Automatic generation system and method of space weather report based on ai

Abstract

The present disclosure relates to an automatic generation system and method of a space weather report based on AI. The present disclosure relates to a technology of generating a space weather report that analyzes / records solar radiation (R), high-energy particles (S), and change in geomagnetic field (G) which are three basic elements of a space environment, describes the analyzed and recorded three basic elements, and predicts / forecasts and notifies the future environment.

Description

AUTOMATIC GENERATION SYSTEM AND METHOD OF SPACE WEATHER REPORT BASED ON AI

[0001] The present disclosure relates to an automatic generation system and method of a space weather report based on artificial intelligence (AI), and more particularly, to an automatic generation system and method of a space weather report based on AI capable of automatic generating, based on the AI, a space weather report that records and predicts a change in space environment by analyzing solar activity, magnetosphere, and ionosphere changes.

[0002]

[0003] A space weather report is data that analyzes and records three basic elements of a space environment, which are solar radiation (R), high-energy particles (S), and geomagnetic field changes (G), describes the analyzed and recorded three basic elements, and predicts / forecasts and notifies the future environment. The space weather report is manually written every day by solar activity and space environment experts (researchers, forecasters, etc.) in the meantime, and is provided to and utilized by airlines, satellite operators, and military.

[0004] In order to write the space weather report, a wide variety of ground and satellite observation data, for example, mainly solar observation images, solar X-ray observation data, solar wind observation data, Earth magnetic field observation data, Earth ionosphere observation data, etc., and various analysis model results are being demanded.

[0005] The solar activity is a source of causing extreme disturbance in the space environment through strong energy activities such as sunspots, flare explosions, and coronal mass emissions. In order to minimize damage to near-Earth satellites and communications caused by the space environmental disturbances due to the solar activity, solar surface energy phenomenon monitoring and analysis forecasts are performed through the space weather report. In addition, the Earth's magnetosphere acts as a shield that protects the Earth from high-energy particles and cosmic radiation emitted from the solar wind. When the solar activity is active, powerful magnetic storms may occur, causing fatal radiation damage to satellites residing in outer space. Among the areas of the Earth's magnetosphere, the Van Allen Radiation Belts, where the high-energy particles are trapped and form a donut shape, are areas, in which geostationary orbits (36,000 km) where many satellites are concentrated, are included. Through the space weather report, monitoring and analysis forecasts for the Van Allen Belts will be performed using ground-received data from Van Allen Probes. The Earth's ionosphere / high atmosphere refers to the area of the space environment that is closest to human society. Since atmospheric particles exist in a plasma state caused by solar radiation energy, they are affected by various electromagnetic dynamics and energy input. As such, the changes in the ionosphere / high level atmospheric conditions due to the impact of the space environment and low level atmosphere cause serious problems in satellites, wireless communications, and terrestrial power systems. Accordingly, the monitoring and analysis forecasts are performed using the observation data, etc., through the space weather report.

[0006] Since the space weather report can only be written by solar activity and space environment experts with superior abilities to analyze a wide variety of ground and satellite observation data, the space weather report contains not only inherent problems of human error, but only analysis results and prediction / forecast results that may vary depending on the expert's capabilities. Therefore, from the perspective of the agencies that receive and utilize the space weather report, the space weather report is data that cannot help but be trusted, but contains problems of inevitably low reliability. In addition, from the perspective of the solar activity and space environment experts with superior capabilities that may analyze a wide variety of ground and satellite observation data, writing the space weather reports every day may be a cumbersome task.

[0007] In this regard, Korean Patent No. 10-1510373 ("The method and apparatus for predicting the solar coronal hole’s geo-effects") discloses a technology of predicting the impact of solar coronal hole on Earth based on image analysis information on solar coronal hole.

[0008]

[0009] An object of the present disclosure provides an automatic generation system and method of a space weather report based on artificial intelligence (AI) capable of automatic generating and providing the space weather report more easily by automating the entire process of analyzing and recording three basic elements of a space environment, which are solar radiation (R), high-energy particles (S), and geomagnetic field changes (G), based on the AI, describing the analyzed and recorded three basic elements, and predicting / forecasting and notifying the future environment.

[0010]

[0011] In one general aspect, an automatic generation system of a space weather report based on AI includes: a data input unit that receives solar and space environment observation-related data; a data preprocessing unit that visualizes the received solar and space environment observation-related data; a model prediction unit that inputs the visualized solar and space environment observation-related data to the stored artificial intelligence model and outputs space weather report data; and a report output unit that provides the output space weather report data and visualized solar and space environment observation-related data to the outside.

[0012] The automatic generation system may further include: prior to providing the space weather report data and visualized solar and space environment observation-related data output through the report output unit to the outside, a review processing unit that receives corresponding instruction information by transmitting the output space weather report data and visualized solar and space environment observation-related data to a previously linked server, in which the report output unit reflects the instruction information received through the review processing unit in the output space weather report data to finally generate the space weather report data.

[0013] The automatic generation system may further include: a data collection unit that collects a plurality of space weather report data written in the past and the solar and space environment observation-related data corresponding to each space weather report data; a data generation unit that generates the collected space weather report data and solar and space environment observation-related data as a data set for artificial intelligence learning; and a learning processing unit that generates an artificial intelligence model by performing learning on the generated data set using the pre-stored artificial intelligence algorithm, in which the model prediction unit may store the artificial intelligence model by the learning processing unit.

[0014] The learning processing unit may extract characteristics of the solar and space environment observation-related data using the pre-stored artificial intelligence algorithm and perform artificial intelligence learning through the space weather report data corresponding to the extracted characteristics.

[0015] In another general aspect, an automatic generation method of a space weather report based on AI by an automatic generation system of a space weather report based on AI in which each step is performed by calculation processing means includes: a data input step (S100) of receiving, by a data input unit, solar and space environment observation-related data when the space weather report is to be generated; a data preprocessing step (S200) of visualizing, by a data preprocessing unit, the solar and space environment observation-related data received by the data input step (S100); a model prediction step (S300) of inputting, by a model prediction unit, the visualized solar and space environment observation-related data through the data preprocessing step (S200) to the stored artificial intelligence model and outputting space weather report data corresponding to the visualized solar and space environment observation-related data; and a repot output step (S400) of integrating, by a report output unit, the visualized solar and space environment observation-related data through the data preprocessing step (S200) and the space weather report data output through the model prediction step (S300) and providing the integrated visualized solar and space environment observation-related data and space weather report data to the outside.

[0016] The automatic generation method may further include: prior to performing the report output step (S400), a review processing step (S410) of transmitting, by a review processing unit, the space weather report data output through the model prediction step (S300) and the visualized solar and space environment observation-related data to a previously linked server and receiving corresponding instruction information, in which in the report output step (S400), the instruction information received through the review processing step (S410) may be reflected in the space weather report data output through the model prediction step (S300) to finally generate the space weather report data to be provided to the outside.

[0017] The automatic generation method may further include: prior to performing the model prediction step (S300), a data collection step (S10) of collecting, by a data collection unit, a plurality of space weather report data written in the past and the solar and space environment observation-related data corresponding to each space weather report data; a data generation step (S20) of generating, by a data generation unit, the space weather report data and solar and space environment observation-related data collected through the data collection step (S10) as a data set for artificial intelligence learning; and a learning processing step (S30) of generating, by a learning processing unit, an artificial intelligence model by performing learning on the data set generated through the data generation step (S20), using a pre-stored artificial intelligence algorithm, in which in the model prediction step (S300), the artificial intelligence model generated by the learning processing step (S30) may be stored.

[0018] In the learning processing step (S30), characteristics of the solar and space environment observation-related data may be extracted using the pre-stored artificial intelligence algorithm and artificial intelligence learning may be performed through the space weather report data corresponding to the extracted characteristics.

[0019]

[0020] According to the present disclosure, by automating the entire process of generating a space weather report through the automatic generation system and method of a space weather report based on AI, it is possible to reduce the cumbersome of having researchers write the report every day.

[0021] In addition, since the system / server such as agencies that receive and use the space weather report can use the automatic generation system and method of a space weather report based on AI, it is possible to more easily acquire the information on the changes and predictions in solar activity and space environment without the help of experts.

[0022]

[0023] FIG. 1 is an exemplary configuration diagram illustrating an automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure.

[0024] FIG. 2 is an exemplary flowchart illustrating an automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure.

[0025]

[0026] Hereinafter, an automatic generation system and method of a space weather report based on AI according to the present disclosure having the configuration described above will be described in detail with reference to the attached drawings. Drawings to be provided below are provided by way of example so that the spirit of the present disclosure may be sufficiently transferred to those skilled in the art. Therefore, the present disclosure is not limited to drawings to be provided below, but may be implemented in other forms. In addition, like reference numerals denote like components throughout the specification.

[0027] In this case, if there is no other definition in the technical and scientific terms used, they have the meaning commonly understood by those with ordinary knowledge in the technical field to which this invention belongs. In the following description and accompanying drawings, descriptions of known functions and configurations that may unnecessarily obscure the gist of the present disclosure will be omitted.

[0028] In addition, a system refers to a set of components including devices, mechanisms, means, and the like, systematized in order to perform required functions and regularly interacting with each other.

[0029] An automatic generation system and method of a space weather report based on AI according to an embodiment of the present disclosure relates to a technology of automatically writing and providing a space weather report which is data that describes solar activity, magnetosphere, and ionosphere changes, and predicts and notifies future phenomena. In particular, the present disclosure goes beyond simply analyzing and providing the solar activity, magnetosphere, and ionosphere changes, and furthermore, relates to a technology of generating prediction results as text, rather than generating general analysis results as text by applying a space weather report that have been manually written every day by solar activity and space environment experts (researcher, forecaster, etc.) as learning data.

[0030] FIG. 1 is an exemplary configuration diagram illustrating an automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure. As illustrated in FIG. 1, the automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure preferably includes a data input unit 100, a data preprocessing unit 200, a model prediction unit 300, and a report output unit 400.

[0031] Each component preferably performs operations by being configured separately in a plurality of calculation processing means including a CPU or being integrated into one calculation processing means.

[0032] Each component will be described in detail.

[0033] The data input unit 100 preferably receives solar and space environment observation-related data for generating a space weather report. Due to the nature of the 'report', the data input unit 100 preferably receives data on a daily basis and performs operations, but may also receive specific data in some cases.

[0034] The solar and space environment observation-related data preferably includes solar observation images, solar X-ray observation data, solar wind observation data, Earth's magnetic field observation data, Earth's ionosphere observation data, etc. In addition to this, it is preferable to receive various solar and space environment-related observation data.

[0035] The data preprocessing unit 200 preferably visualizes the solar and space environment observation-related data input through the data input unit 100.

[0036] In detail, since the solar and space environment observation-related data input through the data input unit 100 is generated according to each set format, it is preferable that the data preprocessing unit 200 preprocesses the data to be easily input to an artificial intelligence model. In the present disclosure, it is preferable to perform visualization processing on data in a text form among the solar and space environment observation-related data input through the data input unit 100 and convert the form of text data into the form of image data. For example, when the solar wind observation data is text data in the form of a numerical model, the text data is analyzed and converted into the image data in the form of a graph. In this case, the visualization is not performed on all the solar and space environment observation-related data received through the data input unit 100, but it is preferable that data that needs to be visualized is specified and visualized, and data that does not need to be visualized is maintained in the form it is received without visualization. It is preferable to receive the type of data, which needs to be visualized, in advance from external experts, etc.

[0037] The model prediction unit 300 preferably receives the solar and space environment observation-related data from the data preprocessing unit 200, inputs the received solar and space environment observation-related data to the stored artificial intelligence model, and outputs the space weather report data.

[0038] Here, the automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure generates the artificial intelligence model and performs a learning process in advance so that it may be used in the model prediction unit 300. For this purpose, as illustrated in FIG. 1, the automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure preferably includes a data collection unit 10, a data generation unit 20, and a learning processing unit 30.

[0039] The data collection unit 10 preferably collects a plurality of space weather report data (text format data) written in the past and the solar and space environment observation-related data corresponding to each space weather report data. In other words, it is preferable to collect the space weather report data manually written by solar activity and space environment experts, as well as base data (corresponding solar and space environment observation-related data) used to write the space weather report data.

[0040] The data generation unit 20 preferably generates the space weather report data and solar and space environment observation-related data collected by the data collection unit 10 as a data set for artificial intelligence learning.

[0041] In detail, the data set is generated using the space weather report data and the base data (solar and space environment observation-related data) used to write the space weather report data, but it is preferable to perform the visualization processing on the data in the text form among the solar and space environment observation-related data which is the base data, and convert the form of the text data into the form of the image data. In other words, the form of the text data is analyzed and converted into the form of the image data in the form of a graph.

[0042] In this way, the solar and space environment observation-related data is in the data in the image form, and the space weather report data is in the data in the text form, so each data set is generated.

[0043] It is preferable that the learning processing unit 30 generates the artificial intelligence model by performing the learning on the data set generated through the data generation unit 20 using a pre-stored artificial intelligence algorithm. Here, the model prediction unit 300 preferably stores the artificial intelligence model generated through the learning processing unit 30.

[0044] It is preferable that the learning processing unit 30 extracts characteristics of the solar and space environment observation-related data using the pre-stored artificial intelligence algorithm and performs the artificial intelligence learning through the space weather report data corresponding to the extracted characteristics.

[0045] In detail, it is preferable to extract the characteristics of the solar and space environment observation-related data, which is the data in the image form, using a convolutional neural network (CNN) algorithm.

[0046] In addition, it is preferable that the learning processing unit 30 extracts the characteristics of the space weather report data in the form of the text data using the text generation algorithm. In detail, the texts related to the analysis / recording contents of the solar radiation (R), high-energy particles (S), and magnetic field changes (G) included in the space weather report data written in the past are analyzed, which is extracted as the characteristics.

[0047] In this way, the learning processing unit 30 finally performs the artificial intelligence learning by configuring the characteristics of the solar and space environment observation-related data in the form of the image data and the characteristics of the space weather report data in the form of the text data into sets.

[0048] During the learning process, a loss function value between the output data and the correct answer data is calculated, and an optimization algorithm is applied to minimize the calculated loss function value. The applied loss function value itself or optimization algorithm itself is not limited.

[0049] It is preferable to use a transformer-based algorithm as the text generation algorithm, and to summarize, it is formed of an encoder-decoder based structure. The text is generated in a manner that the decoder receives results of embedding the text data input from the encoder and an output token generated up to the previous step to predict a token with the highest probability in the predicted token probability distribution, or a next token by a sampling method based on probability, etc., and generates tokens until an end token appears or a specified maximum length is reached. In the present disclosure, the text generation algorithm is not necessarily limited to the transformer-based algorithm, and various text generation algorithms may be applied according to the development of technology.

[0050] However, as described above, since the learning processing unit 30 receives the solar and space environment observation-related data in the form of the image data and the space weather report data in the form of the text data as a set of learning data to perform the learning processing, it will be included in the space weather report data written in the past by the solar activity and space environment experts and will be included as the space environment forecast according to the experts' capabilities. In other words, the artificial intelligence model by the learning processing unit 30 does not only simply analyze the input data and output the results, but also analyzes the input data and outputs the prediction results according to the analysis results.

[0051] In this way, it is preferable that the model prediction unit 300 uses the artificial intelligence model by the learning processing unit 30 to receive the solar and space environment observation-related data transmitted through the data preprocessing unit 200, and output the space weather report data accordingly. The output space weather report data includes the text data related to the contents of the solar radiation (R), high-energy particles (S), and magnetic field changes (G) corresponding to the characteristics of the input solar and space environment observation-related data. Accordingly, the space weather report data output through the model prediction unit 300 includes not only highly accurate analysis results of the solar and space environment observation-related data, but also highly reliable forecast results.

[0052] It is preferable that the report output unit 400 synthesizes the space weather report data output through the model prediction unit 300 and the base data of the output space weather report data (the solar and space environment observation-related data transmitted through the data preprocessing unit 200), and provides the synthesized space weather report data and base data to the linked external server / agency, etc.

[0053] In this case, prior to providing comprehensive results (the space weather report data output through the model prediction unit 300 and the base data of the space weather report data) to external servers / agencies, etc., through the report output unit 400, as illustrated in FIG. 1, the automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure performs an inspection by a solar activity and space environment expert through a review processing unit 500.

[0054] The space weather report data is not information that the general public should check a weather forecast, etc., every day, but is clearly very important information for the linked external servers / agencies, etc., and key information that can cause communication damage in some cases even to ordinary people. Therefore, the space weather report data output from the artificial intelligence model is reviewed through the review processing unit 500.

[0055] It is preferable that the review processing unit 500 transmits the space weather report data output through the model prediction unit 300 and the base data of the space weather report data output to the pre-linked server (solar activity and space environment expert server) and receives the corresponding instruction information from the server. The instruction information input through the server may be correction request information or confirmation information for the space weather report data. That is, the space weather report data output through the model prediction unit 300 and the solar and space environment observation-related data by the data preprocessing unit 200, which is the base data of the output space weather report data, are transmitted to the solar activity and space environment experts, and by comparing the information on the solar radiation (R), high-energy particles (S), and magnetic field changes (G) and the solar and space environment observation-related data directly interpreted by the solar activity and space environment experts with the text contents of the space weather report data output through the model prediction unit 300, the correction request information or confirmation information is received.

[0056] In this way, the report output unit 400 reflects the instruction information input through the review processing unit 500 in the output space weather report data to finally generate the space weather report data, synthesizes the base data of the generated space weather report data, and provides the synthesized base data to the linked external server / agency, etc.

[0057] FIG. 2 is an exemplary flowchart illustrating the automatic generation system of a space weather report based on AI according to an embodiment of the present disclosure. As illustrated in FIG. 2, the automatic generation method of a space weather report based on AI according to an embodiment of the present disclosure includes a data input step (S100), a data preprocessing step (200), a model prediction step (S300), and a report output step (S400).

[0058] Each step is performed through the automatic generation system of a space weather report based on AI that operates by being configured separately in a plurality of calculation processing means including a CPU or being integrated into one calculation processing means.

[0059] Each step will be described in detail.

[0060] In the data input step (S100), the data input unit 100 receives the solar and space environment observation-related data for generating the space weather report. Due to the nature of the 'report', it is preferable to receive data on a daily basis and perform operations, but in some cases, specific data may be received.

[0061] The solar and space environment observation-related data preferably includes solar observation images, solar X-ray observation data, solar wind observation data, Earth's magnetic field observation data, Earth's ionosphere observation data, etc. In addition to this, it is preferable to receive various solar and space environment-related observation data.

[0062] The data preprocessing step (S200) visualizes the solar and space environment observation-related data received by the data input step (S100) in the data preprocessing unit (200). In detail, since the solar and space environment observation-related data input by the data input step (S100) is generated according to each set format, in the data preprocessing step (S200), the preprocessing is performed to facilitate the input to the artificial intelligence model. For this purpose, it is preferable to perform visualization processing on data in a text form among the input solar and space environment observation-related data and convert the data in the form of the text data into the form of the image data. For example, when the solar wind observation data is text data in the form of a numerical model, the text data is analyzed and converted into the image data in the form of a graph. In this case, the visualization is not performed on all the solar and space environment observation-related data received by the data input step (S100), but it is preferable that data to be visualized is specified and visualized, and data that does not need to be visualized is maintained in the form it is received without visualization. It is preferable to receive the type of data, which needs to be visualized, in advance from external experts, etc.

[0063] In the model prediction step (S300), the model prediction unit 300 inputs the solar and space environment observation-related data to the stored artificial intelligence model through the data preprocessing step (S200) and outputs the corresponding space weather report.

[0064] In this case, as illustrated in FIG. 2, prior to performing the model prediction step (S300), the automatic generation method of a space weather report based on AI according to an embodiment of the present disclosure further performs a data collection step (S10), a data generation step (S20), and a learning processing step (S30) to store the artificial intelligence model.

[0065] In the data collection step (S10), the data collection unit 10 preferably collects a plurality of space weather report data (text format data) written in the past and the solar and space environment observation-related data corresponding to each space weather report data. In other words, the space weather report data manually written by solar activity and space environment experts and the base data (corresponding solar and space environment observation-related data) used to write the space weather report data are collected.

[0066] In the data generation step (S20), the data generation unit 20 generates the space weather report data and solar and space environment observation-related data collected through the data collection step (S10) as the data set for the artificial intelligence learning.

[0067] In detail, the data set is generated using the space weather report data and the basic data (solar and space environment observation-related data) used to write the space weather report data, but the visualization processing is performed on the form of the text data among the solar and space environment observation-related data which is the basic data to convert the form of the text data into the form of the image data. In other words, the form of the text data is analyzed and converted into the form of the image data in the form of a graph. In this way, the solar and space environment observation-related data is in the form of the image data, and the space weather report data is in the data in the text form, so each data set is generated.

[0068] In the learning processing step (S30), the learning processing unit 30 generates the artificial intelligence model by performing the learning on the data set generated through the data generation step (S20) using the pre-stored artificial intelligence algorithm. In this case, the generated artificial intelligence model is used in the model prediction step (S300).

[0069] In detail, in the learning processing step (S30), the characteristics of the solar and space environment observation-related data, which is the form of the image data, are extracted using the convolutional neural network (CNN) algorithm.

[0070] Thereafter, in the learning processing step (S30), the characteristics of the space weather report data in the form of the text data are extracted using the text generation algorithm.

[0071] In detail, the texts related to the analysis / recording of the solar radiation (R), high-energy particles (S), and magnetic field changes (G) included in the space weather report data written in the past are analyzed, which is extracted as the characteristics.

[0072] In this way, in the learning processing step (S30), the artificial intelligence learning is performed by configuring the characteristics of the solar and space environment observation-related data in the form of the image data and the characteristics of the space weather report data in the form of the text data into sets. During the learning process, the loss function value between the output data and the correct answer data is calculated, and an optimization algorithm is applied to minimize the calculated loss function value. The applied loss function value itself or optimization algorithm itself is not limited.

[0073] It is preferable to use the transformer-based algorithm as the text generation algorithm, and to summarize, it is formed of an encoder-decoder based structure. The text is generated in a manner that the decoder receives results of embedding the text data input from the encoder and an output token generated up to the previous step to predict a token with the highest probability in the predicted token probability distribution, a next token by a sampling method based on probability, etc., and generates tokens until an end token appears or a specified maximum length is reached. In the present disclosure, the text generation algorithm is not necessarily limited to the transformer-based algorithm, and various text generation algorithms may be applied according to the development of technology.

[0074] However, as described above, in the learning processing step (S30), since the solar and space environment observation-related data in the form of the image data and the space weather report data in the form of the text data are received as a set of learning data to perform the learning processing, it will be included in the space weather report data written in the past by the solar activity and space environment experts and will be included as the space environment forecast according to the experts' capabilities. In other words, the artificial intelligence model by the learning processing step (S30) does not only simply analyze the input data and output the results, but also analyzes the input data and outputs the prediction results according to the analysis results.

[0075] In this way, in the model prediction step (S300), by using the artificial intelligence model by the learning processing step (S30), the solar and space environment observation-related data transmitted through the data preprocessing unit 200 is received, and the space weather report data is output accordingly. The output space weather report data includes the text data related to the solar radiation (R), high-energy particles (S), and magnetic field changes (G) corresponding to the characteristics of the input solar and space environment observation-related data. Accordingly, the space weather report data output through the model prediction step (S300) includes not only highly accurate analysis results of the solar and space environment observation-related data, but also highly reliable forecast results.

[0076] In the report output step (S400), the report output unit 400 synthesizes the space weather report data output through the model prediction unit 300 and the base data of the output space weather report data (the solar and space environment observation-related data transmitted through the data preprocessing unit 200), and provides the synthesized space weather report data and base data to the linked external server / agency, etc.

[0077] However, the space weather report data is not information that the general public should check a weather forecast, etc., every day, but is clearly very important information for the linked external servers / agencies, etc., and key information that can cause communication damage in some cases even to ordinary people. Therefore, the space weather report data output from the artificial intelligence model is reviewed.

[0078] To this end, as illustrated in FIG. 2, prior to performing the report output step (S400), the automatic generation method of a space weather report based on AI according to an embodiment of the present disclosure includes a review processing step (S410).

[0079] In the review processing step (S410), it is preferable that the review processing unit 500 transmits the space weather report data output through the model prediction step (300) and the base data of the space weather report data output to the pre-linked server (solar activity and space environment expert server) and receives the corresponding instruction information from the server. The instruction information input through the server may be correction request information or confirmation information for the space weather report data. That is, the space weather report data output through the model prediction step (S300) and the solar and space environment observation-related data by the data preprocessing step (S200), which is the base data of the output space weather report data, are transmitted to the solar activity and space environment experts, and by comparing the information on the solar radiation (R), high-energy particles (S), and magnetic field changes (G) and the solar and space environment observation-related data directly interpreted by the solar activity and space environment experts with the text contents of the space weather report data output through the model prediction step (S300), the correction request information or confirmation information is received.

[0080] In this way, in the report output step (S400), the instruction information input through the review processing step (S410) is reflected in the output space weather report data to finally generate the space weather report data, synthesize the base data of the generated space weather report data, and provide the synthesized base data to the linked external server / agency, etc.

[0081] Meanwhile, in one embodiment of the present disclosure, the automatic generation system and method of a space weather report based on AI may be implemented in the form of program commands that may be executed through various electronic information processing means and recorded on the storage medium. The storage medium may include program commands, data files, data structures, or the like, alone or a combination thereof.

[0082] Hereinabove, although the present disclosure is described by specific matters such as concrete components, and the like, exemplary embodiments, and drawings, they are provided only for assisting in the entire understanding of the present disclosure. Therefore, the present disclosure is not limited to the exemplary embodiments. Various modifications and changes may be made by those skilled in the art to which the present disclosure pertains from this description.

[0083] Accordingly, the spirit of the present disclosure should not be limited to these embodiments, and the claims and all of modifications equal or equivalent to the claims are intended to fall within the scope and spirit of the present disclosure.

Claims

1.An automatic generation system of a space weather report based on AI, comprising:a data input unit that receives solar and space environment observation-related data;a data preprocessing unit that visualizes the received solar and space environment observation-related data;a model prediction unit that inputs the visualized solar and space environment observation-related data to the stored artificial intelligence model and outputs space weather report data; anda report output unit that provides the output space weather report data and visualized solar and space environment observation-related data to the outside.2.The automatic generation system of claim 1, further comprising:prior to providing the space weather report data and visualized solar and space environment observation-related data output through the report output unit to the outside, a review processing unit that receives corresponding instruction information by transmitting the output space weather report data and visualized solar and space environment observation-related data to a previously linked server,wherein the report output unit reflects the instruction information received through the review processing unit in the output space weather report data to finally generate the space weather report data.3.The automatic generation system of claim 1, further comprising:a data collection unit that collects a plurality of space weather report data written in the past and the solar and space environment observation-related data corresponding to each space weather report data;a data generation unit that generates the collected space weather report data and solar and space environment observation-related data as a data set for artificial intelligence learning; anda learning processing unit that generates an artificial intelligence model by performing learning on the generated data set using the pre-stored artificial intelligence algorithm,wherein the model prediction unit stores the artificial intelligence model by the learning processing unit.4.The automatic generation system of claim 3, wherein the learning processing unit extracts characteristics of the solar and space environment observation-related data using the pre-stored artificial intelligence algorithm and performs artificial intelligence learning through the space weather report data corresponding to the extracted characteristics.5.An automatic generation method of a space weather report based on AI by an automatic generation system of a space weather report based on AI in which each step is performed by calculation processing means, the automatic generation method comprising:a data input step of receiving, by a data input unit, solar and space environment observation-related data when the space weather report is to be generated;a data preprocessing step of visualizing, by a data preprocessing unit, the solar and space environment observation-related data received by the data input step;a model prediction step of inputting, by a model prediction unit, the visualized solar and space environment observation-related data through the data preprocessing step to the stored artificial intelligence model and outputting space weather report data corresponding to the visualized solar and space environment observation-related data; anda repot output step of integrating, by a report output unit, the visualized solar and space environment observation-related data through the data preprocessing step and the space weather report data output through the model prediction step and providing the integrated visualized solar and space environment observation-related data and space weather report data to the outside.6.The automatic generation method of claim 5, further comprising:prior to performing the report output step,a review processing step of transmitting, by a review processing unit, the space weather report data output through the model prediction step and the visualized solar and space environment observation-related data to a previously linked server and receiving corresponding instruction information,wherein in the report output step, the instruction information received through the review processing step is reflected in the space weather report data output through the model prediction step to finally generate the space weather report data to be provided to the outside.7.The automatic generation method of claim 5, further comprising:prior to performing the model prediction step,a data collection step of collecting, by a data collection unit, a plurality of space weather report data written in the past and the solar and space environment observation-related data corresponding to each space weather report data;a data generation step of generating, by a data generation unit, the space weather report data and solar and space environment observation-related data collected through the data collection step as a data set for artificial intelligence learning; anda learning processing step of generating, by a learning processing unit, an artificial intelligence model by performing learning on the data set generated through the data generation step, using a pre-stored artificial intelligence algorithm,wherein, in the model prediction step, the artificial intelligence model generated by the learning processing step is stored.8.The automatic generation method of claim 7, wherein in the learning processing step, characteristics of the solar and space environment observation-related data are extracted using the pre-stored artificial intelligence algorithm and artificial intelligence learning is performed through the space weather report data corresponding to the extracted characteristics.

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