Method and server for supporting aviation safety

A method for supporting aviation safety, comprising:

• (a) an aviation safety-supporting server regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• (b) the aviation safety-supporting server preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein each of the weather observation apparatuses includes:
• a sensor unit, on assumption that at least one of all measuring items is E1, E2, . . . , En and a sensor group for measuring the measuring item E1 is G1, that for measuring the measuring item E2 is G2, and that for measuring the measuring item En is Gn, which is configured to include the sensor group G1 having at least three sensors S11, S12, and S13, the sensor group G2 having at least three sensors S21, S22, and S23, and the sensor group Gn having at least three sensors Sn1, Sn2, and Sn3;
• and
• a measuring value-determining unit for obtaining a selected measuring value for the measuring item E1 by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors S11, S12, and S13 included in the sensor group G1, obtaining a selected measuring value for the measuring item E2 by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors S21, S22, and S23 included in the sensor group G2, and obtaining a selected measuring value for the measuring item En by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors Sn1, Sn2, and Sn3 included in the sensor group Gn.

A method for supporting aviation safety, comprising:

• (a) an aviation safety-supporting server regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• (b) the aviation safety-supporting server preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein the machine learning database further includes reference data created by learning previous observation data observed in each of multiple zones;
• and
• wherein, if a zone where a plurality of weather observation apparatuses are placed is a first zone and a zone adjacent to the first zone is a second zone, the aviation safety-supporting server prepares and manages the observation data obtained from the first zone as a map, and analyzes the prepared map by referring to the reference data of the machine learning database, thereby predicting weather condition of the second zone at the future time coming after the certain period of time from the current time.

A method for supporting aviation safety, comprising:

• (a) an aviation safety-supporting server regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• (b) the aviation safety-supporting server preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein the machine learning database further includes reference data created by learning previous observation data observed in each of multiple zones;
• and
• wherein the aviation safety-supporting server supports each of the weather observation apparatuses to be placed at each location where error rate, calculated by comparing the previously predicted weather information with actually observed weather information at a previous time corresponding to the previously predicted weather information, is analyzed to be equal to or less than a preset scope, by referring to the reference data of the machine learning database.

An aviation safety-supporting server, comprising:

• a communication unit regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• a processor preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein the observation data includes atmospheric pressure data, temperature data, and humidity data
• wherein the processor calculates atmospheric pressure data at sea level by referring to (i) the atmospheric pressure data, the temperature data, and the humidity data obtained through the communication unit from the weather observation apparatuses having the relative positioning with each other, and (ii) height data of each of the weather observation apparatuses;
• and
• wherein the map manages information on the atmospheric pressure data, the temperature data, the humidity data, and the atmospheric pressure data at sea level at each of corresponding locations of each of the weather observation apparatuses.

An aviation safety-supporting server, comprising:

• a communication unit regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• a processor preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein each of the weather observation apparatuses includes:
• a sensor unit, on assumption that at least one of all measuring items is E1, E2, . . . , En and a sensor group for measuring the measuring item E1 is G1, that for measuring the measuring item E2 is G2, and that for measuring the measuring item En is Gn, which is configured to include the sensor group G1 having at least three sensors S11, S12, and S13, the sensor group G2 having at least three sensors S21, S22, and S23, and the sensor group Gn having at least three sensors Sn1, Sn2, and Sn3;
• and
• a measuring value-determining unit for obtaining a selected measuring value for the measuring item E1 by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors S11, S12, and S13 included in the sensor group G1, obtaining a selected measuring value for the measuring item E2 by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors S21, S22, and S23 included in the sensor group G2, and obtaining a selected measuring value for the measuring item En by referring to the rest of measuring values except a specific measuring value by a specific sensor, that is beyond error range, among at least three sensors Sn1, Sn2, and Sn3 included in the sensor group Gn.

An aviation safety-supporting server, comprising:

• a communication unit regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• a processor preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein the machine learning database further includes reference data created by learning previous observation data observed in each of multiple zones;
• and
• wherein, if a zone where a plurality of weather observation apparatuses are placed is a first zone and a zone adjacent to the first zone is a second zone, the processor prepares and manages the observation data obtained from the first zone as a map, and analyzes the prepared map by referring to the reference data of the machine learning database, thereby predicting weather condition of the second zone at the future time coming after the certain period of time from the current time.

An aviation safety-supporting server, comprising:

• a communication unit regularly or irregularly obtaining observation data measured from a plurality of weather observation apparatuses that are located in a specific zone and that are placed having relative positioning with each other with a specified spacing;
• and
• a processor preparing and managing the obtained observation data as a map by referring to information on the relative positioning, and analyzing the prepared map by referring to machine learning database that is created by learning previous observation data observed in the specific zone, thereby predicting weather condition in the specific zone at a future time coming after a certain period of time from a current time;
• wherein the machine learning database further includes reference data created by learning previous observation data observed in each of multiple zones;
• and
• wherein the processor supports each of the weather observation apparatuses to be placed at each location where the error rate, calculated by comparing the previously predicted weather information with actually observed weather information at a previous time corresponding to the previously predicted weather information, is analyzed to be equal to or less than a preset scope, by referring to the reference data of the machine learning database.