequent analysis; engineering data is stored separately for operational status analysis. Simultaneously with reading and unpacking the data, the timecode needs to be parsed and the time uniformly converted to International Standard Time (UTC) to achieve time synchronization across missions.

[0064] The main tasks of preprocessing include sorting the data by time, cleaning up duplicate data, and filling in missing data. First, the raw data is sorted by time to ensure the continuity of the time series. Duplicate data is directly deleted to avoid redundancy. For missing data segments, specific values ​​are used to fill them in, and corresponding time codes are assigned to the missing segments to maintain their integrity on the timeline.

[0065] Common sources of interference in plasma data fall into two main categories: first, ultraviolet radiation from the sun, which, after multiple reflections, partially enters the electrostatic analyzer (MCP), where ultraviolet photons and their generated secondary electrons produce signals on the microchannel plate (MSP); second, high-energy particles in the radiation bands, which can penetrate the instrument and directly generate signals on the MCP, or generate signals through secondary particles. However, these interference signals are characterized by being independent of the voltage selection of the electrostatic analyzer, meaning they exhibit consistent characteristics across all energy levels for a short period.

[0066] Because satellites may accumulate charge due to the distribution of ambient plasma and the influence of solar wind during their orbital operation, they may possess a certain positive or negative potential. This potential can prevent the electrostatic analyzer from effectively detecting low-energy plasma particles at the lowest energy level. Therefore, the signal from the lowest energy level of the electrostatic analyzer is considered to consist primarily of noise. The average value of the raw plasma count at the lowest energy level (less than 15 eV) is used as the noise value. When the number of energy levels less than 15 eV is greater than one, the average value of multiple energy levels can be taken:

[0067]

[0068] Where, N noise (t) represents the noise value of the original plasma count, and M represents the energy level E. i The number of plasma detection energy levels that satisfy 15 eV or less. The initial plasma count for energy level Ei is less than 15 eV. Subtracting noise from the initial counts of other energy levels yields the contamination-separated data:

[0069] N clean (E i ,t)=N i (E i ,t)-N noise

[0070] Where, N clean (E i ,t) represents the plasma count after contamination separation, N i (E i ,t) represents the original count of plasmas with energy levels greater than 15 eV for which interference signals to be removed.

[0071] S204. Perform noise separation processing on the plasma counting sequence;

[0072] Understandably, common sources of interference in plasma data fall into two main categories: first, ultraviolet radiation from the sun, which, after multiple reflections, partially enters the electrostatic analyzer, where ultraviolet photons and their generated secondary electrons produce signals on the microchannel plate; second, high-energy particles in the radiation bands, which can penetrate the instrument and directly generate signals on the MCP, or generate signals through secondary particles. However, these interference signals are characterized by being independent of the voltage selection of the electrostatic analyzer, meaning they exhibit consistent characteristics across all energy levels for a short period.

[0073] In some embodiments, noise separation processing is performed on the original count, including:

[0074] Calculate the average value of the original plasma counts at energy levels below a preset threshold, and use it as the background noise value;

[0075] The original counts of each energy level with energy higher than a preset threshold are subtracted from the background noise value to obtain the plasma counts after noise separation.

[0076] During satellite operation, charge accumulation may occur due to the distribution of ambient plasma and the influence of solar wind, resulting in a positive or negative electric potential. This potential can prevent the electrostatic analyzer from effectively detecting low-energy plasma particles at the lowest energy level. Therefore, the signal from the lowest energy level of the electrostatic analyzer is considered to consist primarily of noise. The average value of the raw plasma count at the lowest energy level (less than 15 eV) is used as the noise value. When the number of energy levels less than 15 eV is greater than one, the average value of multiple energy levels can be taken.

[0077]

[0078] Where, N noise (t) represents the noise value of the original plasma count, and M represents the energy level E. i The number of plasma detection energy levels that satisfy 15 eV or less. The initial plasma count for energy level Ei is less than 15 eV. Subtracting noise from the initial counts of other energy levels yields the contamination-separated data (e.g., ...). Figure 3 As shown):

[0079] N clean (E i ,t)=N i (E i ,t)-N noise

[0080] Where, N clean (E i ,t) represents the plasma count after contamination separation, N i (E i ,t) represents the original count of plasmas with energy levels greater than 15 eV for which interference signals to be removed.

[0081] This embodiment is not based on purely mathematical assumptions, but rather on the well-defined space physics mechanism that "satellite electric potential causes low-energy level detection failure, making its readings primarily noise." This provides a solid physical basis for the denoising operation, resulting in more reliable results. It directly utilizes the statistical average of the lowest energy level data as the global noise benchmark. The algorithm is simple, computationally inexpensive, and highly suitable for real-time, batch data processing by satellite payloads in orbit or on the ground. Specifically targeting the most challenging non-particle noise in space plasma detection, such as ultraviolet radiation and high-energy particle penetration, it effectively removes these energy-independent background interferences. Simultaneously, during noise removal, it preserves the shape and intensity of the true plasma signal in the original data to the greatest extent possible.

[0082] S206. Based on the preset correlation model between plasma count and space environment parameters, the predicted value of plasma count at each moment of the plasma count sequence after noise separation is obtained.

[0083] Understandably, given the high correlation between the plasma environment and the space environment, a plasma perturbation model can be established, and plasma data can be calibrated using other environmental data. First, physically correlated environmental parameters are selected, including solar wind parameters (dynamic pressure P). sw Interplanetary magnetic field strength B z Solar wind speed v sw ), Geomagnetic activity index (K p (Dst, AE index). Then, through machine learning or statistical methods (including parametric and non-parametric models such as generalized linear correlation and random forest), the E values ​​for each energy level are constructed.i Plasma Count N clean (E i ,t) model of fluctuations with space environment.

[0084] For each energy level E i Establish plasma counting N clean (E i Mapping relationship between ,t) and environmental parameters:

[0085] N clean (E i ,t)=Model(e rel,1 (t),e rel,2 (t),…)

[0086] Among them, e rel,k (t) represents the k-th strongly correlated environmental parameter. The environmental data is then compared with the energy level E. i Plasma data are aligned to time t, and the time lag characteristics of each parameter are considered. Training and test sets are divided to complete the construction of a plasma perturbation model in response to space environment disturbances.

[0087] In some embodiments, the method further includes constructing an association model, wherein constructing the association model includes:

[0088] The correlation model is constructed using machine learning or statistical methods to establish a mapping relationship between space environment parameters and plasma counts after noise separation;

[0089] The training data used in building the model has been corrected for the time lag effect of space environment parameters relative to plasma counts.

[0090] The pre-established correlation model is a mathematical or computational model that learns the hidden, complex correspondence between "space environment parameters" and "actual plasma counts" from a large amount of historical data through machine learning or statistical methods. Input: Historical space environment parameters (such as solar wind speed, geomagnetic index, etc.). Output: Historical plasma target reference counts after noise separation. Once trained, the model encapsulates the physics of how the space environment affects plasma behavior. Given a new set of environmental parameters, it can predict the plasma count that "theoretically should" be observed under that environment. Directly detected data (even after noise separation) still contains errors and limitations. Calibration aims to further correct these residual, systematic biases. Specifically: compensating for model prediction bias: In the data fusion calibration stage of the process, the model's predictions and actual observations are weighted and fused. When the model has high uncertainty in certain situations (σ increases), the system automatically reduces the weight of the predictions (ω decreases), placing more trust in the actual observations, and vice versa. This adaptive fusion mechanism ensures the robustness of the output results.

[0091] In this embodiment, single observational data, which may contain errors, is cross-validated and intelligently fused with model predictions based on broad physical laws (summarized from historical data). It no longer treats observational data as absolute truth, but rather combines it with theoretical predictions through an adaptive mechanism, ultimately outputting more reliable, accurate, and physically consistent data.

[0092] S208. Based on the plasma count and predicted value after noise separation at each time point of the plasma counting sequence, the plasma count after noise separation is calibrated to obtain the calibrated plasma count.

[0093] In some embodiments, the plasma count after noise separation is calibrated based on a pre-established correlation model between plasma counts and space environment parameters, including:

[0094] By inputting the current space environment parameters into the correlation model, the predicted value of the plasma count is obtained;

[0095] The predicted values ​​and the observed values ​​of the plasma count after noise separation are weighted and fused, and the fused result is used as the calibrated plasma count.

[0096] The weights ω used in the weighted fusion are determined based on the uncertainty σ of the model prediction and satisfy the following relationship: ω=1 / (1+σ 2 ).

[0097] Input environment parameters {e rel,1 (t),e rel,2From the plasma perturbation model with respect to the space environment (t),…}, the predicted flux N is obtained. pred (E i Based on the model prediction results, the model predicted value N is... pred (E i ,t) and the observed plasma count N(E) after contamination separation i ,t) Weighted by confidence level:

[0098] N final (E i ,t)=ωN pred (E i ,t)+(1-ω)N(E i ,t)

[0099] in, σ represents the uncertainty in the model prediction.

[0100] By inputting environmental parameters such as dynamic pressure Psw, interplanetary magnetic field strength Bz, solar wind speed vsw, geomagnetic index SYM / H, Kp, Dst, and AE into the plasma perturbation model, the predicted flux N is obtained. pred (E i Based on the model prediction results, the model predicted value N is... pred (E i ,t) and the observed plasma count N(E) after contamination separation i ,t) weighted by confidence level N final (E i ,t)=ωN pred (E i ,t)+(1-ω)N(E i ,t).

[0101] In this embodiment, the weight ω is dynamically adjusted based on the uncertainty σ of the model prediction, making the calibration process an adaptive algorithm that optimizes based on the confidence level of each prediction. It comprehensively utilizes the advantages of both theory and observation: this method leverages both "physical laws" (embodied in the model trained on historical data) and "experimental data." When the model's prediction confidence is high (small σ, large ω), it relies more on theoretical predictions; when the model faces unknown or extreme situations and the prediction uncertainty is high (large σ, small ω), it relies more on actual observations, thus effectively avoiding the shortcomings of using either alone.

[0102] In some embodiments, after obtaining the calibrated plasma count, the method further includes:

[0103] Based on the calibrated plasma count, the differential flux and number density of the plasma are calculated.

[0104] The calculation of plasma count to differential flux data includes geometric factor correction, energy conversion, and time serialization. The specific calculation formula is shown below. Particle number flux R i (t)(unit m-2sr-1s-1):

[0105]

[0106] Where Δt is the time step, G i This is the geometric factor (unit: cm²sr).

[0107] Plasma differential flux j(E) i (t)(unit m-2sr-1s-1eV-1):

[0108]

[0109] Where ΔEi is the energy bandwidth of the i-th energy channel.

[0110] like Figure 6 As shown, in a space environment, the velocity distribution of plasma particles can usually be approximated by a Maxwellian distribution, with the distribution function being:

[0111]

[0112] Where n is the particle number density, m is the particle mass, T is the temperature, and k is the particle mass. B Here, is the Boltzmann constant. Based on the Maxwellian distribution, data processing first involves fitting the particle velocity or energy distribution to estimate the plasma temperature using the fitted parameters. This process requires nonlinear fitting methods (such as least squares) to match the observed particle velocity or energy data with the theoretical distribution, thereby extracting the key parameter of temperature. The plasma number density can be calculated by numerical integration, summing the velocity or energy spatial distribution:

[0113] n=∫f(v)d 2 v

[0114] This method ensures that density calculations depend solely on the integrity and resolution of the observational data, avoiding the introduction of fitting errors. This workflow allows for the simultaneous acquisition of plasma temperature and density, providing reliable physical parameters for analyzing its spatial distribution and dynamic behavior.

[0115] In the example, the ion detector has an energy range of 5 eV to 25 keV, a detection solid angle of 2π in space, and a time resolution of 635 seconds for a single complete measurement; the electron detector has an energy range of 5 eV to 5 keV, a detection solid angle of 2π in space, and a time resolution of 32 seconds for a single complete measurement. The plasma detector obtains counts based on combinations of azimuth, elevation, and energy levels; these counts constitute the raw scientific data. The omnidirectional ion detector has 16 channels corresponding to its azimuth angle, evenly distributed across the scanning sector, ranging from 0° to 360°, resulting in a field of view of 22.5° for each channel. The calibration results of the geometric factors for the omnidirectional electron detector in eight elevation modes are shown in Table 1.

[0116] Table 1 Geometric Factors of Omnidirectional Electron Detectors

[0117] Pitch angle <![CDATA[Geometric factor (cm 2 sreV / eV)]]> 0° <![CDATA[1.33×10 -3 ]]> 12.857° <![CDATA[1.33×10 -3 ]]> 25.714° <![CDATA[1.33×10 -3 ]]> 38.571° <![CDATA[1.60×10 -3 ]]> 51.429° <![CDATA[1.84×10 -3 ]]> 64.286° <![CDATA[1.95×10 -3 ]]> 77.143° <![CDATA[1.95×10 -3 ]]> 90° <![CDATA[1.95×10 -3 ]]>

[0118] The differential flux is calculated by combining the count with parameters such as detector geometry, energy level, etc., and the count is converted into particle number flux R. i (t). The number density n of charged particles can then be calculated using integration. Plasma differential flux j(E) i (t)(unit m-2sr-1s-1eV-1):

[0119]

[0120] Where ΔEi is the energy bandwidth of the i-th energy channel.

[0121] In a space environment, the velocity distribution of plasma particles can usually be approximated by a Maxwellian distribution, with the distribution function being:

[0122]

[0123] Where n is the particle number density, m is the particle mass, T is the temperature, and k is the particle mass. B Here, is the Boltzmann constant. Based on the Maxwellian distribution, data processing first involves fitting the particle velocity or energy distribution to estimate the plasma temperature using the fitted parameters. This process requires nonlinear fitting methods (such as least squares) to match the observed particle velocity or energy data with the theoretical distribution, thereby extracting the key parameter of temperature. The plasma number density can be calculated by numerical integration, summing the velocity or energy spatial distribution:

[0124] n=∫f(v)d 2 v

[0125] This method ensures that density calculations depend solely on the integrity and resolution of the observational data, avoiding the introduction of fitting errors. This workflow allows for the simultaneous acquisition of plasma temperature and density, providing reliable physical parameters for analyzing its spatial distribution and dynamic behavior.

[0126] like Figure 2 As shown in the figure, this embodiment provides a flowchart of a method for calibrating plasma detection data, which includes:

[0127] Satellite downlink raw binary plasma count: Satellite downlink plasma detection packet data contains binary scientific data and engineering data. Due to the dispersed and mixed nature of the raw data, it must be systematically processed to support subsequent scientific analysis.

[0128] Reading (parsing, verification): The process of reading raw binary data, including parsing the data structure, automated batch processing, and verifying errors and missing metadata;

[0129] Remove invalid data and record the location of missing and invalid values: Remove data that is found to be incorrect during verification and record the location of errors and missing data for later filling in during the calibration stage;

[0130] Decimal plasma count with timestamp (UTC): Read the unpacked satellite downlink raw binary plasma count, parse the time code, and convert the time to International Standard Time (UTC) to achieve time synchronization of cross-mission data;

[0131] Preprocessing (duplicate removal, sorting, cleaning): Sort the original data by time, remove duplicate data, fill missing data segments with specific values, and assign corresponding time codes to the missing segments to maintain their integrity on the timeline.

[0132] Continuous plasma counting data: Plasma detector data needs to be preprocessed, including time sorting of data, cleaning up duplicate data, and supplementing missing data;

[0133] Average plasma count for energy levels less than 15 eV: The average value of the original plasma count for energy levels less than 15 eV is used as the noise value. When the number of energy levels less than 15 eV is greater than 1, the average value of multiple energy levels is taken.

[0134] Plasma counts for energy levels greater than 15 eV: The signal of the lowest energy level is mainly composed of noise. The average value of plasma counts for energy levels less than 15 eV is used as the noise value. Plasma counts for energy levels greater than 15 eV are used after contamination separation.

[0135] Contamination separation: The average plasma count of continuous data minus the plasma count of energy levels less than 15 eV;

[0136] Plasma count after noise removal: The count values ​​of each energy level after subtracting the noise value (the average value of plasma counts for energy levels less than 15 eV) from the plasma counts of each energy level greater than 15 eV.

[0137] Space environment parameters: environmental parameters that are physically strongly correlated with plasma counting, including solar wind parameters (dynamic pressure Psw, interplanetary magnetic field strength Bz, solar wind speed vsw) and geomagnetic activity indices (Kp, Dst, AE index).

[0138] Plasma fluctuation model with space environment: By using machine learning or statistical methods (including parametric and non-parametric models such as generalized linear correlation and random forest), a model of the Ei plasma count at each energy level fluctuates with the space environment.

[0139] Model-predicted plasma count: The plasma count prediction obtained by incorporating space environment parameters into the plasma perturbation model.

[0140] Based on model uncertainty, a confidence-weighted approach is used: for missing data points in the original data, predicted values ​​are directly used to fill in the gaps. For outlier data, the model prediction results are weighted according to confidence levels.

[0141] Calibrated plasma count: Plasma count after noise removal, calibrated using a plasma perturbation model of the space environment;

[0142] Plasma data reading and verification

[0143] Downlink data from plasma detection packets obtained by the electrostatic analyzer on the GEO satellite in orbit includes binary scientific and engineering data from the plasma detection packets. The data includes verification information at the time of packaging, transmission verification information within the data packet, and timecode information provided by the detector. The binary data is read and transmission verification is performed. Data that fails verification is discarded to ensure data quality. Scientific data, including azimuth, elevation, energy level, and timecode information, is read from the verified data. For omnidirectional electron / ion data packets, a checksum is used, summing bytes from the first byte to the 12343rd byte to form a two-byte checksum.

[0144] Plasma data preprocessing

[0145] The original plasma data count values ​​are sorted according to time, and duplicate data are found and deleted to avoid data redundancy; missing data are found and filled with specific values, and the missing segments are assigned corresponding time codes to ensure the integrity of all data on the timeline.

[0146] Plasma data contamination separation

[0147] A denoising method based on the lowest energy level signal of the electrostatic analyzer is employed. Specifically, the counts corresponding to the two lowest energy levels in the original count are used as background counts. The rationale for this approach is as follows: 1) The energy levels of the electrostatic analyzer are set exponentially, with very close intervals between low energy levels. For example, in this example, the lowest energy level of the ion detector is 10 eV, and the next lowest is 12.22 eV; 2) For ion detectors, due to the common negative potential of satellites, low-energy ions at lower energy levels are accelerated and thus enter higher energy levels. Theoretically, these lower energy levels should be empty, and the counts measured at this point may largely come from noise. The average value of the 10 eV and 12.22 eV energy levels is used as the background noise. This background noise is then subtracted from the original count.

[0148] Plasma model construction under space environment disturbance

[0149] Environmental parameters with strong physical correlation to the plasma data were selected, including solar wind parameters (dynamic pressure Psw, interplanetary magnetic field strength Bz, solar wind velocity vsw) and geomagnetic indices (SYM / H, Kp, Dst, AE index). For missing or low-temporal-resolution space environmental data, interpolation methods were used to obtain values ​​at the corresponding time. The environmental data and the plasma data at energy level Ei were aligned by time t, and the time lag characteristics of each parameter were considered. A machine learning model was constructed using the random forest algorithm, and for each energy level Ei, a plasma count N was established. clean (E i Mapping relationship between N,t) and environmental parameters clean (E i ,t)=Model(e rel,1 (t),e rel,2 (t),…), the model is iteratively optimized based on AIC (Akaike Information Content) or other similar features to reduce irrelevant environmental factors, and finally the plasma perturbation model with space environment is obtained.

[0150] Plasma data calibration

[0151] By inputting environmental parameters such as dynamic pressure Psw, interplanetary magnetic field strength Bz, solar wind speed vsw, geomagnetic index SYM / H, Kp, Dst, and AE into the plasma perturbation model, the predicted flux N is obtained. pred (E i Based on the model prediction results, the model predicted value N is... pred (E i ,t) and the observed plasma count N(E) after contamination separation i,t) weighted by confidence level N final (E i ,t)=ωN pred (E i ,t)+(1-ω)N(E i ,t).

[0152] Plasma differential flux and number density calculation

[0153] In the example, the ion detector has an energy range of 5 eV to 25 keV, a detection solid angle of 2π in space, and a time resolution of 635 seconds for a single complete measurement; the electron detector has an energy range of 5 eV to 5 keV, a detection solid angle of 2π in space, and a time resolution of 32 seconds for a single complete measurement. The plasma detector obtains counts based on combinations of azimuth, elevation, and energy levels; these counts constitute the raw scientific data. The omnidirectional ion detector has 16 channels corresponding to its azimuth angle, evenly distributed across the scanning sector, ranging from 0° to 360°, resulting in a field of view of 22.5° for each channel. The calibration results of the geometric factors for the omnidirectional electron detector in eight elevation modes are shown in Table 1.

[0154] The differential flux is calculated by combining the count with parameters such as detector geometry, energy level, etc., and the count is converted into particle number flux R. i (t). The number density n of charged particles can then be calculated using integration. Plasma differential flux j(E) i (t)(unit m-2sr-1s-1eV-1):

[0155]

[0156] Where ΔEi is the energy bandwidth of the i-th energy channel.

[0157] In a space environment, the velocity distribution of plasma particles can usually be approximated by a Maxwellian distribution, with the distribution function being:

[0158]

[0159] Where n is the particle number density, m is the particle mass, T is the temperature, and k is the particle mass. B Here, is the Boltzmann constant. Based on the Maxwellian distribution, data processing first involves fitting the particle velocity or energy distribution to estimate the plasma temperature using the fitted parameters. This process requires nonlinear fitting methods (such as least squares) to match the observed particle velocity or energy data with the theoretical distribution, thereby extracting the key parameter of temperature. The plasma number density can be calculated by numerical integration, summing the velocity or energy spatial distribution:

[0160] n=∫f(v)d2 v

[0161] This method ensures that density calculations depend solely on the integrity and resolution of the observational data, avoiding the introduction of fitting errors. This workflow allows for the simultaneous acquisition of plasma temperature and density, providing reliable physical parameters for analyzing its spatial distribution and dynamic behavior.

[0162] This application also provides apparatus embodiments that follow the above embodiments, for implementing the method steps of the above embodiments. The interpretation of the same names is the same as that of the above embodiments, and they have the same technical effects as those of the above embodiments, so they will not be repeated here.

[0163] like Figure 7 As shown, this application provides a calibration device for plasma detection data, the device comprising:

[0164] Acquisition unit 702 is used to acquire the downlink plasma counting sequence from the satellite;

[0165] Noise reduction unit 704 is used to perform noise separation processing on the plasma counting sequence;

[0166] The prediction unit 706 is used to obtain the predicted value of the plasma count at each moment of the plasma count sequence after noise separation, based on a preset correlation model between plasma count and space environment parameters.

[0167] The calibration unit 708 is used to calibrate the plasma count after noise separation based on the plasma count and predicted value at each time point of the plasma counting sequence, so as to obtain the calibrated plasma count.

[0168] like Figure 8 As shown, this embodiment provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by a processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method steps of the above embodiment.

[0169] This application provides a non-volatile computer storage medium storing computer-executable instructions that can execute the method steps of the above embodiments.

[0170] The following is for reference. Figure 8The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The terminal devices in the embodiments of this application may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0171] like Figure 4 As shown, the electronic device may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device. The processing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0172] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0173] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by the processing device 801, it performs the functions defined in the methods of the embodiments of this application.

[0174] It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0175] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0176] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0177] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0178] The units described in the embodiments of this application can be implemented in software or hardware. The names of the units are not, in some cases, limiting the scope of the unit itself.

Claims

1. A method for calibrating plasma detection data, characterized in that the method... include: Acquire the downlink plasma data sequence from the satellite; Noise separation processing is performed on the plasma data sequence; Based on a pre-defined correlation model between plasma data and space environment parameters, the target reference value corresponding to the plasma data at each moment of the plasma data sequence is determined according to the space environment parameters when the plasma data sequence is collected; wherein, the correlation model is configured to: determine the target reference value corresponding to the plasma data at a preset energy level based on the space environment parameters; Based on the plasma data and target reference values ​​at each moment of the plasma data sequence, the plasma data is calibrated to obtain calibrated plasma data.

2. The method according to claim 1, characterized in that, Noise separation processing is performed on the plasma data sequence, including: Calculate the arithmetic mean of plasma data with energy less than a preset threshold. Using the arithmetic mean as the noise reduction benchmark, noise reduction correction is performed on data points in the plasma data sequence where the particle energy is greater than a preset threshold, resulting in a denoised plasma data sequence.

3. The method according to claim 1, characterized in that, Based on the plasma data and target reference values ​​at each time point in the plasma data sequence, the raw plasma data is calibrated, including: Based on preset data instruction detection rules, the data quality status of the denoised plasma data is determined. When the data quality does not meet the preset quality conditions, the plasma data after noise separation at each moment of the plasma data sequence and the corresponding target reference value are used to perform calibration processing on the plasma data after noise separation.

4. The method according to claim 1, characterized in that, Based on the plasma data and target reference values ​​at each time point in the plasma data sequence, the raw plasma data is calibrated, including: When the plasma data at the current moment is identified as an outlier, the plasma data is weighted and fused with the corresponding target reference value, and the fused result is used as the calibrated plasma data; wherein, the weights of the weighted fusion are determined based on the uncertainty of the target reference value; or, When plasma data for the current moment is missing, the data is calibrated using the target reference value at that moment.

5. The method according to claim 4, characterized in that, The weights are negatively correlated with the uncertainty of the target reference value; where uncertainty is the standard deviation of the target reference value output by the correlation model.

6. The method according to claim 1, characterized in that, The association model can be a machine learning model or a mathematical statistical model; the methods for constructing the association model include: Acquire historical datasets, which include time-aligned historical spatial environment parameter sequences and historical plasma data that correspond one-to-one with each time point in the historical spatial environment parameter sequences after noise separation. Based on machine learning algorithms or statistical models, the parameters in the historical spatial environment parameter sequence are used as input features, and historical plasma data is used as the training target to train the correlation model. The trained model is fine-tuned based on a pre-set validation dataset to obtain an association model.

7. The method according to claim 1, characterized in that, Fine-tuning the trained model based on a pre-defined validation dataset includes iterative fine-tuning of the model based on the AIC criterion or test set error.

8. A calibration device for plasma detection data, characterized in that, The device includes: Acquisition unit, used to acquire downlink plasma data sequences from the satellite; A noise reduction unit is used to perform noise separation processing on plasma data sequences; The prediction unit is used to determine the target reference value corresponding to the plasma data at each moment of the plasma data sequence based on the space environment parameters when the plasma data sequence is collected, according to a preset correlation model between plasma data and space environment parameters; wherein, the correlation model is configured to determine the target reference value corresponding to the plasma data at a preset energy level based on the space environment parameters. The calibration unit is used to calibrate the plasma data based on the plasma data at each moment of the plasma data sequence and the target reference value, so as to obtain the calibrated plasma data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as claimed in any one of claims 1 to 7.

10. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as claimed in any one of claims 1 to 7.

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