WO2020249039A1 - 一种宇宙空间数据系统、方法、计算机设备和存储介质 - Google Patents

一种宇宙空间数据系统、方法、计算机设备和存储介质 Download PDF

Info

Publication number
WO2020249039A1
WO2020249039A1 PCT/CN2020/095571 CN2020095571W WO2020249039A1 WO 2020249039 A1 WO2020249039 A1 WO 2020249039A1 CN 2020095571 W CN2020095571 W CN 2020095571W WO 2020249039 A1 WO2020249039 A1 WO 2020249039A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
related data
observation
microparticle
particles
Prior art date
Application number
PCT/CN2020/095571
Other languages
English (en)
French (fr)
Inventor
黄亚娟
常斐然
Original Assignee
黄亚娟
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 黄亚娟 filed Critical 黄亚娟
Priority to EP20822912.0A priority Critical patent/EP3985525A4/en
Priority to US17/618,876 priority patent/US11675818B2/en
Publication of WO2020249039A1 publication Critical patent/WO2020249039A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/254—Extract, transform and load [ETL] procedures, e.g. ETL data flows in data warehouses
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/288—Entity relationship models

Definitions

  • This article relates to the field of space data technology, especially to a space data system, method, computer equipment and storage medium.
  • the embodiments of this paper provide a space data system, method, computer equipment and storage medium to classify and summarize the related data of various particles to form an orderly and recognizable data system , To facilitate the researcher.
  • the data import unit is used to store the input related data of the particles according to the type of the particles;
  • the data import unit further includes an encoding mapping module, which is used to map and encode string related data to X, subatomic related data to Y, and neutrino related data to map to Z, and phonon
  • the related data mapping code is S
  • the metadata import module is used to store the related data of Xianzi in the database space coded as X, and store the subatomic related data into the database space coded as Y, and the correlation of neutrinos
  • the data is stored in the database space coded as Z
  • the phonon related data is stored in the database space coded as S;
  • the labeling unit is used to label the stored microparticle related data
  • the feature association unit is configured to establish an association relationship of the stored microparticle related data according to the annotation.
  • the embodiment of this article also provides a method for collecting space data, including:
  • the input microparticle related data is stored; among them, the string related data is mapped and encoded as X, the subatomic related data is mapped and encoded as Y, and the neutrino related data is mapped and encoded as Z, and the sound
  • the relevant data mapping code of the zi is S; the relevant data of the string is stored in the database space coded as X, the relevant data of the subatomic is stored in the database space coded as Y, and the relevant data of the neutrino is stored in the code Z
  • the database space of phonon is stored in the database space coded as S;
  • an association relationship of the stored microparticle related data is established.
  • the embodiments herein also provide a computer device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor implements the above system when the computer program is executed.
  • the embodiments herein also provide a computer-readable storage medium with a computer program stored on the computer-readable storage medium, and the computer program is executed by a processor to implement the above-mentioned system.
  • Figure 1 shows a schematic structural diagram of a space data system according to an embodiment of this article
  • FIG. 2 shows the detailed structure diagram of the space data system of the embodiment of this paper
  • FIG. 3A shows a schematic diagram of the interface of the researcher input unit in this embodiment
  • FIG. 3B shows a schematic diagram of the interface of another researcher input unit in this embodiment
  • Figure 4 shows a flow chart of the smart engine unit calibrating the external observation equipment in the embodiment of this paper
  • Figure 5 shows a flow chart of a method for collecting space data in an embodiment of this article
  • Figure 6 shows a schematic diagram of the structure of a space data system according to an embodiment of this article
  • Fig. 7 is a schematic diagram of the structure of the space data system of the embodiment of this paper.
  • the fine particles described herein may include, for example, string particles, subatoms, neutrinos, and phonons, and may also include other particles that have been discovered, or particles that have not yet been discovered.
  • string theory is a theory in theoretical physics. It believes that the basic units of nature are not only subatoms, neutrinos and other fine particles, and that these things that look like particles are made of smaller strings. It is composed of closed loops (known as closed strings or closed strings), and the different vibrations and motions of the closed strings produce various basic particles that have been observed.
  • Subatomic refers to the standard model of the level of fine particles smaller than atoms, such as electrons, neutrons, protons, mesons, quarks, gluons, photons and other elementary particles.
  • the physics department that studies these more microscopic particles is called For subatomic physics. Theorists use state vectors in Hilbert space to describe them.
  • the currently known Standard Model contains the quantum field theory of 47 elementary particles.
  • Neutrino refers to the elementary particles in the universe that are not charged, have a very light mass, can freely traverse the earth, and have very weak interaction with other matter. They are widespread in nature and are also called fermions.
  • the research results of particle physics show that every kind of neutrino has a corresponding antimatter.
  • the nuclear reaction involving the weak interaction inside the sun produces a large number of neutrinos, which flow unimpeded into space.
  • the detector mainly records the speed of movement, the process of decay, and whether it has quality.
  • Phononon It is a quasi-particle excited by the quantization of the collective oscillation mode of the crystal structure in the crystal, also called a boson. In dynamics, the coordinate position, momentum and motion equation of the oscillating wave are used to describe, and the annihilation operator is used to express the life of phonons as quasiparticles.
  • FIG. 1 is a schematic diagram of the structure of a cosmic space data system in this embodiment of this article.
  • a system for storing observation data of various particles in the universe and corresponding theoretical research is provided, and related data of different particles, such as observation Data and theoretical research data are stored according to the types of particles, and by labeling the stored data, all stored data has corresponding tags to facilitate the establishment of correlations between multiple data related to the same type of particles, which can facilitate research
  • the researcher uses the related data of the microparticles and the input interface for the researcher to enrich the related data of a certain microparticle.
  • each unit and module can be implemented by a software module, or can also be implemented by a distributed computer cluster, or can also be implemented by a general-purpose chip, which specifically includes:
  • the data import unit 101 is configured to store the input related data of the particles according to the type of the particles.
  • the labeling unit 102 is configured to label the stored microparticle related data.
  • the feature association unit 103 is configured to establish an association relationship of the stored microparticle related data according to the annotation.
  • Figure 2 shows the detailed structure diagram of the cosmic space data system of the embodiment of this paper.
  • the figure further describes the structure of each unit in the system. Among them, it also includes a database 100 for storing particle-related data and data import
  • the unit 101 further includes an encoding mapping module 1011 and a data importing module 1012.
  • the space data system of this embodiment also includes a researcher input unit 104, a site memory unit 105, an intelligent engine unit 106, and an algorithm service unit 107.
  • the data import unit 101 further includes a code mapping module 1011 and a metadata import module 1012, where
  • the coding mapping module 1011 is used for coding and mapping the related data of different particles
  • the metadata import module 1012 is used to store the related data of the microparticles into the database space of the corresponding code mapping.
  • the microparticle-related data can include observation data and theoretical research data.
  • the data import process only the observation data of microparticles can be imported, or only the theoretical research data of microparticles can be imported, or the observation data and theoretical research data of microparticles can also be imported. Research data is imported at the same time.
  • the related data of different particles can be encoded and mapped to generate the identity IDs of four types of particles of X (string), Y (subatomic), Z (neutrino), and S (phonon), among which the identity of the particle
  • the ID can be, for example, region code + time stamp + feature attribute + other distinguishing codes, where the region code can be the code of a designated block in the storage medium, and the code can be sequential coding as required; the feature attribute includes the XYZS microparticle identifier
  • the X logo represents Xianzi, and it can also include scene modeling attributes, business operation attributes, etc., where scene modeling attributes can further include researcher input, site memory, intelligent engine, and algorithm service attributes, which are used to mark the particles From which functional unit the relevant data comes from, the business operation attributes can further include attributes such as public media, which are used to mark that the microparticle-related data comes from public media; other distinguishing codes can be the identification of various state machines, which are used to perform data on microparticle-related data.
  • the existing two or more kinds of particles can be encoded and mapped, and the corresponding particle-related data can be stored in the corresponding database space of the corresponding encoding map.
  • the metadata import module can also import through different data channels.
  • the data channels can be parallel channels, that is, different types of microparticle related data can be stored in the corresponding database space at the same time, And open the write permission in the database space to write the imported microparticle related data.
  • the encoding mapping module 1011 further maps and encodes chord related data as X, subatomic related data as Y, neutrino related data as Z, and phonon
  • the relevant data mapping code is S;
  • the metadata import module 1012 further stores the relevant data of Xianzi in the database space coded as X, stores the relevant subatomic data in the database space coded as Y, and stores the relevant data of the neutrino in the database space coded as Z , Store the related data of phonons into the database space coded as S.
  • the data import unit 101 may only receive two or more of the four types of XYZS particles. Therefore, the encoding mapping module 1011 in the data import unit 101 may only encode the received string and subatomic related data Map to X and Y, or map subatomic and neutrino-related data encoding to Y and Z, or map chord, subatomic, and phonon-related data encoding to X, Y, and S, and so on. Map the particle code that has not been received to NULL.
  • the labeling unit 102 is further configured to label the source, data content, and theoretical research data of the stored microparticle-related data.
  • the source of the microparticle-related data further includes the provider information and source auxiliary information of the microparticle-related data
  • the provider information may include, for example, the name (or number) of the research institution, the name (or number) of the research project, and the source auxiliary information
  • the data content further includes observation events, observation data, etc., where the data content can also include the discovery process, data accuracy, data format, base point, Vector trajectories, unit rulers, etc.
  • the calibration method means that the base point is the starting point of measurement of the reference object, the unit ruler is the recorded data, and the vector trajectory is generally obtained according to the dynamic data.
  • the theoretical research data further includes the content of the opinion and the information of the person who posted the opinion ( It can be an individual or an organization), where the content of the opinions can be stored in the form of including papers, signed articles, research reports, etc.
  • the labeling unit 102 can also label the time stamp of the data related to the particles to record the source of the data related to the particles, the content of the data, and the input time of the opinions.
  • the labeling unit 102 can also be used to record the behavior of operating the above-mentioned microparticle-related data, for example, who has invoked which types of microparticles in what role (such as ordinary users, registered members, etc.) in when, where, and what scenarios.
  • Related data, and what kind of data is generated for example, theoretical research data
  • these behaviors of manipulating microparticle-related data can serve the management of cognitive changes.
  • the observation data in the microparticle-related data includes the source and data content of the above-mentioned microparticle-related data
  • the theoretical research data in the microparticle-related data includes the above viewpoints.
  • the observational data of microparticle-related data may include all of the above-mentioned data sources and data content, or may only include some of them.
  • the theoretical research data may also include all of the above-mentioned opinions, or may only include some of them, including the time stamp. It can belong to theoretical research data, it can also belong to observation data, or both have time stamps.
  • the feature association unit 103 is further configured to associate related data sources of the same type of particles, associate the data content of the same type of particles, and associate the theoretical research data of the same type of particles.
  • the association relationship of the particle related information is established, and other related data related to one of the related data (source, data content, theoretical research data) can be found according to the association relationship , which can facilitate researchers to make full use of microparticle related data.
  • the data import unit distributes the identity ID of the particle-related data, and completes the metadata import through a specific parallel channel. This original association management and correspondence is completed by the feature association unit.
  • the feedback and supplements of callers and visitors after metadata processing are also clustered and managed by the feature association unit.
  • the sub-library operation management of the initial library, the alternative library, the comprehensive knowledge base, the application scenario library, and the business operation library is also realized through the feature association unit.
  • the researcher obtains the observation data D (which can be one of the X, Y, Z, and S4 types of particles, or a mixture of several of them) of the observation event C using the instrument B of the institute A
  • the researcher It is also possible to find the observation data D'obtained by another research institute E using the instrument F based on the observation event C that is the same as the observation event.
  • the researcher can study the observation event C by comparing the observation data of D and D'.
  • the information of the person who posted the opinion can obtain other opinions of the person who posted the opinion, as well as the source and content of the data related to each opinion. Or, according to this viewpoint, you can search for the information of other opinion publishers that are the same as this viewpoint, as well as the source and data content of the corresponding microparticle-related data.
  • associating the related data sources of the same type of particles, associating the data content of the same type of particles, and associating the theoretical research data of the same type of particles further includes:
  • a stacking method can also be used to establish the association relationship of microparticle-related data.
  • the microparticle-related data may be It is rich and includes a variety of content, and the researcher may only push a part of the microparticle-related data into the stack. For example, only observation events, observation instruments, observation instrument parameters, and readings of observation instruments are needed, and there is no need for research institutions or research. For information such as items, you only need to push the required data content into the stack, where the stack can form different stacks according to the researcher’s ID, the ID of the acquired particle-related data, and the type of particles.
  • the researcher conducts research based on the microparticle-related data in the above stack, and then obtains the research result (ie theoretical research data).
  • the above-mentioned feature correlation unit 103 associates the data items of the microparticle-related data in the stack with corresponding theoretical research data. Relationship, that is, the data items of the microparticle-related data in the stack form an association relationship with the corresponding theoretical research data.
  • a researcher input unit 104 is also included, which is used to write new particle-related data in the particle-related data designated by the researcher.
  • the researcher uses Hawking’s fruit core theory to construct a particle verification experiment.
  • Hawking the identity ID of a certain type of particle and the particle data of the corresponding channel at a certain step, and to understand the data of the verification process change.
  • the researcher establishes new theoretical research data through the researcher input unit 104, conducts experiments by calling the corresponding microparticle-related data, and records these microparticle-related data for the new theoretical research, thereby forming a complete data record of the new theoretical research ( Including microparticle related data and new theoretical research data).
  • the researcher can input the researcher’s microparticle theory research data into the corresponding microparticle related data through a graphical user interface (GUI) such as the web interface shown in Figure 3A or the interface of the application software.
  • GUI graphical user interface
  • the researcher By studying the observation data of particles in the data related to particles, new ideas can be obtained, and then theoretical research data can be enriched for the observation data, and the researcher’s information, such as name, contact information, etc. can be retained. Or you can also store the researcher's observation data in the microparticle related data.
  • GUI graphical user interface
  • the researcher input unit 104 herein can provide researchers or institutions with an interface for inputting microparticle-related data, so that all researchers can enrich the microparticle-related data of interest.
  • the site memory unit 105 is also included, which is used to provide the researcher with a horizontal comparison of data related to particles and a historical comparison of data related to particles.
  • providing the researcher with a horizontal comparison of data related to particles refers to the correlation of similar observation conditions, observation times, observation locations, and particles of the same type of particles according to the observation conditions, observation time, observation location, and type of particles.
  • the observation data in the data and the theoretical research data are compared horizontally, so that researchers can compare the influence of observation conditions, observation time, and observation location on the observation data.
  • the observation conditions can include the weather conditions at the time of observation, the instrument model at the time of observation, the instrument parameters at the time of observation, and the observation event based on it. In this way, the observation data and theoretical research data can be horizontally based on the same observation event. Compare.
  • To provide researchers with historical comparison of data related to particles refers to comparing the observation data of a certain particle at multiple observation times based on the observation time and the type of particles. It is also possible to further add comparison conditions such as observation events, observation locations, and observation conditions. That is to say, it is possible to compare the observation data and theoretical research data of the same observation event in a certain particle-related data of similar observation locations and observation conditions. Therefore, researchers can conveniently compare based on historical observation data.
  • an intelligent engine unit 106 which is used to connect with an external observation device and perform data interaction with the external observation device.
  • the data interaction between the intelligent engine unit and the external observation device means that the external observation device can obtain the microparticle-related data in the system of this text through the intelligent engine unit 106 for the external device to observe the microparticle, and the external observation device also
  • the observation data of a certain kind of particles can be input to the system of this paper, and it can also include data such as the instrument model and instrument parameters at the time of observation, so as to enrich the related data of particles of this system.
  • the external observation device in this embodiment is an observation device that can be driven remotely by a program, for example, it can be connected to the observation device through a network, and the observation device can be driven by issuing instructions to observe according to specified parameters and time. jobs.
  • FIG. 4 shows the flow chart of the smart engine unit calibrating the external observation equipment in the embodiment of this paper.
  • the process of the smart engine unit calibrating the external observation equipment is described as follows:
  • step 401 the external observation device is connected to the intelligent engine unit 106 through the network.
  • the intelligent engine unit 106 may be notified of the information of the external observation device by reporting the unique identifier such as the model of the external observation device to the intelligent engine unit 106.
  • step 402 the intelligent engine unit 106 finds the setting parameters of the same equipment that has been calibrated and corresponding observation data according to the unique identifier of the external observation equipment (these data can be other calibrated external observation equipment of the same model passed
  • the network mode is stored in the intelligent engine unit 106).
  • step 403 the intelligent engine unit 106 sends the setting parameters and observation data to the external observation device.
  • the external observation device can set the parameters of the external observation device according to the set parameters, and perform observation tasks to obtain observation data.
  • Step 405 Compare the obtained observation data with the received observation data.
  • step 406 If the comparison results are consistent, go to step 406. If the comparison results are inconsistent, it means that the external observation equipment is inaccurate and further calibration is needed, and then step 407 is entered.
  • step 406 the comparison results are consistent, indicating that there is no problem with the external observation equipment and the observation task can be performed.
  • the external observation equipment can display this information to the manager for subsequent operations.
  • Step 407 If the comparison results are inconsistent, it means that the external observation device is inaccurate and further calibration is required. At this time, the information that needs further calibration can be sent to the manager of the external observation device (this information can be connected to the smart Engine unit 106). Or it can be automatically corrected by adjusting the setting parameters of the external observation equipment.
  • the intelligent engine unit 106 of this embodiment can set constraint conditions to control the operation of the external observation device.
  • the external observation device is not always in the observation state, but needs to determine whether the observation condition meets the constraint condition.
  • the constraint condition can be, When a certain (some) observation value of the observed particle is within a certain threshold range, the observation instrument runs silently, and the normal channel records the observation data. When a certain (some) observation value of the example character exceeds the threshold
  • the scope triggers warnings, starts emergency procedures, and triggers batch actions and complex call associations, such as starting a (some) specific observation equipment or computing equipment, or notifying researchers.
  • the external observation device starts the observation task, or the first external observation device is notified to start the observation task according to whether other second external observation devices (which may be more than a certain proportion of external observation devices) connected to it start the observation task, For example, there are multiple second external observation devices related to the first external observation device.
  • the second external observation device starts to perform an observation task, it will send a notification to the intelligent engine unit 106.
  • the intelligent engine unit 106 counts more than half of the first 2.
  • the external observation device performs an observation task, it notifies the first external observation device connected to it to start to perform the observation task, thereby saving energy and the loss of corresponding observation equipment.
  • an algorithm service unit 107 is also included, which is configured to connect with an external computing device and perform data interaction with the external computing device.
  • the data exchange between the algorithm service unit 107 and the external computing device means that the external computing device can be connected to the algorithm service unit 107 via the Internet, and the algorithm service unit 107 obtains the particle-related data, and the external computing device correlates the particles
  • the data is input into a mathematical model in the external computing device for analysis and calculation, so that an analysis result based on the data related to the particles can be obtained.
  • the mathematical model may be, for example, a mathematical model that converts low-dimensional data of particles into high-dimensional data;
  • the external computing device can also send the analysis and calculation results to the system of this text through the algorithm service unit 107, and store them in the corresponding microparticle related data, and can also input the mathematical model or algorithm used by the external computing device It can be stored in the database of the system in this article, which can enrich the related data of particles.
  • the system in this article provides external computing equipment, such as computers, computer clusters, etc., with microparticle observation data and corresponding theoretical research data for theoretical research, including specific observation data and various algorithms Models, etc., and provide input interfaces for microparticle-related data to external computing devices.
  • external computing equipment such as computers, computer clusters, etc.
  • microparticle observation data and corresponding theoretical research data for theoretical research including specific observation data and various algorithms Models, etc.
  • models for external computing devices
  • the algorithm model, calculation process, and calculation and analysis results of external computing devices can be collected, which can further enrich the microparticle-related data in the database. It is convenient for other researchers to learn from and participate.
  • Figure 5 shows a flowchart of a method for collecting space data in the embodiment of this article.
  • the concepts of elementary particles in the universe are clearly divided, and The classification and storage of several microparticles can help researchers to grasp the relevant data of different microparticles more quickly, which is beneficial to the progress of research, including the following steps:
  • Step 501 Store the input related data of particles according to the type of particles;
  • Step 502 Mark the stored microparticle related data
  • Step 503 Establish an association relationship of the stored microparticle related data according to the annotation.
  • the step 501 further includes:
  • the encoding and mapping of related data of different particles further includes:
  • Storing the relevant data of the particles into the corresponding coded and mapped database space further includes:
  • labeling the stored microparticle-related data further includes:
  • the source, data content, and theoretical research data of the stored microparticle-related data are marked.
  • establishing an association relationship of the stored microparticle-related data further includes:
  • associating the related data sources of the same type of particles, associating the data content of the same type of particles, and associating the theoretical research data of the same type of particles further includes:
  • it also includes a researcher input step to write new particle-related data in the particle-related data designated by the researcher.
  • this article also includes a step of site memory, which provides researchers with a horizontal comparison of data related to particles and a historical comparison of data related to particles.
  • it also includes an intelligent engine step to perform data interaction with the external observation device.
  • it also includes an algorithm service step to perform data interaction with the external computing device.
  • microparticles Through the method in the examples in this article, several types of microparticles can be classified into storage and management, and the researcher can be provided with an access interface, which can input microparticle-related data into the system, or use the system to obtain microparticle-related data. It is used for theoretical research, and by labeling and correlating data related to microparticles, it makes it easier for researchers to obtain data related to several microparticles.
  • Figure 6 shows a schematic diagram of the structure of a cosmic space data system in this embodiment.
  • four types of particles are used as examples to establish a data system.
  • the particles in this embodiment include string, subatomic, neutrino, and phonon. ,among them:
  • the initial cognitive space of particulate matter relies on the material space that humans can directly perceive, that is, the three-dimensional geographic coordinate space. Observe and record the data description provided by string theory, and mark it as string.
  • Xianzi is used to describe the position measurement in the universe.
  • the measurement uses mathematical notation that humans can understand and explain, or the recording mode of existing equipment.
  • the higher dimension of the cognizable parallel space is related to the limitation of the cognitive object itself. When the cognitive unit sets limits on its own, it stays in a lower dimensional space.
  • the ten-dimensional space described in current mathematics is limited by the subject tool of modern and contemporary mathematics.
  • Subatomic The cosmic space consists of elementary particles that are smaller than atoms. These elementary particles are collectively called subatoms.
  • subatomic particles include quarks, gluons, 2 kinds of photons (hadrons, nucleons), 3 kinds of electrons (miao, Taozi, leptons), and leptons are subatoms lighter than the Planck energy scale.
  • Higgs boson is a god particle with non-zero mass.
  • Neutrinos The emergence of neutrinos is related to the proton decay activity of cosmic matter. Neutrinos are a type of fermions. The proton decay activity is related to the formation of the mass spectrum of cosmic matter, which chemically refers to the mass spectrum as the periodic table of elements.
  • the activity of proton decay forms the mass spectrum of cosmic elements.
  • Large-scale decay activity of the cosmic body will release ray bursts and possibly neutrinos.
  • the blast of supernova decay releases neutrinos, and neutrinos are a type of fermions.
  • Phononon The smallest mechanical unit of particle motion in multi-particle systems such as crystals, called phonons. Phonons are related to the fusion of collisions or oscillating activities between cosmic matter, and phonons are a type of boson.
  • Phonon is a quasi-particle unit that exists in the multi-particle system of the universe from ground state to excited state, which is also called elementary excitation.
  • the unit of the transition activity of the elementary excitation state is defined as a phonon, and the fusion effect of the phonon forms a field.
  • the embodiment of this document provides a coding mapping module 601 for the above four types of particles, which performs coding mapping of various particles related data received from various observatories or research institutions. For example, it can be identified through keywords (or through Recognition of observation data format and observation data content), identify which of the above 4 types of particles the received microparticle-related data is about, and encode the received microparticle-related data into corresponding characters, for example, when Recognize that the received microparticle related data is related to Xianzi, then encode the related data of the string as X.
  • the subatomic related data is encoded as Y
  • the neutrino related data is coded as Z
  • the received microparticle related data is recognized as phonon related data
  • the The phonon related data is coded as S.
  • Observation data sent by a certain observatory or research institution may only involve a few particles, or all four types of particles. When the sent data does not include any of the particles, it will be recorded in the corresponding code. The data related to this kind of particles is empty.
  • Metadata refers to the basic information marks made by the phenomena observed by each observatory during the operation of the universe.
  • the data obtained by each space station or the universe observation center is imported into the data system of the XYZS cognitive framework composed of the above four kinds of particles through the metadata import module 602.
  • four types of tool interfaces of XYZS are preset.
  • the interfaces of the four types of particles include the observation station as the output source of metadata, and the data system characterized by the XYZS cognitive framework as the input side.
  • a third party can also be introduced to import metadata, that is, the metadata output source provides a data plug-in through the third party to correlate the particles output by the metadata output source
  • the data is adapted to the metadata import module 602 of the embodiment of this document, which may include converting the format of the data related to the particles, unifying the accuracy of the data related to the particles, and so on.
  • the observation data and/or theoretical research data of various observatories and research institutions are imported into the database 600 space characterized by the XYZS cognitive framework according to the encoding of the respective particle-related data, that is, Store the relevant data of the X string in the X string database space, store the relevant data of the Y subatom in the Y subatomic database space, store the relevant data of the Z neutrino into the Z neutrino database space, and store the relevant data of the S phonon The relevant data is stored in the S phonon database space.
  • the four types of particles in this embodiment are all coded and marked by the code mapping module 601 to identify and mark each type of particles.
  • Observation stations or research institutions that provide the above four types of particles send the corresponding particle-related data to the metadata import module 602 through different channels for each kind of particles, such as X-channel for string-related data, Y-channel for subatomic-related data, and Z-channel
  • the neutrino-related data is transmitted, and the S-channel transmits phonon-related data.
  • the specific channel name can be named according to the actual situation.
  • the 4 types of particle-related data input by multiple observation stations or research institutions are identified and labeled by the labeling unit 603, and the observation data and theoretical research data in each type of particle-related data are labeled, for example, in the labeled observation data Including a certain professional organization, project team, a certain field expert group, a certain instrument tool, etc., in the annotation of theoretical research data, including the interpretation of the observation data of the above-mentioned particles and other theoretical research data, new views and opinions are proposed. All the above-mentioned particles
  • the content labeling unit 603 of the related data will perform labeling.
  • the content of different parts of the related data of each particle can include the type of particle, research institution, research project, instrument, certifier, observation event, and observed data.
  • particle-related data may also include a time stamp to mark the time of observation or opinion generation.
  • microparticle-related data research institutions, research projects, instruments, and certifiers can be summarized as the source of microparticle-related data, and observation events and observed data can be summarized as the data content, views, and opinions of microparticle-related data.
  • the source of opinions and related papers published can be summarized as theoretical research data related to microparticles.
  • the source, data content, and opinions of the microparticle-related data can also include other content
  • the source and data content of the microparticle-related data can also be summarized as the observation data of the microparticle-related data.
  • the labeling unit 603 will label the particle-related data input into the database 600 from the left side.
  • the particle-related data imported into the database 600 from other databases or observation stations (institutes) will also be input into the database 600 from the right side.
  • the observation data input into the database 600 by external observation equipment or theoretical research data such as new opinions generated by other researchers after referring to the particle-related data in the data system of this article.
  • the feature associating unit 604 associates the annotated particle related data, and the feature associating unit 604 associates the same or similar observation data or theoretical research data according to the annotation information in the particle related data.
  • Feature association is a tool box for data analysis and data relationship management for the annotation information in the microparticle-related data imported in batches and the matrix data of the universe.
  • the method of feature association is closely related to the theoretical development of cosmic space data classified according to XYZS, which mainly includes the feature value extraction of the data content in the same label, the definition of the association relationship, and the demand adaptation of application modes in various scenarios.
  • the methods and tools used in data analysis and relationship management are not limited to factor analysis, classification, clustering, decision trees, neural networks, and knowledge graphs.
  • Feature association can, for example, perform keyword matching or semantic analysis on the annotation content in microparticle-related data, such as theoretical research data, and associate theoretical research data with the same keywords or semantically similar ones, such as associating similar opinions, and linking
  • Multiple theoretical research data are marked with the same cluster identification, and certain particle-related data related to the theoretical research data will also have a corresponding cluster identification.
  • the researcher obtains a certain kind of particle-related data, he agrees with the corresponding point of view.
  • Other theoretical research data with similar views and other related observation data can be obtained through the theoretical research data of the microparticle-related data.
  • observation data of the current research finds observation data similar to the observation data of the current research, for example, the observation instrument is similar, and the instrument is configured with similar parameters, and the similar observation data is used to find other researchers based on the similar observation data.
  • Theoretical research data ie opinions).
  • the above-mentioned feature association can also adopt the method of flag bit.
  • the flag bit is set in the label of each data in the microparticle related data, and the flag in the flag bit is matched with the flag in the same label. For example, the flag of one particle-related data source is marked as Observation Station 01, and the flag of the other particle-related data source is marked as Observation Station 02, then the two particle-related data sources are not correlated. If the other particle-related data source is The flag of is marked as Observation Station 01, then the two data sources related to particles are associated and the association relationship is recorded.
  • the researcher can also initiate a data request to the data system of the embodiment of this paper through the researcher input unit 605, requesting the data system of the embodiment of this paper to obtain the specified microparticle-related data from a designated research institution or observatory, or the researcher can also input data through the researcher
  • the unit 605 writes new particle-related data, such as observation data of a certain particle, to the data system of this embodiment, so as to enrich the particle-related data of the data system of this embodiment.
  • the researcher can also obtain multiple related microparticle-related data of the data system of the embodiment of this paper through the site memory unit 606 for horizontal comparison.
  • the site memory unit 606 finds all the particulate-related data related to the observation event, for example, including the observation data of different observation institutions for the observation event And theoretical research data, or include observation data and theoretical research data for the observation event under different observation conditions, or may also include observation data and theoretical research data for the observation event in a time axis manner.
  • the horizontal comparison of multiple microparticle related data is convenient for researchers to conduct research.
  • the researcher can also obtain multiple related micro-particle-related data of the data system of the embodiment of this paper through the site memory unit 606 for historical comparison. For example, taking a certain observation instrument as an example, the researcher can input the historical observation data of the observation instrument of a certain observation station to the ruins memory unit 606, and the ruins memory unit 606 finds the historical observation data of the specific observation instrument of the observation station and carries it There are information such as the observation conditions and instrument setting parameters during a certain observation, which can facilitate the researcher to compare historical observation data.
  • the data system of this embodiment is connected to external observation equipment through the intelligent engine unit 607 and the network, for example, is connected to the observation equipment of the observation station through the network.
  • the external observation equipment can not only obtain particle-related data from the data system of this embodiment through the intelligent engine unit 607
  • the intelligent engine unit 607 can also store microparticle-related data (mainly observation data obtained during observation by the external observation device) in the data system of the embodiment of this document.
  • the external observation device can obtain the observation parameters and observation data of the same observation device from the intelligent engine unit 607 (the same observation device is an accurate observation device whose false positioning has been adjusted), and use the observation parameters to set the local observation device and perform Observe, and then compare the observation data with the received observation data. If the comparison results are inconsistent, it means that there is an error in the observation equipment of the machine and need to be adjusted. The corresponding setting parameters can be revised until the observation data is consistent with the received observation. If the data is the same or similar, if the comparison results are consistent, the observation results of the observation equipment of this machine are accurate and the observation task can be performed.
  • the data system of this embodiment can also be connected to an external computing device through an algorithm service unit 608 and a network, for example, connected to a computing device of an observatory through a network.
  • the external computing device can not only obtain particles from the data system of this embodiment through the algorithm service unit 608
  • Related data can also be stored in the data system of the embodiment of this paper through the algorithm service unit 608.
  • the data is mainly calculated by the external computing device based on the microparticle related device, or includes the external computing device based on some microparticle related data.
  • Algorithm models, formulas, etc. used in data calculations may also include theoretical research data calculated by computing equipment based on microparticle-related data.
  • FIG. 7 shows a schematic diagram of the structure of the cosmic space data system in the embodiment of this paper.
  • the cosmic space data system is called a computing device in this embodiment, and the computing device 702 may
  • One or more processing devices 704 are included, such as one or more central processing units (CPUs), and each processing unit can implement one or more hardware threads.
  • the computing device 702 may also include any storage resources 706 for storing any kind of information such as codes, settings, data, and the like.
  • the storage resource 706 may include any one or a combination of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any storage resource can use any technology to store information.
  • any storage resource can provide volatile or non-volatile retention of information.
  • any storage resource may represent a fixed or removable component of the computing device 702.
  • the processing device 704 executes an associated instruction stored in any storage resource or combination of storage resources, the computing device 702 can perform any operation of the associated instruction.
  • the computing device 702 also includes one or more drive mechanisms 708 for interacting with any storage resource, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like.
  • the computing device 702 may also include an input/output module 710 (I/O) for receiving various inputs (via the input device 712) and for providing various outputs (via the output device 714).
  • a specific output device may include a presentation device 716 and an associated graphical user interface (GUI) 718.
  • GUI graphical user interface
  • the input/output module 710 (I/O), the input device 712, and the output device 714 may not be included, and it is only used as a computing device in the network.
  • the computing device 702 may also include one or more network interfaces 720 for exchanging data with other devices via the one or more communication links 722.
  • One or more communication buses 724 couple the components described above together.
  • the communication link 722 may be implemented in any manner, for example, through a local area network, a wide area network (for example, the Internet), a point-to-point connection, etc., or any combination thereof.
  • the communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.
  • the embodiments herein also provide a computer device, including a memory, a processor, and a computer program stored on the memory and capable of running on the processor, and the processor implements the following steps when the processor executes the computer program:
  • an association relationship of the stored microparticle related data is established.
  • the embodiments of this document also provide a computer-readable storage medium with a computer program stored on the computer-readable storage medium, which executes the steps of the above method when the computer program is run by a processor . And can realize all data processing and control processes in the corresponding system.
  • the embodiments herein also provide a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the solutions shown in FIGS. 1 to 6.
  • the term "and/or” is merely an association relationship describing associated objects, and indicates that there may be three relationships.
  • a and/or B can mean: A alone exists, A and B exist at the same time, and B exists alone.
  • the character "/" in this text generally indicates that the associated objects before and after are in an "or" relationship.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may also be electrical, mechanical or other forms of connection.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments herein.
  • the functional units in the various embodiments herein may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution in this article is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , Including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of this document.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code .

Landscapes

  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Economics (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Game Theory and Decision Science (AREA)
  • Marketing (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • General Business, Economics & Management (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

一种宇宙空间数据系统、方法、计算机设备和存储介质。其中系统包括数据导入单元（101），用于根据微粒子的种类将输入的微粒子相关数据进行存储；标注单元（102），用于对所述存储的微粒子相关数据进行标注；特征关联单元（103），用于根据所述标注，建立所述存储的微粒子相关数据的关联关系。利用该系统，可以实现一个系统中同时存储多种微粒子的相关数据，研究者可以根据研究所需从该系统中获取各种微粒子的相关数据，并且通过微粒子相关数据之间的特征关系，可以方便研究者很快的得到所需的微粒子相关数据。

Description

一种宇宙空间数据系统、方法、计算机设备和存储介质 技术领域
本文涉及宇宙空间数据技术领域，尤其涉及一种宇宙空间数据系统、方法、计算机设备和存储介质。
背景技术
在研究宇宙运动等空间科学的观测活动中，会针对不同的微小粒子进行观测记录，并进行相应的理论研究，例如针对亚原子、中微子、声子(玻色子)等微粒子的观测以及相应的理论文献，甚至对于一些理论微粒子例如弦子(基于弦论)也会有相应的观测数据以及理论分析等数据，大量的观测数据以及相应的理论研究成果都未构成相应体系，不利于相关的研究者或者机构获取所需要的观测数据以及理论研究成果的扩散。现在，亟需一种可以将多种微粒子观测数据、理论研究成果分门别类的归集，方便研究者可以利用、丰富现有微粒子的观测数据和理论研究。
发明内容
为解决现有技术中的技术问题，本文实施例提供了一种宇宙空间数据系统、方法、计算机设备和存储介质将多种微粒子的相关数据进行分门别类的归纳，构成有序的可认识的数据系统，方便研究者研究。
本文实施例提供了一种宇宙空间数据系统，包括，
数据导入单元，用于根据微粒子的种类将输入的微粒子相关数据进行存储；
其中，数据导入单元进一步包括，编码映射模块，用于将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；元数据导入模块，用于将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间；
标注单元，用于对所述存储的微粒子相关数据进行标注；
特征关联单元，用于根据所述标注，建立所述存储的微粒子相关数据的关联关系。
本文实施例还提供了一种宇宙空间数据收录方法，包括，
根据微粒子的种类将输入的微粒子相关数据进行存储；其中，将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间；
对所述存储的微粒子相关数据进行标注；
根据所述标注，建立所述存储的微粒子相关数据的关联关系。
本文实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述系统。
本文实施例还提供了一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器运行时实现上述系统。
利用本文实施例，可以实现在一个系统中同时存储各种微粒子的相关数据，研究者可以根据研究所需从该系统中获取各种微粒子的相关数据，并且通过微粒子相关数据之间的特征关系，可以方便研究者很快的得到所需的微粒子相关数据。
附图说明
为了更清楚地说明本文实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本文的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1所示为本文实施例一种宇宙空间数据系统的结构示意图；
图2所示为本文实施例宇宙空间数据系统的详细结构示意图；
图3A所示为本文实施例研究者输入单元的界面示意图；
图3B所示为本文实施例另一研究者输入单元的界面示意图；
图4所示为本文实施例智能引擎单元校准外界观测设备的流程图；
图5所示为本文实施例一种宇宙空间数据收录方法的流程图；
图6所示为本文实施例一种宇宙空间数据系统结构示意图；
图7所示为本文实施例宇宙空间数据系统的结构示意图。
具体实施方式
下面将结合本文实施例中的附图，对本文实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本文一部分实施例，而不是全部的实施例。基于本文中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本文保护的范围。
在本文中所述的微粒子例如可以包括弦子、亚原子、中微子以及声子，还可能包括其他已经发现的微粒子，或者还未发现的微粒子。
其中，弦子：弦论是理论物理学上的一门学说，它认为自然界的基本单元不仅仅只有亚原子、中微子等微粒子，并且认为这些看起来像粒子的东西都是由更小的弦的闭合圈组成(成为闭合弦或闭弦)，闭弦不同的振动和运动就产生出各种已经观测到的基本粒子。
亚原子：是指比原子更小的微粒子物质层次的标准模型，例如电子、中子、质子、介子、夸克、胶子、光子等基本粒子，研究这种更微观粒子的物理学科，都被称为亚原子物理学。理论学家用希尔伯特空间中的状态向量来描述它们。目前所知的标准模型包含47种基本粒子的量子场论。
中微子：是指宇宙中不带电、质量非常轻、可自由穿越地球，与其他物质的相互作用十分微弱的基本粒子，在自然界广泛存在，又叫费米子。微粒子物理的研究结果表明，每一种中微子都有与其相对应的反物质。太阳内部的弱相互作用参与的核反应产生大量中微子，畅通无阻流向太空。每秒钟通过我们眼睛的中微子数以十亿计，穿过每个人身体有数以千万亿个中微子。目前探测器主要记录运动速度，衰变产生的过程，是否具有质量的情况。
声子：是晶体中晶体结构集体振荡模式量子化激发的准粒子，又叫玻色子。动力学中用振荡波的坐标位置、动量和运动方程来描述，用湮灭算符来表示声子作为准粒子的寿命。
如图1所示为本文实施例一种宇宙空间数据系统的结构示意图，在本实施例中提供一个存储宇宙中多种微粒子观测数据以及相应理论研究的系统，将不同微粒子的相关数据，例如观测数据和理论研究数据，根据微粒子种类进行存储，并通过对存储数据进行标注，所有存储的数据都具有相应的标签，以便于建立多个同一种微粒子相关数据之间的关联性，从而可以方便研究者利用所述微粒子的相关数据以及输入接口供研究者丰富某种微粒子的相关数据。在本实施例中的各个单元以及模块均可以通过软件模块来实 现，或者还可以通过分布式的计算机集群来实现，或者还可以通过通用芯片来完成单元、模块的功能，具体包括：
数据导入单元101，用于根据微粒子的种类将输入的微粒子相关数据进行存储。
标注单元102，用于对所述存储的微粒子相关数据进行标注。
特征关联单元103，用于根据所述标注，建立所述存储的微粒子相关数据的关联关系。
通过上述构成的系统，可以实现在一个系统中同时存储各种微粒子的相关数据，研究者可以根据研究所需从该系统中获取各种微粒子的相关数据，并且通过微粒子相关数据之间的特征关系，可以方便研究者很快的得到所需的微粒子相关数据。
如图2所示为本文实施例宇宙空间数据系统的详细结构示意图，图中对该系统中各单元的结构进行了进一步的描述，其中，还包括数据库100，用于存储微粒子相关数据，数据导入单元101进一步包括了编码映射模块1011和数据导入模块1012，本文实施例的宇宙空间数据系统还包括研究者输入单元104，遗址记忆单元105，智能引擎单元106以及算法服务单元107。
作为本文实施例的一个方面，所述数据导入单元101进一步包括，编码映射模块1011以及元数据导入模块1012，其中，
编码映射模块1011，用于将不同微粒子的相关数据进行编码映射；
元数据导入模块1012，用于将微粒子的相关数据存储入相应编码映射的数据库空间。
在本步骤中，所述的微粒子相关数据可以包括观测数据以及理论研究数据，数据导入过程中可以仅导入微粒子的观测数据也可以仅导入微粒子的理论研究数据，或者还可以将微粒子观测数据以及理论研究数据同时导入。并且，还可以将不同微粒子的相关数据进行编码映射，产生X(弦子)、Y(亚原子)、Z(中微子)和S(声子)四个类别微粒子的身份ID，其中微粒子的身份ID可以例如为，区域码+时间戳+特征属性+其他区分码，其中，区域码可以为存储介质中指定区块的编码，所述编码可以根据需要采用顺序编码方式；特征属性包括XYZS微粒子标识，例如X标识代表弦子，以及还可以包括场景建模属性、业务运营属性等，其中，场景建模属性可以进一步包括研究者输入、遗址记忆、智能引擎以及算法服务属性，用于标记该微粒子的相关数据来源于哪个功能单元，业务运营属性可以进一步包括公众媒体等属性，用于标记该微粒子相关数据来源于公众媒体；其他区分码可以为各种状态机的标识，用于对微粒子相关数据进行区分，例如由 谁操作的读的状态标识、写的状态标识、由谁调用的角色策略的过程状态标识等。例如可以对现有的两种或者两种以上的微粒子进行编码映射，并将相应的微粒子相关数据存储入相应编码映射对应的数据库空间中，其中，元数据导入模块还可以通过不同的数据通道导入不同观测站或者机构、个人等观测到的微粒子数据以及该微粒子理论研究数据，所述数据通道可以为并行通道，也就是是说，不同种类的微粒子相关数据可以同时存储到相应的数据库空间中，并在所述数据库空间中开启写入权限对导入的微粒子相关数据进行写入。
作为本文实施例的一个方面，编码映射模块1011进一步将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；
元数据导入模块1012进一步将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间。
在本步骤中，数据导入单元101可能只接收XYZS四类微粒子中的两种或者两种以上，因此，数据导入单元101中的编码映射模块1011可以仅将接收到的弦子和亚原子相关数据编码映射为X以及Y，或者将亚原子和中微子相关数据编码映射为Y和Z，或者将弦子、亚原子和声子相关数据编码映射为X、Y和S等等。将没有接收到的微粒子编码映射为空(NULL)。
作为本文实施例的一个方面，所述标注单元102进一步用于，将所述存储的微粒子相关数据的来源、数据内容、理论研究数据进行标注。
其中，微粒子相关数据的来源进一步包括，微粒子相关数据的提供方信息和来源辅助信息，其中，提供方信息例如可以包括研究机构的名称(或者编号)、研究项目名称(或者编号)，来源辅助信息例如可以包括仪器的型号(或者编号)、仪器的配置参数、证明人等信息；数据内容进一步包括，观测事件、观测数据等，其中数据内容还可以包括发现过程、数据精度、数据格式、基点、矢量轨迹、单元标尺等内容，例如，当研究机构记录数据时，他们的数据一般都是来自于实验设备或仪器的读数，或者根据实验模型推测得出，那么基点就来源于设备和仪器设定的校准方式，也就是说基点就是测量参照物的起始计量点，单元标尺就是记录的数据，矢量轨迹一般按照动态数据作图得到；理论研究数据进一步包括，观点内容以及发布观点者的信息(可以为个人或者机构)，其中，观点内容可以以包括论文、署名文章、研究报告等形式存储。所述标注单 元102还可以标注微粒子相关数据的时间戳，用以记录微粒子相关数据来源、数据内容、观点的录入时间。并且，标注单元102还可以用于记录操作上述微粒子相关数据的行为，例如，谁在何时何地什么场景下以什么样的角色(例如普通用户、注册会员等角色)调用了哪些种类的微粒子相关数据，并生成了何种数据(例如理论研究数据)，这些操作微粒子相关数据的行为可以服务于认知变化的管理。
其中，微粒子相关数据中的观测数据包括了上述微粒子相关数据的来源、数据内容，微粒子相关数据中的理论研究数据包括了上述的观点。当然，微粒子相关数据的观测数据可能包括了上述全部的数据来源和数据内容，也可能仅包括其中一部分，理论研究数据也可以包括了上述观点的全部内容，也可能仅包括其中一部分，其中时间戳可以属于理论研究数据，也可能属于观测数据，或者两者都分别具有时间戳。
作为本文实施例的一个方面，所述特征关联单元103进一步用于，将相同类型微粒子的相关数据来源建立关联，将相同类型微粒子的数据内容建立关联，将相同类型微粒子的理论研究数据建立关联。
在本步骤中，根据相同种类微粒子相关信息的标注信息建立微粒子相关信息的关联关系，就可以根据关联关系寻找与其中某一项相关数据(来源、数据内容、理论研究数据)有关的其他相关数据，从而可以方便研究者充分利用微粒子相关数据。初始库建立状态下，数据导入单元通过发放微粒子相关数据的身份ID，并由特定并行通道完成元数据导入，这个原始的关联管理和对应关系是由特征关联单元完成。调用者和访问者对元数据处理之后的反馈和补充，也是由特征关联单元进行聚类管理。初始库、备选库、综合知识库、应用场景库、业务运营库的分库操作管理也通过特征关联单元来实现。
为了更加清楚的说明以上步骤，可以例举如下示例：
当研究者获得了研究所A采用仪器B对观测事件C的观测数据D(可以是X、Y、Z、S4类微粒子中的一种，也可以是其中几种的混合数据)，该研究者还可以根据与该观测事件相同的观测事件C寻找到另一个研究所E采用仪器F获得的观测数据D’，此时研究者可以通过对比D和D’的观测数据研究该观测事件C。
当研究者查询到某个观点，以及该观点发布者的信息，通过该发布观点者的信息可以获得该发布观点者的其他观点，以及与每个观点相关的微粒子相关数据的来源、数据内容。或者还可以根据该观点，寻找与该观点相同的其他观点发布者的信息，以及相应的微粒子相关数据的来源和数据内容。
作为本文实施例的一个方面，将相同类型微粒子的相关数据来源建立关联，将相同类型微粒子的数据内容建立关联，将相同类型微粒子的理论研究数据建立关联进一步包括，
向所述相同类型微粒子相关数据来源、数据内容和理论研究数据中加入标志位，
当微粒子相关数据来源、数据内容或理论研究数据相同时，在相应的标志位写入相同的标记。
在本步骤中，还可以采用堆栈的方式建立微粒子相关数据的关联关系，例如，当研究者获取微粒子相关数据后，将与研究有关的微粒子相关数据压入堆栈，此时，微粒子相关数据可能及其丰富，包括多种内容，而该研究者可能仅将一部分微粒子相关数据压入堆栈，例如只需要观测事件、观测仪器、观测仪器参数、观测仪器的读数即可，并不需要研究机构、研究项目等信息，只需要将上述需要的数据内容压入堆栈，其中，堆栈可以根据研究者的身份ID、所获取的微粒子相关数据的ID、微粒子的种类等形成不同的堆栈。研究者根据上述堆栈中的微粒子相关数据进行研究，而后得到研究结果(即理论研究数据)，上述的特征关联单元103将所述堆栈中的微粒子相关数据的数据项以及相应的理论研究数据建立关联关系，也就是说，在堆栈中的微粒子相关数据的数据项与相应的理论研究数据形成关联关系。
作为本文实施例的一个方面，还包括研究者输入单元104，用于在研究者指定的微粒子相关数据中写入新的微粒子相关数据。
在本步骤中，例如研究者通过运用霍金的果核理论构建微粒子验证的实验，期间需要在某个步骤调用某类微粒子的身份ID和对应通道的微粒子数据，同时要了解验证过程变化的数据，研究者通过研究者输入单元104建立新的理论研究数据，并通过调用相应微粒子相关数据来进行实验，并记录这些用于新理论研究的微粒子相关数据，从而构成一个新理论研究的完整数据记录(包括微粒子相关数据以及新的理论研究数据)。研究者可以通过例如图3A所示的web界面或则应用软件的界面等图形用户界面(GUI)将研究者的微粒子理论研究数据输入到相应的微粒子相关数据中，在本实施例中，研究者通过对微粒子相关数据中的微粒子观测数据进行研究，得到了新的观点，则可以针对该观测数据进行理论研究数据的丰富，并且留存下该研究者的信息，例如姓名、联系方式等。或者还可以将研究者的观测数据存储到微粒子相关数据中。
或者还可以如图3B所示的web界面或则应用软件的界面等图形用户界面(GUI)将研究者的需求信息输入到微粒子相关数据中。也就是说，本文研究者输入单元104可以向研 究者或者机构提供微粒子相关数据输入的接口，方便所有研究者对感兴趣的微粒子相关数据进行丰富。
作为本文实施例的一个方面，还包括遗址记忆单元105，用于向研究者提供微粒子相关数据的横向对比，以及微粒子相关数据的历史对比。
在本步骤中，向研究者提供微粒子相关数据的横向对比是指，根据观测条件、观测时间、观测地点以及微粒子种类为条件，将类似观测条件、观测时间、观测地点以及相同微粒子种类的微粒子相关数据中的观测数据以及理论研究数据进行横向比较，这样方便研究者对比观测条件、观测时间、观测地点给观测数据带来的影响。其中，观测条件可以包括观测时的天气情况、观测时的仪器型号、观测时的仪器参数，还可以包括基于什么观测事件，这样还可以根据同一个观测事件，将观测数据以及理论研究数据进行横向比较。
向研究者提供微粒子相关数据的历史对比是指，根据观测时间以及微粒子种类为条件，将某种微粒子在多个观测时间的观测数据进行对比。还可以进一步加入观测事件、观测地点、观测条件等对比条件，也就是说，可以将相同观测事件在类似的观测地点、观测条件的某种微粒子相关数据中的观测数据以及理论研究数据进行对比，从而研究者可以方便的根据历史观测数据进行对比。
作为本文实施例的一个方面，还包括智能引擎单元106，用于与外界观测设备连接，与所述外界观测设备进行数据交互。
在本步骤中，智能引擎单元与外界观测设备进行数据交互是指，外界观测设备可以通过智能引擎单元106获取本文系统中的微粒子相关数据用于该外界设备对微粒子进行观测，并且外界观测设备还可以向本文系统输入某种微粒子的观测数据，还可以包括观测时的仪器型号、仪器参数等数据，从而可以丰富本文系统的微粒子相关数据。
在本实施例中的外界观测设备为可以通过程序远距离驱动的观测设备，例如可以通过网络方式与该观测设备连接，并通过下发指令的方式驱动该观测设备按照指定的参数和时间进行观测工作。
如图4所示为本文实施例智能引擎单元校准外界观测设备的流程图，在该图中描述了智能引擎单元对外界观测设备进行校准的过程，具体如下：
步骤401，外界观测设备通过网络与智能引擎单元106连接。
在本步骤中，可以通过向智能引擎单元106报告该外界观测设备的型号等唯一标识符的方式通知智能引擎单元106该外界观测设备的信息。
步骤402，智能引擎单元106根据该外界观测设备的唯一标识符寻找到已经被校准过的相同设备的设定参数以及相应的观测数据(这些数据可以是其他相同型号的校准后的外界观测设备通过网络的方式存储到智能引擎单元106中)。
步骤403，智能引擎单元106将该设定参数以及观测数据发送给该外界观测设备。
步骤404，该外界观测设备可以根据设定参数设定该外界观测设备的参数，并执行观测任务，得到观测数据。
步骤405，将得到的观测数据与接收到的观测数据进行比较。
如果比较结果一致，进入步骤406。如果比较结果不一致，则说明该外界观测设备不准确，还需要进一步校准，则进入步骤407。
步骤406，比较结果一致，说明该外界观测设备没有问题，可以执行观测任务。外界观测设备可以向管理者显示该信息，以进行后续操作。
步骤407，如果比较结果不一致，说明该外界观测设备不准确，还需要进一步校准，此时可以将需要进一步校准的信息发送给该外界观测设备的管理人员(该信息可以从该外界观测设备连接智能引擎单元106时获得)。或者还可以通过调节外界观测设备的设定参数来进行自动校正。
本文实施例的智能引擎单元106可以设置约束条件控制外界观测设备工作，在本实施例中外界观测设备不是始终处于观测状态，而是需要判断观测条件是否满足约束条件，其中，约束条件可以为，当被观测的微粒子的某个(某些)观测值在一定阈值范围内时，该观测仪器静默运行，正常通道记录观测数据，当该为例字的某个(某些)观测值超出该阈值范围触发警告，启动应急程序，并触发批量动作和复杂调用关联关系，例如启动某个(某些)特定的观测设备或者计算设备，或者通知研究人员。如果满足约束条件则该外界观测设备开始观测任务，或者根据其他与其连接的第二外界观测设备(可以是超过一定比例的外界观测设备)是否启动观测任务来通知第一外界观测设备启动观测任务，例如，与第一外界观测设备相关的第二外界观测设备有多个，第二外界观测设备开始执行观测任务时，都会向智能引擎单元106发送通知，当智能引擎单元106统计超过一半数量的第二外界观测设备执行观测任务时，则通知与其连接的第一外界观测设备启动以执行观测任务，从而可以节省能源以及相应观测设备的损耗。
作为本文实施例的一个方面，还包括算法服务单元107，用于与外界计算设备连接，与所述外界计算设备进行数据交互。
在本步骤中，算法服务单元107与外界计算设备进行数据交换是指，外界计算设备可以通过互联网与算法服务单元107相连接，通过算法服务单元107获得微粒子相关数据，外界计算设备通过将微粒子相关数据输入到该外界计算设备中的数学模型中进行分析、计算，从而能够得到基于该微粒子相关数据的分析结果，其中数学模型可以例如为将微粒子的低维数据转换为高维数据的数学模型；所述外界计算设备还可以将分析、计算结果通过算法服务单元107发送给本文的系统，在相应的微粒子相关数据中进行存储，并且，还可以将外界计算设备所使用的数学模型或算法也输入到本文系统的数据库中进行存储，从而可以丰富微粒子相关数据。
通过算法服务单元，本文的系统向外界计算设备，例如计算机、计算机集群等，提供了用于进行理论研究的微粒子观测数据以及相应的理论研究数据，其中包括具体的观测数据以及各种不同的算法模型等，并且向外界计算设备提供了微粒子相关数据的输入接口，通过该算法服务单元可以收录外界计算设备的算法模型、计算过程、以及计算、分析结果，可以进一步丰富数据库中的微粒子相关数据，便于其他研究者的借鉴和参与。
如图5所示为本文实施例一种宇宙空间数据收录方法的流程图，在本实施例中，通过对多种微粒子进行分门别类的记录，清晰的划分了几种宇宙中基本粒子的概念，并且通过几种微粒子的分类存储可以帮助研究者更快的掌握不同微粒子的相关数据，有利于研究的进展，具体包括如下步骤：
步骤501，根据微粒子的种类将输入的微粒子相关数据进行存储；
步骤502，对所述存储的微粒子相关数据进行标注；
步骤503，根据所述标注，建立所述存储的微粒子相关数据的关联关系。
作为本文实施例的一个方面，在所述步骤501中进一步包括，
将不同微粒子的相关数据进行编码映射；
将微粒子的相关数据存储入相应编码映射的数据库空间。
作为本文实施例的一个方面，在将不同微粒子的相关数据进行编码映射中进一步包括，
将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；
将微粒子的相关数据存储入相应编码映射的数据库空间中进一步包括，
将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间。
作为本文实施例的一个方面，对所述存储的微粒子相关数据进行标注进一步包括，
将所述存储的微粒子相关数据的来源、数据内容、理论研究数据进行标注。
作为本文实施例的一个方面，根据所述标注，建立所述存储的微粒子相关数据的关联关系进一步包括，
将相同类型微粒子的相关数据来源建立关联，将相同类型微粒子的数据内容建立关联，将相同类型微粒子的理论研究数据建立关联。
作为本文实施例的一个方面，将相同类型微粒子的相关数据来源建立关联，将相同类型微粒子的数据内容建立关联，将相同类型微粒子的理论研究数据建立关联进一步包括，
向所述相同类型微粒子相关数据来源、数据内容和理论研究数据中加入标志位，
当微粒子相关数据来源、数据内容或理论研究数据相同时，在相应的标志位写入相同的标记。
作为本文实施例的一个方面，还包括研究者输入步骤，在研究者指定的微粒子相关数据中写入新的微粒子相关数据。
作为本文实施例的一个方面，还包括遗址记忆步骤，向研究者提供微粒子相关数据的横向对比，以及微粒子相关数据的历史对比。
作为本文实施例的一个方面，还包括智能引擎步骤，与所述外界观测设备进行数据交互。
作为本文实施例的一个方面，还包括算法服务步骤，与所述外界计算设备进行数据交互。
通过本文实施例中的方法，可以通过将几种微粒子分门别类的进行存储管理，并向研究者提供了接入的接口，既可以向系统中输入微粒子相关数据，也可以利用系统获取微粒子相关数据以用于理论研究，并且通过将微粒子相关数据进行标注和关联，使得研究者更容易的获得几种微粒子相关数据。
如图6所示为本文实施例一种宇宙空间数据系统结构示意图，在本实施例中以4种微粒子为例建立数据系统，本实施例的微粒子包括弦子、亚原子、中微子和声子，其中：
弦子，微粒子物质的初始认知空间依托于人类可直接感知的物质空间，即3维地理位置坐标空间。用弦论的方式观测并记录下来提供的数据描述，标记为弦子。
弦子用于描述宇宙空间的位置量度，量度采用人类可理解可解释的数学记号，或现有仪器设备的记录模式。可认知的平行空间的更高维度与认知对象本身的设限有关。当认知单元自我设限，就会停留在较低维度空间。目前数学中描述的十维空间，是受了现当代数学这门学科工具的限制。
亚原子：宇宙空间组成物质比原子更小的基本粒子，这些基本粒子统称为亚原子。
目前已知的亚原子粒子包括夸克、胶子，2种光子(强子、核子)，3种电子(缪子、陶子、轻子)，轻子是比普朗克能标更轻的亚原子，希格斯玻色子是具有非零质量的上帝粒子。
中微子：中微子的出现与宇宙物质的质子衰变活动相关。中微子是费米子的一种。质子衰变活动与宇宙物质的质谱形成有关，化学上将质谱称元素周期表。
质子衰变的活动形成了宇宙元素质谱，宇宙体大规模衰变活动会释放射线暴并有可能出现中微子。例如超新星衰变的耀变体释放射线暴出现中微子，中微子是费米子的一种。
声子：晶体等多粒子体系中粒子运动的最小力学单位，称为声子。声子与宇宙物质之间的碰撞或振荡活动的聚变相关，声子是玻色子的一种。
声子是宇宙多粒子体系存在从基态到激发态的准粒子单元，也称作元激发。元激发状态跃迁活动单位就定义为声子，声子的聚变效应形成场。
本文实施例提供了针对于上述4种微粒子的编码映射模块601，将从各个观测站或者研究机构接收到的各种微粒子的相关数据进行编码映射，例如，可以通过关键字识别的方式(或者通过对观测数据格式、观测数据内容的识别)，识别出接收到的微粒子相关数据是关于上述4种微粒子中的哪一种微粒子，将该接收到的微粒子相关数据编码为相应的字符，例如，当识别出接收到的微粒子相关数据为弦子的相关数据，则将该弦子的相关数据编码为X，当识别出接收到的微粒子相关数据为亚原子的相关数据，则将该亚原子相关数据编码为Y，当识别出接收到的微粒子相关数据为中微子的相关数据，则将该中微子相关数据编码为Z，当识别出接收到的微粒子相关数据为声子的相关数据，则将该声子相关数据编码为S。
某个观测站或者研究机构发送的观测数据可能仅涉及某几个微粒子，也可能涉及全部的4种微粒子，当发送的微粒子相关数据中不包括其中哪种微粒子时，则在相应的编码记录中记录该种微粒子的相关数据为空。
元数据是指宇宙空间运行时各观测站所观测到的现象做出的基础信息标记。各太空站或者宇宙观测中心得到的数据通过元数据导入模块602导入上述4种微粒子构成的XYZS认知框架的数据系统。本实施例中预设了XYZS四类工具接口，4种微粒子的接口包括由观测站作为元数据的输出源，以XYZS认知框架为特征的数据系统作为输入方。在某些实施例中由于保密性隔离和专业性保护的要求，还可以引入第三方进行元数据导入，即由元数据输出源，经由第三方提供数据插件，将元数据输出源输出的微粒子相关数据做与本文实施例元数据导入模块602进行适配，其中可能包括转换微粒子相关数据的格式、统一微粒子相关数据的精度等。通过元数据导入模块602，将各个观测站以及研究机构的观测数据和/或理论研究数据根据各自微粒子相关数据的编码，导入到以XYZS认知框架为特征的数据库600空间中，也就是说，将X弦子的相关数据存储入X弦子数据库空间，将Y亚原子的相关数据存储入Y亚原子数据库空间，将Z中微子的相关数据存储入Z中微子数据库空间，将S声子的相关数据存储入S声子数据库空间。
通过附图可以看出，本实施例中的4种微粒子均由编码映射模块601进行编码标记，识别并标记出每一种微粒子。提供上述4种微粒子的观测站或者研究机构通过每种微粒子不同的通道将相应的微粒子相关数据传送给元数据导入模块602，例如X通道传送弦子相关数据，Y通道传送亚原子相关数据，Z通道传送中微子相关数据，S通道传送声子相关数据，其中，具体的通道名称可以根据实际情况命名。
上述由多个观测站或者研究机构输入的4种微粒子相关数据，经过标注单元603的识别和标注，将每一种微粒子相关数据中的观测数据和理论研究数据进行标注，例如，标注观测数据中包括了某专业机构、项目组、某领域专家组、某仪器工具等，在标注理论研究数据中包括在解读上述微粒子的观测数据和其他理论研究数据后提出了新的看法和观点，上述所有微粒子相关数据的内容标注单元603都会进行标注，其中，在标注完成后，各微粒子的相关数据的不同部分的内容可以包括微粒子种类、研究机构、研究项目、仪器、证明人、观测事件、观测到的数据、观点、观点的出处、发表的相关论文等多属性内容中的一部分或者全部的标注信息，此外，微粒子相关数据还可以包括时间戳，用于标记观测或者观点产生的时间。
在上述对微粒子相关数据进行标注的例子中，研究机构、研究项目、仪器、证明人可以概括为微粒子相关数据的来源，观测事件、观测到的数据可以概括为微粒子相关数据的数据内容，观点、观点的出处、发表的相关论文可以概括为微粒子相关数据的理论研究数据。其中，微粒子相关数据的来源、数据内容以及观点还可以包括其他内容，并且，还可以将微粒子相关数据的来源、数据内容概括为微粒子相关数据的观测数据。
其中，标注单元603会将由左侧输入到数据库600的微粒子相关数据进行标注，例如从其他数据库或者观测站(研究所)导入数据库600的微粒子相关数据，同样会对从右侧输入到数据库600中的微粒子相关数据进行标注，例如，由外界观测设备输入到数据库600的观测数据或者由其他研究者在参考了本文数据系统中的微粒子相关数据后产生的新的观点等理论研究数据等。
特征关联单元604将上述标注后的微粒子相关数据进行关联，特征关联单元604根据微粒子相关数据中的标注信息将相同的或者相近的观测数据或者理论研究数据进行关联。
特征关联是针对分批分次导入的微粒子相关数据中的标注信息和宇宙空间的矩阵数据进行数据分析和数据关系管理的工具盒子。特征关联的方式是根据XYZS分类的宇宙空间数据的理论发展密切相关，主要包括相同标注中数据内容的特征值提取、关联关系的定义、各种场景下应用模式的需求适配等。数据分析和关系管理采用的方法和工具不限于因子分析、分类、聚类、决策树、神经网络、知识图谱等。
特征关联可以例如，对微粒子相关数据中的标注内容，例如理论研究数据进行关键字匹配或者语义分析，将关键字相同或者语义上相近的理论研究数据，例如将相近的观点进行关联，将关联的多个理论研究数据标记为同一个聚类标识，与理论研究数据相关的某种微粒子相关数据也将具有相应的聚类标识，当研究者获得某种微粒子相关数据后，认同相应的观点，就可以通过该微粒子相关数据的理论研究数据获得其他具有类似观点的理论研究数据以及其他相关的观测数据。当研究者通过某些观测数据研究后，产生了新的观点(即理论研究数据)，可以寻找具有类似观点的理论研究数据，并调阅相应的观测数据，进一步完善其观点，或者还可以通过当前研究的观测数据找到与当前研究的观测数据类似的观测数据，例如，观测仪器相似，并且该仪器配置了类似的参数，通过类似的观测数据寻找到其他研究者根据该类似的观测数据得到的理论研究数据(即观点)。
上述的特征关联还可以采用标志位的方式，在微粒子相关数据中每个数据的标注中设置标志位，通过标志位中的标识来匹配相同标注中标志位的标识。例如，一个微粒子相关数据来源的标志位标识为观测站01，另一个微粒子相关数据来源的标志位标识位观测站02，则这两个微粒子相关数据来源不做关联，如果另一个微粒子相关数据来源的标志位标识为观测站01，则将这两个微粒子相关数据来源进行关联，并记录该关联关系。
分散于世界各地的研究者可以通过本文实施例的数据系统获得关于上述4种微粒子的相关数据以进行研究，研究者还可以通过研究者输入单元605向所研究的微粒子先关数据中加入新的理论研究数据，并通过标注单元603进行标注，以及特征关联单元604将新加入的理论研究数据与其他相关数据进行关联。研究者通过研究者输入单元605还可以向本文实施例数据系统发起数据要求，请求本文实施例数据系统从指定的研究机构或观测站获取指定的微粒子相关数据，或者研究者还可以通过研究者输入单元605向本文实施例数据系统写入新的微粒子相关数据，例如某种微粒子的观测数据，从而可以丰富本文实施例数据系统的微粒子相关数据。
研究者还可以通过遗址记忆单元606获得本文实施例数据系统的多个相关的微粒子相关数据进行横向对比。例如以观测事件为例，研究者可以向遗址记忆单元606输入某个观测事件，遗址记忆单元606查找出所有与该观测事件相关的微粒子相关数据，例如包括不同观测机构针对该观测事件的观测数据以及理论研究数据，或者包括不同观测条件下针对该观测事件的观测数据以及理论研究数据，或者还可以包括以时间轴的方式针对该观测事件的观测数据以及理论研究数据。将多个微粒子相关数据进行横向对比，方便研究者进行研究。
研究者还可以通过遗址记忆单元606获得本文实施例数据系统的多个相关的微粒子相关数据进行历史对比。例如以某个观测仪器为例，研究者可以向遗址记忆单元606输入某个观测站的观测仪器的历史观测数据，遗址记忆单元606查找出该观测站的特定观测仪器的历史观测数据，并携带有例如某一次观测时的观测条件，仪器设定参数等信息，从而可以方便研究者对历史观测数据进行对比。
本文实施例的数据系统通过智能引擎单元607以及网络与外界观测设备连接，例如通过网络与观测站的观测设备连接，外界观测设备不仅可以通过智能引擎单元607从本文实施例数据系统获取微粒子相关数据，还可以通过智能引擎单元607向本文实施例数据系统存储微粒子相关数据(其中主要是由该外界观测设备进行观测时得到的观测数据)。
其中，外界观测设备可以从智能引擎单元607获取相同观测设备的观测参数和观测数据(该相同观测设备假定位已经调节完毕的，准确的观测设备)，利用该观测参数设置本机观测设备并进行观测，然后通过观测数据以及接收到的观测数据进行比较，如果比较结果不一致，则说明本机的观测设备存在误差，需要调节，可以进行相应的设定参数修订，直至观测数据与接收到的观测数据相同或者相近，如果比较结果一致，则说明本机的观测设备观测结果准确，可以执行观测任务。
本文实施例的数据系统还可以通过算法服务单元608以及网络与外界计算设备连接，例如通过网络与观测站的计算设备连接，外界计算设备不仅可以通过算法服务单元608从本文实施例数据系统获取微粒子相关数据，还可以通过算法服务单元608向本文实施例数据系统存储微粒子相关数据，其中主要是由该外界计算设备根据微粒子相关设备进行计算后的结果，或者包括该外界计算设备根据某些微粒子相关数据进行计算时所使用的算法模型、公式等，还可以包括计算设备根据微粒子相关数据进行计算后的理论研究数据等。
如图7所示为本文实施例宇宙空间数据系统的结构示意图，在本实施例中描述了数据系统的结构，所述宇宙空间数据系统在本实施例中被称为计算设备，计算设备702可以包括一个或多个处理设备704，诸如一个或多个中央处理单元(CPU)，每个处理单元可以实现一个或多个硬件线程。计算设备702还可以包括任何存储资源706，其用于存储诸如代码、设置、数据等之类的任何种类的信息。非限制性的，比如，存储资源706可以包括以下任一项或多种组合：任何类型的RAM，任何类型的ROM，闪存设备，硬盘，光盘等。更一般地，任何存储资源都可以使用任何技术来存储信息。进一步地，任何存储资源可以提供信息的易失性或非易失性保留。进一步地，任何存储资源可以表示计算设备702的固定或可移除部件。在一种情况下，当处理设备704执行被存储在任何存储资源或存储资源的组合中的相关联的指令时，计算设备702可以执行相关联指令的任一操作。计算设备702还包括用于与任何存储资源交互的一个或多个驱动机构708，诸如硬盘驱动机构、光盘驱动机构等。
计算设备702还可以包括输入/输出模块710(I/O)，其用于接收各种输入(经由输入设备712)和用于提供各种输出(经由输出设备714)。一个具体输出设备可以包括呈现设备716和相关联的图形用户接口(GUI)718。在其他实施例中，还可以不包括输入/输出模块710(I/O)、输入设备712以及输出设备714，仅作为网络中的一台计算设备。计 算设备702还可以包括一个或多个网络接口720，其用于经由一个或多个通信链路722与其他设备交换数据。一个或多个通信总线724将上文所描述的部件耦合在一起。
通信链路722可以以任何方式实现，例如，通过局域网、广域网(例如，因特网)、点对点连接等、或其任何组合。通信链路722可以包括由任何协议或协议组合支配的硬连线链路、无线链路、路由器、网关功能、名称服务器等的任何组合。
本文实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如下步骤：
根据微粒子的种类将输入的微粒子相关数据进行存储；
对所述存储的微粒子相关数据进行标注；
根据所述标注，建立所述存储的微粒子相关数据的关联关系。
并且本文实施例中的计算机设备还可以实现图1-图6中的所有方案。
对应于图1-图6中的方案，本文实施例还提供了一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器运行时执行上述方法的步骤。并可以实现相应系统中的所有数据处理和控制的过程。
本文实施例还提供一种计算机可读指令，其中当处理器执行所述指令时，其中的程序使得处理器执行如图1至图6所示的方案。
应理解，在本文的各种实施例中，上述各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本文实施例的实施过程构成任何限定。
还应理解，在本文实施例中，术语“和/或”仅仅是一种描述关联对象的关联关系，表示可以存在三种关系。例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在上述说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本文的范围。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本文所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另外，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口、装置或单元的间接耦合或通信连接，也可以是电的，机械的或其它的形式连接。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本文实施例方案的目的。
另外，在本文各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本文的技术方案本质上或者说对现有技术做出贡献的部分，或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本文各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
本文中应用了具体实施例对本文的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本文的方法及其核心思想；同时，对于本领域的一般技术人员，依据本文的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本文的限制。

Claims (10)

  1. 一种宇宙空间数据系统，其特征在于，包括，
    数据导入单元，用于根据微粒子的种类将输入的微粒子相关数据进行存储；
    其中，数据导入单元进一步包括，编码映射模块，用于将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；元数据导入模块，用于将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间；
    标注单元，用于对所述存储的微粒子相关数据进行标注；
    特征关联单元，用于根据所述标注，建立所述存储的微粒子相关数据的关联关系。
  2. 根据权利要求1所述的系统，其特征在于，所述标注单元进一步用于，将所述存储的微粒子相关数据的来源、数据内容、理论研究数据进行标注。
  3. 根据权利要求2所述的系统，其特征在于，所述特征关联单元进一步用于，将相同类型微粒子的相关数据来源建立关联，将相同类型微粒子的数据内容建立关联，将相同类型微粒子的理论研究数据建立关联。
  4. 根据权利要求1所述的系统，其特征在于，还包括研究者输入单元、遗址记忆单元、智能引擎单元以及算法服务单元中的一个或者多个；
    其中，所述研究者输入单元，用于在研究者指定的微粒子相关数据中写入新的微粒子相关数据；
    所述遗址记忆单元，用于向研究者提供微粒子相关数据的横向对比，以及微粒子相关数据的历史对比；
    所述智能引擎单元，用于与外界观测设备连接，与所述外界观测设备进行数据交互；
    所述算法服务单元，用于与外界计算设备连接，与所述外界计算设备进行数据交互。
  5. 根据权利要求4所述的系统，其特征在于，所述智能引擎单元将与所述外界观测设备相同的微粒子相关数据发送给所述外界观测设备，使得所述外界观测设备根据接收到的微粒子相关数据进行校准。
  6. 根据权利要求4所述的系统，其特征在于，所述智能引擎单元根据约束条件控制所述外界观测设备工作。
  7. 根据权利要求4所述的系统，其特征在于，所述智能引擎单元根据第二外界观测设备的观测状态来控制第一外界观测设备的工作。
  8. 一种宇宙空间数据收录方法，其特征在于，包括，
    根据微粒子的种类将输入的微粒子相关数据进行存储；其中，将弦子的相关数据映射编码为X，将亚原子的相关数据映射编码为Y，将中微子的相关数据映射编码为Z，将声子的相关数据映射编码为S；将弦子的相关数据存储入编码为X的数据库空间，将亚原子的相关数据存储入编码为Y的数据库空间，将中微子的相关数据存储入编码为Z的数据库空间，将声子的相关数据存储入编码为S的数据库空间；
    对所述存储的微粒子相关数据进行标注；
    根据所述标注，建立所述存储的微粒子相关数据的关联关系。
  9. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1-7任一项所述的系统。
  10. 一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，其特征在于，该计算机程序被处理器运行时实现上述权利要求1-7任一项所述的系统。
PCT/CN2020/095571 2019-06-13 2020-06-11 一种宇宙空间数据系统、方法、计算机设备和存储介质 WO2020249039A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP20822912.0A EP3985525A4 (en) 2019-06-13 2020-06-11 SPATIAL DATA SYSTEM, COMPUTER DEVICE AND STORAGE MEDIUM
US17/618,876 US11675818B2 (en) 2019-06-13 2020-06-11 Cosmic space data system, method, computer device, and storage medium

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
CN201910509981.5 2019-06-13
CN201910509981 2019-06-13
CN202010355309.8 2020-04-29
CN202010355309.8A CN111444191B (zh) 2019-06-13 2020-04-29 一种宇宙空间数据系统、方法、计算机设备和存储介质

Publications (1)

Publication Number Publication Date
WO2020249039A1 true WO2020249039A1 (zh) 2020-12-17

Family

ID=71656138

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/095571 WO2020249039A1 (zh) 2019-06-13 2020-06-11 一种宇宙空间数据系统、方法、计算机设备和存储介质

Country Status (4)

Country Link
US (1) US11675818B2 (zh)
EP (1) EP3985525A4 (zh)
CN (1) CN111444191B (zh)
WO (1) WO2020249039A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114488261B (zh) * 2022-01-13 2023-06-30 西南交通大学 基于lhaaso实验的低能量段伽马质子高能粒子鉴别方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101261865A (zh) * 2007-04-20 2008-09-10 炬力集成电路设计有限公司 媒体电子文件的制作方法、装置及其播放设备及方法
CN101278294A (zh) * 2005-08-02 2008-10-01 卢米尼克斯股份有限公司 用于分类微粒的方法、数据结构和系统
US20100145931A1 (en) * 2003-12-02 2010-06-10 International Business Machines Corporation System and method for indexing, searching and retrieving semantic objects
CN104252665A (zh) * 2014-09-16 2014-12-31 国家海洋信息中心 一种海洋环境监测数据管理方法及系统
CN104657459A (zh) * 2015-02-09 2015-05-27 中国科学院信息工程研究所 一种基于文件粒度的海量数据存储方法

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002163299A (ja) * 2000-11-28 2002-06-07 Sakai Musen:Kk 広域総合情報収集制御システム。
WO2007106514A2 (en) * 2006-03-13 2007-09-20 Smi Holding, Inc. Automatic microparticle mark reader
US8896605B2 (en) * 2011-10-07 2014-11-25 Hewlett-Packard Development Company, L.P. Providing an ellipsoid having a characteristic based on local correlation of attributes
US20130325924A1 (en) * 2012-06-05 2013-12-05 Mehran Moshfeghi Method and system for server-assisted remote probing and data collection in a cloud computing environment
CN104572645B (zh) * 2013-10-11 2020-07-10 阿里巴巴（中国）有限公司 兴趣点数据关联方法及装置
CN104199848B (zh) * 2014-08-08 2017-10-24 亿赞普(北京)科技有限公司 一种不同域下用户数据的关联方法和装置
CN106228165A (zh) * 2016-07-27 2016-12-14 维沃移动通信有限公司 一种照片分类的方法和移动终端
JP7203356B2 (ja) * 2017-03-31 2023-01-13 パナソニックＩｐマネジメント株式会社 撮像システム
US10600024B2 (en) * 2017-05-16 2020-03-24 Walmart Apollo, Llc Automated smart peg system monitoring items
US11143532B2 (en) * 2017-10-19 2021-10-12 International Business Machines Corporation Adaptive calibration of sensors through cognitive learning
WO2019200240A1 (en) 2018-04-13 2019-10-17 Board Of Regents, The University Of Texas System Transmembrane stem cell factor (tm-scf) lipid nanocarriers and methods of use thereof
CN108959247B (zh) * 2018-06-19 2022-09-09 深圳市元征科技股份有限公司 一种数据处理方法、服务器及计算机可读介质
WO2020123956A1 (en) * 2018-12-14 2020-06-18 Denso International America, Inc. System and method of calibration for establishing real-time location
CA3070689C (en) * 2019-02-11 2021-12-07 Hexo Operations Inc. Methods and systems for industrial processes

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100145931A1 (en) * 2003-12-02 2010-06-10 International Business Machines Corporation System and method for indexing, searching and retrieving semantic objects
CN101278294A (zh) * 2005-08-02 2008-10-01 卢米尼克斯股份有限公司 用于分类微粒的方法、数据结构和系统
CN101261865A (zh) * 2007-04-20 2008-09-10 炬力集成电路设计有限公司 媒体电子文件的制作方法、装置及其播放设备及方法
CN104252665A (zh) * 2014-09-16 2014-12-31 国家海洋信息中心 一种海洋环境监测数据管理方法及系统
CN104657459A (zh) * 2015-02-09 2015-05-27 中国科学院信息工程研究所 一种基于文件粒度的海量数据存储方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP3985525A4 *

Also Published As

Publication number Publication date
CN111444191A (zh) 2020-07-24
CN111444191B (zh) 2021-10-12
US20220261424A1 (en) 2022-08-18
EP3985525A4 (en) 2022-12-14
EP3985525A1 (en) 2022-04-20
US11675818B2 (en) 2023-06-13

Similar Documents

Publication Publication Date Title
US20170300657A1 (en) Computerized Event Simulation Using Synthetic Populations
EP3550444B1 (en) Query generation based on a logical data model
CN104050223A (zh) 用于文本挖掘和搜索的数据透视面
US20200341978A1 (en) Query Generation Based On A Logical Data Model With One-to-One Joins
CN104536987B (zh) 一种查询数据的方法及装置
EP3944149A1 (en) Data classification method, and classifier training method and system
TW202347123A (zh) 基於文檔底板實現附著資源動態組合應用的方法
US10402428B2 (en) Event clustering system
WO2020249039A1 (zh) 一种宇宙空间数据系统、方法、计算机设备和存储介质
WO2023134134A1 (zh) 一种关联查看模型的生成方法、装置、计算机设备及存储介质
Padmanabhan et al. Supersymmetric many-body systems from partial symmetries—integrability, localization and scrambling
Yadav et al. Big data analytics for health systems
CN104346393B (zh) 原子级数据元素模型的建模方法
Madhulatha Graph partitioning advance clustering technique
CN113836235B (zh) 基于数据中台的数据处理方法及其相关设备
WO2019006638A1 (zh) 一种基于云计算的文具销售大数据挖掘和分析系统
Zou et al. An integrated disaster rapid cloud service platform using remote sensing data
CN112801545A (zh) 一种基于脑图的项目管理系统
Gilligan-Lee Computation in a general physical setting
Chandola et al. Knowledge discovery from sensor data (SensorKDD)
US20230195673A1 (en) Pluggable data adaptor
Cui et al. A Task-Oriented Hybrid Cloud Architecture with Deep Cognition Mechanism for Intelligent Space.
Glawischnig et al. A distributed generic data structure for urban level building data monitoring
Li Performance management of event processing systems
Babu et al. IoT Driven with Big Data Analytics and Block Chain Application Scenarios.

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20822912

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 2020822912

Country of ref document: EP

Effective date: 20220113