System

Abstract

An object of a system according to an embodiment is to collect and analyze ocean data and provide ocean conditions and prediction information.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects marine data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a state of the ocean or prediction information based on the data analyzed by the analysis unit. The provision unit provides the user with the information generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately collect and analyze oceanographic data, leaving room for improvement in providing information on ocean conditions and forecasts.

[0005] The system according to the embodiment aims to collect and analyze oceanographic data and provide information on ocean conditions and forecasts. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects oceanographic data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates oceanographic state or forecast information based on the data analyzed by the analysis unit. The provision unit provides the information generated by the generation unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze oceanographic data and provide information on ocean conditions and forecasts. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An ocean data provision system according to an embodiment of the present invention provides ocean data by utilizing short-wave ocean radar and other data. The ocean data provision system uses short-wave ocean radar to collect ocean data and other data (e.g., weather data and satellite data). These data are analyzed using AI to generate ocean condition and forecast information. The generated information is provided to users, enabling them to grasp the ocean situation in real time. For example, the ocean data provision system uses short-wave ocean radar to collect detailed data such as ocean wave height and current speed. The ocean data provision system then collects weather data and satellite data, thereby enabling them to more accurately grasp the ocean condition. The collected data is analyzed using AI to generate ocean condition and forecast information. For example, AI can analyze wave height and current speed to predict future ocean conditions. The generated information is provided to users, enabling them to grasp the ocean situation in real time. This allows fishermen to select optimal fishing grounds and ensure the safe navigation of their vessels. The ocean data provision system thus efficiently collects and analyzes ocean data and provides it to users. For example, it will be possible to grasp the situation in the ocean in real time and take appropriate action.

[0029] The ocean data providing system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects ocean data. The ocean data includes, for example, water temperature, salinity, ocean currents, and wave height, but is not limited to these examples. The collection unit collects ocean wave height and current speed using, for example, a short-wave ocean radar. The collection unit can also collect meteorological data and satellite data. For example, the meteorological data includes temperature, wind speed, and precipitation. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the collected data using a machine learning algorithm to understand the state of the ocean. The generation unit generates ocean state and forecast information based on the data analyzed by the analysis unit. The generation is performed using, for example, a prediction model or a simulation algorithm, but is not limited to these examples. For example, the generation unit predicts future ocean conditions using a prediction model. The provision unit provides the information generated by the generation unit to a user. The provision may be, for example, real-time provision, batch provision, or through a user interface, but is not limited to these examples. For example, the provision unit provides the generated information to the user through a web application or a mobile application. This allows the oceanographic data provision system according to the embodiment to efficiently collect, analyze, generate, and provide oceanographic data.

[0030] The ocean data providing system includes a weather data collection unit that collects weather data. The weather data collection unit collects weather data. Weather data includes, but is not limited to, temperature, wind speed, and precipitation. The weather data collection unit collects temperature and wind speed using, for example, a weather sensor. The weather data collection unit can also collect data from weather satellites. For example, data from weather satellites can be used to collect weather information over a wide area. By collecting weather data, the state of the ocean can be more accurately understood.

[0031] The ocean data providing system includes a satellite data collection unit that collects satellite data. The satellite data collection unit collects satellite data. Examples of satellite data include, but are not limited to, remote sensing data and image data. The satellite data collection unit collects ocean image data using, for example, remote sensing technology. The satellite data collection unit can also collect ocean information over a wide area using data from satellites. For example, the satellite data is used to collect ocean surface temperature and salinity concentration. In this way, by collecting satellite data, ocean data over a wide area can be obtained.

[0032] The marine data providing system includes a storage unit that stores collected data. The storage unit stores the collected data. For example, a database is used for storage, but the storage unit is not limited to this example. For example, the storage unit stores the collected data in the database. The storage unit can also back up the data. For example, the data is backed up periodically to ensure the safety of the data. In this way, by storing the collected data, it becomes possible to analyze or refer to it later.

[0033] The marine data providing system includes an update unit that manages the data update frequency. The update unit manages the data update frequency. Examples of update frequencies include, but are not limited to, real-time updates, periodic updates, and event-driven updates. The update unit, for example, sets the data update frequency and updates the data in real time. The update unit can also update the data periodically. For example, the data is updated at a fixed time every day. In this way, by managing the data update frequency, the latest data can always be provided.

[0034] The providing unit can provide the user with ocean conditions in real time. The providing unit provides the user with ocean conditions in real time. Real time includes, for example, a data delay time and an update interval, but is not limited to these examples. The providing unit, for example, provides the generated information to the user in real time. The providing unit can also display the information in real time through a user interface. For example, the information is provided in real time through a web application or a mobile application. This allows the user to understand the ocean conditions in real time.

[0035] The providing unit can provide fishermen with appropriate fishing grounds. The providing unit provides fishermen with appropriate fishing grounds. Suitable fishing grounds include, but are not limited to, fish detection data and past fishing data, for example. The providing unit provides optimal fishing grounds based on, for example, fish detection data. The providing unit can also provide fishing grounds by referring to past fishing data. For example, the past fishing data can be analyzed to identify optimal fishing grounds. This allows fishermen to select optimal fishing grounds.

[0036] The providing unit can provide information to ensure the navigation safety of the ship. The providing unit provides information to ensure the navigation safety of the ship. Navigation safety includes, for example, maritime traffic information and weather warnings, but is not limited to these examples. The providing unit provides navigation safety information based on, for example, maritime traffic information. The providing unit can also provide navigation safety information based on weather warnings. For example, the weather warnings are analyzed and information that will affect the navigation of the ship is provided. This makes it possible to ensure the navigation safety of the ship.

[0037] The collection unit can select an optimal method for collecting data in a specific area of ​​the ocean based on past data. The collection unit selects an optimal method for collecting data in a specific area of ​​the ocean based on past data. Examples of optimal methods include, but are not limited to, types of algorithms and evaluation criteria. For example, the collection unit analyzes past data and prioritizes data collection in areas where wave heights fluctuate frequently. The collection unit can also focus monitoring on areas where current speeds change rapidly based on past data. Furthermore, the collection unit focuses data collection on specific seasons and time periods based on past data. This allows data collection to be optimized based on past data.

[0038] The collection unit can simultaneously collect data from different depths in the ocean during data collection. The collection unit simultaneously collects data from different depths in the ocean during data collection. Different depths include, but are not limited to, the surface, mid-depth, and deep layers, for example. The collection unit simultaneously collects data from, for example, the surface, mid-depth, and deep layers of the ocean to grasp the overall state of the ocean. The collection unit can also collect data from different depths to analyze changes in temperature and salinity. Furthermore, the collection unit performs detailed analysis of the speed and direction of ocean currents based on the data from different depths. In this way, by simultaneously collecting data from different depths, the overall state of the ocean can be grasped.

[0039] The collection unit can collect multifaceted data by combining different types of sensors during data collection. The collection unit collects multifaceted data by combining different types of sensors during data collection. Different types of sensors include, but are not limited to, temperature sensors, pressure sensors, and salinity sensors, for example. The collection unit can, for example, combine a short-wave ocean radar with a weather sensor to simultaneously collect wave height and wind strength. The collection unit can also combine satellite data with an ocean sensor to collect a wide range of ocean data. Furthermore, the collection unit can combine a water temperature sensor with a salinity sensor to obtain a detailed understanding of changes in the ocean environment. In this way, multifaceted data can be collected by combining different types of sensors.

[0040] The collection unit can adjust the collection range during data collection, taking into account geographical features of the ocean. The collection unit adjusts the collection range during data collection, taking into account geographical features of the ocean. Geographical features include, but are not limited to, for example, ocean bottom topography and tidal current patterns. For example, the collection unit takes into account ocean topography and focuses data collection on specific areas, such as shallow waters or deep waters. The collection unit can also dynamically adjust the data collection range based on the geographical features of the ocean. Furthermore, the collection unit optimizes the efficiency of data collection by taking into account geographical features. This allows the collection range to be adjusted, taking into account geographical features of the ocean.

[0041] The collection unit can integrate and collect data from other ocean data provision services when collecting data. The collection unit integrates and collects data from other ocean data provision services when collecting data. Other ocean data provision services include, but are not limited to, databases from government agencies and data provision services from private companies. For example, the collection unit acquires data from other ocean data provision services in real time and integrates and collects it. The collection unit can also improve the accuracy of the collected data based on the data from other services. Furthermore, the collection unit optimizes the collection range by referring to the data from other services. This allows data from other ocean data provision services to be integrated and collected.

[0042] The collection unit can apply a correction algorithm to improve the accuracy of the collected data when collecting data. The collection unit applies a correction algorithm to improve the accuracy of the collected data when collecting data. Examples of correction algorithms include, but are not limited to, data filtering and noise removal. For example, the collection unit applies a correction algorithm to remove noise when collecting data. The collection unit can also apply an algorithm to perform correction according to environmental conditions when collecting data. Furthermore, the collection unit applies an algorithm to detect and correct outliers when collecting data. This allows the application of a correction algorithm to improve the accuracy of the collected data.

[0043] During analysis, the analysis unit can apply an algorithm that compares data with past data to detect outliers. During analysis, the analysis unit applies an algorithm that compares data with past data to detect outliers. Outliers include, but are not limited to, statistical outliers and anomaly detection using machine learning. For example, the analysis unit applies an algorithm that compares data with past data to detect outliers in wave height. The analysis unit can also apply an algorithm that compares data with past data to detect outliers in flow speed. Furthermore, the analysis unit applies an algorithm that compares data with past data to detect outliers in temperature and salinity. This makes it possible to apply an algorithm that compares data with past data to detect outliers.

[0044] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. The analysis unit improves the accuracy of the analysis by combining different analysis methods during analysis. Different analysis methods include, but are not limited to, regression analysis, clustering, deep learning, and the like. For example, the analysis unit improves the accuracy by combining different analysis methods when analyzing wave height. The analysis unit can also improve the accuracy by combining different analysis methods when analyzing flow speed. Furthermore, the analysis unit improves the accuracy by combining different analysis methods when analyzing temperature or salinity concentration. This makes it possible to improve the accuracy of the analysis by combining different analysis methods.

[0045] The analysis unit can generate analysis results by taking into account temporal fluctuations in the data during analysis. The analysis unit generates analysis results by taking into account temporal fluctuations in the data during analysis. Temporal fluctuations include, but are not limited to, time series analysis and consideration of seasonal fluctuations, for example. For example, the analysis unit generates analysis results by taking into account temporal fluctuations when analyzing wave height. The analysis unit can also generate analysis results by taking into account temporal fluctuations when analyzing flow speed. Furthermore, the analysis unit generates analysis results by taking into account temporal fluctuations when analyzing temperature and salinity concentration. This allows analysis results to be generated by taking into account temporal fluctuations in the data.

[0046] During analysis, the analysis unit can integrate information from other data sources to improve analysis accuracy. During analysis, the analysis unit can integrate information from other data sources to improve analysis accuracy. Examples of other data sources include, but are not limited to, meteorological data, satellite data, and sensor data. For example, the analysis unit can integrate meteorological data from other data sources to improve analysis accuracy. The analysis unit can also integrate satellite data from other data sources to improve analysis accuracy. Furthermore, the analysis unit can integrate oceanographic data from other data sources to improve analysis accuracy. This allows analysis accuracy to be improved by integrating information from other data sources.

[0047] The analysis unit can generate analysis results by taking into account spatial variations in the data during analysis. The analysis unit generates analysis results by taking into account spatial variations in the data during analysis. Spatial variations include, but are not limited to, geographical distribution, spatial clustering, and the like. For example, the analysis unit generates analysis results by taking into account spatial variations when analyzing wave height. The analysis unit can also generate analysis results by taking into account spatial variations when analyzing flow speed. Furthermore, the analysis unit generates analysis results by taking into account spatial variations when analyzing temperature or salinity concentration. This allows analysis results to be generated by taking into account spatial variations in the data.

[0048] The generation unit can optimize the generation algorithm by referring to past generation results during generation. The generation unit optimizes the generation algorithm by referring to past generation results during generation. Examples of the generation algorithm include, but are not limited to, a prediction model, a simulation algorithm, etc. The generation unit, for example, analyzes past generation results and selects an optimal generation algorithm. The generation unit can also adjust parameters of the generation algorithm based on past generation results. Furthermore, the generation unit improves the generation algorithm by referring to past generation results. This allows the generation algorithm to be optimized by referring to past generation results.

[0049] The generation unit can improve generation accuracy by combining different generation methods during generation. The generation unit improves generation accuracy by combining different generation methods during generation. Different generation methods include, but are not limited to, statistical models, machine learning models, etc. For example, the generation unit can improve prediction accuracy of wave height by combining different generation methods. The generation unit can also improve prediction accuracy of flow speed by combining different generation methods. Furthermore, the generation unit can improve prediction accuracy of temperature and salinity concentration by combining different generation methods. This makes it possible to improve generation accuracy by combining different generation methods.

[0050] The generation unit can generate the generation result by taking into account temporal fluctuations in the data during generation. The generation unit generates the generation result by taking into account temporal fluctuations in the data during generation. Temporal fluctuations include, but are not limited to, consideration of time-series data and seasonal fluctuations, for example. For example, the generation unit generates the generation result by taking into account temporal fluctuations when predicting wave height. The generation unit can also generate the generation result by taking into account temporal fluctuations when predicting flow speed. Furthermore, the generation unit generates the generation result by taking into account temporal fluctuations when predicting temperature or salinity concentration. This allows the generation result to be generated by taking into account temporal fluctuations in the data.

[0051] The generation unit can improve generation accuracy by integrating information from other data sources during generation. The generation unit can improve generation accuracy by integrating information from other data sources during generation. Examples of other data sources include, but are not limited to, meteorological data, satellite data, and sensor data. For example, the generation unit can integrate meteorological data from other data sources to improve generation accuracy. The generation unit can also integrate satellite data from other data sources to improve generation accuracy. Furthermore, the generation unit can integrate oceanographic data from other data sources to improve generation accuracy. This allows generation accuracy to be improved by integrating information from other data sources.

[0052] The generation unit can apply different generation methods during generation to generate multiple generation results. The generation unit applies different generation methods during generation to generate multiple generation results. Different generation methods include, but are not limited to, statistical models and machine learning models, for example. The generation unit, for example, applies different generation methods to generate multiple prediction results for wave height. The generation unit can also apply different generation methods to generate multiple prediction results for flow speed. Furthermore, the generation unit applies different generation methods to generate multiple prediction results for temperature and salinity concentration. This allows multiple generation results to be generated by applying different generation methods.

[0053] The generation unit can generate the generated result by taking into account spatial variation of the data during generation. The generation unit generates the generated result by taking into account spatial variation of the data during generation. Spatial variation includes, but is not limited to, for example, geographical distribution, spatial clustering, etc. For example, the generation unit generates the generated result by taking into account spatial variation when predicting wave height. The generation unit can also generate the generated result by taking into account spatial variation when predicting flow speed. Furthermore, the generation unit generates the generated result by taking into account spatial variation when predicting temperature or salinity concentration. In this way, the generation result can be generated by taking into account spatial variation of the data.

[0054] The providing unit can provide optimal information by referring to the user's past usage history when providing the information. The providing unit can provide optimal information by referring to the user's past usage history when providing the information. Past usage history includes, for example, access logs, usage patterns, etc., but is not limited to these examples. The providing unit can provide optimal information based on, for example, the user's past usage history. The providing unit can also prioritize frequently used information based on the user's past usage history. Furthermore, the providing unit analyzes the user's past usage history and provides related information. This makes it possible to provide optimal information by referring to the user's past usage history.

[0055] The providing unit can adjust the priority of information based on the user's current situation when providing the information. The providing unit adjusts the priority of information based on the user's current situation when providing the information. The current situation includes, but is not limited to, current location information, current activity status, etc. For example, when the user is in a hurry, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a specific situation, the providing unit prioritizes providing information according to the situation. This makes it possible to adjust the priority of information based on the user's current situation.

[0056] The providing unit can improve the method of providing information by reflecting user feedback when providing the information. The providing unit improves the method of providing information by reflecting user feedback when providing the information. Feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The providing unit improves the content of the information to be provided, for example, based on user feedback. The providing unit can also improve the method of displaying information by referring to user feedback. Furthermore, the providing unit adjusts the priority of the information to be provided by reflecting user feedback. This makes it possible to improve the method of providing information by reflecting user feedback.

[0057] The providing unit can provide optimal information by taking into consideration the user's geographical location information when providing the information. The providing unit can provide optimal information by taking into consideration the user's geographical location information when providing the information. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the providing unit provides optimal information based on the user's current location. The providing unit can also provide related information by referring to the user's geographical location information. Furthermore, the providing unit adjusts the priority of information by taking into consideration the user's geographical location information. This makes it possible to provide optimal information by taking into consideration the user's geographical location information.

[0058] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can analyze the user's social media activity and provide related information at the time of providing. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The providing unit can provide, for example, information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be provided.

[0059] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing the information. The providing unit customizes the method of providing information by reflecting the user's past feedback when providing the information. Past feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The providing unit customizes the content of the information to be provided, for example, based on the user's past feedback. The providing unit can also customize the method of displaying information by referring to the user's past feedback. Furthermore, the providing unit customizes the priority of the information to be provided by reflecting the user's past feedback. This makes it possible to customize the method of providing information by reflecting the user's past feedback.

[0060] The weather data collection unit can select the optimal collection method by referring to past weather data when collecting weather data. The weather data collection unit selects the optimal collection method by referring to past weather data when collecting weather data. The optimal collection method includes, for example, but is not limited to, the type of algorithm and evaluation criteria. The weather data collection unit, for example, analyzes past weather data and selects the optimal collection method. The weather data collection unit can also adjust parameters of the collection method based on the past weather data. Furthermore, the weather data collection unit improves the collection method by referring to the past weather data. In this way, the optimal collection method can be selected by referring to the past weather data.

[0061] The weather data collection unit can collect multifaceted data by combining different weather sensors when collecting weather data. The weather data collection unit collects multifaceted data by combining different weather sensors when collecting weather data. Different weather sensors include, but are not limited to, temperature sensors, humidity sensors, and wind speed sensors, for example. The weather data collection unit can, for example, combine a weather sensor with a temperature sensor to simultaneously collect temperature and wind speed. The weather data collection unit can also combine a weather sensor with a humidity sensor to simultaneously collect humidity and wind direction. Furthermore, the weather data collection unit can combine a weather sensor with a barometric pressure sensor to simultaneously collect barometric pressure and wind speed. This allows multifaceted data to be collected by combining different weather sensors.

[0062] The weather data collection unit can integrate and collect data from other weather data provision services when collecting weather data. The weather data collection unit integrates and collects data from other weather data provision services when collecting weather data. Other weather data provision services include, but are not limited to, databases from government agencies and data provision services from private companies. The weather data collection unit, for example, acquires data from other weather data provision services in real time and integrates and collects the data. The weather data collection unit can also improve the accuracy of the collected data based on the weather data from other services. Furthermore, the weather data collection unit optimizes the collection range by referring to the weather data from other services. This allows for integrated collection of data from other weather data provision services.

[0063] The satellite data collection unit can select the optimal collection method by referring to past satellite data when collecting satellite data. The satellite data collection unit selects the optimal collection method by referring to past satellite data when collecting satellite data. The optimal collection method includes, for example, but is not limited to, the type of algorithm and evaluation criteria. The satellite data collection unit, for example, analyzes past satellite data and selects the optimal collection method. The satellite data collection unit can also adjust parameters of the collection method based on the past satellite data. Furthermore, the satellite data collection unit improves the collection method by referring to the past satellite data. In this way, the optimal collection method can be selected by referring to the past satellite data.

[0064] The satellite data collection unit can collect multifaceted data by combining different satellite sensors when collecting satellite data. The satellite data collection unit collects multifaceted data by combining different satellite sensors when collecting satellite data. Examples of different satellite sensors include, but are not limited to, remote sensing sensors and image sensors. For example, the satellite data collection unit can combine a satellite sensor with a weather sensor to simultaneously collect temperature and wind speed. The satellite data collection unit can also combine a satellite sensor with a humidity sensor to simultaneously collect humidity and wind direction. Furthermore, the satellite data collection unit can combine a satellite sensor with a barometric pressure sensor to simultaneously collect barometric pressure and wind speed. This allows multifaceted data to be collected by combining different satellite sensors.

[0065] The satellite data collection unit can adjust the collection range when collecting satellite data, taking geographical features into account. The satellite data collection unit adjusts the collection range when collecting satellite data, taking geographical features into account. Geographical features include, but are not limited to, topography and land use patterns. For example, the satellite data collection unit takes geographical features into account and focuses satellite data collection on specific areas, such as mountainous regions and coastal areas. The satellite data collection unit can also dynamically adjust the range of satellite data collection based on the geographical features. Furthermore, the satellite data collection unit optimizes the efficiency of satellite data collection by taking geographical features into account. This allows the collection range to be adjusted taking geographical features into account.

[0066] The satellite data collection unit can integrate and collect data from other satellite data provision services when collecting satellite data. The satellite data collection unit integrates and collects data from other satellite data provision services when collecting satellite data. Examples of other satellite data provision services include, but are not limited to, government agency databases and data provision services from private companies. The satellite data collection unit, for example, acquires data from other satellite data provision services in real time and integrates and collects it. The satellite data collection unit can also improve the accuracy of the collected data based on satellite data from other services. Furthermore, the satellite data collection unit optimizes the collection range by referring to satellite data from other services. This allows for integrated collection of data from other satellite data provision services.

[0067] The storage unit can adjust the storage period based on the importance of the data when storing the data. The storage unit adjusts the storage period based on the importance of the data when storing the data. The importance of the data includes, for example, the frequency of data use and the degree of impact, but is not limited to these examples. For example, the storage unit stores important data for a long period of time and less important data for a short period of time. The storage unit can also dynamically adjust the storage period according to the importance of the data. Furthermore, the storage unit optimizes storage efficiency by adjusting the storage period. This allows the storage period to be adjusted based on the importance of the data.

[0068] The storage unit can apply different storage methods depending on the type of data when storing the data. The storage unit applies different storage methods depending on the type of data when storing the data. Examples of different storage methods include, but are not limited to, cloud storage, local storage, archive storage, etc. The storage unit, for example, compresses and stores image data, and stores text data as is. The storage unit can also store video data in a streaming format, and compress and store still image data. Furthermore, the storage unit selects the optimal storage method depending on the type of data. This allows different storage methods to be applied depending on the type of data.

[0069] The storage unit can add a function to automatically back up data when saving. The storage unit adds a function to automatically back up data when saving. Backups include, but are not limited to, periodic backups, incremental backups, and differential backups, for example. The storage unit automatically creates a backup when saving data, for example. The storage unit can also set the backup frequency and perform backups periodically. Furthermore, the storage unit stores the backup data in a separate storage device to ensure the safety of the data. In this way, the safety of the data can be ensured by automatically backing up the data.

[0070] The storage unit can encrypt data when it is stored to enhance security. The storage unit can encrypt data when it is stored to enhance security. Encryption methods include, but are not limited to, AES encryption and RSA encryption. The storage unit can automatically encrypt data when it is stored, for example. The storage unit can also set an encryption algorithm to enhance data security. Furthermore, the storage unit safely stores the encrypted data and prevents data leakage. Thus, data encryption can enhance security.

[0071] The storage unit can compress data when storing it to optimize storage capacity. The storage unit compresses data when storing it to optimize storage capacity. Compression includes, but is not limited to, ZIP compression, GZIP compression, and the like. For example, the storage unit automatically compresses data when storing it. The storage unit can also set a compression algorithm to optimize storage capacity. Furthermore, the storage unit efficiently stores compressed data to reduce storage usage. In this way, data compression can optimize storage capacity.

[0072] The update unit can select the optimal update timing by referring to the past update history when updating. The update unit selects the optimal update timing by referring to the past update history when updating. The optimal update timing includes, for example, past data analysis, user usage patterns, etc., but is not limited to these examples. The update unit, for example, analyzes the past update history and selects the optimal update timing. The update unit can also adjust update timing parameters based on the past update history. Furthermore, the update unit improves the update timing by referring to the past update history. In this way, the optimal update timing can be selected by referring to the past update history.

[0073] The update unit can determine the update priority based on the importance of the data when updating. The update unit determines the update priority based on the importance of the data when updating. The importance of the data includes, but is not limited to, for example, the importance of the data, the urgency, and the user's interest. For example, the update unit prioritizes updating important data and postpones updating less important data. The update unit can also dynamically adjust the update priority according to the importance of the data. Furthermore, the update unit optimizes system efficiency by adjusting the update priority. This allows the update priority to be determined based on the importance of the data.

[0074] The update unit can add an automatic data update function when updating. The update unit adds an automatic data update function when updating. The automatic update function includes, for example, schedule settings, trigger conditions, etc., but is not limited to these examples. The update unit, for example, adds a function to automatically update when updating data. The update unit can also set the frequency of automatic updates and perform updates periodically. Furthermore, by adding the automatic update function, the update unit ensures that the data is up-to-date. As a result, by adding the automatic data update function, the up-to-dateness of the data can be ensured.

[0075] The update unit can customize the update content by reflecting user feedback during an update. The update unit customizes the update content by reflecting user feedback during an update. Feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The update unit customizes the update content based on user feedback, for example. The update unit can also adjust the priority of the update content by referring to user feedback. Furthermore, the update unit improves the update content by reflecting user feedback. In this way, the update content can be customized by reflecting user feedback.

[0076] The update unit can integrate information from different data sources to enhance the update content during an update. The update unit integrates information from different data sources to enhance the update content during an update. The different data sources include, but are not limited to, weather data, satellite data, and sensor data, for example. The update unit integrates information from different data sources to enhance the update content, for example. The update unit can also improve the accuracy of the update content based on information from other data sources. Furthermore, the update unit optimizes the update content by referring to information from different data sources. This allows the update content to be enhanced by integrating information from different data sources.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The collection unit may also collect activity data of marine organisms when collecting oceanographic data. For example, the collection unit may use sensors to monitor the migration patterns and habitat changes of marine organisms. The collection unit may also analyze the behavioral data of marine organisms to understand changes in the marine environment. Furthermore, the collection unit may integrate the activity data of marine organisms with other oceanographic data to perform a comprehensive assessment of the marine environment. In this way, by collecting the activity data of marine organisms, changes in the marine environment can be understood in more detail.

[0079] The meteorological data collection unit can monitor the urban heat island effect when collecting meteorological data. For example, the meteorological data collection unit collects data on temperature and humidity in urban areas and analyzes the impact of the heat island effect. The meteorological data collection unit can also collect data on green spaces and water areas in urban areas and evaluate measures to mitigate the heat island effect. Furthermore, the meteorological data collection unit collects data on traffic volume and energy consumption in urban areas and analyzes the relationship with the heat island effect. In this way, by monitoring the urban heat island effect, the effectiveness of environmental measures can be evaluated.

[0080] The satellite data collection unit can monitor the distribution of marine plastic litter when collecting satellite data. For example, the satellite data collection unit collects image data of plastic litter on the ocean surface and analyzes its distribution. The satellite data collection unit can also track the movement patterns of marine plastic litter and identify pollution sources. Furthermore, the satellite data collection unit can integrate the distribution data of marine plastic litter with other ocean data to conduct a comprehensive assessment of the marine environment. This allows for monitoring the distribution of marine plastic litter to strengthen measures to protect the marine environment.

[0081] The storage unit can add a function to evaluate the reliability of collected data when storing the data. For example, the storage unit records the source and method of data collection and prioritizes storing highly reliable data. The storage unit can also apply an algorithm to evaluate the reliability of data and filter out unreliable data. Furthermore, the storage unit can share the results of the data reliability evaluation with other systems to improve the quality of the data. In this way, the quality of stored data can be ensured by evaluating the reliability of the data.

[0082] The update unit can add a function to optimize energy consumption when managing the data update frequency. For example, the update unit can set the data update frequency to a time period with low energy consumption, thereby improving the energy efficiency of the system. The update unit can also monitor energy consumption in real time and adjust the update frequency if energy consumption is high. Furthermore, the update unit analyzes energy consumption data and generates an optimal update schedule. This optimizes energy consumption, thereby improving the sustainability of the system.

[0083] The processing flow of the first embodiment will be briefly explained below.

[0084] Step 1: The collection unit collects oceanographic data. Oceanographic data includes water temperature, salinity, ocean currents, and wave height. The collection unit uses a short-wave ocean radar to collect ocean wave height and current speed. It can also collect meteorological and satellite data, and meteorological data includes temperature, wind speed, and precipitation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the collected data is analyzed using machine learning algorithms to understand the state of the ocean. Step 3: The generation unit generates ocean state and forecast information based on the data analyzed by the analysis unit. This is done using a prediction model and simulation algorithm. For example, a prediction model is used to predict future ocean state. Step 4: The providing unit provides the information generated by the generating unit to the user. The provision may be performed in real time, in batches, or through a user interface. For example, the generated information may be provided to the user through a web application or a mobile application.

[0085] (Example 2) An ocean data provision system according to an embodiment of the present invention provides ocean data by utilizing short-wave ocean radar and other data. The ocean data provision system uses short-wave ocean radar to collect ocean data and other data (e.g., weather data and satellite data). These data are analyzed using AI to generate ocean condition and forecast information. The generated information is provided to users, enabling them to grasp the ocean situation in real time. For example, the ocean data provision system uses short-wave ocean radar to collect detailed data such as ocean wave height and current speed. The ocean data provision system then collects weather data and satellite data, thereby enabling them to more accurately grasp the ocean condition. The collected data is analyzed using AI to generate ocean condition and forecast information. For example, AI can analyze wave height and current speed to predict future ocean conditions. The generated information is provided to users, enabling them to grasp the ocean situation in real time. This allows fishermen to select optimal fishing grounds and ensure the safe navigation of their vessels. The ocean data provision system thus efficiently collects and analyzes ocean data and provides it to users. For example, it will be possible to grasp the situation in the ocean in real time and take appropriate action.

[0086] The ocean data providing system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects ocean data. The ocean data includes, for example, water temperature, salinity, ocean currents, and wave height, but is not limited to these examples. The collection unit collects ocean wave height and current speed using, for example, a short-wave ocean radar. The collection unit can also collect meteorological data and satellite data. For example, the meteorological data includes temperature, wind speed, and precipitation. The analysis unit analyzes the data collected by the collection unit. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit analyzes the collected data using a machine learning algorithm to understand the state of the ocean. The generation unit generates ocean state and forecast information based on the data analyzed by the analysis unit. The generation is performed using, for example, a prediction model or a simulation algorithm, but is not limited to these examples. For example, the generation unit predicts future ocean conditions using a prediction model. The provision unit provides the information generated by the generation unit to a user. The provision may be, for example, real-time provision, batch provision, or through a user interface, but is not limited to these examples. For example, the provision unit provides the generated information to the user through a web application or a mobile application. This allows the oceanographic data provision system according to the embodiment to efficiently collect, analyze, generate, and provide oceanographic data.

[0087] The ocean data providing system includes a weather data collection unit that collects weather data. The weather data collection unit collects weather data. Weather data includes, but is not limited to, temperature, wind speed, and precipitation. The weather data collection unit collects temperature and wind speed using, for example, a weather sensor. The weather data collection unit can also collect data from weather satellites. For example, data from weather satellites can be used to collect weather information over a wide area. By collecting weather data, the state of the ocean can be more accurately understood.

[0088] The ocean data providing system includes a satellite data collection unit that collects satellite data. The satellite data collection unit collects satellite data. Examples of satellite data include, but are not limited to, remote sensing data and image data. The satellite data collection unit collects ocean image data using, for example, remote sensing technology. The satellite data collection unit can also collect ocean information over a wide area using data from satellites. For example, the satellite data is used to collect ocean surface temperature and salinity concentration. In this way, by collecting satellite data, ocean data over a wide area can be obtained.

[0089] The marine data providing system includes a storage unit that stores collected data. The storage unit stores the collected data. For example, a database is used for storage, but the storage unit is not limited to this example. For example, the storage unit stores the collected data in the database. The storage unit can also back up the data. For example, the data is backed up periodically to ensure the safety of the data. In this way, by storing the collected data, it becomes possible to analyze or refer to it later.

[0090] The marine data providing system includes an update unit that manages the data update frequency. The update unit manages the data update frequency. Examples of update frequencies include, but are not limited to, real-time updates, periodic updates, and event-driven updates. The update unit, for example, sets the data update frequency and updates the data in real time. The update unit can also update the data periodically. For example, the data is updated at a fixed time every day. In this way, by managing the data update frequency, the latest data can always be provided.

[0091] The providing unit can provide the user with ocean conditions in real time. The providing unit provides the user with ocean conditions in real time. Real time includes, for example, a data delay time and an update interval, but is not limited to these examples. The providing unit, for example, provides the generated information to the user in real time. The providing unit can also display the information in real time through a user interface. For example, the information is provided in real time through a web application or a mobile application. This allows the user to understand the ocean conditions in real time.

[0092] The providing unit can provide fishermen with appropriate fishing grounds. The providing unit provides fishermen with appropriate fishing grounds. Suitable fishing grounds include, but are not limited to, fish detection data and past fishing data, for example. The providing unit provides optimal fishing grounds based on, for example, fish detection data. The providing unit can also provide fishing grounds by referring to past fishing data. For example, the past fishing data can be analyzed to identify optimal fishing grounds. This allows fishermen to select optimal fishing grounds.

[0093] The providing unit can provide information to ensure the navigation safety of the ship. The providing unit provides information to ensure the navigation safety of the ship. Navigation safety includes, for example, maritime traffic information and weather warnings, but is not limited to these examples. The providing unit provides navigation safety information based on, for example, maritime traffic information. The providing unit can also provide navigation safety information based on weather warnings. For example, the weather warnings are analyzed and information that will affect the navigation of the ship is provided. This makes it possible to ensure the navigation safety of the ship.

[0094] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit can adjust the timing of data collection based on the user's emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the load on the system. Also, if the user is relaxed, the frequency of data collection can be increased to provide more detailed data. Furthermore, if the user is in a hurry, only important data can be collected preferentially. This allows the timing of data collection to be adjusted according to the user's emotions.

[0095] The collection unit can select an optimal method for collecting data in a specific area of ​​the ocean based on past data. The collection unit selects an optimal method for collecting data in a specific area of ​​the ocean based on past data. Examples of optimal methods include, but are not limited to, types of algorithms and evaluation criteria. For example, the collection unit analyzes past data and prioritizes data collection in areas where wave heights fluctuate frequently. The collection unit can also focus monitoring on areas where current speeds change rapidly based on past data. Furthermore, the collection unit focuses data collection on specific seasons and time periods based on past data. This allows data collection to be optimized based on past data.

[0096] The collection unit can simultaneously collect data from different depths in the ocean during data collection. The collection unit simultaneously collects data from different depths in the ocean during data collection. Different depths include, but are not limited to, the surface, mid-depth, and deep layers, for example. The collection unit simultaneously collects data from, for example, the surface, mid-depth, and deep layers of the ocean to grasp the overall state of the ocean. The collection unit can also collect data from different depths to analyze changes in temperature and salinity. Furthermore, the collection unit performs detailed analysis of the speed and direction of ocean currents based on the data from different depths. In this way, by simultaneously collecting data from different depths, the overall state of the ocean can be grasped.

[0097] The collection unit can collect multifaceted data by combining different types of sensors during data collection. The collection unit collects multifaceted data by combining different types of sensors during data collection. Different types of sensors include, but are not limited to, temperature sensors, pressure sensors, and salinity sensors, for example. The collection unit can, for example, combine a short-wave ocean radar with a weather sensor to simultaneously collect wave height and wind strength. The collection unit can also combine satellite data with an ocean sensor to collect a wide range of ocean data. Furthermore, the collection unit can combine a water temperature sensor with a salinity sensor to obtain a detailed understanding of changes in the ocean environment. In this way, multifaceted data can be collected by combining different types of sensors.

[0098] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The collection unit determines the priority of data to be collected based on the user's emotions. For example, if the user is stressed, only important data can be collected with priority. Also, if the user is relaxed, detailed data can be collected with priority. Furthermore, if the user is in a hurry, data that can be collected quickly can be prioritized. In this way, the priority of data to be collected can be determined according to the user's emotions.

[0099] The collection unit can adjust the collection range during data collection, taking into account geographical features of the ocean. The collection unit adjusts the collection range during data collection, taking into account geographical features of the ocean. Geographical features include, but are not limited to, for example, ocean bottom topography and tidal current patterns. For example, the collection unit takes into account ocean topography and focuses data collection on specific areas, such as shallow waters or deep waters. The collection unit can also dynamically adjust the data collection range based on the geographical features of the ocean. Furthermore, the collection unit optimizes the efficiency of data collection by taking into account geographical features. This allows the collection range to be adjusted, taking into account geographical features of the ocean.

[0100] The collection unit can integrate and collect data from other ocean data provision services when collecting data. The collection unit integrates and collects data from other ocean data provision services when collecting data. Other ocean data provision services include, but are not limited to, databases from government agencies and data provision services from private companies. For example, the collection unit acquires data from other ocean data provision services in real time and integrates and collects it. The collection unit can also improve the accuracy of the collected data based on the data from other services. Furthermore, the collection unit optimizes the collection range by referring to the data from other services. This allows data from other ocean data provision services to be integrated and collected.

[0101] The collection unit can apply a correction algorithm to improve the accuracy of the collected data when collecting data. The collection unit applies a correction algorithm to improve the accuracy of the collected data when collecting data. Examples of correction algorithms include, but are not limited to, data filtering and noise removal. For example, the collection unit applies a correction algorithm to remove noise when collecting data. The collection unit can also apply an algorithm to perform correction according to environmental conditions when collecting data. Furthermore, the collection unit applies an algorithm to detect and correct outliers when collecting data. This allows the application of a correction algorithm to improve the accuracy of the collected data.

[0102] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. Examples of user emotions include, but are not limited to, tension, relaxation, and hurry. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. The analysis unit can adjust the display method of the analysis results based on the user's emotions. For example, if the user is tension, a simple, highly visible display method can be provided. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the analysis results according to the user's emotions.

[0103] During analysis, the analysis unit can apply an algorithm that compares data with past data to detect outliers. During analysis, the analysis unit applies an algorithm that compares data with past data to detect outliers. Outliers include, but are not limited to, statistical outliers and anomaly detection using machine learning. For example, the analysis unit applies an algorithm that compares data with past data to detect outliers in wave height. The analysis unit can also apply an algorithm that compares data with past data to detect outliers in flow speed. Furthermore, the analysis unit applies an algorithm that compares data with past data to detect outliers in temperature and salinity. This makes it possible to apply an algorithm that compares data with past data to detect outliers.

[0104] The analysis unit can improve the accuracy of the analysis by combining different analysis methods during analysis. The analysis unit improves the accuracy of the analysis by combining different analysis methods during analysis. Different analysis methods include, but are not limited to, regression analysis, clustering, deep learning, and the like. For example, the analysis unit improves the accuracy by combining different analysis methods when analyzing wave height. The analysis unit can also improve the accuracy by combining different analysis methods when analyzing flow speed. Furthermore, the analysis unit improves the accuracy by combining different analysis methods when analyzing temperature or salinity concentration. This makes it possible to improve the accuracy of the analysis by combining different analysis methods.

[0105] The analysis unit can generate analysis results by taking into account temporal fluctuations in the data during analysis. The analysis unit generates analysis results by taking into account temporal fluctuations in the data during analysis. Temporal fluctuations include, but are not limited to, time series analysis and consideration of seasonal fluctuations, for example. For example, the analysis unit generates analysis results by taking into account temporal fluctuations when analyzing wave height. The analysis unit can also generate analysis results by taking into account temporal fluctuations when analyzing flow speed. Furthermore, the analysis unit generates analysis results by taking into account temporal fluctuations when analyzing temperature and salinity concentration. This allows analysis results to be generated by taking into account temporal fluctuations in the data.

[0106] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The analysis unit can estimate the user's emotions using, for example, facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotion. The analysis unit determines the analysis priority based on the user's emotions. For example, if the user is stressed, only important analyses can be prioritized. Also, if the user is relaxed, detailed analyses can be prioritized. Furthermore, if the user is in a hurry, data that can be analyzed quickly can be prioritized. In this way, the analysis priority can be determined according to the user's emotions.

[0107] During analysis, the analysis unit can integrate information from other data sources to improve analysis accuracy. During analysis, the analysis unit can integrate information from other data sources to improve analysis accuracy. Examples of other data sources include, but are not limited to, meteorological data, satellite data, and sensor data. For example, the analysis unit can integrate meteorological data from other data sources to improve analysis accuracy. The analysis unit can also integrate satellite data from other data sources to improve analysis accuracy. Furthermore, the analysis unit can integrate oceanographic data from other data sources to improve analysis accuracy. This allows analysis accuracy to be improved by integrating information from other data sources.

[0108] The analysis unit can generate analysis results by taking into account spatial variations in the data during analysis. The analysis unit generates analysis results by taking into account spatial variations in the data during analysis. Spatial variations include, but are not limited to, geographical distribution, spatial clustering, and the like. For example, the analysis unit generates analysis results by taking into account spatial variations when analyzing wave height. The analysis unit can also generate analysis results by taking into account spatial variations when analyzing flow speed. Furthermore, the analysis unit generates analysis results by taking into account spatial variations when analyzing temperature or salinity concentration. This allows analysis results to be generated by taking into account spatial variations in the data.

[0109] The generation unit can estimate the user's emotion and adjust the level of detail of the generated information based on the estimated user's emotion. The generation unit can estimate the user's emotion and adjust the level of detail of the generated information based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, relaxed, rushed, and excited. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit adjusts the level of detail of the generated information based on the user's emotion. For example, if the user is relaxed, detailed information can be generated. If the user is rushed, information that focuses on the main points can be generated. Furthermore, if the user is excited, visually stimulating information can be generated. This makes it possible to adjust the level of detail of the generated information according to the user's emotion.

[0110] The generation unit can optimize the generation algorithm by referring to past generation results during generation. The generation unit optimizes the generation algorithm by referring to past generation results during generation. Examples of the generation algorithm include, but are not limited to, a prediction model, a simulation algorithm, etc. The generation unit, for example, analyzes past generation results and selects an optimal generation algorithm. The generation unit can also adjust parameters of the generation algorithm based on past generation results. Furthermore, the generation unit improves the generation algorithm by referring to past generation results. This allows the generation algorithm to be optimized by referring to past generation results.

[0111] The generation unit can improve generation accuracy by combining different generation methods during generation. The generation unit improves generation accuracy by combining different generation methods during generation. Different generation methods include, but are not limited to, statistical models, machine learning models, etc. For example, the generation unit can improve prediction accuracy of wave height by combining different generation methods. The generation unit can also improve prediction accuracy of flow speed by combining different generation methods. Furthermore, the generation unit can improve prediction accuracy of temperature and salinity concentration by combining different generation methods. This makes it possible to improve generation accuracy by combining different generation methods.

[0112] The generation unit can generate the generation result by taking into account temporal fluctuations in the data during generation. The generation unit generates the generation result by taking into account temporal fluctuations in the data during generation. Temporal fluctuations include, but are not limited to, consideration of time-series data and seasonal fluctuations, for example. For example, the generation unit generates the generation result by taking into account temporal fluctuations when predicting wave height. The generation unit can also generate the generation result by taking into account temporal fluctuations when predicting flow speed. Furthermore, the generation unit generates the generation result by taking into account temporal fluctuations when predicting temperature or salinity concentration. This allows the generation result to be generated by taking into account temporal fluctuations in the data.

[0113] The generation unit can estimate the user's emotion and determine the priority of information to be generated based on the estimated user's emotion. The generation unit can estimate the user's emotion and determine the priority of information to be generated based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit determines the priority of information to be generated based on the user's emotion. For example, if the user is stressed, only important information can be generated preferentially. Also, if the user is relaxed, detailed information can be generated preferentially. Furthermore, if the user is in a hurry, information that can be generated quickly is prioritized. In this way, the priority of information to be generated can be determined according to the user's emotion.

[0114] The generation unit can improve generation accuracy by integrating information from other data sources during generation. The generation unit can improve generation accuracy by integrating information from other data sources during generation. Examples of other data sources include, but are not limited to, meteorological data, satellite data, and sensor data. For example, the generation unit can integrate meteorological data from other data sources to improve generation accuracy. The generation unit can also integrate satellite data from other data sources to improve generation accuracy. Furthermore, the generation unit can integrate oceanographic data from other data sources to improve generation accuracy. This allows generation accuracy to be improved by integrating information from other data sources.

[0115] The generation unit can apply different generation methods during generation to generate multiple generation results. The generation unit applies different generation methods during generation to generate multiple generation results. Different generation methods include, but are not limited to, statistical models and machine learning models, for example. The generation unit, for example, applies different generation methods to generate multiple prediction results for wave height. The generation unit can also apply different generation methods to generate multiple prediction results for flow speed. Furthermore, the generation unit applies different generation methods to generate multiple prediction results for temperature and salinity concentration. This allows multiple generation results to be generated by applying different generation methods.

[0116] The generation unit can generate the generated result by taking into account spatial variation of the data during generation. The generation unit generates the generated result by taking into account spatial variation of the data during generation. Spatial variation includes, but is not limited to, for example, geographical distribution, spatial clustering, etc. For example, the generation unit generates the generated result by taking into account spatial variation when predicting wave height. The generation unit can also generate the generated result by taking into account spatial variation when predicting flow speed. Furthermore, the generation unit generates the generated result by taking into account spatial variation when predicting temperature or salinity concentration. In this way, the generation result can be generated by taking into account spatial variation of the data.

[0117] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. Examples of the user's emotion include, but are not limited to, tension, relaxation, and hurry. The providing unit can estimate the user's emotion using, for example, facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. The providing unit adjusts the display method of the information to be provided based on the user's emotion. For example, if the user is tension, a simple, highly visible display method can be provided. Furthermore, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the display method of the information to be provided according to the user's emotion.

[0118] The providing unit can provide optimal information by referring to the user's past usage history when providing the information. The providing unit can provide optimal information by referring to the user's past usage history when providing the information. Past usage history includes, for example, access logs, usage patterns, etc., but is not limited to these examples. The providing unit can provide optimal information based on, for example, the user's past usage history. The providing unit can also prioritize frequently used information based on the user's past usage history. Furthermore, the providing unit analyzes the user's past usage history and provides related information. This makes it possible to provide optimal information by referring to the user's past usage history.

[0119] The providing unit can adjust the priority of information based on the user's current situation when providing the information. The providing unit adjusts the priority of information based on the user's current situation when providing the information. The current situation includes, but is not limited to, current location information, current activity status, etc. For example, when the user is in a hurry, the providing unit can prioritize providing important information. Furthermore, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a specific situation, the providing unit prioritizes providing information according to the situation. This makes it possible to adjust the priority of information based on the user's current situation.

[0120] The providing unit can improve the method of providing information by reflecting user feedback when providing the information. The providing unit improves the method of providing information by reflecting user feedback when providing the information. Feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The providing unit improves the content of the information to be provided, for example, based on user feedback. The providing unit can also improve the method of displaying information by referring to user feedback. Furthermore, the providing unit adjusts the priority of the information to be provided by reflecting user feedback. This makes it possible to improve the method of providing information by reflecting user feedback.

[0121] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The providing unit can estimate the user's emotion using, for example, facial expression recognition technology. The providing unit can also estimate the user's emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotion. The providing unit determines the priority of information to be provided based on the user's emotion. For example, if the user is feeling stressed, only important information can be provided preferentially. Also, if the user is relaxed, detailed information can be provided preferentially. Furthermore, if the user is in a hurry, information that can be provided quickly can be provided preferentially. In this way, the priority of information to be provided can be determined according to the user's emotion.

[0122] The providing unit can provide optimal information by taking into consideration the user's geographical location information when providing the information. The providing unit can provide optimal information by taking into consideration the user's geographical location information when providing the information. Geographical location information includes, but is not limited to, GPS data, map information, etc. For example, the providing unit provides optimal information based on the user's current location. The providing unit can also provide related information by referring to the user's geographical location information. Furthermore, the providing unit adjusts the priority of information by taking into consideration the user's geographical location information. This makes it possible to provide optimal information by taking into consideration the user's geographical location information.

[0123] The providing unit can analyze the user's social media activity and provide related information at the time of providing. The providing unit can analyze the user's social media activity and provide related information at the time of providing. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. The providing unit can provide, for example, information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. Furthermore, the providing unit can provide information about related places and events by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related information can be provided.

[0124] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing the information. The providing unit customizes the method of providing information by reflecting the user's past feedback when providing the information. Past feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The providing unit customizes the content of the information to be provided, for example, based on the user's past feedback. The providing unit can also customize the method of displaying information by referring to the user's past feedback. Furthermore, the providing unit customizes the priority of the information to be provided by reflecting the user's past feedback. This makes it possible to customize the method of providing information by reflecting the user's past feedback.

[0125] The weather data collection unit can estimate the user's emotions and adjust the timing of weather data collection based on the estimated user emotions. The weather data collection unit can estimate the user's emotions and adjust the timing of weather data collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The weather data collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The weather data collection unit can also estimate the user's emotions using voice analysis technology. For example, the weather data collection unit can analyze the tone and speed of the user's voice to estimate emotions. The weather data collection unit can adjust the timing of weather data collection based on the user's emotions. For example, if the user is stressed, the weather data collection frequency can be reduced to reduce the load on the system. Also, if the user is relaxed, the weather data collection frequency can be increased to provide more detailed data. Furthermore, if the user is in a hurry, only important weather data can be collected preferentially. This allows the timing of weather data collection to be adjusted according to the user's emotions.

[0126] The weather data collection unit can select the optimal collection method by referring to past weather data when collecting weather data. The weather data collection unit selects the optimal collection method by referring to past weather data when collecting weather data. The optimal collection method includes, for example, but is not limited to, the type of algorithm and evaluation criteria. The weather data collection unit, for example, analyzes past weather data and selects the optimal collection method. The weather data collection unit can also adjust parameters of the collection method based on the past weather data. Furthermore, the weather data collection unit improves the collection method by referring to the past weather data. In this way, the optimal collection method can be selected by referring to the past weather data.

[0127] The weather data collection unit can collect multifaceted data by combining different weather sensors when collecting weather data. The weather data collection unit collects multifaceted data by combining different weather sensors when collecting weather data. Different weather sensors include, but are not limited to, temperature sensors, humidity sensors, and wind speed sensors, for example. The weather data collection unit can, for example, combine a weather sensor with a temperature sensor to simultaneously collect temperature and wind speed. The weather data collection unit can also combine a weather sensor with a humidity sensor to simultaneously collect humidity and wind direction. Furthermore, the weather data collection unit can combine a weather sensor with a barometric pressure sensor to simultaneously collect barometric pressure and wind speed. This allows multifaceted data to be collected by combining different weather sensors.

[0128] The weather data collection unit can estimate the user's emotions and determine the priority of weather data to be collected based on the estimated user's emotions. The weather data collection unit can estimate the user's emotions and determine the priority of weather data to be collected based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The weather data collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The weather data collection unit can also estimate the user's emotions using voice analysis technology. For example, the weather data collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The weather data collection unit determines the priority of weather data to be collected based on the user's emotions. For example, if the user is stressed, only important weather data can be collected with priority. Also, if the user is relaxed, detailed weather data can be collected with priority. Furthermore, if the user is in a hurry, weather data that can be collected quickly can be prioritized. In this way, the priority of weather data to be collected can be determined according to the user's emotions.

[0129] The weather data collection unit can integrate and collect data from other weather data provision services when collecting weather data. The weather data collection unit integrates and collects data from other weather data provision services when collecting weather data. Other weather data provision services include, but are not limited to, databases from government agencies and data provision services from private companies. The weather data collection unit, for example, acquires data from other weather data provision services in real time and integrates and collects the data. The weather data collection unit can also improve the accuracy of the collected data based on the weather data from other services. Furthermore, the weather data collection unit optimizes the collection range by referring to the weather data from other services. This allows for integrated collection of data from other weather data provision services.

[0130] The satellite data collection unit can estimate a user's emotions and adjust the timing of satellite data collection based on the estimated user emotions. The satellite data collection unit can estimate a user's emotions and adjust the timing of satellite data collection based on the estimated user emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The satellite data collection unit can estimate a user's emotions using, for example, facial expression recognition technology. The satellite data collection unit can also estimate a user's emotions using voice analysis technology. For example, the satellite data collection unit can analyze the tone and speed of the user's voice to estimate emotions. The satellite data collection unit adjusts the timing of satellite data collection based on the user's emotions. For example, if the user is stressed, the frequency of satellite data collection can be reduced to reduce the load on the system. Furthermore, if the user is relaxed, the frequency of satellite data collection can be increased to provide more detailed data. Furthermore, if the user is in a hurry, only important satellite data can be collected preferentially. This allows the timing of satellite data collection to be adjusted according to the user's emotions.

[0131] The satellite data collection unit can select the optimal collection method by referring to past satellite data when collecting satellite data. The satellite data collection unit selects the optimal collection method by referring to past satellite data when collecting satellite data. The optimal collection method includes, for example, but is not limited to, the type of algorithm and evaluation criteria. The satellite data collection unit, for example, analyzes past satellite data and selects the optimal collection method. The satellite data collection unit can also adjust parameters of the collection method based on the past satellite data. Furthermore, the satellite data collection unit improves the collection method by referring to the past satellite data. In this way, the optimal collection method can be selected by referring to the past satellite data.

[0132] The satellite data collection unit can collect multifaceted data by combining different satellite sensors when collecting satellite data. The satellite data collection unit collects multifaceted data by combining different satellite sensors when collecting satellite data. Examples of different satellite sensors include, but are not limited to, remote sensing sensors and image sensors. For example, the satellite data collection unit can combine a satellite sensor with a weather sensor to simultaneously collect temperature and wind speed. The satellite data collection unit can also combine a satellite sensor with a humidity sensor to simultaneously collect humidity and wind direction. Furthermore, the satellite data collection unit can combine a satellite sensor with a barometric pressure sensor to simultaneously collect barometric pressure and wind speed. This allows multifaceted data to be collected by combining different satellite sensors.

[0133] The satellite data collection unit can estimate the user's emotions and determine the priority of satellite data to be collected based on the estimated user's emotions. The satellite data collection unit can estimate the user's emotions and determine the priority of satellite data to be collected based on the estimated user's emotions. Examples of user emotions include, but are not limited to, stress, relaxation, and hurry. The satellite data collection unit can estimate the user's emotions using, for example, facial expression recognition technology. The satellite data collection unit can also estimate the user's emotions using voice analysis technology. For example, the satellite data collection unit can analyze the tone and speed of the user's voice to estimate the emotion. The satellite data collection unit determines the priority of satellite data to be collected based on the user's emotions. For example, if the user is stressed, only important satellite data can be collected with priority. Also, if the user is relaxed, detailed satellite data can be collected with priority. Furthermore, if the user is in a hurry, satellite data that can be collected quickly can be prioritized. In this way, the priority of satellite data to be collected can be determined according to the user's emotions.

[0134] The satellite data collection unit can adjust the collection range when collecting satellite data, taking geographical features into account. The satellite data collection unit adjusts the collection range when collecting satellite data, taking geographical features into account. Geographical features include, but are not limited to, topography and land use patterns. For example, the satellite data collection unit takes geographical features into account and focuses satellite data collection on specific areas, such as mountainous regions and coastal areas. The satellite data collection unit can also dynamically adjust the range of satellite data collection based on the geographical features. Furthermore, the satellite data collection unit optimizes the efficiency of satellite data collection by taking geographical features into account. This allows the collection range to be adjusted taking geographical features into account.

[0135] The satellite data collection unit can integrate and collect data from other satellite data provision services when collecting satellite data. The satellite data collection unit integrates and collects data from other satellite data provision services when collecting satellite data. Examples of other satellite data provision services include, but are not limited to, government agency databases and data provision services from private companies. The satellite data collection unit, for example, acquires data from other satellite data provision services in real time and integrates and collects it. The satellite data collection unit can also improve the accuracy of the collected data based on satellite data from other services. Furthermore, the satellite data collection unit optimizes the collection range by referring to satellite data from other services. This allows for integrated collection of data from other satellite data provision services.

[0136] The storage unit can estimate a user's emotion and determine the priority of data to be saved based on the estimated user's emotion. The storage unit can estimate a user's emotion and determine the priority of data to be saved based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The storage unit can estimate a user's emotion using, for example, facial expression recognition technology. The storage unit can also estimate a user's emotion using voice analysis technology. For example, the storage unit can analyze the tone and speed of the user's voice to estimate the emotion. The storage unit determines the priority of data to be saved based on the user's emotion. For example, if the user is stressed, only important data can be saved with priority. Also, if the user is relaxed, detailed data can be saved with priority. Furthermore, if the user is in a hurry, data that can be saved quickly can be saved with priority. In this way, the priority of data to be saved can be determined according to the user's emotion.

[0137] The storage unit can adjust the storage period based on the importance of the data when storing the data. The storage unit adjusts the storage period based on the importance of the data when storing the data. The importance of the data includes, for example, the frequency of data use and the degree of impact, but is not limited to these examples. For example, the storage unit stores important data for a long period of time and less important data for a short period of time. The storage unit can also dynamically adjust the storage period according to the importance of the data. Furthermore, the storage unit optimizes storage efficiency by adjusting the storage period. This allows the storage period to be adjusted based on the importance of the data.

[0138] The storage unit can apply different storage methods depending on the type of data when storing the data. The storage unit applies different storage methods depending on the type of data when storing the data. Examples of different storage methods include, but are not limited to, cloud storage, local storage, archive storage, etc. The storage unit, for example, compresses and stores image data, and stores text data as is. The storage unit can also store video data in a streaming format, and compress and store still image data. Furthermore, the storage unit selects the optimal storage method depending on the type of data. This allows different storage methods to be applied depending on the type of data.

[0139] The storage unit can add a function to automatically back up data when saving. The storage unit adds a function to automatically back up data when saving. Backups include, but are not limited to, periodic backups, incremental backups, and differential backups, for example. The storage unit automatically creates a backup when saving data, for example. The storage unit can also set the backup frequency and perform backups periodically. Furthermore, the storage unit stores the backup data in a separate storage device to ensure the safety of the data. In this way, the safety of the data can be ensured by automatically backing up the data.

[0140] The storage unit can encrypt data when it is stored to enhance security. The storage unit can encrypt data when it is stored to enhance security. Encryption methods include, but are not limited to, AES encryption and RSA encryption. The storage unit can automatically encrypt data when it is stored, for example. The storage unit can also set an encryption algorithm to enhance data security. Furthermore, the storage unit safely stores the encrypted data and prevents data leakage. Thus, data encryption can enhance security.

[0141] The storage unit can compress data when storing it to optimize storage capacity. The storage unit compresses data when storing it to optimize storage capacity. Compression includes, but is not limited to, ZIP compression, GZIP compression, and the like. For example, the storage unit automatically compresses data when storing it. The storage unit can also set a compression algorithm to optimize storage capacity. Furthermore, the storage unit efficiently stores compressed data to reduce storage usage. In this way, data compression can optimize storage capacity.

[0142] The update unit can estimate the user's emotion and adjust the data update frequency based on the estimated user's emotion. The update unit can estimate the user's emotion and adjust the data update frequency based on the estimated user's emotion. Examples of user's emotion include, but are not limited to, stress, relaxation, and hurry. The update unit can estimate the user's emotion using, for example, facial expression recognition technology. The update unit can also estimate the user's emotion using voice analysis technology. For example, the update unit can analyze the tone and speed of the user's voice to estimate the emotion. The update unit can adjust the data update frequency based on the user's emotion. For example, if the user is feeling stressed, the update frequency of the data can be reduced to reduce the load on the system. Furthermore, if the user is relaxed, the update frequency of the data can be increased to provide more detailed data. Furthermore, if the user is in a hurry, only important data can be updated preferentially. This allows the data update frequency to be adjusted according to the user's emotion.

[0143] The update unit can select the optimal update timing by referring to the past update history when updating. The update unit selects the optimal update timing by referring to the past update history when updating. The optimal update timing includes, for example, past data analysis, user usage patterns, etc., but is not limited to these examples. The update unit, for example, analyzes the past update history and selects the optimal update timing. The update unit can also adjust update timing parameters based on the past update history. Furthermore, the update unit improves the update timing by referring to the past update history. In this way, the optimal update timing can be selected by referring to the past update history.

[0144] The update unit can determine the update priority based on the importance of the data when updating. The update unit determines the update priority based on the importance of the data when updating. The importance of the data includes, but is not limited to, for example, the importance of the data, the urgency, and the user's interest. For example, the update unit prioritizes updating important data and postpones updating less important data. The update unit can also dynamically adjust the update priority according to the importance of the data. Furthermore, the update unit optimizes system efficiency by adjusting the update priority. This allows the update priority to be determined based on the importance of the data.

[0145] The update unit can add an automatic data update function when updating. The update unit adds an automatic data update function when updating. The automatic update function includes, for example, schedule settings, trigger conditions, etc., but is not limited to these examples. The update unit, for example, adds a function to automatically update when updating data. The update unit can also set the frequency of automatic updates and perform updates periodically. Furthermore, by adding the automatic update function, the update unit ensures that the data is up-to-date. As a result, by adding the automatic data update function, the up-to-dateness of the data can be ensured.

[0146] The update unit can customize the update content by reflecting user feedback during an update. The update unit customizes the update content by reflecting user feedback during an update. Feedback includes, for example, user surveys, evaluations after use, etc., but is not limited to these examples. The update unit customizes the update content based on user feedback, for example. The update unit can also adjust the priority of the update content by referring to user feedback. Furthermore, the update unit improves the update content by reflecting user feedback. In this way, the update content can be customized by reflecting user feedback.

[0147] The update unit can integrate information from different data sources to enhance the update content during an update. The update unit integrates information from different data sources to enhance the update content during an update. The different data sources include, but are not limited to, weather data, satellite data, and sensor data, for example. The update unit integrates information from different data sources to enhance the update content, for example. The update unit can also improve the accuracy of the update content based on information from other data sources. Furthermore, the update unit optimizes the update content by referring to information from different data sources. This allows the update content to be enhanced by integrating information from different data sources. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the function of collecting ocean wave height and current speed using a short-wave marine radar is realized by the camera 42 of the smart device 14 and the specific processing unit 290 of the data processing device 12. Furthermore, collection of weather data and satellite data is realized by the communication I / F 44 of the smart device 14 and the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates ocean state and forecast information using a prediction model or simulation algorithm. The providing unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides the generated information to the user through a web application or a mobile application. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the function of collecting ocean wave height and current speed using a short-wave ocean radar is realized by the camera 42 of the smart glasses 214 and the specific processing unit 290 of the data processing device 12. Furthermore, collection of weather data and satellite data is realized by the communication I / F 44 of the smart glasses 214 and the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates ocean state and forecast information using a prediction model or simulation algorithm. The providing unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides the generated information to the user through a web application or a mobile application. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by at least one of the headset terminal 314 and the data processing device 12. For example, the function of collecting ocean wave height and current speed using a short-wave marine radar is realized by the camera 42 of the headset terminal 314 and the specific processing unit 290 of the data processing device 12. Furthermore, collection of weather data and satellite data is realized by the communication I / F 44 of the headset terminal 314 and the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates ocean state and forecast information using a prediction model or simulation algorithm. The providing unit is realized by, for example, the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and provides the generated information to the user through a web application or a mobile application. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the function of collecting ocean wave height and current speed using a short-wave marine radar is realized by the camera 42 of the robot 414 and the specific processing unit 290 of the data processing device 12. Furthermore, collection of weather data and satellite data is realized by the communication I / F 44 of the robot 414 and the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using statistical analysis or machine learning algorithms. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates ocean state and prediction information using a prediction model or simulation algorithm. The providing unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides the generated information to the user through a web application or a mobile application.

[0148] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0149] The collection unit may also collect activity data of marine organisms when collecting oceanographic data. For example, the collection unit may use sensors to monitor the migration patterns and habitat changes of marine organisms. The collection unit may also analyze the behavioral data of marine organisms to understand changes in the marine environment. Furthermore, the collection unit may integrate the activity data of marine organisms with other oceanographic data to perform a comprehensive assessment of the marine environment. In this way, by collecting the activity data of marine organisms, changes in the marine environment can be understood in more detail.

[0150] The meteorological data collection unit can monitor the urban heat island effect when collecting meteorological data. For example, the meteorological data collection unit collects data on temperature and humidity in urban areas and analyzes the impact of the heat island effect. The meteorological data collection unit can also collect data on green spaces and water areas in urban areas and evaluate measures to mitigate the heat island effect. Furthermore, the meteorological data collection unit collects data on traffic volume and energy consumption in urban areas and analyzes the relationship with the heat island effect. In this way, by monitoring the urban heat island effect, the effectiveness of environmental measures can be evaluated.

[0151] The satellite data collection unit can monitor the distribution of marine plastic litter when collecting satellite data. For example, the satellite data collection unit collects image data of plastic litter on the ocean surface and analyzes its distribution. The satellite data collection unit can also track the movement patterns of marine plastic litter and identify pollution sources. Furthermore, the satellite data collection unit can integrate the distribution data of marine plastic litter with other ocean data to conduct a comprehensive assessment of the marine environment. This allows for monitoring the distribution of marine plastic litter to strengthen measures to protect the marine environment.

[0152] The storage unit can add a function to evaluate the reliability of collected data when storing the data. For example, the storage unit records the source and method of data collection and prioritizes storing highly reliable data. The storage unit can also apply an algorithm to evaluate the reliability of data and filter out unreliable data. Furthermore, the storage unit can share the results of the data reliability evaluation with other systems to improve the quality of the data. In this way, the quality of stored data can be ensured by evaluating the reliability of the data.

[0153] The update unit can add a function to optimize energy consumption when managing the data update frequency. For example, the update unit can set the data update frequency to a time period with low energy consumption, thereby improving the energy efficiency of the system. The update unit can also monitor energy consumption in real time and adjust the update frequency if energy consumption is high. Furthermore, the update unit analyzes energy consumption data and generates an optimal update schedule. This optimizes energy consumption, thereby improving the sustainability of the system.

[0154] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide concise, visually easy-to-understand information. If the user is relaxed, the providing unit can provide detailed information or additional analysis results. Furthermore, if the user is in a hurry, important information is displayed preferentially and provided in a format that can be quickly understood. In this way, adjusting the format of information according to the user's emotions improves user satisfaction.

[0155] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, the providing unit can provide less information when the user is feeling stressed and provide information when the user is relaxed. It can also provide important information quickly when the user is in a hurry and provide detailed information when the user is calm. Furthermore, the frequency of providing information can be adjusted according to the user's emotions, reducing the burden on the user. In this way, the user's stress can be reduced by adjusting the timing of providing information according to the user's emotions.

[0156] The providing unit can estimate the user's emotions and customize the information display method based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points, allowing the user to quickly understand the information. In this way, the information display method can be customized according to the user's emotions, thereby improving user convenience.

[0157] The providing unit can estimate the user's emotions and determine the priority of information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide only important information with priority. Also, if the user is relaxed, the providing unit can provide detailed information with priority. Furthermore, if the user is in a hurry, the providing unit can provide information that can be provided quickly with priority. In this way, by determining the priority of information according to the user's emotions, it is possible to provide information that meets the user's needs.

[0158] The providing unit can estimate the user's emotions and adjust the content of the information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide positive information or encouraging messages. Also, if the user is relaxed, the providing unit can provide interesting information or new discoveries. Furthermore, if the user is in a hurry, the providing unit can provide important information in a concise summary. In this way, adjusting the content of the information according to the user's emotions improves user satisfaction.

[0159] The processing flow of the second embodiment will be briefly explained below.

[0160] Step 1: The collection unit collects oceanographic data. Oceanographic data includes water temperature, salinity, ocean currents, and wave height. The collection unit uses a short-wave ocean radar to collect ocean wave height and current speed. It can also collect meteorological and satellite data, and meteorological data includes temperature, wind speed, and precipitation. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis and machine learning algorithms. For example, the collected data is analyzed using machine learning algorithms to understand the state of the ocean. Step 3: The generation unit generates ocean state and forecast information based on the data analyzed by the analysis unit. This is done using a prediction model and simulation algorithm. For example, a prediction model is used to predict future ocean state. Step 4: The providing unit provides the information generated by the generating unit to the user. The provision may be performed in real time, in batches, or through a user interface. For example, the generated information may be provided to the user through a web application or a mobile application.

[0161] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0163] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0165] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0166] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0181] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0182] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0183] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0184] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0185] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0186] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0187] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0188] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0189] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0190] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0191] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0192] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0193] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0194] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0195] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0196] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0197] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0198] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0199] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0200] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0201] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0202] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0203] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0204] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0205] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0206] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0208] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0209] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0210] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0211] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0212] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0213] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0214] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0215] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0216] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0217] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0218] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0219] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0220] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0221] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0222] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0223] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0224] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0225] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0226] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0227] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0228] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0229] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0230] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0231] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0232] [Explanation of symbols]

[0233] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects oceanographic data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates ocean state or prediction information based on the data analyzed by the analysis unit; a providing unit that provides the information generated by the generating unit to a user; Equipped with A system characterized by:

2. Equipped with a weather data collection unit that collects weather data 2. The system of claim 1.

3. Equipped with a satellite data collection unit that collects satellite data 2. The system of claim 1.

4. Equipped with a storage unit for storing collected data 2. The system of claim 1.

5. Equipped with an update unit that manages the frequency of data updates 2. The system of claim 1.

6. The providing unit Providing users with real-time ocean conditions 2. The system of claim 1.

7. The providing unit Providing suitable fishing grounds for fishermen 2. The system of claim 1.

8. The providing unit Providing information to ensure the safety of vessel navigation 2. The system of claim 1.

9. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images