Spectral data classification method and device based on multi-source star catalog cross matching

Abstract

The present application provides a spectral data classification method and device based on multi-source star catalog cross-matching, which can be applied to the field of astrophysics. The method includes: cross-matching the target star catalog with the first star catalog and the second star catalog to obtain first homologous data and second homologous data; the first homologous data and the second homologous data respectively represent the classification of the homologous celestial body spectral data in the first star catalog and the second star catalog; based on the first homologous data and the second homologous data, a classification fusion strategy is used to classify the spectral data to be classified in the target star catalog; the spectral data to be classified includes spectral data whose spectral characteristics meet at least one of the first preset evaluation criteria, spectral data when the target star catalog and the first homologous data have different classifications for the same celestial body, and spectral data when the target star catalog and the second homologous data have different classifications for the same celestial body. In this way, the cost of manual review is greatly reduced and the accuracy and quality of spectral classification are improved.

Description

Technical Field

[0001] The present application relates to the field of astrophysics, specifically to the field of spectral data classification, and more specifically to a spectral data classification method, apparatus, device, medium and program product based on multi-source star catalog cross-matching. Background Art

[0002] Spectral data classification is an important part of studying the laws of celestial evolution. However, due to factors such as observation conditions, signal-to-noise ratio, and observation instruments, the quality of some existing spectral data is poor, resulting in unreliable classification results. Therefore, manual inspection and reclassification of spectral data with low confidence in classification results and poor signal-to-noise ratio are required. This method is extremely labor-intensive and time-consuming, thereby reducing the efficiency of data release. Summary of the Invention

[0003] In view of the above problems, the present application provides a spectral data classification method, apparatus, device, medium and program product based on multi-source star catalog cross-matching to improve the accuracy of spectral data classification.

[0004] According to a first aspect of the present application, a spectral data classification method based on multi-source star catalog cross-matching is provided, comprising:

[0005] Cross-matching the target star catalog with the first star catalog to obtain a first homologous data set; cross-matching the target star catalog with the second star catalog to obtain a second homologous data set; wherein the first homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the first star catalog in the first star catalog, and the second homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the second star catalog in the second star catalog;

[0006] According to the first homologous data set and the second homologous data set, a classification fusion strategy is adopted to classify the spectral data to be classified in the target star catalog; wherein the spectral data to be classified include spectral data in the target star catalog whose spectral data characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous data set is different, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set is different.

[0007] According to an embodiment of the present application, cross-matching a target star catalog with a first star catalog to obtain a first homologous data set includes: determining the position of a celestial body represented by each spectral data in the target star catalog; searching the first celestial body corresponding to each celestial body within the first preset radius in the first star catalog with the position of each celestial body as the center, representing the first celestial body as the same celestial body as the corresponding celestial body located at the center position, and using spectral classification information of the spectral data of the first celestial body in the first star catalog as the first homologous data set;

[0008] Cross-matching the target star catalog with the second star catalog to obtain a second homologous data set includes: taking the position of each celestial body as the center, searching the second star catalog for a second object corresponding to each celestial body and located within the second preset radius according to a second preset radius, representing the second object as the same celestial body as the corresponding celestial body located at the center position, and using spectral classification information of the spectral data of the second object in the second star catalog as the second homologous data set.

[0009] According to an embodiment of the present application, the first homologous data set includes spectral classification information without warnings and spectral classification information of a type of galaxy spectrum or quasar spectrum; wherein, no warnings indicates that the quality of the spectral data meets the first preset requirement and the spectral classification information is unambiguous;

[0010] The second homologous data set includes spectral classification information of spectral data having single spectral classification information and spectral classification information of which spectral type differences between multiple spectral classification information representing the same celestial body are respectively within a first preset spectral subtype range.

[0011] According to an embodiment of the present application, a classification fusion strategy is used to classify the spectral data to be classified in the target star catalog based on the first homologous data set and the second homologous data set, including:

[0012] If there is first spectral classification information corresponding to a celestial body whose distance from the celestial body represented by the spectral data to be classified is less than a first preset threshold in the first homologous dataset, the first spectral classification information is used as the final spectral classification information of the spectral data to be classified;

[0013] If the first spectral classification information does not exist in the first homologous dataset, but the second spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a second preset threshold exists in the second homologous dataset, then when the spectral data to be classified meets the second preset requirement, the second spectral classification information is used as the final spectral classification information of the spectral data to be classified;

[0014] If the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, when the characteristic parameters of the spectral data to be classified respectively meet the second preset evaluation criteria, the spectral classification information of the spectral data to be classified in the target star catalog is used as the final spectral classification information of the spectral data to be classified.

[0015] According to an embodiment of the present application, the spectral data to be classified meets the second preset requirement, including:

[0016] The spectral classification information of the spectral data to be classified in the target star catalog is consistent with the second spectral classification information, or the distance between the celestial body corresponding to the second spectral classification information in the second homologous data set and the celestial body represented by the spectral data to be classified is less than or equal to a third preset threshold.

[0017] According to an embodiment of the present application, the second preset evaluation criteria include:

[0018] The redshift value of the spectral data matches the spectral classification information of the corresponding spectral data, the Hertzsprung-Russell diagram distribution of the spectral data matches the spectral classification information of the corresponding spectral data, and each characteristic parameter of the spectral data is located within the corresponding boundary value.

[0019] According to an embodiment of the present application, the first preset evaluation criteria include:

[0020] The first field corresponding to the spectral data is not 0, the redshift confidence of the spectral data is less than the preset redshift confidence threshold, the r-band signal-to-noise ratio of the spectral data is less than the preset signal-to-noise ratio threshold, the redshift value of the spectral data does not match the spectral classification information of the corresponding spectral data, and the spectral classification information is uncertain; wherein, the first field represents the reliability of the spectral data.

[0021] A second aspect of the present application provides a spectral data classification device based on multi-source star catalog cross-matching, comprising:

[0022] A homologous data acquisition module is configured to cross-match a target star catalog with a first star catalog to obtain a first homologous data set; and cross-match the target star catalog with a second star catalog to obtain a second homologous data set; wherein the first homologous data set includes spectral classification information in the first star catalog for spectral data representing the same celestial body in the target star catalog and the first star catalog, and the second homologous data set includes spectral classification information in the second star catalog for spectral data representing the same celestial body in the target star catalog and the second star catalog;

[0023] The spectral classification information acquisition module is used to classify the spectral data to be classified in the target star catalog based on the first homologous data set and the second homologous data set using a classification fusion strategy; wherein the spectral data to be classified include spectral data in the target star catalog whose spectral data characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous data set are different, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set are different.

[0024] The third aspect of the present application provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0025] The fourth aspect of the present application further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0026] The fifth aspect of the present application further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0027] One or more of the above embodiments have the following beneficial effects: further classifying low-quality spectral data in the target star catalog using an external high-quality star catalog can improve the accuracy of low-quality spectral data classification, greatly reduce the cost of manual review, improve the accuracy and quality of spectral data classification, and thus improve the data publishing efficiency of subsequent processing flows. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The above contents and other objects, features and advantages of the present application will become more apparent through the following description of the embodiments of the present application with reference to the accompanying drawings, in which:

[0029] Figure 1 Schematically illustrates an application scenario diagram of a spectral data classification method, apparatus, device, medium, and program product based on multi-source star catalog cross-matching according to an embodiment of the present application;

[0030] Figure 2 The flowchart of the spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application is schematically shown;

[0031] Figure 3 The following schematically shows a flow chart of obtaining spectral data to be classified according to an embodiment of the present application;

[0032] Figure 4The following schematically shows a flow chart of classifying spectral data to be classified according to an embodiment of the present application;

[0033] Figure 5 The following schematically shows a structural block diagram of a spectral data classification device based on multi-source star catalog cross-matching according to an embodiment of the present application;

[0034] Figure 6 The block diagram of an electronic device suitable for implementing a spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0035] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present application. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0036] The terms used herein are only for describing specific embodiments and are not intended to limit this application. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0037] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0038] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0039] The embodiments of the present application provide a spectral data classification method based on cross-matching of multiple source star catalogs. For low-quality spectral data in the target star catalog, an external high-quality star catalog is used to improve the accuracy of low-quality spectral data classification, thereby greatly reducing the cost of manual review, improving the accuracy and quality of spectral data classification, and further improving the data publishing efficiency of subsequent processing flows.

[0040] Figure 1 The following schematically illustrates an application scenario of a spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application.

[0041] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0042] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0043] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0044] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0045] It should be noted that the spectral data classification method based on multi-source star catalog cross-matching provided in the embodiment of the present application can generally be executed by the server 105. Accordingly, the spectral data classification device based on multi-source star catalog cross-matching provided in the embodiment of the present application can generally be set in the server 105. The spectral data classification method based on multi-source star catalog cross-matching provided in the embodiment of the present application can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the spectral data classification device based on multi-source star catalog cross-matching provided in the embodiment of the present application can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0046] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0047] The following will be based on Figure 1 The scene described by Figures 2 to 4 A spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application is described in detail.

[0048] Figure 2 The flowchart of the spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application is schematically shown.

[0049] like Figure 2 As shown, the spectral data classification method 200 based on multi-source star catalog cross-matching of this embodiment includes operations S210 to S220.

[0050] In operation S210, the target star catalog is cross-matched with the first star catalog to obtain a first homologous data set; the target star catalog is cross-matched with the second star catalog to obtain a second homologous data set; wherein the first homologous data set includes spectral classification information of the spectral data representing the same celestial body in the target star catalog and the first star catalog in the first star catalog, and the second homologous data set includes spectral classification information of the spectral data representing the same celestial body in the target star catalog and the second star catalog in the second star catalog.

[0051] In operation S220, the spectral data to be classified in the target star catalog is classified using a classification fusion strategy based on the first homologous data set and the second homologous data set; wherein the spectral data to be classified includes spectral data in the target star catalog whose spectral data characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous data set is different, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set is different.

[0052] In some embodiments, in operation S210, before cross-matching, the coordinates in the target star catalog, the first star catalog, and the second star catalog are respectively unified into J2000 coordinates; wherein, the target star catalog may be the LAMOST star catalog obtained based on the spectral data obtained by the Guo Shoujing Telescope, the first star catalog may be the DESI star catalog, and the second star catalog may be the SIMBAD star catalog. The DESI star catalog is a data set obtained based on the observation and research of the Dark Energy Spectroscopic Survey project DESI, which contains information such as the position, magnitude, redshift, and spectral characteristics of a large number of celestial bodies. It aims to reveal the nature of dark energy, the evolutionary history of the universe, and the formation and evolution of galaxies by obtaining spectral information of a large number of galaxies, quasars and other celestial bodies. The SIMBAD star catalog integrates a large amount of celestial body data from published astronomical literature, covering almost all data of known stars, galaxies, nebulae and other celestial bodies, including the name of the celestial body, position, magnitude, spectral type, redshift, astronomical observation data, and literature citation information.

[0053] In some embodiments, in operation S210 , a Topcat tool may be used for cross-matching, or other cross-matching methods may be used to cross-match the target star catalog with the first star catalog and cross-match the target star catalog with the second star catalog.

[0054] In some embodiments, in operation S210, homologous data refers to spectral classification information of spectral data marked as the same celestial body in different star catalogs in the current star catalog. For example, spectral classification information of spectral data marked as the same celestial body in the target star catalog and the first star catalog in the first star catalog can be recorded as first homologous data, and the first homologous data belongs to the first homologous data set; spectral classification information of spectral data marked as the same celestial body in the target star catalog and the second star catalog in the second star catalog can be recorded as second homologous data, and the second homologous data belongs to the second homologous data set.

[0055] In some embodiments, in operation S210, due to factors such as observation conditions, celestial body characteristics, and observation time, the spectral classification information representing the same celestial body in different star catalogs may be different. For example, a star catalog using the Harvard spectral classification classifies Sirius as type A0, whose spectral characteristics include a high surface temperature, mainly hydrogen absorption lines, and some neutral helium lines. In a star catalog using the Morgan-Keenan spectral classification, Sirius belongs to type A0m, whose spectral characteristics, in addition to a high surface temperature, also include some special metal absorption lines. Therefore, the spectral type is further divided into type M, which indicates that the spectral characteristics include metal lines. For another example, for a certain galaxy, the spectral characteristics in some star catalogs are relatively simple, so the spectral classification information of the galaxy given by the star catalog is relatively simple, such as an elliptical galaxy or an irregular galaxy, but some star catalogs record more specific and detailed spectral data of the galaxy. Based on this more specific spectral data, more specific spectral classification information can be given, such as a metal line galaxy or a galaxy with strong ionized gas emission lines.

[0056] In some embodiments, in operation S210, the first homologous data set may include galaxy spectra, quasar spectra, emission-line star spectra, etc., the second homologous data set may include white dwarf spectra, main-sequence star spectra, carbon star spectra, quasar spectra, etc., and the spectral classification information in the target star catalog may include emission-line star spectra, quasar spectra, main-sequence star spectra, white dwarf spectra, etc.

[0057] According to the embodiments of the present application, by introducing external star catalogs (the first star catalog and the second star catalog) to classify low-quality spectral data in the target star catalog, manual review can be greatly reduced, time costs can be reduced, the accuracy and quality of spectral classification can be improved, and the data publishing efficiency of subsequent processing flows can be improved. The spectral data classification method given in method 200 has strong versatility and can be applied to various types of astronomical spectral data and is widely used in various spectral survey projects.

[0058] In some embodiments, in operation S210, the target star catalog is cross-matched with the first star catalog to obtain a first homologous data set, including: determining the position of the celestial body represented by each spectral data in the target star catalog; taking the position of each celestial body as the center, and searching the first celestial body corresponding to each celestial body within the first preset radius in the first star catalog according to the first preset radius, representing the first celestial body as the same celestial body as the corresponding celestial body located at the center position, and using the spectral classification information of the spectral data of the first celestial body in the first star catalog as the first homologous data set.

[0059] For example, using the spectral data in the target star catalog as the main list, determine the position of the celestial body represented by each spectral data in the target star catalog. Assuming that there are three celestial bodies in the target star catalog, namely the third celestial body, the fourth celestial body and the fifth celestial body, with the third celestial body as the center, according to the first preset radius, search in the first star catalog for a celestial body located within the first preset radius with the third celestial body as the center, and treat the found celestial body as the same celestial body as the third celestial body, that is, it can be considered that the found celestial body and the third celestial body represent the same celestial body, and use the spectral classification information of the spectral data of the found celestial body in the first star catalog as a first homologous data. In the same way, with the fourth celestial body as the center, according to the first preset radius, search in the first star catalog for a celestial body located within the first preset radius with the fourth celestial body as the center. It is considered that the found celestial body and the fourth celestial body represent the same celestial body, and the spectral classification information of the spectral data of the found celestial body in the first star catalog is used as a first homologous data. With the fifth celestial body as the center, according to the first preset radius, the first star catalog is searched for celestial bodies within the first preset radius with the fifth celestial body as the center. It can be considered that the found celestial body and the fifth celestial body represent the same celestial body, and the spectral classification information of the spectral data of the found celestial body in the first star catalog is used as a first homologous data. All the above first homologous data are combined to obtain a first homologous data set; further, the first preset radius can be 1 arc second, etc. Since the measurement accuracy of astronomical objects in the first star catalog is generally high, a first homologous data set with higher confidence can be obtained by reducing the first preset radius.

[0060] Furthermore, if there are multiple spectral classification information representing the same celestial body found in the first star catalog and the spectral classification information is different, it is necessary to manually check these spectral classification information and provide the final spectral classification information.

[0061] In some embodiments, in operation S210, the target star catalog is cross-matched with the second star catalog to obtain a second homologous data set, including: taking the position of each celestial body as the center, according to the second preset radius, searching the second star catalog for a second object corresponding to each celestial body and located within the second preset radius, representing the second object as the same celestial body as the corresponding celestial body located at the center position, and using the spectral classification information of the spectral data of the second object in the second star catalog as the second homologous data set.

[0062] For example, with the third celestial body as the center, according to the second preset radius, the second star catalog is searched for a celestial body located within the second preset radius with the third celestial body as the center. It can be considered that the celestial body found and the third celestial body represent the same celestial body, and the spectral classification information of the spectral data of the found celestial body in the second star catalog is used as a second homologous data. In the same way, with the fourth celestial body as the center, according to the second preset radius, the second star catalog is searched for a celestial body located within the second preset radius with the fourth celestial body as the center. It can be considered that the celestial body found and the fourth celestial body represent the same celestial body, and the spectral classification information of the spectral data of the found celestial body in the second star catalog is used as a second homologous data. With the fifth celestial body as the center, according to the second preset radius, the second star catalog is searched for a celestial body located within the second preset radius with the fifth celestial body as the center. It can be considered that the celestial body found and the fifth celestial body represent the same celestial body, and the spectral classification information of the spectral data of the found celestial body in the second star catalog is used as a second homologous data. All the above second homologous data are combined to obtain a second homologous data set. Furthermore, the second preset radius can be 5 arc seconds, etc.

[0063] In some embodiments, in operation S210, the first homologous data set includes spectral classification information without warnings and spectral classification information of the type of galaxy spectrum or quasar spectrum; wherein, no warnings indicates that the quality of the spectral data meets the first preset requirement and the spectral classification information is unambiguous; further, the first preset requirement includes that the signal-to-noise ratio of the spectral data in the first star catalog is within the preset signal-to-noise ratio range and the integrity and continuity of the spectral data meet the requirements; the second homologous data set includes spectral classification information of spectral data with single spectral classification information and spectral classification information in which the spectral type gaps between multiple spectral classification information representing the same celestial body are respectively within the first preset spectral sub-type range.

[0064] Furthermore, because different spectral classification methods focus on different spectral features, different spectral classification information may be obtained for the same celestial object. For example, for a certain celestial object, some literature in the second star catalog uses the Morgan-Keenan classification method to classify the spectrum of the celestial object as a main sequence star spectrum, while some literature in the second star catalog uses the spectral energy distribution method to classify the spectrum of the celestial object as a subgiant star spectrum. If the spectral type difference between the main sequence star spectrum and the subgiant star spectrum is within a first preset spectral subtype range, it can be considered that there is no conflict between the two spectral classification information and both spectral classification information are retained. For another example, for another celestial object, some literature in the second star catalog uses a classification method based on the intensity ratio of emission lines in the visible light band to classify the spectrum of the celestial object as a planetary nebula spectrum, while some literature in the second star catalog combines the radiation of different components in the nebula to classify the spectrum of the celestial object as a planetary nebula spectrum with certain special chemical compositions. It is determined whether the spectral type difference between the two spectral classification information is within the first preset spectral subtype range. If not, the spectral classification information is discarded; if so, the spectral classification information is retained. Furthermore, the first preset spectral subtype can be five spectral subtypes, etc.

[0065] Figure 3 The following schematically shows a flow chart of obtaining spectral data to be classified according to an embodiment of the present application.

[0066] like Figure 3 As shown, the spectral data in the target star catalog is used as the main list to determine the celestial bodies represented by each spectral data in the target star catalog. With these celestial bodies as the center, the spectral classification information of the celestial bodies identical to these central celestial bodies is searched in the first star catalog according to a first preset radius to obtain a first homologous data set. Similarly, with these celestial bodies as the center, the spectral classification information of the celestial bodies identical to these central celestial bodies is searched in the second star catalog according to a second preset radius to obtain a second homologous data set.

[0067] Furthermore, the spectral classification information included in the first homologous data set is classification information without warning and of type galaxy spectrum or quasi-galaxy spectrum, and these spectral classification information are taken as first classification information (CLASS1); the spectral classification information included in the second homologous data set is spectral classification information of spectral data with single spectral classification information and spectral classification information with small differences among spectral classification information representing the same celestial body (that is, spectral classification information in which the spectral type differences between multiple spectral classification information representing the same celestial body are respectively within the first preset spectral sub-type range), and these spectral classification information are merged and sorted to obtain second classification information (CLASS2), and the spectral classification information of all celestial bodies in the target star catalog is obtained, and these spectral classification information are taken as target classification information (CLASS obj), the spectral classification information of the spectral data in the target star catalog whose characteristic parameters do not meet the first preset judgment standard is regarded as the more reliable classification information, and the CLASS obj1 Said that later by comparing CLASS obj1 With CLASS1 and CLASS obj1 With CLASS1, the final spectral classification information of each celestial body in the target star catalog can be determined. Furthermore, the target classification information includes CLASS obj1 .

[0068] Furthermore, the spectral data whose characteristic parameters of the spectral data in the target star catalog meet at least one of the first preset evaluation criteria, the spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the spectral classification information representing the same celestial body in the first homologous data set are different, and the spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set are different are regarded as spectral data to be classified with lower quality; further, the first preset evaluation criteria include that the redshift value of the spectral data does not match the spectral classification information of the corresponding spectral data (that is, the redshift value does not match the classification), the first field corresponding to the spectral data is not 0, and the redshift confidence of the spectral data is less than the preset redshift confidence A redshift confidence threshold is determined (for example, the preset redshift confidence threshold may be 20%, etc.), the spectral classification information is uncertain, and the r-band signal-to-noise ratio of the spectral data is less than a preset signal-to-noise ratio threshold (for example, the preset signal-to-noise ratio threshold may be 5, etc.), wherein the first field represents the reliability of the spectral data, and the field name of the first field may be represented by fibermask; further, it is determined whether the characteristic parameters of the spectral data in the target star catalog meet the first preset evaluation criteria, that is, whether the redshift value matches the classification, whether the first field is 0, whether the redshift confidence is less than the preset redshift confidence threshold, whether the spectral classification information is determined, and whether the r-band signal-to-noise ratio is less than the preset signal-to-noise ratio threshold.

[0069] In some embodiments, in order to unify the second classification information with the target classification information as much as possible, the spectral classification information with finer divisions in the second star catalog is merged so that the second classification information includes three categories: stellar spectra, galaxy spectra and quasar spectra, among which stellar spectra include O-type star spectra, B-type star spectra, A-type star spectra, F-type star spectra, G-type star spectra, K-type star spectra, M-type star spectra, WD-type star spectra, CV-type star spectra, DoubleStar-type star spectra and Carbon-type star spectra.

[0070] Figure 4 The following schematically shows a flow chart of classifying spectral data to be classified according to an embodiment of the present application.

[0071] like Figure 4As shown, based on the first homologous data set and the second homologous data set, a classification fusion strategy is adopted to classify the spectral data to be classified in the target star catalog, including:

[0072] If the first homologous data set contains first spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a first preset threshold, the first spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set, but the second spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a second preset threshold exists in the second homologous data set, then when the spectral data to be classified meets the second preset requirement, the second spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, then when the characteristic parameters of the spectral data to be classified respectively meet the second preset evaluation criteria, the spectral classification information of the spectral data to be classified in the target star catalog is used as the final spectral classification information of the spectral data to be classified.

[0073] Furthermore, the spectral data to be classified meets a second preset requirement, including: the spectral classification information of the spectral data to be classified in the target classification result is consistent with the second spectral classification information, or the distance between the celestial body corresponding to the second spectral classification information in the second homologous data set and the celestial body represented by the spectral data to be classified is less than or equal to a third preset threshold.

[0074] For example, Figure 4 As shown, if there is first spectral classification information corresponding to a celestial body whose distance from the celestial body represented by the spectral data to be classified is less than a first preset threshold in the first homologous data set, that is, there is first spectral classification information in the first classification information (CLASS1), then regardless of whether the first spectral classification information is the same as the spectral classification information of the spectral data to be classified in the target classification information, the first spectral classification information is used as the final spectral classification information of the spectral data to be classified, wherein the first preset threshold may be the same as the first preset radius; if there is no first spectral classification information in the first homologous data set, but there is second spectral classification information corresponding to a celestial body whose distance from the celestial body represented by the spectral data to be classified is less than a second preset threshold in the second homologous data set, that is, there is no first spectral classification information in the first classification information (CLASS1), but there is second spectral classification information in the second classification information (CLASS2), then it is determined whether the second spectral classification information is the same as the spectral classification information of the spectral data to be classified in the target classification information, that is, it is determined whether the second spectral classification information in CLASS2 is the same as the spectral classification information in CLASS1. obj Are the spectral classification information of the spectral data to be classified the same? If they are the same (i.e. CLASS objSame as CLASS2), the second spectrum classification information is used as the final spectrum classification information of the spectrum data to be classified, wherein the second preset threshold value can be the same as the second preset radius; if different (ie, CLASS obj Different from CLASS2), it is further determined whether the distance between the celestial body corresponding to the second spectral classification information and the celestial body represented by the spectral data to be classified is less than or equal to a third preset threshold; if the distance between the celestial body corresponding to the second spectral classification information and the celestial body represented by the spectral data to be classified is less than or equal to the third preset threshold, it is considered that the classification given by the second spectral classification information is more accurate, and the second spectral classification information is used as the final spectral classification information of the spectral data to be classified, wherein the third preset threshold is less than the second preset threshold, and the third preset threshold can be 1 arc second, etc. If the distance between the celestial body corresponding to the second spectral classification information and the celestial body represented by the spectral data to be classified is greater than the third preset threshold, it is indicated that the spectral classification information of the spectral data to be classified is ambiguous. Manual inspection is required; if the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, the classification information of the spectral data to be classified in the target classification information will be used as the final classification result only when the characteristic parameters of the spectral data to be classified all meet the second preset evaluation criteria. As long as the characteristic parameters of the spectral data to be classified do not meet one of the second preset evaluation criteria, manual inspection is required to manually determine the final spectral classification information of the spectral data to be classified; further, whether the spectral classification information mentioned above is the same, the same here means that the spectral type difference between the two spectral classification information is within the second preset spectral sub-type range, and the second preset spectral sub-type can be 5 spectral sub-types, etc.

[0075] Furthermore, the second preset evaluation criteria include that the redshift value of the spectral data matches the spectral classification information of the corresponding spectral data (i.e., the redshift value is adapted to the classification), the Hertzsprung-Russell diagram distribution of the spectral data matches the spectral classification information of the corresponding spectral data (i.e., the HR diagram distribution is reasonable), and each characteristic parameter of the spectral data is respectively located within the corresponding boundary values ​​(i.e., not at the boundary values); further, it is judged whether the characteristic parameters of the spectral data to be classified meet the second preset evaluation criteria, that is, whether the redshift value is adapted to the classification, whether the HR diagram distribution is reasonable, and whether it is within the boundary value.

[0076] For example, for a spectrum whose type is a star, the redshift value is between -0.05 and +0.05. If the redshift value is between -0.05 and +0.05, but the corresponding spectrum is classified as a non-stellar spectrum, the redshift value does not match the classification, does not meet the second preset evaluation criteria, and requires manual inspection; for a spectrum whose type is a galaxy, the redshift value is between 0.0 and 1.0. If the redshift value is not between 0.0 and 1.0, but the corresponding spectrum is classified as a galaxy spectrum, the redshift value does not match the classification, does not meet the second preset evaluation criteria, and requires manual inspection; for a spectrum whose type is a quasar, the redshift value is between 0.0 and 7.0.

[0077] For example, the HR diagram refers to the Hertzsprung-Russell diagram of spectral data. The HR diagram can be used to obtain the spectral color and the surface temperature of the celestial body represented by the spectrum. For example, for main sequence stars (O / B / A / F / G / K / M), the distribution range of their surface temperatures on the HR diagram is shown in Table 1. It can be seen that the surface temperature range of stars represented by O-type spectra is 29000K–60000K, the surface temperature range of stars represented by B-type spectra is 9600K–29000K, the surface temperature range of stars represented by A-type spectra is 7200K–9600K, the surface temperature range of stars represented by F-type spectra is 6000K–7200K, the surface temperature range of stars represented by G-type spectra is 5150K–6000K, the surface temperature range of stars represented by K-type spectra is 3850K–5150K, and the surface temperature range of stars represented by M-type spectra is 1900K–3850K.

[0078] Table 1

[0079] type Temperature range O 29000K – 60000K B 9600K – 29000K A 7200K – 9600K F 6000K – 7200K G 5150K – 6000K K 3850K – 5150K M 1900K – 3850K

[0080] For another example, when the celestial body represented by the spectrum is a white dwarf, the color range of the white dwarf needs to satisfy the following formula:

[0081] ;

[0082] ;

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] in, Indicates that the celestial body represented by the spectrum is The apparent magnitude of the band (for example, in this embodiment, it refers to the apparent magnitude of a white dwarf in band), Indicates that the celestial body represented by the spectrum is The apparent magnitude of the band (for example, in this embodiment, it refers to the apparent magnitude of a white dwarf in band), Indicates that the celestial body represented by the spectrum is The apparent magnitude of the band (for example, in this embodiment, it refers to the apparent magnitude of a white dwarf in band), Indicates that the celestial body represented by the spectrum is The apparent magnitude of the band (for example, in this embodiment, it refers to the apparent magnitude of a white dwarf in band), Indicates that the celestial body represented by the spectrum is The apparent magnitude of the band (for example, in this embodiment, it refers to the apparent magnitude of a white dwarf in band).

[0090] For example, when the celestial body represented by the spectrum is a carbon star, the color range of the carbon star needs to satisfy the following formula:

[0091] ; ; ; ; ; ; ; ;in, 、 、 、 、 、 、 、 、 、 and represent the apparent magnitude of the celestial body represented by the spectrum in different bands. For example, in this embodiment, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band, Indicates that carbon stars are The apparent magnitude of the band.

[0092] For example, when the celestial body represented by the spectrum is a quasar, the color range of the quasar needs to satisfy the following formula:

[0093] when hour, , ;

[0094] when Between 2 and 3, , ;

[0095] when hour, , ;

[0096] For any Value, must also meet ; where y is the apparent magnitude of the quasar in the y band, 、 、 、 、 、 and represent the apparent magnitude of the celestial body represented by the spectrum in different bands. For example, in this embodiment, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band, Indicates that the quasar is The apparent magnitude of the band.

[0097] In the process of obtaining spectral classification information of the spectral data to be classified in the target star catalog, when calculating the redshift value of the spectral data to be classified, if the calculated redshift value reaches the upper and lower limits, it is considered that the corresponding boundary value is reached and the second preset evaluation standard is not met. When performing spectral line fitting, if the interpolation reaches the upper and lower limits, it is also considered that the corresponding boundary value is reached and the second preset evaluation standard is not met.

[0098] In some embodiments, method 200 further includes: for a case where the spectral classification information of high-quality spectral data other than the spectral data to be classified in the target star catalog, the spectral classification information of the high-quality spectral data in the first star catalog, and the spectral classification information of the high-quality spectral data in the second star catalog are inconsistent, marking the high-quality spectral data in this case in the target star catalog, and setting a corresponding marking field for it to facilitate searching for the high-quality spectral data. This case may indicate that there is uncertainty in the celestial body data represented by the high-quality spectral data, or may indicate that the high-quality spectral data was shot incorrectly, or may indicate that the celestial body represented by the high-quality spectral data is a variable star, which needs to be further verified through manual inspection; further, the target star catalog contains not only target classification information, but also first classification information, second classification information, and final spectral classification information CLASS FINAL , and mark their corresponding sources respectively. For example, the first classification information is marked as coming from the first star catalog, the second classification information is marked as coming from the second star catalog, and the final spectral classification information is marked as coming from the first star catalog, the second star catalog, and the target star catalog of the classification fusion. In this way, it is convenient for users to verify the accuracy of the spectral classification information through the target star catalog, providing users with greater transparency.

[0099] According to the embodiments of the present application, a transparent classification fusion process and detailed classification source markings improve the traceability of the data. By introducing an external high-quality star catalog to classify the low-quality spectral data to be classified in the target star catalog, it can not only reduce labor costs and time costs, but also improve classification accuracy, enhance the citation rate and competitiveness of the classified target star catalog, etc.

[0100] Based on the above-mentioned spectral data classification method based on multi-source star catalog cross matching, the present application also provides a spectral data classification device based on multi-source star catalog cross matching. Figure 5 The device is described in detail.

[0101] Figure 5 The structural block diagram of the spectral data classification device based on multi-source star catalog cross-matching according to an embodiment of the present application is schematically shown.

[0102] like Figure 5As shown, the spectral data classification device 500 based on multi-source star catalog cross-matching in this embodiment includes a homologous data acquisition module 510 and a spectral classification information acquisition module 520 .

[0103] Homologous data acquisition module 510 is configured to cross-match the target star catalog with the first star catalog to obtain a first homologous dataset; and cross-match the target star catalog with the second star catalog to obtain a second homologous dataset. The first homologous dataset includes spectral classification information in the first star catalog for spectral data representing the same celestial body in the target star catalog and the first star catalog, and the second homologous dataset includes spectral classification information in the second star catalog for spectral data representing the same celestial body in the target star catalog and the second star catalog. In one embodiment, homologous data acquisition module 510 can be configured to perform operation S210 described above, which will not be further described here.

[0104] The spectral classification information acquisition module 520 is configured to classify the spectral data to be classified in the target star catalog using a classification fusion strategy based on the first homologous dataset and the second homologous dataset. The spectral data to be classified includes spectral data in the target star catalog whose spectral data characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous dataset differ, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous dataset differ. In one embodiment, the spectral classification information acquisition module 520 can be configured to perform operation S220 described above, which will not be further described here. Furthermore, the first preset evaluation criteria include: a first field corresponding to the spectral data is not zero, a redshift confidence level of the spectral data is less than a preset redshift confidence threshold, an r-band signal-to-noise ratio of the spectral data is less than a preset signal-to-noise ratio threshold, a redshift value of the spectral data does not match the spectral classification information of the corresponding spectral data, and uncertainty of the spectral classification information. The first field represents the reliability of the spectral data.

[0105] In some embodiments, the homologous data acquisition module 510 is specifically used to:

[0106] Determining the position of each celestial body represented by each spectral data in the target star catalog; searching for the first celestial body corresponding to each celestial body within the first preset radius in the first star catalog with the position of each celestial body as the center, representing the first celestial body as the same celestial body as the corresponding celestial body located at the center position, and using the spectral classification information of the spectral data of the first celestial body in the first star catalog as a first homologous data set;

[0107] Taking the position of each celestial body as the center and according to the second preset radius, the second star catalog is searched for the second object corresponding to each celestial body and located within the second preset radius. The second object is represented as the same celestial body as the corresponding celestial body located at the center position, and the spectral classification information of the spectral data of the second object in the second star catalog is used as the second homologous data set.

[0108] In some embodiments, the homologous data acquisition module 510 is further configured to:

[0109] A first homologous data set is obtained, wherein the first homologous data set includes spectral classification information without warnings and spectral classification information of the type of galaxy spectrum or quasar spectrum; wherein no warning indicates that the quality of the spectral data meets the first preset requirement and the spectral classification information is unambiguous; a second homologous data set is obtained, wherein the second homologous data set includes spectral classification information of spectral data with single spectral classification information and spectral classification information in which the spectral type gap between multiple spectral classification information representing the same celestial body is within the first preset spectral sub-type range.

[0110] In some embodiments, the spectrum classification information acquisition module 520 is specifically configured to:

[0111] If the first homologous data set contains first spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a first preset threshold, the first spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set, but the second spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a second preset threshold exists in the second homologous data set, then when the spectral data to be classified meets the second preset requirement, the second spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, then when the characteristic parameters of the spectral data to be classified respectively meet the second preset evaluation criteria, the spectral classification information of the spectral data to be classified in the target star catalog is used as the final spectral classification information of the spectral data to be classified.

[0112] Furthermore, the spectral data to be classified meets a second preset requirement, including: the spectral classification information of the spectral data to be classified in the target star catalog is consistent with the second spectral classification information, or the distance between the celestial body corresponding to the second spectral classification information in the second homologous data set and the celestial body represented by the spectral data to be classified is less than or equal to a third preset threshold.

[0113] Furthermore, the second preset evaluation criteria include: the redshift value of the spectral data matches the spectral classification information of the corresponding spectral data, the Hertzsprung-Russell diagram distribution of the spectral data matches the spectral classification information of the corresponding spectral data, and each characteristic parameter of the spectral data is within the corresponding boundary value.

[0114] According to the embodiment of the present application, the device 500 can be used to improve the accuracy of spectral data classification, reduce the cost and time cost of manual review, and thus improve the efficiency of subsequent data processing procedures.

[0115] According to embodiments of the present application, any multiple modules in the homology data acquisition module 510 and the spectral classification information acquisition module 520 can be combined into a single module, or any one of them can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present application, at least one of the homology data acquisition module 510 and the spectral classification information acquisition module 520 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented in hardware or firmware through any other reasonable means of circuit integration or packaging, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the homology data acquisition module 510 and the spectral classification information acquisition module 520 can be at least partially implemented as a computer program module that, when executed, can perform the corresponding functions.

[0116] Figure 6 The block diagram of an electronic device suitable for implementing a spectral data classification method based on multi-source star catalog cross-matching according to an embodiment of the present application is schematically shown.

[0117] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present application includes a processor 601, which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 602 or a program loaded from a storage unit 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present application.

[0118] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to the embodiment of the present application by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the above programs can also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 can also perform various operations of the method flow according to the embodiment of the present application by executing the programs stored in one or more memories.

[0119] According to an embodiment of the present application, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0120] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of this application is implemented.

[0121] According to an embodiment of the present application, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present application, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0122] Embodiments of the present application also include a computer program product comprising a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to cause the computer system to implement the spectral data classification method based on multi-source star catalog cross-matching provided in the embodiments of the present application.

[0123] The computer program executes the above functions defined in the system / device of the embodiment of the present application when the computer program is executed by the processor 601. According to the embodiment of the present application, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0124] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0125] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present application are performed. According to the embodiment of the present application, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0126] According to an embodiment of the present application, the program code for executing the computer program provided by the embodiment of the present application can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0128] Those skilled in the art will appreciate that the features described in the various embodiments of this application may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in this application. In particular, the features described in the various embodiments of this application may be combined and / or coupled in various ways without departing from the spirit and teachings of this application. All such combinations and / or couplings fall within the scope of this application.

Claims

1. A spectral data classification method based on multi-source star catalog cross-matching, characterized in that: The method comprises: Cross-matching a target star catalog with a first star catalog to obtain a first homologous data set; cross-matching the target star catalog with a second star catalog to obtain a second homologous data set; wherein the first homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the first star catalog in the first star catalog, and the second homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the second star catalog in the second star catalog, the target star catalog is the LAMOST star catalog, the first star catalog is the DESI star catalog, and the second star catalog is the SIMBAD star catalog; According to the first homologous data set and the second homologous data set, a classification fusion strategy is adopted to classify the spectral data to be classified in the target star catalog, including: if the first homologous data set contains first spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a first preset threshold, then the first spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set, but the second spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a second preset threshold exists in the second homologous data set, then when the spectral data to be classified meets the second preset requirement, the second spectral classification information is used as the final spectral classification information of the spectral data to be classified. spectral classification information; if the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, then when the characteristic parameters of the spectral data to be classified respectively meet the second preset evaluation criteria, the spectral classification information of the spectral data to be classified in the target star catalog is used as the final spectral classification information of the spectral data to be classified; wherein, the spectral data to be classified includes spectral data in the target star catalog whose spectral data characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous data set are different, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set are different.

2. The method according to claim 1, characterized in that Cross-matching the target star catalog with the first star catalog to obtain a first homologous data set includes: Determining the position of the celestial body represented by each spectral data in the target star catalog; Taking the position of each celestial body as the center and according to a first preset radius, searching the first star catalog for the first celestial body corresponding to each celestial body and located within the first preset radius, representing the first celestial body as the same celestial body as the corresponding celestial body located at the center position, and using spectral classification information of the spectral data of the first celestial body in the first star catalog as the first homologous data set; Cross-matching the target star catalog with the second star catalog to obtain a second homologous data set includes: Taking the position of each celestial body as the center and according to the second preset radius, search the second star catalog for a second object corresponding to each celestial body and located within the second preset radius, represent the second object as the same celestial body as the corresponding celestial body located at the center position, and use spectral classification information of the spectral data of the second object in the second star catalog as the second homologous data set.

3. The method according to claim 1, characterized in that The first homologous data set includes spectral classification information without warnings and spectral classification information of a galaxy spectrum or a quasar spectrum; wherein the absence of warnings indicates that the spectral data quality meets the first preset requirement and the spectral classification information is unambiguous; The second homologous data set includes spectral classification information of spectral data having single spectral classification information and spectral classification information of spectral data having spectral type differences between multiple spectral classification information representing the same celestial body within a first preset spectral subtype range.

4. The method according to claim 1, wherein The spectral data to be classified meets the second preset requirement, including: The spectral classification information of the spectral data to be classified in the target star catalog is consistent with the second spectral classification information, or the distance between the celestial body corresponding to the second spectral classification information in the second homologous data set and the celestial body represented by the spectral data to be classified is less than or equal to a third preset threshold.

5. The method according to claim 1, wherein The second preset evaluation criteria include: The redshift value of the spectral data matches the spectral classification information of the corresponding spectral data, the Hertzsprung-Russell diagram distribution of the spectral data matches the spectral classification information of the corresponding spectral data, and each characteristic parameter of the spectral data is located within the corresponding boundary value.

6. The method according to claim 1, characterized in that The first preset evaluation criteria include: The first field corresponding to the spectral data is not 0, the redshift confidence of the spectral data is less than the preset redshift confidence threshold, the r-band signal-to-noise ratio of the spectral data is less than the preset signal-to-noise ratio threshold, the redshift value of the spectral data does not match the spectral classification information of the corresponding spectral data, and the spectral classification information is uncertain; wherein, the first field represents the reliability of the spectral data.

7. A spectral data classification device based on multi-source star catalog cross-matching, characterized in that: The device comprises: A homologous data acquisition module is configured to cross-match a target star catalog with a first star catalog to obtain a first homologous data set; and cross-match the target star catalog with a second star catalog to obtain a second homologous data set; wherein the first homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the first star catalog in the first star catalog, and the second homologous data set includes spectral classification information of spectral data representing the same celestial body in the target star catalog and the second star catalog in the second star catalog, the target star catalog is the LAMOST star catalog, the first star catalog is the DESI star catalog, and the second star catalog is the SIMBAD star catalog; The spectral classification information acquisition module is used to classify the spectral data to be classified in the target star catalog based on the first homologous data set and the second homologous data set using a classification fusion strategy, including: if the first homologous data set contains first spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a first preset threshold, then the first spectral classification information is used as the final spectral classification information of the spectral data to be classified; if the first spectral classification information does not exist in the first homologous data set, but the second homologous data set contains second spectral classification information corresponding to a celestial body whose distance to the celestial body represented by the spectral data to be classified is less than a second preset threshold, then when the spectral data to be classified meets the second preset requirement, the second spectral classification information is used as the spectral data to be classified. final spectral classification information of the data; if the first spectral classification information does not exist in the first homologous data set and the second spectral classification information does not exist in the second homologous data set, then when the characteristic parameters of the spectral data to be classified respectively meet the second preset evaluation criteria, the spectral classification information of the spectral data to be classified in the target star catalog is used as the final spectral classification information of the spectral data to be classified; wherein, the spectral data to be classified includes spectral data of the spectral data in the target star catalog whose characteristic parameters meet at least one of the first preset evaluation criteria, spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the first homologous data set are different, and spectral data corresponding to the target star catalog when the spectral classification information representing the same celestial body in the target star catalog and the second homologous data set are different.

8. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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