A method and system for producing a circular array solar radio imaging telescope

Abstract

This invention provides a method and system for generating products for a circular array solar radio imaging telescope, comprising: generating raw complex visibility function data and raw solar radio spectrum data based on the output signals of each antenna element of the telescope; obtaining calibrated data through multi-dimensional calibration of channels, phase, the entire system, and the spectrum; generating a solar radio brightness temperature image based on the calibrated complex visibility function data, and generating two types of fast-view products by combining the calibrated spectrum data; performing quality inspection on all data products, and classifying and storing qualified products after encapsulation according to standardized naming rules and metadata specifications. This invention achieves end-to-end processing from raw signals to standardized products, balancing data accuracy, real-time performance, and quality controllability, fully complying with relevant technical specifications, and adapting to the high-resolution observation requirements of the telescope.

Description

Technical Field

[0001] This invention relates to the field of solar radio observation technology, specifically to a method and system for producing a circular array solar radio imaging telescope. Background Technology

[0002] Currently, solar radio radiation contains key physical information about the structure of the solar atmosphere, the evolution of the magnetic field, and the formation of solar storms, making it a core observational object in solar physics research.

[0003] Existing circular array solar radio imaging telescopes can achieve high spatial, temporal, and frequency resolution observations of solar meter-wave radio bursts. However, the massive terabytes of data and complex data types generated by these telescope observations pose significant challenges to current processing technologies: existing technologies lack a workflow designed for the integrated characteristics of interferometric imaging and spectral observation, resulting in a disconnect between the complex visibility function and solar radio spectrum data processing, leading to low data integration efficiency; due to antenna gain drift, phase inconsistency, and environmental interference, existing calibration methods struggle to cover the three-level correction requirements of channels, phase, and the entire system, resulting in insufficient accuracy of physical quantities in data products; the lack of consistent naming rules, metadata specifications, and storage formats hinders data archiving and multi-device collaborative analysis; the suddenness of solar radio bursts requires rapid product generation, but existing technologies, while increasing speed, tend to neglect interference removal and data integrity checks, leading to decreased product reliability. Therefore, an integrated method that combines data acquisition, calibration, inversion, product generation, and quality control is urgently needed to address these technical pain points. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method for producing a circular array solar radio imaging telescope, comprising: Based on the signals output by each antenna element of the circular array solar radio imaging telescope, the original complex visibility function data and the original solar radio spectrum data are generated. The original complex visibility function data and the original solar radio spectrum data are calibrated in multiple dimensions to obtain calibrated complex visibility function data and solar radio spectrum data; Based on the calibrated complex visibility function data, a solar radio brightness temperature image is generated; Based on the solar radio brightness temperature image and the calibrated solar radio spectrum data, a solar radio imaging quick-view product and a solar radio spectrum quick-view product are generated. The calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product, and solar radio spectrum fast-view product are subjected to quality inspection to obtain standardized data products that pass the quality inspection. The standardized data products that have passed the quality inspection are packaged according to the preset standardized naming rules and metadata specifications, and then classified and stored in the corresponding storage nodes.

[0005] Optionally, the step of generating raw complex visibility function data and raw solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope includes: The signals output from each antenna element of the acquired circular array solar radio imaging telescope are amplified, bandpass filtered, and frequency-converted to obtain a preprocessed signal, which is then converted into a preset intermediate frequency signal. The intermediate frequency signal is subjected to high-speed digital sampling, digital down-conversion, and Fourier transform to obtain the transformed signal; The transformed signal is cross-correlationly analyzed to generate the original complex visibility function data. The transformed signal is subjected to autocorrelation operation to generate the original solar radio spectrum data.

[0006] Optionally, the multi-dimensional calibration includes: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; The process of multi-dimensionally calibrating the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data includes: By injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, the original complex visibility function data is calibrated by channel calibration, and the equivalent gain and input noise temperature of each antenna receiving channel are calculated to obtain the original complex visibility function data after channel calibration. By transmitting a standard signal to the antenna receiving channel through a simulated fiber optic loop, phase calibration is performed on the original complex visibility function data after the channel calibration to obtain the phase-calibrated original complex visibility function data. Using the standard radio signal emitted by the central calibration tower, the antenna receiving channel is used to receive and perform full-system calibration on the original complex visibility function data after phase calibration, thereby obtaining the calibrated complex visibility function data; The calibrated complex visibility function data is spectrally calibrated using a preset conversion model to obtain calibrated solar radio spectrum data.

[0007] Optionally, generating a solar radio brightness temperature image based on the calibrated complex visibility function data includes: Baseline processing is performed on the calibrated complex visibility function data, and valid baselines are retained; The non-uniform spatial frequency domain sampling points corresponding to the effective baseline are interpolated to a regular grid to match the telescope's spatial resolution. The interpolated regular grid data is weighted using a hybrid weighting strategy to obtain the weighted data. The weighted data is subjected to inverse Fourier transform, and combined with the antenna pattern and coordinate system parameters, the solar radio brightness temperature distribution is calculated to obtain the initial brightness temperature image; The initial brightness temperature image is cleaned to obtain a solar radio brightness temperature image.

[0008] Optionally, the hybrid weighting strategy is as follows: uniform weighting is used in the central region of the spatial frequency domain, and natural weighting is used in the edge region; The cleansing process uses the CLEAN algorithm, and the iteration stops when the sidelobe intensity is lower than the main lobe intensity by a preset percentage threshold.

[0009] Optionally, the step of generating solar radio imaging quick-view products and solar radio spectrum quick-view products based on the solar radio brightness temperature image and the calibrated solar radio spectrum data includes: Select solar radio brightness temperature images with typical frequencies from the solar radio brightness temperature images; The solar radio brightness temperature image with typical frequency points is subjected to brightness stretching and color mapping processing to obtain a preprocessed solar radio brightness temperature image. Information annotation is performed on the preprocessed solar radio brightness temperature image to generate a solar radio imaging quick-view product; The calibrated solar radio spectrum data is smoothed and preprocessed to generate a multipolar spectrum diagram and a time-varying curve of characteristic frequency point flux according to a preset layout. The time axis, frequency axis, physical quantity unit and observation mode are marked to generate a solar radio spectrum quick view product. The information annotation includes one or more of the following: observation time, frequency, polarization components, and coordinate system information.

[0010] Optionally, the quality inspection includes: data integrity inspection, signal quality inspection, and calibration accuracy inspection; The metadata specification includes: Binary data products record the product name, observation mode, equipment parameters, and data start and end times in the file header; Image and spectrum data products store observation mode, physical quantity type, calibration time, and coordinate system information through key fields; QuickView products store quality levels, observation parameters, and annotation information through preset data segments.

[0011] Based on the same inventive concept, the present invention also provides a product manufacturing system for a circular array solar radio imaging telescope, comprising: The data generation module is used to generate raw complex visibility function data and raw solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope. The data calibration module is used to perform multi-dimensional calibration on the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data. The image brightness temperature module is used to generate a solar radio brightness temperature image based on the calibrated complex visibility function data. The product manufacturing module is used to generate solar radio imaging quick-view products and solar radio spectrum quick-view products based on the solar radio brightness temperature image and the calibrated solar radio spectrum data. The quality inspection module is used to perform quality inspection on the calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product and solar radio spectrum fast-view product to obtain standardized data products that pass the quality inspection. The data encapsulation module is used to encapsulate the standardized data products that have passed the quality inspection according to preset standardized naming rules and metadata specifications, and to classify and store them to the corresponding storage nodes.

[0012] Optionally, the data generation module includes: The preprocessing submodule is used to amplify, bandpass filter and frequency convert the signals output by each antenna unit of the acquired circular array solar radio imaging telescope to obtain a preprocessed signal, and convert the preprocessed signal into a preset intermediate frequency signal. The transformation submodule is used to perform high-speed digital sampling, digital down-conversion, and Fourier transform on the intermediate frequency signal to obtain the transformed signal; The cross-correlation operation submodule is used to generate the original complex visibility function data by performing cross-correlation operation on the transformed signal; The autocorrelation operation submodule is used to generate raw solar radio spectrum data by performing autocorrelation operations on the transformed signal.

[0013] Optionally, the multi-dimensional calibration includes: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; The data calibration module includes: The channel calibration submodule is used to perform channel calibration on the original complex visibility function data by injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, calculating the equivalent gain and input noise temperature of each antenna receiving channel, and obtaining the original complex visibility function data after channel calibration. The phase calibration submodule is used to transmit a standard signal to the antenna receiving channel through an analog fiber optic loop, and to perform phase calibration on the original complex visibility function data after the channel calibration, so as to obtain the phase-calibrated original complex visibility function data. The system-wide calibration submodule is used to receive the standard radio signal emitted by the central calibration tower through the antenna receiving channel and perform system-wide calibration on the original complex visibility function data after phase calibration to obtain calibrated complex visibility function data. The spectrum calibration submodule is used to perform spectrum calibration on the calibrated complex visibility function data using a preset conversion model to obtain calibrated solar radio spectrum data.

[0014] Optionally, the image brightness temperature module includes: The baseline processing submodule is used to perform baseline processing on the calibrated complex visibility function data and retain the valid baseline; The spatial sampling submodule is used to interpolate the non-uniform spatial frequency domain sampling points corresponding to the effective baseline to a regular grid to match the telescope's spatial resolution. The hybrid weighting submodule is used to perform weighting processing on the interpolated regular grid data using a hybrid weighting strategy to obtain the weighted data. The brightness temperature distribution submodule is used to perform inverse Fourier transform on the weighted data, and calculate the solar radio brightness temperature distribution by combining the antenna pattern and coordinate system parameters to obtain the initial brightness temperature image. The cleaning processing submodule is used to clean the initial brightness temperature image to obtain a solar radio brightness temperature image.

[0015] Optionally, the hybrid weighting strategy is as follows: uniform weighting is used in the central region of the spatial frequency domain, and natural weighting is used in the edge region; The cleansing process uses the CLEAN algorithm, and the iteration stops when the sidelobe intensity is lower than the main lobe intensity by a preset percentage threshold.

[0016] Optionally, the product manufacturing module includes: The frequency selection submodule is used to select solar radio brightness temperature images with typical frequencies from the solar radio brightness temperature images; The preprocessing submodule is used to perform brightness stretching and color mapping processing on the solar radio brightness temperature image with typical frequency points to obtain the preprocessed solar radio brightness temperature image. The information annotation submodule is used to annotate the preprocessed solar radio brightness temperature image to generate a solar radio imaging quick-view product. The smoothing submodule is used to perform smoothing preprocessing on the calibrated solar radio spectrum data, generate multipolar spectrum diagrams and time-varying curves of characteristic frequency point fluxes according to a preset layout, label the time axis, frequency axis, physical quantity units and observation mode, and generate solar radio spectrum quick-view products. The information annotation includes one or more of the following: observation time, frequency, polarization components, and coordinate system information.

[0017] Optionally, the quality inspection includes: data integrity inspection, signal quality inspection, and calibration accuracy inspection; The metadata specification includes: Binary data products record the product name, observation mode, equipment parameters, and data start and end times in the file header; Image and spectrum data products store observation mode, physical quantity type, calibration time, and coordinate system information through key fields; QuickView products store quality levels, observation parameters, and annotation information through preset data segments.

[0018] In another aspect, the present invention also provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for generating a circular array solar radio imaging telescope product as described above is implemented.

[0019] In another aspect, the present invention also provides a computer device readable storage medium having an executable program stored thereon, wherein when the executable program is executed, it implements the aforementioned method for generating a circular array solar radio imaging telescope product.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method and system for generating products from a circular array solar radio imaging telescope, comprising: generating original complex visibility function data and original solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope; performing multi-dimensional calibration on the original complex visibility function data and original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data; generating a solar radio brightness temperature image based on the calibrated complex visibility function data; generating a solar radio imaging fast-view product and a solar radio spectrum fast-view product based on the solar radio brightness temperature image and the calibrated solar radio spectrum data; performing quality inspection on the calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product, and solar radio spectrum fast-view product to obtain standardized data products that pass quality inspection; and further... Standardized data products that pass quality inspection are packaged according to preset standardized naming rules and metadata specifications, and stored in corresponding storage nodes. This invention adopts a hierarchical calibration scheme to comprehensively cover error correction at the channel, phase, and system levels, effectively eliminating the effects of antenna gain drift, phase inconsistency, and environmental interference, significantly improving the accuracy of physical quantities in data products, and meeting the data precision requirements of scientific research. By formulating unified standardized naming rules, metadata encapsulation specifications, and storage strategies, the invention addresses the pain point of lacking product standardization, facilitating data archiving management and multi-device joint analysis. Furthermore, through a full-process quality inspection mechanism covering data integrity, signal purity, and calibration accuracy, it can effectively identify and process abnormal data, ensuring both real-time product generation and quality reliability, providing stable and reliable data support for solar activity monitoring, solar radio burst research, and space weather early warning. Attached Figure Description

[0021] Figure 1 A flowchart illustrating a method for generating a circular array solar radio imaging telescope product according to the present invention; Figure 2 A schematic diagram of the structural composition of a circular array solar radio imaging telescope product manufacturing system provided by the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0022] This invention proposes a method, system, equipment, and medium for producing circular array solar radio imaging telescope products. The specific embodiments of this invention will be further described in detail below with reference to the accompanying drawings.

[0023] Example 1: This invention provides a method for producing a circular array solar radio imaging telescope, the process of which is illustrated in the following diagram. Figure 1As shown, it includes: Step 1: Based on the signals output by each antenna element of the circular array solar radio imaging telescope, generate the original complex visibility function data and the original solar radio spectrum data; Step 2: Perform multi-dimensional calibration on the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data; Step 3: Based on the calibrated complex visibility function data, generate a solar radio brightness temperature image; Step 4: Based on the solar radio brightness temperature image and the calibrated solar radio spectrum data, generate a solar radio imaging quick-view product and a solar radio spectrum quick-view product; Step 5: Perform quality inspection on the calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product, and solar radio spectrum fast-view product to obtain standardized data products that pass the quality inspection. Step 6: Package the standardized data products that have passed the quality inspection according to the preset standardized naming rules and metadata specifications, and classify and store them to the corresponding storage nodes.

[0024] In one implementation, the process of generating the original complex visibility function data and the original solar radio spectrum data based on the signals output by each antenna element of the circular array solar radio imaging telescope in step 1 above may include: The signals output from each antenna element of the acquired circular array solar radio imaging telescope are amplified, bandpass filtered, and frequency-converted to obtain a preprocessed signal, which is then converted into a preset intermediate frequency signal. The intermediate frequency signal is subjected to high-speed digital sampling, digital down-conversion, and Fourier transform to obtain the transformed signal; The transformed signal is cross-correlationly analyzed to generate the original complex visibility function data. The transformed signal is subjected to autocorrelation operation to generate the original solar radio spectrum data.

[0025] For example, the multi-dimensional calibration may include: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; For example, in this implementation, the signals output from each antenna element of the acquired circular array solar radio imaging telescope are amplified, bandpass filtered, and frequency-converted to obtain a preprocessed signal, which may include: The analog signal in the 150MHz-450MHz frequency band is amplified (e.g., gain ≥25dB), bandpass filtered (to suppress out-of-band interference), and frequency converted to a 70MHz intermediate frequency signal to ensure that the signal-to-noise ratio meets the requirements of subsequent processing. For example, the process of generating the original complex visibility function data by cross-correlation operation on the transformed signal described above may include: A high-speed AD acquisition chip is used to sample the intermediate frequency signal. The sampling rate is designed according to the frequency resolution requirements (imaging observation supports 2MHz resolution, and spectrum observation supports 7.6kHz resolution). Then, digital down-conversion, FFT transformation (i.e. Fourier transform) and related operations are performed. Taking 313 antenna elements as an example, by calculating the cross-correlation coefficients between each pair of the 313 antenna elements, the original complex visibility function data containing four polarization modes, HH, HV, VH, and VV, is generated. The number of correlations can be 313×312 / 2×4=195312. By calculating the autocorrelation power spectrum of a single antenna element signal, raw solar radio spectrum data containing horizontal (H) and vertical (V) polarization can be generated, covering 39,322 frequency points; The raw data can be stored in a cache server in a custom binary format, and divided according to time segmentation rules: for example, the complex visibility function data is divided into 1 minute / file (time segmentation code 01M), and the raw spectrum data is divided into 5 minutes / file (time segmentation code 05M).

[0026] In one implementation, step 2 above, which involves multi-dimensional calibration of the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data, may include: By injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, the original complex visibility function data is calibrated by channel calibration, and the equivalent gain and input noise temperature of each antenna receiving channel are calculated to obtain the original complex visibility function data after channel calibration. By transmitting a standard signal to the antenna receiving channel through a simulated fiber optic loop, phase calibration is performed on the original complex visibility function data after the channel calibration to obtain the phase-calibrated original complex visibility function data. Using the standard radio signal emitted by the central calibration tower, the antenna receiving channel is used to receive and perform full-system calibration on the original complex visibility function data after phase calibration, thereby obtaining the calibrated complex visibility function data; The calibrated complex visibility function data is spectrally calibrated using a preset conversion model to obtain calibrated solar radio spectrum data. In this implementation, the generation of calibrated complex visibility function and solar radio spectrum data is mainly used to eliminate systematic errors and environmental interference. The specific process is as follows: A Dicke-type radiometer calibration scheme based on high and low temperature noise injection is adopted. Low-temperature noise signals (liquid nitrogen-cooled load, temperature can be set to 77K) and high-temperature noise signals (matched load, temperature close to ambient temperature) are injected into the receiving channel. By measuring the channel's output response to these two types of signals, the channel's equivalent gain G and the input noise temperature T are calculated. The calculation formula is as follows: G=(P 高温 -P 低温 ) / (T 高温 -T 低温 ), T=(P 低温 / G)-T 低温 ; Achieve amplitude consistency correction for antenna channels and complete channel calibration; Based on the analog fiber optic loop calibration scheme, a standard sinusoidal signal is generated by the digital subsystem in the central control room, distributed to the analog fiber optic links of each antenna unit via a power divider, and the signal loops back to the central control room. The phase difference of the signal in each channel is then measured. Perform phase correction on the complex visibility function data: ; in, This represents the corrected complex visibility function value; This represents the original complex visibility function value; This represents the imaginary unit; this formula ensures that the channel phase consistency is ≤ ±2.5°. Full-system calibration: Using a 100-meter-high central calibration tower (equipped with an omnidirectional calibration antenna) at the center of the circular array, standard radio signals in the 150MHz-450MHz range can be emitted, controlling 313 antenna elements to simultaneously point towards the calibration tower. The overall system response can be measured, and corrections can be made, including adjustments to the antenna pattern. The total system error, including The direction cosine of the heliocentric coordinate system; Spectrum calibration: Converting the relative power values ​​of the raw spectrum data into solar flux units (SFU). , Indicates Watt, Indicates per square meter, (represented per hertz), a conversion model is built using observational data from known sources (such as the solar quiet region). ( This is the proportionality coefficient. This is the offset. This represents the standard solar flux value. The original relative power value is represented by the solar radio spectrum data, which is calculated at each frequency point (e.g., 7.6 kHz resolution) to generate calibrated solar radio spectrum data.

[0027] In one implementation, step 3 above, which involves generating a solar radio brightness temperature image based on the calibrated complex visibility function data, may include: Baseline processing is performed on the calibrated complex visibility function data, and valid baselines are retained; The non-uniform spatial frequency domain sampling points corresponding to the effective baseline are interpolated to a regular grid to match the telescope's spatial resolution. The interpolated regular grid data is weighted using a hybrid weighting strategy to obtain the weighted data. The weighted data is subjected to inverse Fourier transform, and combined with the antenna pattern and coordinate system parameters, the solar radio brightness temperature distribution is calculated to obtain the initial brightness temperature image; The initial brightness temperature image is cleaned to obtain a solar radio brightness temperature image.

[0028] In this implementation, the hybrid weighting strategy can be: uniform weighting is used in the central region of the spatial frequency domain, and natural weighting is used in the edge region; The cleaning process can employ the CLEAN algorithm, and the iteration stopping condition can be a preset threshold for the sidelobe intensity being lower than the main lobe intensity. Specifically, the process of generating a solar radio brightness temperature image in this implementation may include: Based on the observation time (UTC) and antenna position, the projected coordinates of each baseline in the spatial frequency domain (uv plane) are calculated. According to the array antenna occlusion model and occlusion threshold, occluded baselines with a signal-to-noise ratio (SNR) < 5 are removed, and the effective baselines are retained for subsequent processing. A convolutional interpolation algorithm is used to interpolate non-uniformly distributed UV sampling points onto a regular grid. The grid density matches the telescope's spatial resolution (≈4′50″ at 150MHz and ≈1′37″ at 450MHz), ensuring that the interpolated data covers complete spatial frequency information. A hybrid weighting strategy is adopted to meet scientific needs: uniform weighting is used for UV data in the central region (to ensure spatial resolution), and natural weighting is used for edge regions (to improve signal-to-noise ratio). The weighting coefficients are positively correlated with the baseline length and signal strength. Inverse Fourier Transform: Perform a two-dimensional inverse Fourier transform on the weighted UV data, combine it with the normalized average antenna pattern to calculate the solar radio brightness temperature distribution, and calculate the solar radio brightness temperature image according to the following formula: ; in, The direction cosine of the heliocentric coordinate system is... Image of solar radio brightness temperature at that time. Corresponding to the X-axis of the heliocentric coordinate system, Corresponding to the Y-axis of the heliocentric coordinate system; Represents spatial frequency coordinates; Represents the calibrated complex visibility function; Represents the two-dimensional inverse Fourier transform kernel; The imaginary unit; The direction cosine of the heliocentric coordinate system is... The antenna pattern at that time; based on this calculation formula, an initial brightness temperature image of 512×512 pixels can be generated; The CLEAN algorithm is used to eliminate sidelobe interference in the initial brightness temperature image. Combined with the telescope's dynamic range of ≥75dB, the iteration stopping condition is set to the sidelobe intensity being less than 1% of the main lobe intensity, ultimately generating a clean solar radio brightness temperature image.

[0029] In one implementation, step 4 above, which involves generating a solar radio imaging quick-view product and a solar radio spectrum quick-view product based on the solar radio brightness temperature image and the calibrated solar radio spectrum data, may include: Select solar radio brightness temperature images with typical frequencies from the solar radio brightness temperature images; The solar radio brightness temperature image with typical frequency points is subjected to brightness stretching and color mapping processing to obtain a preprocessed solar radio brightness temperature image. Information annotation is performed on the preprocessed solar radio brightness temperature image to generate a solar radio imaging quick-view product; The calibrated solar radio spectrum data is smoothed and preprocessed to generate a multipolar spectrum diagram and a time-varying curve of characteristic frequency point flux according to a preset layout. The time axis, frequency axis, physical quantity unit and observation mode are marked to generate a solar radio spectrum quick view product. The information annotation may include one or more of the following: observation time, frequency, polarization components, and coordinate system information; Specifically, in this implementation, the process of generating a solar radio imaging quick-view product based on a solar radio brightness temperature image may include: Solar radio brightness temperature images (I polarization component) were selected at three typical frequencies: 164MHz, 300MHz, and 432MHz. Linear stretching of the brightness temperature image data (e.g., the value range can be matched to the sensitivity index of the Quiet Sun 8900K-23200K@5ms) and Jet color mapping is used to improve contrast; Label the heliocentric coordinate system (X-axis east-west, Y-axis north-south), observation time (UTC), frequency, polarization components (I), and color scale on the right side of the image; It adopts JPG format, with a resolution of 812 (horizontal) × 612 (vertical) pixels (brightness temperature information area 512 × 512 pixels, annotation area 200 × 612 pixels), and is stored in single time point (time segmentation encoding STP); Specifically, in this implementation, the process of generating a solar radio spectrum quick-view product based on the calibrated solar radio spectrum data may include: The vertical axis (frequency) of the solar radio spectrum data was smoothed at 20 frequency points, and 1966 effective frequency points (150MHz-450MHz) were retained. The left side can be a horizontal polarization spectrum diagram, the middle can be a vertical polarization spectrum diagram (the horizontal axis is time and the vertical axis is frequency), and the right side can be a time-varying curve of solar flux at a frequency of 300MHz. Label the time axis (e.g., 0-5 minutes for a 5-minute quick view, 0-8 hours for a full-day quick view), frequency axis (150MHz-450MHz), flow unit (sfu), and observation mode; Use JPG format, resolution 1500 (horizontal) × 2100 (vertical) pixels, stored in 5-minute (0.5M) or 1-day (DAY) segments; In one implementation, the quality inspection in step 5 above may include: data integrity inspection, signal quality inspection, and calibration accuracy inspection; The metadata specification may include: Binary data products record the product name, observation mode, equipment parameters, and data start and end times in the file header; Image and spectrum data products store observation mode, physical quantity type, calibration time, and coordinate system information through key fields; QuickView products store quality levels, observation parameters, and annotation information through preset data segments; In this implementation, product reliability can be ensured through end-to-end data quality inspection. Specific quality inspection processes may include: Check whether the size and number of records of the data files at each stage meet the preset standards (e.g., a 1-minute file of the multiple visibility function should contain 195,312 relevant values ​​× 60 / 0.75 records). If the missing rate is >5%, trigger the data re-collection mechanism. Signal quality inspection: Narrowband and pulse interference in the spectrum data are analyzed, and abnormal frequency points are identified and removed using a threshold method (interference intensity > 3 times the background noise), thereby ensuring that the effective data ratio is > 95%; Calibration accuracy test: Compare the amplitude deviation (≤5%) and phase deviation (≤±2.5°) of the data before and after calibration. If the values ​​exceed the threshold, the calibration process must be repeated. Product quality marking: The quality level is marked according to the test results: "Excellent" (data complete, interference <1%), "Good" (data complete, interference 1%-5%), "Poor" (data missing or interference >5%), and the marking information is written into the product metadata.

[0030] In one implementation, step 6 above, which involves encapsulating the standardized data products that have passed quality inspection according to preset standardized naming rules and metadata specifications, and classifying and storing them to corresponding storage nodes, may include: (1) Standardized naming process: Named according to the rule "OBSID_CARTnn_subclass encoding level time segmentation encoding_YYYYMMDDhhmmss_Vnn.nn.extension", the field definitions can be as follows; OBSID: Represents the 5-digit observation station code (according to the "Rules for Naming and Coding Entities of xx Project", xx station is in a radio quiet zone, with longitude 100.246E, latitude 29.011N, and altitude 3750m). CARTnn: Represents a 4-digit device type code ("CART" is the identifier for a circular array telescope, and nn is the serial number, starting from 01). Subclass encoding: CPVF (Complex Visibility Function), SRIM (Solar Radio Image), SRQP (Synchronous Quick View), SRSR (Raw Spectrum), SRSP (Solar Radio Spectrum), SRSQ (Synchronous Quick View); Levels: L0 (raw data), L1 (calibrated data), L2 (image products), L2Q (imaging quick view), L1Q (spectral quick view); Time segmentation coding: 01M (1 minute), 05M (5 minutes), STP (single time point), DAY (1 day); File extensions: DAT (binary data), FITS (image / spectral data), JPG (fast-view image); (2) Metadata encapsulation: Binary / DAT files: can use the xx engineering specification file header to record product name, level, observation mode, equipment parameters (such as number of antennas 313, frequency range 150MHz-450MHz), data start and end time, etc. FITS files store metadata through key fields such as "OPERMODE" (observation mode), "QUANTITY" (physical quantities, such as BrightTemp(K), SolarFlux(sfu)), "CALI_TM" (calibration time), and "COORDNAM" (heliocentric coordinate system). JPG file: indicates that “Chinese Meridian Project\0” + FileMarker + metadata is stored in the APP15 data segment, including quality markers, observation parameters, etc. Long-term storage: Plan storage nodes based on average annual data volume; High-performance nodes: Storage complex visibility function (1842.2TB per year); Typical nodes store solar radio images (41.2TB per year), solar radio spectra (2.5TB per year), and raw spectra (2.5TB per year). Cost nodes: Store QuickView images (Imaging QuickView 63.3GB / year, Spectrum QuickView 191.8GB / year); simultaneously build Elasticsearch indexes to support fast queries by time, frequency, and quality level.

[0031] In summary, this invention addresses the technical problems in existing circular array solar radio imaging telescope data processing, such as fragmented workflows, insufficient calibration accuracy, lack of product standardization, and imbalance between real-time performance and quality. It proposes an integrated product generation method that, through an end-to-end design encompassing raw data generation, multi-dimensional calibration, brightness temperature image inversion, fast-view product generation, end-to-end quality inspection, and standardized packaging and storage, achieves a complete processing chain from antenna unit output signals to standardized data products. Specifically, the multi-dimensional calibration scheme comprehensively covers channel, phase, and system-wide error correction, effectively eliminating the effects of antenna gain drift and environmental interference, thus improving the accuracy of data physical quantities. Brightness temperature image inversion accurately reconstructs the spatial distribution of solar radio radiation through baseline screening, regular grid interpolation, hybrid weighting, and cleansing processing. End-to-end quality inspection ensures product reliability from multiple dimensions, including data integrity, signal purity, and calibration accuracy. Standardized naming, metadata encapsulation, and classification storage mechanisms solve the challenges of data archiving and joint analysis. Therefore, the method of this invention can be adapted to the high spatial, high temporal and high frequency resolution observation requirements of telescopes, taking into account data accuracy, real-time performance and quality controllability, and providing key technical support for solar activity monitoring, solar radio burst research and space weather early warning.

[0032] Example 2: Taking the full-band event mode observations of the XX station of the XX Phase II Project as an example, this paper discusses the implementation process of a method for generating products for a XX solar radio imaging telescope based on the XX array proposed in this invention. The steps may include: System hardware configuration: 1. Data acquisition hardware: 313 sets of dual-polarized antenna units (6-meter aperture, gain ≥25dBi), 626 receiving links (noise figure ≤3dB), 16-channel high-speed AD acquisition card (meeting the frequency resolution requirements of 2MHz / 7.6kHz), and central computing server (CPU: Intel Xeon Gold 6348, memory 256GB).

[0033] 2. Calibration hardware: high and low temperature noise source (77K / ambient temperature), simulated fiber optic loop (loss ≤0.5dB / km), 100-meter central calibration tower (omnidirectional calibration antenna gain ≥10dBi).

[0034] 3. Storage hardware: Distributed storage system (total capacity 10PB, high-performance node read / write speed ≥10GB / s, ordinary node ≥5GB / s).

[0035] System software configuration: Operating system: Linux CentOS 8.4.

[0036] Development languages: C++ (core algorithms: correlation operations, inverse Fourier transform), Python (data visualization, metadata processing); Third-party libraries: FFTW (Fourier Transform), OpenCV (Image Enhancement), CFITSIO (FITS File Processing); Databases: MySQL (metadata management), Elasticsearch (data indexing).

[0037] 3. Implementation steps: Step S1: Data Acquisition (Observation Time: UTC 2024-05-20 12:00:00): The antenna unit receives solar radio signals in the 150MHz-450MHz range, which are then amplified and filtered by the receiving link and converted into a 70MHz intermediate frequency signal. The AD acquisition card samples the intermediate frequency signal, performs digital down-conversion to 10MHz, and performs 1024-point FFT transformation. Cross-correlation operations generate 195,312 raw complex visibility function values ​​(HH / HV / VH / VV polarization), which are stored at 1 minute / file (filename: DCXXXX_CART01_CPVF_L0_01M_20240520120000_V1.00.DAT, where DCXXXX is the 5-bit code for station xx); Autocorrelation operation generates 39,322 raw spectral values ​​(H / V polarization), which are stored at 5-minute intervals per file (filename: DCXXXX_CART01_SRSR_L0_05M_20240520120000_V1.00.DAT).

[0038] Step S2: Data calibration Channel calibration: Inject a noise signal of 77K / 25℃ (ambient temperature), measure the channel output power P_low temperature = 0.1mV, P_high temperature = 0.5mV, calculate G = (0.5-0.1) / (25-77) = -0.0077mV / K (absolute value used for amplitude correction), T = (0.1 / 0.0077)-77≈-64K (corrected noise temperature); Phase calibration: Simulate a 10MHz signal transmission in a fiber optic loop, measure the phase difference Δφ1-Δφ313 (range 0.5°-2.0°) between channels 1-313, and perform phase correction on the complex visibility function data; System-wide calibration: Point the control antenna at a 100-meter calibration tower, receive a 300MHz standard signal (10μW power), measure the system response, and correct the antenna pattern. ; Spectrum calibration: Using observational data from the solar quiet zone, we established S=0.002×P+0.1 (where P is the relative power), and converted the original spectrum P=500 to S=0.002×500+0.1=1.1sfu, generating solar radio spectrum data (filename: DCXXXX_CART01_SRSP_L1_05M_20240520120000_V1.00.FITS).

[0039] Step S3: Image Inversion Baseline processing: UV coordinates were calculated, 23 occluded baselines (SNR<5) were removed, and 195,289 valid baselines were retained; Grid interpolation: Interpolate to a 2048×2048 regular grid (grid spacing matches 4′50″ resolution at 150MHz); Weighting: The central UV region (radius < 500m) is uniformly weighted, while the edge regions are naturally weighted; Inverse Fourier Transform: Substitute into the formula to calculate brightness temperature The range is 1000K-10000K; Cleaning process: The CLEAN algorithm was iterated 200 times, and the side lobe intensity was reduced to less than 1% of the main lobe intensity, generating a 512×512 pixel image (filename: DCXXXX_CART01_SRIM_L2_STP_20240520120000_V1.00.FITS).

[0040] Step S4: QuickView Product Generation Imaging quick-view image: Select the I-polarization image at 300MHz, linearly stretch it to 1000K-10000K, label it "UTC2024-05-20 12:00:00, 300MHz, I-polarization", and generate a JPG file (filename: DCXXXX_CART01_SRQP_L2Q_STP_20240520120000_V1.00_F300.JPG); Spectrum quick view image: Smooth the 300-305MHz spectrum at 20 points to generate H / V polarization spectrum and 300MHz time-varying curve, labeled "0-5 minutes, 150-450MHz, sfu", and generate a JPG file (filename: DCXXXX_CART01_SRSQ_L1Q_05M_20240520120000_V1.00.JPG).

[0041] Step S5: Quality Control Integrity check: Number of records in the file with multiple visibility function = 195312 × 60 / 0.75 = 15624960, which meets the standard (missing rate 0%). Signal detection: The interference intensity at the 302.5MHz frequency point is 4 times the background noise, so this frequency point is eliminated; Calibration accuracy: amplitude deviation 3.2%, phase deviation 1.8°, meeting the requirements; Quality Marker: All products are marked with "Excellent" rating and written into the metadata.

[0042] Step S6: Product Archiving Metadata encapsulation: Add the keywords "OPERMODE=Full-FreqMode", "QUANTITY=BrightTemp (K)", and "CALI_TM=2024Y5M20D11H30M" to FITS files; write quality markers to the APP15 section of JPG files; Storage allocation: Complex visibility functions are stored on high-performance nodes, while other products are stored on their respective nodes; Index creation: The Elasticsearch index fields contain "20240520120000, 300MHz, and excellent", supporting fast queries.

[0043] This specific embodiment illustrates that the method for generating products for a circular array solar radio imaging telescope provided by the present invention, by building a compliant hardware and software environment, completed the entire process of processing solar radio observation data during the UTC 2024-05-20 12:00:00 period. The entire implementation process verifies that the method of the present invention can stably and efficiently process massive amounts of observation data in practical applications, and the generated standardized products meet the needs of scientific research and business applications, fully demonstrating the technical effectiveness and engineering practicality of the method of the present invention.

[0044] Example 3: Based on the same inventive concept, this invention also provides a product manufacturing system for a circular array solar radio imaging telescope, the structural composition of which is shown in the schematic diagram below. Figure 2 As shown, it includes: The data generation module is used to generate raw complex visibility function data and raw solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope. The data calibration module is used to perform multi-dimensional calibration on the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data. The image brightness temperature module is used to generate a solar radio brightness temperature image based on the calibrated complex visibility function data. The product manufacturing module is used to generate solar radio imaging quick-view products and solar radio spectrum quick-view products based on the solar radio brightness temperature image and the calibrated solar radio spectrum data. The quality inspection module is used to perform quality inspection on the calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product and solar radio spectrum fast-view product to obtain standardized data products that pass the quality inspection. The data encapsulation module is used to encapsulate the standardized data products that have passed the quality inspection according to preset standardized naming rules and metadata specifications, and to classify and store them to the corresponding storage nodes.

[0045] In one implementation, the data generation module may include: The preprocessing submodule is used to amplify, bandpass filter and frequency convert the signals output by each antenna unit of the acquired circular array solar radio imaging telescope to obtain a preprocessed signal, and convert the preprocessed signal into a preset intermediate frequency signal. The transformation submodule is used to perform high-speed digital sampling, digital down-conversion, and Fourier transform on the intermediate frequency signal to obtain the transformed signal; The cross-correlation operation submodule is used to generate the original complex visibility function data by performing cross-correlation operation on the transformed signal; The autocorrelation operation submodule is used to generate raw solar radio spectrum data by performing autocorrelation operations on the transformed signal.

[0046] In one implementation, the multi-dimensional calibration may include: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; The data calibration module may include: The channel calibration submodule is used to perform channel calibration on the original complex visibility function data by injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, calculating the equivalent gain and input noise temperature of each antenna receiving channel, and obtaining the original complex visibility function data after channel calibration. The phase calibration submodule is used to transmit a standard signal to the antenna receiving channel through an analog fiber optic loop, and to perform phase calibration on the original complex visibility function data after the channel calibration, so as to obtain the phase-calibrated original complex visibility function data. The system-wide calibration submodule is used to receive the standard radio signal emitted by the central calibration tower through the antenna receiving channel and perform system-wide calibration on the original complex visibility function data after phase calibration to obtain calibrated complex visibility function data. The spectrum calibration submodule is used to perform spectrum calibration on the calibrated complex visibility function data using a preset conversion model to obtain calibrated solar radio spectrum data.

[0047] In one implementation, the image brightness temperature module may include: The baseline processing submodule is used to perform baseline processing on the calibrated complex visibility function data and retain the valid baseline; The spatial sampling submodule is used to interpolate the non-uniform spatial frequency domain sampling points corresponding to the effective baseline to a regular grid to match the telescope's spatial resolution. The hybrid weighting submodule is used to perform weighting processing on the interpolated regular grid data using a hybrid weighting strategy to obtain the weighted data. The brightness temperature distribution submodule is used to perform inverse Fourier transform on the weighted data, and calculate the solar radio brightness temperature distribution by combining the antenna pattern and coordinate system parameters to obtain the initial brightness temperature image. The cleaning processing submodule is used to clean the initial brightness temperature image to obtain a solar radio brightness temperature image.

[0048] For example, the hybrid weighting strategy can be: uniform weighting is used in the central region of the spatial frequency domain, and natural weighting is used in the edge region; The cleansing process uses the CLEAN algorithm, and the iteration stops when the sidelobe intensity is lower than the main lobe intensity by a preset percentage threshold.

[0049] In one implementation, the product manufacturing module may include: The frequency selection submodule is used to select solar radio brightness temperature images with typical frequencies from the solar radio brightness temperature images; The preprocessing submodule is used to perform brightness stretching and color mapping processing on the solar radio brightness temperature image with typical frequency points to obtain the preprocessed solar radio brightness temperature image. The information annotation submodule is used to annotate the preprocessed solar radio brightness temperature image to generate a solar radio imaging quick-view product. The smoothing submodule is used to perform smoothing preprocessing on the calibrated solar radio spectrum data, generate multipolar spectrum diagrams and time-varying curves of characteristic frequency point fluxes according to a preset layout, label the time axis, frequency axis, physical quantity units and observation mode, and generate solar radio spectrum quick-view products. The information annotation includes one or more of the following: observation time, frequency, polarization components, and coordinate system information.

[0050] For example, the quality inspection may include: data integrity inspection, signal quality inspection, and calibration accuracy inspection; The metadata specification includes: Binary data products record the product name, observation mode, equipment parameters, and data start and end times in the file header; Image and spectrum data products store observation mode, physical quantity type, calibration time, and coordinate system information through key fields; QuickView products store quality levels, observation parameters, and annotation information through preset data segments.

[0051] Example 4: like Figure 3 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0052] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the method for generating a circular array solar radio imaging telescope product in the above embodiments.

[0053] Example 5: Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the method for generating a circular array solar radio imaging telescope product in the above embodiments.

[0054] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the scope of protection of the claims pending approval.

Claims

1. A method for producing a circular array solar radio imaging telescope, characterized in that, include: Based on the signals output by each antenna element of the circular array solar radio imaging telescope, the original complex visibility function data and the original solar radio spectrum data are generated. The original complex visibility function data and the original solar radio spectrum data are calibrated in multiple dimensions to obtain calibrated complex visibility function data and solar radio spectrum data; Based on the calibrated complex visibility function data, a solar radio brightness temperature image is generated; Based on the solar radio brightness temperature image and the calibrated solar radio spectrum data, a solar radio imaging quick-view product and a solar radio spectrum quick-view product are generated. The calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product, and solar radio spectrum fast-view product are subjected to quality inspection to obtain standardized data products that pass the quality inspection. The standardized data products that have passed the quality inspection are packaged according to the preset standardized naming rules and metadata specifications, and then classified and stored in the corresponding storage nodes.

2. The method as described in claim 1, characterized in that, The process of generating raw complex visibility function data and raw solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope includes: The signals output from each antenna element of the acquired circular array solar radio imaging telescope are amplified, bandpass filtered, and frequency-converted to obtain a preprocessed signal, which is then converted into a preset intermediate frequency signal. The intermediate frequency signal is subjected to high-speed digital sampling, digital down-conversion, and Fourier transform to obtain the transformed signal; The transformed signal is cross-correlationly analyzed to generate the original complex visibility function data. The transformed signal is subjected to autocorrelation operation to generate the original solar radio spectrum data.

3. The method as described in claim 1, characterized in that, The multi-dimensional calibration includes: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; The process of multi-dimensionally calibrating the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data includes: By injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, the original complex visibility function data is calibrated by channel calibration, and the equivalent gain and input noise temperature of each antenna receiving channel are calculated to obtain the original complex visibility function data after channel calibration. By transmitting a standard signal to the antenna receiving channel through a simulated fiber optic loop, phase calibration is performed on the original complex visibility function data after the channel calibration to obtain the phase-calibrated original complex visibility function data. Using the standard radio signal emitted by the central calibration tower, the antenna receiving channel is used to receive and perform full-system calibration on the original complex visibility function data after phase calibration, thereby obtaining the calibrated complex visibility function data; The calibrated complex visibility function data is spectrally calibrated using a preset conversion model to obtain calibrated solar radio spectrum data.

4. The method as described in claim 1, characterized in that, The process of generating a solar radio brightness temperature image based on the calibrated complex visibility function data includes: Baseline processing is performed on the calibrated complex visibility function data, and valid baselines are retained; The non-uniform spatial frequency domain sampling points corresponding to the effective baseline are interpolated to a regular grid to match the telescope's spatial resolution. The interpolated regular grid data is weighted using a hybrid weighting strategy to obtain the weighted data. The weighted data is subjected to inverse Fourier transform, and combined with the antenna pattern and coordinate system parameters, the solar radio brightness temperature distribution is calculated to obtain the initial brightness temperature image; The initial brightness temperature image is cleaned to obtain a solar radio brightness temperature image.

5. The method as described in claim 4, characterized in that, The hybrid weighting strategy is as follows: uniform weighting is used in the central region of the spatial frequency domain, and natural weighting is used in the edge region; The cleansing process uses the CLEAN algorithm, and the iteration stops when the sidelobe intensity is lower than the main lobe intensity by a preset percentage threshold.

6. The method as described in claim 1, characterized in that, The process of generating solar radio imaging fast-view products and solar radio spectrum fast-view products based on the solar radio brightness temperature image and calibrated solar radio spectrum data includes: Select solar radio brightness temperature images with typical frequencies from the solar radio brightness temperature images; The solar radio brightness temperature image with typical frequency points is subjected to brightness stretching and color mapping processing to obtain a preprocessed solar radio brightness temperature image. Information annotation is performed on the preprocessed solar radio brightness temperature image to generate a solar radio imaging quick-view product; The calibrated solar radio spectrum data is smoothed and preprocessed to generate a multipolar spectrum diagram and a time-varying curve of characteristic frequency point flux according to a preset layout. The time axis, frequency axis, physical quantity unit and observation mode are marked to generate a solar radio spectrum quick view product. The information annotation includes one or more of the following: observation time, frequency, polarization components, and coordinate system information.

7. The method as described in claim 1, characterized in that, The quality inspection includes: data integrity inspection, signal quality inspection, and calibration accuracy inspection; The metadata specification includes: Binary data products record the product name, observation mode, equipment parameters, and data start and end times in the file header; Image and spectrum data products store observation mode, physical quantity type, calibration time, and coordinate system information through key fields; QuickView products store quality levels, observation parameters, and annotation information through preset data segments.

8. A product manufacturing system for a circular array solar radio imaging telescope, characterized in that, include: The data generation module is used to generate raw complex visibility function data and raw solar radio spectrum data based on the signals output by each antenna element of the acquired circular array solar radio imaging telescope. The data calibration module is used to perform multi-dimensional calibration on the original complex visibility function data and the original solar radio spectrum data to obtain calibrated complex visibility function data and solar radio spectrum data. The image brightness temperature module is used to generate a solar radio brightness temperature image based on the calibrated complex visibility function data. The product manufacturing module is used to generate solar radio imaging quick-view products and solar radio spectrum quick-view products based on the solar radio brightness temperature image and the calibrated solar radio spectrum data. The quality inspection module is used to perform quality inspection on the calibrated complex visibility function data, calibrated solar radio spectrum data, solar radio brightness temperature image, solar radio imaging fast-view product and solar radio spectrum fast-view product to obtain standardized data products that pass the quality inspection. The data encapsulation module is used to encapsulate the standardized data products that have passed the quality inspection according to preset standardized naming rules and metadata specifications, and to classify and store them to the corresponding storage nodes.

9. The system as described in claim 8, characterized in that, The data generation module includes: The preprocessing submodule is used to amplify, bandpass filter and frequency convert the signals output by each antenna unit of the acquired circular array solar radio imaging telescope to obtain a preprocessed signal, and convert the preprocessed signal into a preset intermediate frequency signal. The transformation submodule is used to perform high-speed digital sampling, digital down-conversion, and Fourier transform on the intermediate frequency signal to obtain the transformed signal; The cross-correlation operation submodule is used to generate the original complex visibility function data by performing cross-correlation operation on the transformed signal; The autocorrelation operation submodule is used to generate raw solar radio spectrum data by performing autocorrelation operations on the transformed signal.

10. The system as described in claim 8, characterized in that, The multi-dimensional calibration includes: channel calibration, phase calibration, system-wide calibration, and spectrum calibration; The data calibration module includes: The channel calibration submodule is used to perform channel calibration on the original complex visibility function data by injecting low-temperature noise signals and high-temperature noise signals into the antenna receiving channels of the circular array solar radio imaging telescope, calculating the equivalent gain and input noise temperature of each antenna receiving channel, and obtaining the original complex visibility function data after channel calibration. The phase calibration submodule is used to transmit a standard signal to the antenna receiving channel through an analog fiber optic loop, and to perform phase calibration on the original complex visibility function data after the channel calibration, so as to obtain the phase-calibrated original complex visibility function data. The system-wide calibration submodule is used to receive the standard radio signal emitted by the central calibration tower through the antenna receiving channel and perform system-wide calibration on the original complex visibility function data after phase calibration to obtain calibrated complex visibility function data. The spectrum calibration submodule is used to perform spectrum calibration on the calibrated complex visibility function data using a preset conversion model to obtain calibrated solar radio spectrum data.

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