Solar radio data processing method and system, storage medium and device

Abstract

The present application relates to a solar radio data processing method, system, storage medium and equipment, wherein the solar radio data processing method comprises the following steps: obtaining the spectrum data of solar radio signal and preprocessing, and performing point-by-point difference between the preprocessed data and the preset solar radio spectrum data model in the quiet state to realize solar burst event identification; dividing into burst data and non-burst data based on the solar burst event identification result and performing binary processing, abnormal point elimination and edge detection, extracting solar burst characteristics based on the data after edge detection; calculating the burst duration and burst frequency drift rate according to the extracted solar burst characteristics, and classifying the burst type according to the burst duration and burst frequency drift rate; using the original data in the burst area, and setting the brightness in the non-burst area, and outputting the processed solar burst spectrum.

Description

Technical Field

[0001] This invention relates to the field of geophysics, specifically to methods, systems, storage media, and devices for processing solar radio data. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Based on the intensity of solar activity, solar radio can be divided into three different types: quiet solar radio, slowly varying solar radio, and solar radio bursts. Among them, solar radio bursts are characterized by intense solar activity, with solar radiation intensity far exceeding that of quiet solar radio and slowly varying solar radio. This can cause drastic changes in space weather, which can not only interfere with the normal operation of spacecraft, space stations, and satellites, but also disrupt radio communications, affect navigation systems, and in severe cases, even paralyze power systems.

[0004] Current technologies process acquired solar radio signals to identify solar radio bursts. However, solar radio signal observations are typically routine, generating a large amount of data that consumes significant storage space. Furthermore, current solar radio data processing relies on manual detection of bursts before subsequent detailed analysis. Manual detection is labor-intensive, inefficient, and carries a relatively high probability of missing bursts.

[0005] To address the aforementioned issues, some observation systems employ deep learning to process solar radio data. While this improves efficiency, it presents challenges in obtaining large and reasonable datasets for deep learning, and the models are complex to design and have poor portability. Summary of the Invention

[0006] To address the technical problems mentioned above, this invention provides a solar radio data processing method, system, storage medium, and device, which can identify solar eruption events, extract features of solar eruption events, classify solar eruption types, and acquire processed solar eruption spectrum images. This effectively improves the efficiency of solar radio data processing, reduces data storage pressure, and provides fundamental data for subsequent research on the physical mechanisms of solar eruptions.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The first aspect of the present invention provides a method for processing solar radio data, comprising the following steps:

[0009] Acquire and preprocess the spectrum data of solar radio signals, and then subtract the preprocessed data from the preset solar radio spectrum data model under quiescent conditions point by point to achieve solar eruption event identification.

[0010] Based on the solar eruption event identification results, the data is divided into eruption data and non-eruption data and binarized. Outlier removal and edge detection are performed. Solar eruption features are extracted based on the edge-detected data.

[0011] Based on the extracted solar eruption characteristics, the eruption duration and eruption frequency drift rate are calculated, and the eruption types are classified according to the eruption duration and eruption frequency drift rate.

[0012] Raw data is used in the eruption area, while the non-eruption area is uniformly set to a set brightness, and the processed solar eruption spectrum is output.

[0013] Preprocessing includes converting the input spectrogram into a pixel matrix data, where the matrix rows represent frequency points, the matrix columns represent time frames, and each value in the matrix represents the brightness value of the spectrogram.

[0014] The solar radio model under quiescent conditions is obtained by averaging the solar radio observation spectrum data under quiescent conditions.

[0015] The preprocessed data is compared point by point with the preset solar radio spectrum data model under quiescent conditions to identify solar eruption events. Specifically, if the difference is greater than a set value, it is determined to be an eruption; otherwise, it is determined to be a quiescent state. If the number of eruption points is greater than n, it is considered that there is an eruption event in the spectrum; otherwise, the eruption point is considered to be interference.

[0016] Outlier removal specifically includes:

[0017] Cut the spectrogram horizontally and vertically to divide it into four equal parts. Repeat the above steps for each part in a loop.

[0018] When a part has exactly one point, the point is considered an abnormal point and is removed, that is, the brightness value of the point is set to zero.

[0019] After m cuts, all outliers are removed, while burst areas are not removed due to their high density, thus achieving outlier removal.

[0020] Solar eruption features are extracted from the data after edge detection, specifically as follows:

[0021] After edge detection, the time corresponding to the column containing the foremost edge of the outbreak area is the outbreak start time, and the time corresponding to the column containing the last edge of the outbreak area is the outbreak end time.

[0022] The maximum frequency of the burst is determined by the frequency point corresponding to the row containing the uppermost edge of the burst area, and the minimum frequency of the burst is determined by the frequency point corresponding to the row containing the lowermost edge of the burst area.

[0023] The peak value of this outbreak can be determined by comparing the maximum brightness value of the original data within the outbreak area.

[0024] Based on the extracted solar eruption characteristics, the eruption duration and eruption frequency drift rate are calculated, specifically as follows:

[0025] t burst =t end -t start ;

[0026]

[0027] Among them, t burst For the duration of the outbreak, t start For the start time of the outbreak, t end Let f be the burst end time, k be the burst frequency drift rate, and f be the burst frequency drift rate. max For the maximum burst frequency, f min This is the minimum frequency of the burst.

[0028] Outbreak types are classified according to their duration and frequency drift rate, specifically as follows:

[0029] If the burst duration is less than t and the burst frequency drift rate is greater than k1 and less than k2, then the burst type is considered to be a Type II solar radio burst; t, k1, and k2 are the set thresholds.

[0030] If the burst duration is less than t and the burst frequency drift rate is greater than k2, then the burst type is considered to be a Type III solar radio burst.

[0031] If the burst duration is greater than t or the burst frequency drift rate is less than k1, then the burst type is considered to be other types.

[0032] A second aspect of the present invention provides a solar radio data processing system, comprising:

[0033] The solar eruption event identification module is configured to: acquire and preprocess the spectrum data of solar radio signals, and subtract the preprocessed data from the preset solar radio spectrum data model under quiescent conditions point by point to achieve solar eruption event identification;

[0034] The solar eruption feature extraction module is configured to: divide solar eruption event recognition results into eruption data and non-eruption data and perform binarization processing, outlier removal and edge detection, and extract solar eruption features based on the edge-detected data.

[0035] The solar eruption type classification module is configured to: calculate the eruption duration and eruption frequency drift rate based on the extracted solar eruption characteristics, and classify the eruption type based on the eruption duration and eruption frequency drift rate;

[0036] The solar eruption spectrum output module is configured to use the raw data in the eruption area and the set brightness in the non-eruption area to output the processed solar eruption spectrum.

[0037] A third aspect of the present invention provides a computer-readable storage medium.

[0038] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the solar radio data processing method described above.

[0039] A fourth aspect of the present invention provides a computer device.

[0040] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the solar radio data processing method described above.

[0041] Compared with existing technologies, one or more of the above technical solutions have the following beneficial effects:

[0042] 1. It can automatically detect and identify solar eruption events, extract features from solar eruption events, classify solar eruption types, and obtain processed solar eruption spectrum images. It is highly efficient and accurate, without missing any detections, and also reduces the data storage pressure. The method is universal and can be applied to any frequency band. It only requires importing data to build a new quiet solar radio data model, providing basic data for subsequent research on the physical mechanism of solar eruptions.

[0043] 2. By performing point-to-point subtraction, burst events are identified. The number of burst points n is set during this process. The purpose is that when the signal strength is greater than that of the solar radio model in the quiet state, in addition to the case of solar radio bursts, there may also be random spike pulse interference. Therefore, in order to reduce the burst detection error rate, a threshold burst point number n is set to exclude random spike pulse interference.

[0044] 3. In the method of removing outliers by dividing the spectrum into four equal parts m times, the high density of the burst regions means that they will not be removed. This achieves the goal of removing outliers without affecting the identification of burst regions. Attached Figure Description

[0045] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0046] Figure 1 A flowchart of solar radio data processing provided for one or more embodiments of the present invention. Detailed Implementation

[0047] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0048] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0049] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0050] As described in the background section, the acquired solar radio signals need to be processed to perform tasks such as solar eruption event identification and solar eruption type classification. However, the current process of processing solar radio signals is still mainly manual, which is inefficient and the data processing results are too simplistic. They usually only identify whether an eruption event exists, but cannot obtain the spectrum of the eruption period. In addition, the portability is poor. Many solar radio data processing methods are only applicable to single situations, especially deep learning-based methods, whose models are built on datasets and lack universality.

[0051] Therefore, the following embodiments provide a solar radio data processing method, system, storage medium, and device, which can realize the identification of solar eruption events, feature extraction of solar eruption events, classification of solar eruption types, and acquisition of processed solar eruption spectrum images. This effectively improves the efficiency of solar radio data processing, reduces data storage pressure, and provides basic data for subsequent research on the physical mechanisms of solar eruptions.

[0052] Example 1:

[0053] like Figure 1 As shown, the solar radio data processing method includes the following steps:

[0054] 1. Input spectrogram data and preprocess it.

[0055] 2. Subtract the input data point by point from the preset solar radio spectrum data model under quiescent conditions. If the difference is greater than s, it is determined to be an eruption; otherwise, it is determined to be a quiescent state. If the number of eruption points is greater than n, it is considered that there is an eruption event in the spectrum; otherwise, the above eruption points are considered to be interference.

[0056] 3. Perform black-and-white binarization on burst data and non-burst data.

[0057] 4. Perform outlier removal to eliminate random spike pulse interference.

[0058] 5. Use the Sobel operator for edge detection.

[0059] 6. Extract features from the start time, end time, maximum frequency, minimum frequency, and peak value of the solar radio burst region after edge detection.

[0060] 7. Calculate the burst duration and burst frequency drift rate, and classify the burst types according to the burst duration and burst frequency drift rate.

[0061] 8. Using the original data for the eruption area and uniformly using brightness b for the non-eruption area, draw the processed solar eruption spectrum diagram.

[0062] Steps 1-2 above are for solar eruption event identification, steps 3-6 are for solar eruption feature extraction, step 7 is for solar eruption type classification, and step 8 is for drawing the processed solar eruption spectrum.

[0063] In step 1, data preprocessing converts the input spectrogram into a matrix of fixed pixel points (x rows and y columns) for subsequent processing. At this time, the matrix rows represent frequency points, the matrix columns represent time frames, and each value in the matrix represents the brightness value of the spectrogram.

[0064] In step 2, the solar radio model under quiescent conditions is obtained by averaging a large amount of solar radio observation spectrum data under quiescent conditions. It not only reflects the intensity of solar radio signals under quiescent conditions, but also reflects the influence of internal instrument noise and interference at fixed frequency points within this frequency band. This allows the noise influence to be removed to a certain extent when performing subtraction in the subsequent steps.

[0065] In step 2, the purpose of setting the number of burst points n is that when the signal strength is greater than that of the solar radio model in the quiet state, in addition to the case of solar radio bursts, there may also be random spike pulse interference. Therefore, in order to reduce the burst detection error rate, a threshold number of burst points n is set to basically eliminate random spike pulse interference.

[0066] In step 3, black-and-white binarization refers to setting the brightness value of burst data points to 255 and the brightness value of non-burst data points to 0. At this time, the spectrogram shows white burst areas and black non-burst areas. After black-and-white binarization, the spectrogram only contains black and white points with the greatest color difference, which facilitates subsequent outlier removal and edge detection.

[0067] In step 4, the spectrum is divided into m equal parts based on density to remove outliers, as detailed below:

[0068] The spectrogram is divided into four equal parts horizontally and vertically. The above steps are repeated for each part in a loop. When a part contains exactly one point, that point is considered an outlier and is removed by setting its brightness value to zero. After m cuts, all outliers are removed, while burst regions, due to their high density, are not removed. This achieves the goal of outlier removal.

[0069] In step 5, commonly used edge detection operators include the Sobel operator, Roberts operator, Prewitt operator, Laplacian operator, and Canny operator. Among these, the Laplacian and Canny operators are prone to edge loss, while the Sobel operator performs better than the Roberts and Prewitt operators. A drawback of the Sobel operator is its insensitivity to grayscale changes; however, since the spectral image has already undergone black-and-white binarization processing in this invention, the Sobel operator is chosen for edge detection.

[0070] The features extracted in step 6 are: the start time, end time, maximum frequency, minimum frequency, and peak value of solar flares.

[0071] After edge detection, the overall outline of the outbreak area is clear. At this time, the start time of the outbreak can be determined by the time corresponding to the column where the foremost edge of the outbreak area is located, and the end time of the outbreak can be determined by the time corresponding to the column where the last edge of the outbreak area is located.

[0072] Similarly, the maximum frequency of the burst is determined by detecting the frequency point corresponding to the row containing the uppermost edge of the burst area, and the minimum frequency is determined by detecting the frequency point corresponding to the row containing the lowermost edge of the burst area.

[0073] The peak value of this outbreak can be determined by comparing the maximum brightness value of the original data within the outbreak area.

[0074] In step 7, the types of solar radio bursts are classified as follows:

[0075] The burst duration and burst frequency drift rate are calculated using the following two formulas, where t in formula (1) is... burst For the duration of the outbreak, tstart For the start time of the outbreak, t end The burst end time; in equation (2), k is the burst frequency drift rate, f max For the maximum burst frequency, f min This is the minimum frequency of the burst.

[0076] t burst =t end -t start (1)

[0077]

[0078] If the burst duration is less than t and the burst frequency drift rate is greater than k1 and less than k2, then the burst type is considered to be a type II solar radio burst.

[0079] If the burst duration is less than t and the burst frequency drift rate is greater than k2, then the burst type is considered to be a Type III solar radio burst.

[0080] If the burst duration is greater than t or the burst frequency drift rate is less than k1, then the burst type is considered to be other types.

[0081] The above method establishes a quiet solar radio data model for solar eruption event identification; proposes a method for m-time segmentation to remove outliers; uses the Sobel operator for edge detection; and includes eruption event identification, feature extraction, classification, and acquisition of processed solar eruption spectrum images.

[0082] This method can automatically identify solar eruption events, extract features from solar eruption events, classify solar eruption types, and acquire processed solar eruption spectrum images. It is highly efficient and accurate, with no missed detections, and also reduces data storage pressure. The method is universal and can be applied to any frequency band. It only requires importing data to build a new quiet solar radio data model, providing basic data for subsequent research on the physical mechanisms of solar eruptions.

[0083] Example 2:

[0084] The solar eruption event identification module is configured to: acquire and preprocess the spectrum data of solar radio signals, and subtract the preprocessed data from the preset solar radio spectrum data model under quiescent conditions point by point to achieve solar eruption event identification;

[0085] The solar eruption feature extraction module is configured to: divide solar eruption event recognition results into eruption data and non-eruption data and perform binarization processing, outlier removal and edge detection, and extract solar eruption features based on the edge-detected data.

[0086] The solar eruption type classification module is configured to: calculate the eruption duration and eruption frequency drift rate based on the extracted solar eruption characteristics, and classify the eruption type based on the eruption duration and eruption frequency drift rate;

[0087] The solar eruption spectrum output module is configured to use the raw data in the eruption area and the set brightness in the non-eruption area to output the processed solar eruption spectrum.

[0088] The aforementioned system can automatically identify solar eruption events, extract features from solar eruption events, classify solar eruption types, and acquire processed solar eruption spectrum images. It is highly efficient and accurate, without missing any events, and also reduces the pressure on data storage. The method is universal and can be applied to any frequency band. It only requires importing data to build a new quiet solar radio data model, providing fundamental data for subsequent research on the physical mechanisms of solar eruptions.

[0089] Example 3:

[0090] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the solar radio data processing method as described in Embodiment 1 above.

[0091] The solar radio data processing method can automatically identify solar eruption events, extract features from solar eruption events, classify solar eruption types, and obtain processed solar eruption spectrum images. It is highly efficient and accurate, without missing any events, and also reduces the data storage pressure. The method is universal and can be applied to any frequency band. It only requires importing data to build a new quiet solar radio data model, providing basic data for subsequent research on the physical mechanisms of solar eruptions.

[0092] Example 4:

[0093] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the solar radio data processing method described in Embodiment 1 above.

[0094] The solar radio data processing method can automatically identify solar eruption events, extract features from solar eruption events, classify solar eruption types, and obtain processed solar eruption spectrum images. It is highly efficient and accurate, without missing any events, and also reduces the data storage pressure. The method is universal and can be applied to any frequency band. It only requires importing data to build a new quiet solar radio data model, providing basic data for subsequent research on the physical mechanisms of solar eruptions.

[0095] The steps or modules involved in Embodiments 2 to 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for processing solar radio data, characterized in that: Includes the following steps: Acquire and preprocess the spectrum data of solar radio signals, and then subtract the preprocessed data from the preset solar radio spectrum data model under quiescent conditions point by point to achieve solar eruption event identification. Based on the solar eruption event identification results, the data is divided into eruption data and non-eruption data and binarized. Outlier removal and edge detection are performed. Solar eruption features are extracted based on the edge-detected data. Based on the extracted solar eruption characteristics, the eruption duration and frequency drift rate are calculated, and the eruption types are classified according to these characteristics, specifically: ； ； in, For the duration of the outbreak, The start time of the outbreak. The end time of the outbreak, For burst frequency drift rate, To achieve the highest burst frequency, This represents the minimum frequency of the burst. If the burst duration is less than t and the burst frequency drift rate is greater than k1 and less than k2, then the burst type is considered to be a Type II solar radio burst; t, k1, and k2 are the set thresholds. If the burst duration is less than t and the burst frequency drift rate is greater than k2, then the burst type is considered to be a Type III solar radio burst. If the burst duration is greater than t or the burst frequency drift rate is less than k1, then the burst type is considered to be other types. Raw data is used in the eruption area, while the non-eruption area is uniformly set to a set brightness, and the processed solar eruption spectrum is output.

2. The solar radio data processing method as described in claim 1, characterized in that: The preprocessing includes converting the input spectrogram into a pixel matrix data, where the matrix rows represent frequency points, the matrix columns represent time frames, and each value in the matrix represents the brightness value of the spectrogram.

3. The solar radio data processing method as described in claim 1, characterized in that: The solar radio model under quiescent conditions is obtained by averaging the solar radio observation spectrum data under quiescent conditions.

4. The solar radio data processing method as described in claim 1, characterized in that: The preprocessed data is compared point-by-point with a pre-defined solar radio spectrum data model under quiescent conditions to identify solar eruption events. Specifically: If the difference is greater than the set value, it is determined to be an outbreak; otherwise, it is determined to be a quiet state. If the number of outbreak points is greater than n, it is considered that there is an outbreak event in the spectrum graph; otherwise, the outbreak point is interference.

5. The solar radio data processing method as described in claim 1, characterized in that: The removal of outliers specifically involves: Cut the spectrogram horizontally and vertically to divide it into four equal parts. Repeat the above steps for each part in a loop. When a part has exactly one point, the point is considered an abnormal point and is removed, that is, the brightness value of the point is set to zero. After m cuts, all outliers are removed, while burst areas are not removed due to their high density, thus achieving outlier removal.

6. The solar radio data processing method as described in claim 1, characterized in that: The extraction of solar eruption features based on edge detection data specifically involves: After edge detection, the time corresponding to the column containing the foremost edge of the outbreak area is the outbreak start time, and the time corresponding to the column containing the last edge of the outbreak area is the outbreak end time. The maximum frequency of the burst is determined by the frequency point corresponding to the row containing the uppermost edge of the burst area, and the minimum frequency of the burst is determined by the frequency point corresponding to the row containing the lowermost edge of the burst area. The peak value of this outbreak can be determined by comparing the maximum brightness value of the original data within the outbreak area.

7. A solar radio data processing system, characterized in that: include: The solar eruption event identification module is configured to: acquire and preprocess the spectrum data of solar radio signals, and subtract the preprocessed data from the preset solar radio spectrum data model under quiescent conditions point by point to achieve solar eruption event identification; The solar eruption feature extraction module is configured to: divide solar eruption event recognition results into eruption data and non-eruption data and perform binarization processing, outlier removal and edge detection, and extract solar eruption features based on the edge-detected data. The solar eruption type classification module is configured to: calculate the eruption duration and frequency drift rate based on the extracted solar eruption characteristics, and then classify the eruption based on the eruption duration and frequency drift rate. The outbreak types are classified as follows: ； ； in, For the duration of the outbreak, The start time of the outbreak. The end time of the outbreak, For burst frequency drift rate, To achieve the highest burst frequency, This represents the minimum frequency of the burst. If the burst duration is less than t and the burst frequency drift rate is greater than k1 and less than k2, then the burst type is considered to be a Type II solar radio burst; t, k1, and k2 are the set thresholds. If the burst duration is less than t and the burst frequency drift rate is greater than k2, then the burst type is considered to be a Type III solar radio burst. If the burst duration is greater than t or the burst frequency drift rate is less than k1, then the burst type is considered to be other types. The solar eruption spectrum output module is configured to use the raw data in the eruption area and the set brightness in the non-eruption area to output the processed solar eruption spectrum.

8. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the solar radio data processing method as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the solar radio data processing method as described in any one of claims 1-6.

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