System

Abstract

To provide a system according to an embodiment, capable of predicting a torrential heavy rain or a local climate change, and quickly coping with them.SOLUTION: A system according to an embodiment comprises a collection unit, an analysis unit, a determination unit and an execution unit. The collection unit collects pieces of climate data. The analysis unit analyzes the data collected by the collection unit, and predicts a torrential heavy rain or a local climate change. The determination unit determines concrete countermeasures on the basis of a prediction result obtained by the analysis unit. The execution unit executes the countermeasures determined by the determination 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology was not able to adequately predict sudden heavy rains and localized climate changes, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to predict sudden heavy rains and local climate changes and to respond quickly to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and an execution unit. The collection unit collects meteorological data. The analysis unit analyzes the data collected by the collection unit and predicts torrential rain or localized climate change. The determination unit determines a specific response based on the prediction result obtained by the analysis unit. The execution unit executes the response determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment can predict sudden heavy rains and localized climate changes and respond quickly to them. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed 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 and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The weather forecasting and crop protection system according to the embodiment of the present invention is a system that has AI perform predictions of torrential rains and localized climate changes at any time, and when a response is necessary, automatically stacks sandbags to protect crops from disasters. In this system, AI collects weather data and predicts torrential rains and localized climate changes. Next, the necessary response is determined based on the prediction result. For example, when torrential rains are predicted, a system that automatically stacks sandbags is activated to protect crops. This mechanism can minimize damage to agricultural crops. For example, AI collects weather data. At this time, weather information is obtained in real time using weather sensors and satellite data. For example, data such as temperature, humidity, precipitation, and wind speed are collected and analyzed by AI. Next, AI analyzes the collected data and predicts torrential rains and localized climate changes. AI predicts future weather changes based on past weather data and current weather conditions. For example, if sudden rainfall is predicted in a specific area, AI provides that information. Based on the prediction result, the necessary response is determined. For example, if heavy rain is predicted, a system that automatically piles up sandbags will be activated. The system will follow the instructions of the AI ​​to place sandbags in the appropriate locations to protect crops. This mechanism can minimize damage to agricultural crops. For example, to prevent flood damage caused by heavy rain, sandbags can be quickly piled up to prevent crops from becoming waterlogged. In addition, the system can predict damage caused by localized climate change and take appropriate measures to protect the growing environment of agricultural crops. In this way, the weather forecasting and crop protection system can protect agricultural crops from disasters.

[0029] The weather forecasting and crop protection system according to the embodiment includes a collection unit, an analysis unit, a judgment unit, and an execution unit. The collection unit collects weather data. The weather data includes, for example, temperature, humidity, precipitation, wind speed, and the like, but is not limited to such examples. The collection unit collects weather information using, for example, weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, and the like. The satellite data is collected based on the type of satellite and the frequency of data acquisition. The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and localized climate changes. The analysis is performed, for example, based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. The prediction is performed, for example, based on a prediction model and the accuracy of the prediction. The judgment unit judges a specific response based on the prediction result obtained by the analysis unit. The specific response includes, for example, evacuation instructions and protection of facilities, but is not limited to such examples. The judgment unit can also make a judgment using AI. The execution unit executes the response determined by the determination unit. The execution unit includes, for example, a system for automatically stacking sandbags. The system for automatically stacking sandbags operates based on the machine used and the stacking procedure. For example, it includes a robot arm that stacks sandbags and an automatic stacking device. In this way, the weather forecasting and crop protection system according to the embodiment can protect agricultural crops from disasters. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm that stacks sandbags using AI and execute an optimal stacking procedure.

[0030] The collection unit collects meteorological data. The meteorological data includes, for example, temperature, humidity, precipitation, wind speed, etc., but is not limited to such examples. The collection unit collects meteorological information using, for example, weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, etc. These sensors are installed on farmland or its surroundings and collect data in real time. The temperature sensor records temperature changes during the day and night in detail, and the humidity sensor measures the amount of moisture in the air. The precipitation sensor measures the presence or absence of rainfall and its amount, and the wind speed sensor detects the strength and direction of the wind. These data are transmitted to a central database via wireless communication or the Internet. The satellite data is collected based on the type of satellite and the frequency of data acquisition. For example, a meteorological satellite observes the movement of the earth's atmosphere and clouds and provides meteorological information for a wide area. This allows the collection unit to integrate local meteorological data and wide-area meteorological data to obtain more accurate meteorological information. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and judgment units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and local climate changes. The analysis is performed, for example, based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. Specifically, the analysis unit uses a machine learning algorithm using AI to learn past weather patterns, thereby predicting future weather changes with high accuracy. For example, a recurrent neural network (RNN) or a convolutional neural network (CNN) is used to analyze time series data. This makes it possible to detect the probability of torrential rains occurring and signs of local climate changes at an early stage. Furthermore, the analysis unit can use an anomaly detection algorithm to detect weather patterns that are different from normal and issue an early warning. For example, it detects sudden temperature increases and abnormal fluctuations in precipitation and evaluates the risk to agricultural crops. In addition, the analysis unit can integrate and analyze not only weather data, but also data such as soil humidity and crop growth status. This makes it possible to comprehensively evaluate the impact of weather changes on agricultural crops and plan appropriate measures. The analysis unit processes data in real time and quickly and accurately predicts weather changes, contributing to the protection of crops.

[0032] The judgment unit judges a specific response based on the prediction results obtained by the analysis unit. Specific responses include, for example, evacuation orders and protection of facilities, but are not limited to such examples. The judgment unit can also use AI to make judgments. For example, a decision-making algorithm using deep learning is used to simulate multiple scenarios to select the optimal response. Specifically, the necessity of evacuation and the method of protecting facilities are judged based on weather data and the state of crops. For example, if a sudden downpour is predicted, the judgment unit can issue an instruction to operate the drainage system of the farmland. Also, if a strong wind is predicted, measures can be taken to reinforce the vinyl house and protect the crops. Furthermore, the judgment unit can learn from past response results and feedback to continuously improve the accuracy of judgments. For example, by analyzing the response results during past weather disasters and extracting effective measures, it is useful for improving future response measures. In addition, the judgment unit can integrate multiple data sources to make comprehensive judgments. For example, by integrating not only weather data but also crop growth data and soil data, more accurate judgments can be made. As a result, the judgment unit can take prompt and appropriate measures to protect agricultural crops from disasters.

[0033] The execution unit executes the response determined by the judgment unit. The execution unit includes, for example, a system for automatically stacking sandbags. The system for automatically stacking sandbags operates based on the machine used and the stacking procedure. For example, it includes a robot arm and an automatic stacking device for stacking sandbags. These devices are controlled by AI and can execute the optimal stacking procedure. Specifically, the robot arm detects the position and shape of the sandbags with a sensor and performs an operation to stack them efficiently. In addition, the automatic stacking device protects agricultural land from floods and inundation by stacking sandbags to a certain height. In addition, the execution unit can also execute other protective measures. For example, these include reinforcing vinyl greenhouses, moving crops, and operating drainage systems. These measures are automatically executed based on the instructions of the judgment unit. The execution unit can monitor the situation in real time and modify the response as necessary. For example, if the weather conditions suddenly change, the execution unit immediately receives new instructions and changes the response. In addition, the execution unit can collect the execution results as feedback and use them to improve the entire system. This will enable the department to respond quickly and effectively and protect crops from disaster.

[0034] The execution unit may include a system for automatically stacking sandbags. The system for automatically stacking sandbags includes, for example, a robot arm for stacking sandbags and an automatic stacking device. For example, the robot arm for stacking sandbags lifts the sandbags and places them at a predetermined position. The robot arm can detect the position of the sandbags using a sensor and stack them accurately. In addition, the automatic stacking device can continuously supply and stack sandbags. For example, the automatic stacking device transports sandbags using a belt conveyor and places them at a position to be stacked. In this way, crops can be quickly protected by automatically stacking the sandbags. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm for stacking sandbags using AI and execute an optimal stacking procedure.

[0035] The execution unit may include a system for automatically controlling the drainage system. The automatic control of the drainage system is performed, for example, based on the sensor and control algorithm used. For example, the drainage system detects the amount of precipitation using a precipitation sensor and automatically controls the drainage pump. The precipitation sensor detects changes in the amount of precipitation in real time and adjusts the operation of the drainage pump. The drainage system can also optimize the drainage route based on topographical data. For example, the topographical data is analyzed to determine the optimal drainage route. This makes it possible to prevent flood damage through automatic control of the drainage system. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can optimize the control algorithm of the drainage system using AI to achieve efficient drainage.

[0036] The execution unit may include a system for automatically unfolding the crop protection sheet. The system for automatically unfolding the crop protection sheet operates, for example, based on an unfolding machine and a procedure for unfolding. For example, a robot arm that unfolds the protection sheet lifts the sheet and places it in a predetermined position. The robot arm can detect the position of the sheet using a sensor and unfold it accurately. In addition, the automatic unfolding device can continuously supply and unfold the sheet. For example, the automatic unfolding device transports the sheet using a belt conveyor and places it in a position for unfolding. In this way, crops can be protected from climate change by automatically unfolding the crop protection sheet. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm that unfolds the protection sheet using AI and execute an optimal unfolding procedure.

[0037] The collection unit can collect weather information using weather sensors or satellite data. Weather sensors include, for example, temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, and the like. These sensors collect weather data in real time and provide it to the collection unit. For example, the temperature sensor measures the ambient temperature and transmits the data to the collection unit. The humidity sensor measures the humidity in the air and transmits the data to the collection unit. The precipitation sensor measures the amount of precipitation and transmits the data to the collection unit. The wind speed sensor measures the wind speed and transmits the data to the collection unit. The satellite data is collected based on the type of satellite and the frequency of data acquisition. For example, a weather satellite observes the weather conditions on Earth and transmits the data to the collection unit. As a result, accurate weather information can be collected by using the weather sensors and satellite data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze data from the weather sensor using AI to detect abnormal values.

[0038] The analysis unit can predict future weather changes based on past weather data or current weather conditions. Past weather data includes, for example, data from the past 10 years or data from a specific region. These data are acquired from the Japan Meteorological Agency or a weather observation station. For example, past precipitation data and temperature data are analyzed to predict future weather changes. Current weather conditions include weather data acquired in real time. For example, predictions are made based on data such as current temperature, humidity, precipitation, and wind speed. The analysis unit makes predictions based on these data, based on the algorithm used and the accuracy of the analysis. For example, a machine learning algorithm is used to learn patterns from past data and predict future weather changes. This makes it possible to make highly accurate weather predictions by making predictions based on past weather data and current weather conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze past weather data using AI to predict future weather changes.

[0039] The collection unit can analyze past weather data and select the optimal collection method. The collection unit, for example, increases the collection frequency in a specific time period based on past weather data. The past weather data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the collection unit analyzes past precipitation data and temperature data and selects the optimal collection method. The collection unit can also optimize the collection method under specific weather conditions. For example, the collection unit analyzes past weather data and adjusts the collection method based on specific weather conditions (e.g., high temperature and humidity). The collection unit can also adjust the type and placement of sensors to be collected with reference to past weather data. For example, the placement of sensors is optimized according to specific regions and conditions based on past data. This enables efficient data collection by selecting the optimal collection method based on past data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past weather data to the generation AI and cause the generation AI to select the optimal collection method.

[0040] The collection unit can filter the weather data based on a specific region or condition when collecting the weather data. For example, when a heavy rain forecast is high in a specific region, the collection unit preferentially collects data from that region. The specific region is determined based on geographical location information and past weather data. For example, the collection unit preferentially collects weather data from a specific region to improve the accuracy of the heavy rain forecast. The collection unit can also filter data based on a specific weather condition (e.g., high temperature and humidity). For example, the collection unit preferentially collects data under specific weather conditions to improve the accuracy of the weather forecast. Furthermore, the collection unit can adjust the type of data to be collected according to a specific season or time. For example, the collection unit adjusts the type and arrangement of the sensor to be collected according to a specific season. This allows the necessary data to be efficiently collected by filtering the data based on a specific region or condition. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can optimize an algorithm for filtering data based on a specific region or condition using a generation AI to achieve efficient data collection.

[0041] The collection unit can preferentially collect highly relevant data in consideration of geographical location information when collecting weather data. For example, when a heavy rain forecast is high in a specific area, the collection unit preferentially collects data for that area. The geographical location information is acquired based on GPS data or map information. For example, the collection unit preferentially collects weather data for a specific area to improve the accuracy of the heavy rain forecast. The collection unit can also collect highly relevant weather data based on the geographical location information. For example, the collection unit adjusts the arrangement of the sensor to be collected in consideration of the geographical location information. In this way, highly relevant data can be efficiently collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0042] The collection unit can analyze social media activities and collect related data when collecting weather data. The collection unit collects related weather data, for example, based on reports of heavy rain or storms on social media. The social media activities are analyzed based on analysis of tweets and frequency of posts. For example, the collection unit analyzes posts on social media to grasp the weather conditions in a specific area. The collection unit can also adjust the type of weather data to be collected based on the activities on social media. For example, the collection unit collects data according to specific areas and conditions based on the activities on social media. In this way, related weather data can be collected in real time by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input social media activities to the generation AI and cause the generation AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the weather data during the analysis. For example, when data of heavy rain or storm is important, the analysis unit performs a detailed analysis. The importance of the weather data is determined based on the granularity of the data and the depth of the analysis. For example, the analysis unit performs a detailed analysis of the heavy rain data to improve the accuracy of the prediction. In addition, the analysis unit can perform a simplified analysis in the case of normal weather data. For example, the analysis unit simplifies and analyzes normal weather data to perform efficient data processing. Furthermore, when a specific weather condition is important, the analysis unit can adjust the level of detail of the analysis based on the condition. For example, the analysis unit adjusts the level of detail of the analysis based on a specific weather condition (e.g., high temperature and humidity) to improve the accuracy of the prediction. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the weather data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the weather data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of weather data during analysis. For example, the analysis unit applies an analysis algorithm dedicated to heavy rain prediction to heavy rain data. The category of weather data is determined based on the type and characteristics of the data. For example, the analysis unit applies an algorithm dedicated to heavy rain prediction to heavy rain data to improve the accuracy of the prediction. The analysis unit can also apply an algorithm dedicated to wind speed analysis to wind speed data. For example, the analysis unit applies an algorithm dedicated to wind speed analysis to wind speed data to improve the accuracy of the prediction. Furthermore, the analysis unit can also apply an algorithm dedicated to temperature analysis to temperature data. For example, the analysis unit applies an algorithm dedicated to temperature analysis to temperature data to improve the accuracy of the prediction. As a result, by applying an analysis algorithm depending on the category of weather data, highly accurate analysis is possible. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of weather data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the collection time of the weather data during the analysis. For example, the analysis unit preferentially analyzes the latest weather data. The collection time of the weather data is determined based on the importance of the data and the urgency of collection. For example, the analysis unit preferentially analyzes the latest weather data to improve the accuracy of the prediction. The analysis unit can also preferentially analyze data collected in a specific time period. For example, the analysis unit preferentially analyzes data collected in a specific time period to perform efficient data processing. Furthermore, the analysis unit can also determine the priority of the analysis with reference to past weather data. For example, the analysis unit determines the priority of the analysis under specific weather conditions based on past weather data. As a result, efficient analysis is possible by determining the priority of the analysis based on the collection time of the weather data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the weather data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the weather data during analysis. The analysis unit adjusts the order of analysis, for example, taking into account the relevance of the heavy rain data and the wind speed data. The relevance of the weather data is determined based on the correlation and causal relationship of the data. For example, the analysis unit adjusts the order of analysis, taking into account the relevance of the heavy rain data and the wind speed data, to improve the accuracy of the prediction. The analysis unit can also adjust the order of analysis, taking into account the relevance of the temperature data and the humidity data. For example, the analysis unit adjusts the order of analysis, taking into account the relevance of the temperature data and the humidity data, to perform efficient data processing. Furthermore, the analysis unit can also preferentially analyze data related to specific weather conditions. For example, the analysis unit preferentially analyzes data related based on specific weather conditions (e.g., high temperature and humidity) to improve the accuracy of the prediction. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the weather data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the weather data to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] The judgment unit can optimize the judgment algorithm by referring to past judgment data at the time of judgment. The judgment unit optimizes the judgment algorithm based on, for example, past heavy rain forecast data. The past judgment data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the past heavy rain forecast data is analyzed to optimize the judgment algorithm. The judgment unit can also optimize the judgment algorithm based on past wind speed forecast data. For example, the past wind speed forecast data is analyzed to optimize the judgment algorithm. Furthermore, the judgment unit can also optimize the judgment algorithm based on past temperature forecast data. For example, the past temperature forecast data is analyzed to optimize the judgment algorithm. As a result, the accuracy of the judgment algorithm is improved by referring to the past judgment data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the past judgment data to the generation AI and cause the generation AI to execute optimization of the judgment algorithm.

[0048] The judgment unit can improve the accuracy of the judgment by considering the interrelationship of the weather data when making a judgment. The judgment unit makes a judgment by considering, for example, the interrelationship between the heavy rain data and the wind speed data. The interrelationship of the weather data is determined based on the correlation and causal relationship of the data. For example, the judgment unit makes a judgment by considering the interrelationship between the heavy rain data and the wind speed data, thereby improving the accuracy of the prediction. The judgment unit can also make a judgment by considering the interrelationship between the temperature data and the humidity data. For example, the judgment unit makes a judgment by considering the interrelationship between the temperature data and the humidity data, thereby performing efficient data processing. Furthermore, the judgment unit can also make a judgment based on data related to a specific weather condition. For example, the judgment unit makes a judgment based on data related to a specific weather condition (e.g., high temperature and high humidity), thereby improving the accuracy of the prediction. As a result, the accuracy of the judgment is improved by considering the interrelationship of the weather data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the interrelationship of the weather data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0049] The judgment unit can make a judgment taking into consideration the geographical distribution when making a judgment. For example, when a heavy rain forecast is high in a specific area, the judgment unit prioritizes the data of that area. The geographical distribution is determined based on the characteristics and population density of the area. For example, the judgment unit prioritizes weather data of a specific area to improve the accuracy of the heavy rain forecast. The judgment unit can also prioritize highly relevant data based on the geographical distribution. For example, the judgment unit improves the accuracy of the judgment taking into consideration the geographical distribution. As a result, appropriate judgment according to the area is possible by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the geographical distribution to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0050] The judgment unit can improve the accuracy of the judgment by referring to related literature at the time of judgment. The judgment unit makes a judgment by referring to, for example, the latest research on heavy rain prediction. The related literature is acquired based on academic papers and technical reports. For example, the judgment unit improves the accuracy of the judgment by referring to the latest research on heavy rain prediction. The judgment unit can also make a judgment by referring to related literature on wind speed prediction. For example, the judgment unit improves the accuracy of the judgment by referring to related literature on wind speed prediction. Furthermore, the judgment unit can also make a judgment by referring to related literature on temperature prediction. For example, the judgment unit improves the accuracy of the judgment by referring to related literature on temperature prediction. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0051] The execution unit can optimize the execution algorithm by referring to past execution data at the time of execution. The execution unit optimizes the execution algorithm based on, for example, past heavy rain countermeasure data. The past execution data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the execution unit optimizes the execution algorithm by analyzing the past heavy rain countermeasure data. The execution unit can also optimize the execution algorithm based on past wind speed countermeasure data. For example, the execution unit optimizes the execution algorithm by analyzing the past wind speed countermeasure data. The execution unit can also optimize the execution algorithm based on past temperature countermeasure data. For example, the execution unit optimizes the execution algorithm by analyzing the past temperature countermeasure data. As a result, the accuracy of the execution algorithm is improved by referring to the past execution data. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the past execution data to the generation AI and cause the generation AI to execute optimization of the execution algorithm.

[0052] The execution unit can improve the accuracy of execution by considering the interrelationship of weather data at the time of execution. The execution unit performs execution by considering, for example, the interrelationship of heavy rain data and wind speed data. The interrelationship of weather data is determined based on the correlation and causal relationship of data. For example, the execution unit performs execution by considering the interrelationship of heavy rain data and wind speed data, thereby improving the accuracy of prediction. The execution unit can also perform execution by considering the interrelationship of temperature data and humidity data. For example, the execution unit performs execution by considering the interrelationship of temperature data and humidity data, thereby performing efficient data processing. Furthermore, the execution unit can also perform execution based on data related to specific weather conditions. For example, the execution unit performs execution based on data related to specific weather conditions (e.g., high temperature and humidity), thereby improving the accuracy of prediction. As a result, the accuracy of execution is improved by considering the interrelationship of weather data. Part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the interrelationship of weather data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of execution.

[0053] The execution unit can perform execution while taking into consideration the geographical distribution. For example, when a heavy rain forecast is high in a specific area, the execution unit prioritizes the execution of measures for that area. The geographical distribution is determined based on the characteristics and population density of the area. For example, the execution unit prioritizes the execution of meteorological data for a specific area to improve the accuracy of the heavy rain forecast. The execution unit can also prioritize the execution of highly relevant measures based on the geographical distribution. For example, the execution unit improves the accuracy of the execution by taking into consideration the geographical distribution. As a result, appropriate execution according to the area is possible by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the geographical distribution to the generation AI and cause the generation AI to execute the improvement of the accuracy of the execution.

[0054] The execution unit can improve the accuracy of execution by referring to related literature at the time of execution. The execution unit performs execution by referring to, for example, the latest research on heavy rain countermeasures. The related literature is acquired based on academic papers and technical reports. For example, the execution unit improves the accuracy of execution by referring to the latest research on heavy rain countermeasures. The execution unit can also perform execution by referring to related literature on wind speed countermeasures. For example, the execution unit improves the accuracy of execution by referring to related literature on wind speed countermeasures. Furthermore, the execution unit can also perform execution by referring to related literature on temperature countermeasures. For example, the execution unit improves the accuracy of execution by referring to related literature on temperature countermeasures. As a result, the accuracy of execution is improved by referring to the related literature. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of execution.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The collection unit can monitor the growth status of a specific plant when collecting weather data. For example, the collection unit can capture the color and growth rate of the plant's leaves with a camera and evaluate the growth status using image analysis technology. The collection unit can also measure the soil moisture and nutrient levels with a sensor to grasp the health status of the plant. Furthermore, the collection unit monitors meteorological conditions that affect the growth of the plant (e.g., sunshine hours and wind speed) in real time and collects data. This allows for integrated management of meteorological data and the growth status of the plant, enabling more accurate weather forecasting and crop protection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input plant growth data to the generation AI and have the generation AI select the optimal collection method.

[0057] The analysis unit can take into account the history of pesticide use when analyzing weather data. For example, the analysis unit can evaluate the effectiveness of pesticides under specific weather conditions based on past pesticide use data. The analysis unit can also analyze the frequency and amount of pesticide use to clarify correlations with weather conditions. Furthermore, the analysis unit can propose an optimal pesticide use plan according to future weather conditions based on the history of pesticide use. This enables more effective crop protection by integrating the analysis of weather data and pesticide use data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input pesticide use data to the generation AI and cause the generation AI to select the optimal analysis method.

[0058] The judgment unit can optimize the harvest time of the crop based on the analysis result of the weather data. For example, the judgment unit performs an integrated analysis of the weather data and the crop growth data to propose an optimal harvest time. The judgment unit can also evaluate the relationship between weather conditions and the harvest amount based on past harvest data and adjust the harvest time. Furthermore, the judgment unit can optimize the harvest work schedule based on the weather data. This makes it possible to improve the efficiency of the harvest by managing the weather data and the crop growth data in an integrated manner. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the harvest data to the generation AI and cause the generation AI to execute a proposal for an optimal harvest time.

[0059] The execution unit can automatically control the irrigation system based on weather data. For example, the execution unit can adjust the timing and amount of irrigation based on precipitation data. The execution unit can also monitor soil moisture data in real time and irrigate as necessary. Furthermore, the execution unit can predict future precipitation based on weather forecast data and optimize irrigation plans. This enables efficient use of water resources by managing the weather data and the irrigation system in an integrated manner. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can input irrigation data to a generation AI and have the generation AI execute a proposal for an optimal irrigation plan.

[0060] The collection unit can monitor the occurrence of pests in crops when collecting weather data. For example, the collection unit can take pictures of the leaves and stems of crops with a camera and detect the occurrence of pests using image analysis technology. The collection unit can also monitor the state of the soil and weather conditions to evaluate the risk of pests occurring. Furthermore, the collection unit can predict the risk of pests occurring under specific weather conditions based on past pest occurrence data. This makes it possible to take early measures against pests by managing weather data and pest occurrence data in an integrated manner. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pest occurrence data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects weather data. The weather data includes temperature, humidity, precipitation, wind speed, etc. The collection unit collects weather information using weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, etc. The satellite data is collected based on the type of satellite and the frequency of data acquisition. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and local climate changes. The analysis is performed based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. The prediction is performed based on the prediction model and the accuracy of the prediction. Step 3: The judgment unit determines the specific response based on the prediction results obtained by the analysis unit. Specific responses include evacuation orders and protection of facilities. The judgment unit can also use AI to make decisions. Step 4: The execution unit executes the response determined by the judgment unit. The execution unit is equipped with a system for automatically stacking sandbags. It includes a robotic arm that stacks the sandbags and an automatic stacking device. Some or all of the processing in the execution unit may be performed using AI. For example, the execution unit uses AI to control the operation of the robotic arm that stacks the sandbags and executes the optimal stacking procedure.

[0063] (Example 2) The weather forecasting and crop protection system according to the embodiment of the present invention is a system that has AI perform predictions of torrential rains and localized climate changes at any time, and when a response is necessary, automatically stacks sandbags to protect crops from disasters. In this system, AI collects weather data and predicts torrential rains and localized climate changes. Next, the necessary response is determined based on the prediction result. For example, when torrential rains are predicted, a system that automatically stacks sandbags is activated to protect crops. This mechanism can minimize damage to agricultural crops. For example, AI collects weather data. At this time, weather information is obtained in real time using weather sensors and satellite data. For example, data such as temperature, humidity, precipitation, and wind speed are collected and analyzed by AI. Next, AI analyzes the collected data and predicts torrential rains and localized climate changes. AI predicts future weather changes based on past weather data and current weather conditions. For example, if sudden rainfall is predicted in a specific area, AI provides that information. Based on the prediction result, the necessary response is determined. For example, if heavy rain is predicted, a system that automatically piles up sandbags will be activated. The system will follow the instructions of the AI ​​to place sandbags in the appropriate locations to protect crops. This mechanism can minimize damage to agricultural crops. For example, to prevent flood damage caused by heavy rain, sandbags can be quickly piled up to prevent crops from becoming waterlogged. In addition, the system can predict damage caused by localized climate change and take appropriate measures to protect the growing environment of agricultural crops. In this way, the weather forecasting and crop protection system can protect agricultural crops from disasters.

[0064] The weather forecasting and crop protection system according to the embodiment includes a collection unit, an analysis unit, a judgment unit, and an execution unit. The collection unit collects weather data. The weather data includes, for example, temperature, humidity, precipitation, wind speed, and the like, but is not limited to such examples. The collection unit collects weather information using, for example, weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, and the like. The satellite data is collected based on the type of satellite and the frequency of data acquisition. The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and localized climate changes. The analysis is performed, for example, based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. The prediction is performed, for example, based on a prediction model and the accuracy of the prediction. The judgment unit judges a specific response based on the prediction result obtained by the analysis unit. The specific response includes, for example, evacuation instructions and protection of facilities, but is not limited to such examples. The judgment unit can also make a judgment using AI. The execution unit executes the response determined by the determination unit. The execution unit includes, for example, a system for automatically stacking sandbags. The system for automatically stacking sandbags operates based on the machine used and the stacking procedure. For example, it includes a robot arm that stacks sandbags and an automatic stacking device. In this way, the weather forecasting and crop protection system according to the embodiment can protect agricultural crops from disasters. Some or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm that stacks sandbags using AI and execute an optimal stacking procedure.

[0065] The collection unit collects meteorological data. The meteorological data includes, for example, temperature, humidity, precipitation, wind speed, etc., but is not limited to such examples. The collection unit collects meteorological information using, for example, weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, etc. These sensors are installed on farmland or its surroundings and collect data in real time. The temperature sensor records temperature changes during the day and night in detail, and the humidity sensor measures the amount of moisture in the air. The precipitation sensor measures the presence or absence of rainfall and its amount, and the wind speed sensor detects the strength and direction of the wind. These data are transmitted to a central database via wireless communication or the Internet. The satellite data is collected based on the type of satellite and the frequency of data acquisition. For example, a meteorological satellite observes the movement of the earth's atmosphere and clouds and provides meteorological information for a wide area. This allows the collection unit to integrate local meteorological data and wide-area meteorological data to obtain more accurate meteorological information. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server and made accessible to the analysis and judgment units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and local climate changes. The analysis is performed, for example, based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. Specifically, the analysis unit uses a machine learning algorithm using AI to learn past weather patterns, thereby predicting future weather changes with high accuracy. For example, a recurrent neural network (RNN) or a convolutional neural network (CNN) is used to analyze time series data. This makes it possible to detect the probability of torrential rains occurring and signs of local climate changes at an early stage. Furthermore, the analysis unit can use an anomaly detection algorithm to detect weather patterns that are different from normal and issue an early warning. For example, it detects sudden temperature increases and abnormal fluctuations in precipitation and evaluates the risk to agricultural crops. In addition, the analysis unit can integrate and analyze not only weather data, but also data such as soil humidity and crop growth status. This makes it possible to comprehensively evaluate the impact of weather changes on agricultural crops and plan appropriate measures. The analysis unit processes data in real time and quickly and accurately predicts weather changes, contributing to the protection of crops.

[0067] The judgment unit judges a specific response based on the prediction results obtained by the analysis unit. Specific responses include, for example, evacuation orders and protection of facilities, but are not limited to such examples. The judgment unit can also use AI to make judgments. For example, a decision-making algorithm using deep learning is used to simulate multiple scenarios to select the optimal response. Specifically, the necessity of evacuation and the method of protecting facilities are judged based on weather data and the state of crops. For example, if a sudden downpour is predicted, the judgment unit can issue an instruction to operate the drainage system of the farmland. Also, if a strong wind is predicted, measures can be taken to reinforce the vinyl house and protect the crops. Furthermore, the judgment unit can learn from past response results and feedback to continuously improve the accuracy of judgments. For example, by analyzing the response results during past weather disasters and extracting effective measures, it is useful for improving future response measures. In addition, the judgment unit can integrate multiple data sources to make comprehensive judgments. For example, by integrating not only weather data but also crop growth data and soil data, more accurate judgments can be made. As a result, the judgment unit can take prompt and appropriate measures to protect agricultural crops from disasters.

[0068] The execution unit executes the response determined by the judgment unit. The execution unit includes, for example, a system for automatically stacking sandbags. The system for automatically stacking sandbags operates based on the machine used and the stacking procedure. For example, it includes a robot arm and an automatic stacking device for stacking sandbags. These devices are controlled by AI and can execute the optimal stacking procedure. Specifically, the robot arm detects the position and shape of the sandbags with a sensor and performs an operation to stack them efficiently. In addition, the automatic stacking device protects agricultural land from floods and inundation by stacking sandbags to a certain height. In addition, the execution unit can also execute other protective measures. For example, these include reinforcing vinyl greenhouses, moving crops, and operating drainage systems. These measures are automatically executed based on the instructions of the judgment unit. The execution unit can monitor the situation in real time and modify the response as necessary. For example, if the weather conditions suddenly change, the execution unit immediately receives new instructions and changes the response. In addition, the execution unit can collect the execution results as feedback and use them to improve the entire system. This will enable the department to respond quickly and effectively and protect crops from disaster.

[0069] The execution unit may include a system for automatically stacking sandbags. The system for automatically stacking sandbags includes, for example, a robot arm for stacking sandbags and an automatic stacking device. For example, the robot arm for stacking sandbags lifts the sandbags and places them at a predetermined position. The robot arm can detect the position of the sandbags using a sensor and stack them accurately. In addition, the automatic stacking device can continuously supply and stack sandbags. For example, the automatic stacking device transports sandbags using a belt conveyor and places them at a position to be stacked. In this way, crops can be quickly protected by automatically stacking the sandbags. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm for stacking sandbags using AI and execute an optimal stacking procedure.

[0070] The execution unit may include a system for automatically controlling the drainage system. The automatic control of the drainage system is performed, for example, based on the sensor and control algorithm used. For example, the drainage system detects the amount of precipitation using a precipitation sensor and automatically controls the drainage pump. The precipitation sensor detects changes in the amount of precipitation in real time and adjusts the operation of the drainage pump. The drainage system can also optimize the drainage route based on topographical data. For example, the topographical data is analyzed to determine the optimal drainage route. This makes it possible to prevent flood damage through automatic control of the drainage system. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can optimize the control algorithm of the drainage system using AI to achieve efficient drainage.

[0071] The execution unit may include a system for automatically unfolding the crop protection sheet. The system for automatically unfolding the crop protection sheet operates, for example, based on an unfolding machine and a procedure for unfolding. For example, a robot arm that unfolds the protection sheet lifts the sheet and places it in a predetermined position. The robot arm can detect the position of the sheet using a sensor and unfold it accurately. In addition, the automatic unfolding device can continuously supply and unfold the sheet. For example, the automatic unfolding device transports the sheet using a belt conveyor and places it in a position for unfolding. In this way, crops can be protected from climate change by automatically unfolding the crop protection sheet. Some or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can control the operation of the robot arm that unfolds the protection sheet using AI and execute an optimal unfolding procedure.

[0072] The collection unit can collect weather information using weather sensors or satellite data. Weather sensors include, for example, temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, and the like. These sensors collect weather data in real time and provide it to the collection unit. For example, the temperature sensor measures the ambient temperature and transmits the data to the collection unit. The humidity sensor measures the humidity in the air and transmits the data to the collection unit. The precipitation sensor measures the amount of precipitation and transmits the data to the collection unit. The wind speed sensor measures the wind speed and transmits the data to the collection unit. The satellite data is collected based on the type of satellite and the frequency of data acquisition. For example, a weather satellite observes the weather conditions on Earth and transmits the data to the collection unit. As a result, accurate weather information can be collected by using the weather sensors and satellite data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can analyze data from the weather sensor using AI to detect abnormal values.

[0073] The analysis unit can predict future weather changes based on past weather data or current weather conditions. Past weather data includes, for example, data from the past 10 years or data from a specific region. These data are acquired from the Japan Meteorological Agency or a weather observation station. For example, past precipitation data and temperature data are analyzed to predict future weather changes. Current weather conditions include weather data acquired in real time. For example, predictions are made based on data such as current temperature, humidity, precipitation, and wind speed. The analysis unit makes predictions based on these data, based on the algorithm used and the accuracy of the analysis. For example, a machine learning algorithm is used to learn patterns from past data and predict future weather changes. This makes it possible to make highly accurate weather predictions by making predictions based on past weather data and current weather conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze past weather data using AI to predict future weather changes.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of the weather data based on the estimated user's emotion. For example, when the user feels anxious, the collection unit increases the collection frequency to acquire weather data in real time. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the collection timing is adjusted. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, an emotion score is calculated, and the collection timing is adjusted. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the collection timing is adjusted. In this way, more appropriate weather data can be collected by adjusting the collection timing according to the user's emotion. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0075] The collection unit can analyze past weather data and select the optimal collection method. The collection unit, for example, increases the collection frequency in a specific time period based on past weather data. The past weather data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the collection unit analyzes past precipitation data and temperature data and selects the optimal collection method. The collection unit can also optimize the collection method under specific weather conditions. For example, the collection unit analyzes past weather data and adjusts the collection method based on specific weather conditions (e.g., high temperature and humidity). The collection unit can also adjust the type and placement of sensors to be collected with reference to past weather data. For example, the placement of sensors is optimized according to specific regions and conditions based on past data. This enables efficient data collection by selecting the optimal collection method based on past data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input past weather data to the generation AI and cause the generation AI to select the optimal collection method.

[0076] The collection unit can filter the weather data based on a specific region or condition when collecting the weather data. For example, when a heavy rain forecast is high in a specific region, the collection unit preferentially collects data from that region. The specific region is determined based on geographical location information and past weather data. For example, the collection unit preferentially collects weather data from a specific region to improve the accuracy of the heavy rain forecast. The collection unit can also filter data based on a specific weather condition (e.g., high temperature and humidity). For example, the collection unit preferentially collects data under specific weather conditions to improve the accuracy of the weather forecast. Furthermore, the collection unit can adjust the type of data to be collected according to a specific season or time. For example, the collection unit adjusts the type and arrangement of the sensor to be collected according to a specific season. This allows the necessary data to be efficiently collected by filtering the data based on a specific region or condition. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can optimize an algorithm for filtering data based on a specific region or condition using a generation AI to achieve efficient data collection.

[0077] The collection unit can estimate the user's emotion and determine the priority of the weather data to be collected based on the estimated user's emotion. For example, when the user feels anxious, the collection unit preferentially collects data on heavy rain and storms. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the priority of the data to be collected is determined. The collection unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, an emotion score is calculated, and the priority of the data to be collected is determined. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the priority of the data to be collected is determined. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's emotion data to the generation AI and have the generation AI determine the priority of the data to be collected.

[0078] The collection unit can preferentially collect highly relevant data in consideration of geographical location information when collecting weather data. For example, when a heavy rain forecast is high in a specific area, the collection unit preferentially collects data for that area. The geographical location information is acquired based on GPS data or map information. For example, the collection unit preferentially collects weather data for a specific area to improve the accuracy of the heavy rain forecast. The collection unit can also collect highly relevant weather data based on the geographical location information. For example, the collection unit adjusts the arrangement of the sensor to be collected in consideration of the geographical location information. In this way, highly relevant data can be efficiently collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0079] The collection unit can analyze social media activities and collect related data when collecting weather data. The collection unit collects related weather data, for example, based on reports of heavy rain or storms on social media. The social media activities are analyzed based on analysis of tweets and frequency of posts. For example, the collection unit analyzes posts on social media to grasp the weather conditions in a specific area. The collection unit can also adjust the type of weather data to be collected based on the activities on social media. For example, the collection unit collects data according to specific areas and conditions based on the activities on social media. In this way, related weather data can be collected in real time by analyzing the social media activities. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input social media activities to the generation AI and cause the generation AI to collect related data.

[0080] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user feels anxious, the analysis unit provides a simple and highly visible analysis result. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the expression method of the analysis is adjusted. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, an emotion score is calculated, and the expression method of the analysis is adjusted. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the expression method of the analysis is adjusted. This allows the analysis result to be more easily understood by adjusting the way the analysis is expressed according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the way the analysis is expressed.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the weather data during the analysis. For example, when data of heavy rain or storm is important, the analysis unit performs a detailed analysis. The importance of the weather data is determined based on the granularity of the data and the depth of the analysis. For example, the analysis unit performs a detailed analysis of the heavy rain data to improve the accuracy of the prediction. In addition, the analysis unit can perform a simplified analysis in the case of normal weather data. For example, the analysis unit simplifies and analyzes normal weather data to perform efficient data processing. Furthermore, when a specific weather condition is important, the analysis unit can adjust the level of detail of the analysis based on the condition. For example, the analysis unit adjusts the level of detail of the analysis based on a specific weather condition (e.g., high temperature and humidity) to improve the accuracy of the prediction. As a result, efficient analysis is possible by adjusting the level of detail of the analysis based on the importance of the weather data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the weather data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of weather data during analysis. For example, the analysis unit applies an analysis algorithm dedicated to heavy rain prediction to heavy rain data. The category of weather data is determined based on the type and characteristics of the data. For example, the analysis unit applies an algorithm dedicated to heavy rain prediction to heavy rain data to improve the accuracy of the prediction. The analysis unit can also apply an algorithm dedicated to wind speed analysis to wind speed data. For example, the analysis unit applies an algorithm dedicated to wind speed analysis to wind speed data to improve the accuracy of the prediction. Furthermore, the analysis unit can also apply an algorithm dedicated to temperature analysis to temperature data. For example, the analysis unit applies an algorithm dedicated to temperature analysis to temperature data to improve the accuracy of the prediction. As a result, by applying an analysis algorithm depending on the category of weather data, highly accurate analysis is possible. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of weather data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0083] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is feeling anxious, the analysis unit provides a short and to-the-point analysis result. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the length of the analysis is adjusted. The analysis unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, the emotion score is calculated, and the length of the analysis is adjusted. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the length of the analysis is adjusted. In this way, a more appropriate analysis result can be provided by adjusting the length of the analysis according to the user's emotion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0084] The analysis unit can determine the priority of the analysis based on the collection time of the weather data during the analysis. For example, the analysis unit preferentially analyzes the latest weather data. The collection time of the weather data is determined based on the importance of the data and the urgency of collection. For example, the analysis unit preferentially analyzes the latest weather data to improve the accuracy of the prediction. The analysis unit can also preferentially analyze data collected in a specific time period. For example, the analysis unit preferentially analyzes data collected in a specific time period to perform efficient data processing. Furthermore, the analysis unit can also determine the priority of the analysis with reference to past weather data. For example, the analysis unit determines the priority of the analysis under specific weather conditions based on past weather data. As a result, efficient analysis is possible by determining the priority of the analysis based on the collection time of the weather data. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collection time of the weather data to the generation AI and cause the generation AI to execute the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the weather data during analysis. The analysis unit adjusts the order of analysis, for example, taking into account the relevance of the heavy rain data and the wind speed data. The relevance of the weather data is determined based on the correlation and causal relationship of the data. For example, the analysis unit adjusts the order of analysis, taking into account the relevance of the heavy rain data and the wind speed data, to improve the accuracy of the prediction. The analysis unit can also adjust the order of analysis, taking into account the relevance of the temperature data and the humidity data. For example, the analysis unit adjusts the order of analysis, taking into account the relevance of the temperature data and the humidity data, to perform efficient data processing. Furthermore, the analysis unit can also preferentially analyze data related to specific weather conditions. For example, the analysis unit preferentially analyzes data related based on specific weather conditions (e.g., high temperature and humidity) to improve the accuracy of the prediction. As a result, efficient analysis is possible by adjusting the order of analysis based on the relevance of the weather data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the weather data to the generation AI and cause the generation AI to adjust the order of analysis.

[0086] The judgment unit can estimate the user's emotion and adjust the judgment criteria based on the estimated user's emotion. For example, when the user feels anxious, the judgment unit makes a judgment based on strict criteria. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the judgment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the judgment criteria are adjusted. The judgment unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, the emotion score is calculated, and the judgment criteria are adjusted. Furthermore, the judgment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the judgment criteria are adjusted. This allows for a more appropriate judgment by adjusting the judgment criteria according to the user's emotion. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the judgment criteria.

[0087] The judgment unit can optimize the judgment algorithm by referring to past judgment data at the time of judgment. The judgment unit optimizes the judgment algorithm based on, for example, past heavy rain forecast data. The past judgment data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the past heavy rain forecast data is analyzed to optimize the judgment algorithm. The judgment unit can also optimize the judgment algorithm based on past wind speed forecast data. For example, the past wind speed forecast data is analyzed to optimize the judgment algorithm. Furthermore, the judgment unit can also optimize the judgment algorithm based on past temperature forecast data. For example, the past temperature forecast data is analyzed to optimize the judgment algorithm. As a result, the accuracy of the judgment algorithm is improved by referring to the past judgment data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the past judgment data to the generation AI and cause the generation AI to execute optimization of the judgment algorithm.

[0088] The judgment unit can improve the accuracy of the judgment by considering the interrelationship of the weather data when making a judgment. The judgment unit makes a judgment by considering, for example, the interrelationship between the heavy rain data and the wind speed data. The interrelationship of the weather data is determined based on the correlation and causal relationship of the data. For example, the judgment unit makes a judgment by considering the interrelationship between the heavy rain data and the wind speed data, thereby improving the accuracy of the prediction. The judgment unit can also make a judgment by considering the interrelationship between the temperature data and the humidity data. For example, the judgment unit makes a judgment by considering the interrelationship between the temperature data and the humidity data, thereby performing efficient data processing. Furthermore, the judgment unit can also make a judgment based on data related to a specific weather condition. For example, the judgment unit makes a judgment based on data related to a specific weather condition (e.g., high temperature and high humidity), thereby improving the accuracy of the prediction. As a result, the accuracy of the judgment is improved by considering the interrelationship of the weather data. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the interrelationship of the weather data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0089] The judgment unit can estimate the user's emotion and adjust the order of displaying the judgment results based on the estimated user's emotion. For example, when the user feels anxious, the judgment unit preferentially displays important results. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the judgment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the display order of the results is adjusted. The judgment unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, the emotion score is calculated, and the display order of the results is adjusted. Furthermore, the judgment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the display order of the results is adjusted. In this way, important information can be provided preferentially by adjusting the display order of the results according to the user's emotion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the results.

[0090] The judgment unit can make a judgment taking into consideration the geographical distribution when making a judgment. For example, when a heavy rain forecast is high in a specific area, the judgment unit prioritizes the data of that area. The geographical distribution is determined based on the characteristics and population density of the area. For example, the judgment unit prioritizes weather data of a specific area to improve the accuracy of the heavy rain forecast. The judgment unit can also prioritize highly relevant data based on the geographical distribution. For example, the judgment unit improves the accuracy of the judgment taking into consideration the geographical distribution. As a result, appropriate judgment according to the area is possible by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the geographical distribution to the generation AI and cause the generation AI to improve the accuracy of the judgment.

[0091] The judgment unit can improve the accuracy of the judgment by referring to related literature at the time of judgment. The judgment unit makes a judgment by referring to, for example, the latest research on heavy rain prediction. The related literature is acquired based on academic papers and technical reports. For example, the judgment unit improves the accuracy of the judgment by referring to the latest research on heavy rain prediction. The judgment unit can also make a judgment by referring to related literature on wind speed prediction. For example, the judgment unit improves the accuracy of the judgment by referring to related literature on wind speed prediction. Furthermore, the judgment unit can also make a judgment by referring to related literature on temperature prediction. For example, the judgment unit improves the accuracy of the judgment by referring to related literature on temperature prediction. As a result, the accuracy of the judgment is improved by referring to the related literature. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the judgment.

[0092] The execution unit can estimate the user's emotion and adjust the execution method based on the estimated user's emotion. For example, when the user feels anxious, the execution unit quickly piles up sandbags. The user's emotion is estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the execution unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. An emotion score is calculated based on the change in the facial expression, and the execution method is adjusted. The execution unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, an emotion score is calculated, and the execution method is adjusted. Furthermore, the execution unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. An emotion score is calculated based on the fluctuation of the heart rate, and the execution method is adjusted. This makes it possible to respond more appropriately by adjusting the execution method according to the user's emotion. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the execution method.

[0093] The execution unit can optimize the execution algorithm by referring to past execution data at the time of execution. The execution unit optimizes the execution algorithm based on, for example, past heavy rain countermeasure data. The past execution data is acquired from the Japan Meteorological Agency or a weather observation station. For example, the execution unit optimizes the execution algorithm by analyzing the past heavy rain countermeasure data. The execution unit can also optimize the execution algorithm based on past wind speed countermeasure data. For example, the execution unit optimizes the execution algorithm by analyzing the past wind speed countermeasure data. The execution unit can also optimize the execution algorithm based on past temperature countermeasure data. For example, the execution unit optimizes the execution algorithm by analyzing the past temperature countermeasure data. As a result, the accuracy of the execution algorithm is improved by referring to the past execution data. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the past execution data to the generation AI and cause the generation AI to execute optimization of the execution algorithm.

[0094] The execution unit can improve the accuracy of execution by considering the interrelationship of weather data at the time of execution. The execution unit performs execution by considering, for example, the interrelationship of heavy rain data and wind speed data. The interrelationship of weather data is determined based on the correlation and causal relationship of data. For example, the execution unit performs execution by considering the interrelationship of heavy rain data and wind speed data, thereby improving the accuracy of prediction. The execution unit can also perform execution by considering the interrelationship of temperature data and humidity data. For example, the execution unit performs execution by considering the interrelationship of temperature data and humidity data, thereby performing efficient data processing. Furthermore, the execution unit can also perform execution based on data related to specific weather conditions. For example, the execution unit performs execution based on data related to specific weather conditions (e.g., high temperature and humidity), thereby improving the accuracy of prediction. As a result, the accuracy of execution is improved by considering the interrelationship of weather data. Part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input the interrelationship of weather data to the generation AI, and cause the generation AI to execute the improvement of the accuracy of execution.

[0095] The execution unit can estimate the user's emotions and determine the priority of execution based on the estimated user's emotions. For example, when the user feels anxious, the execution unit executes important measures with priority. The user's emotions are estimated using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, the execution unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The emotion score is calculated based on the change in the facial expression, and the execution priority is determined. The execution unit can also record the user's voice and estimate the emotion using a voice analysis technology. The tone and speed of the voice are analyzed, an emotion score is calculated, and the execution priority is determined. Furthermore, the execution unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. The emotion score is calculated based on the fluctuation of the heart rate, and the execution priority is determined. In this way, important measures can be executed with priority by determining the execution priority according to the user's emotions. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit may input the user's emotion data to the generation AI and have the generation AI determine the priority of execution.

[0096] The execution unit can perform execution while taking into consideration the geographical distribution. For example, when a heavy rain forecast is high in a specific area, the execution unit prioritizes the execution of measures for that area. The geographical distribution is determined based on the characteristics and population density of the area. For example, the execution unit prioritizes the execution of meteorological data for a specific area to improve the accuracy of the heavy rain forecast. The execution unit can also prioritize the execution of highly relevant measures based on the geographical distribution. For example, the execution unit improves the accuracy of the execution by taking into consideration the geographical distribution. As a result, appropriate execution according to the area is possible by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the geographical distribution to the generation AI and cause the generation AI to execute the improvement of the accuracy of the execution.

[0097] The execution unit can improve the accuracy of execution by referring to related literature at the time of execution. The execution unit performs execution by referring to, for example, the latest research on heavy rain countermeasures. The related literature is acquired based on academic papers and technical reports. For example, the execution unit improves the accuracy of execution by referring to the latest research on heavy rain countermeasures. The execution unit can also perform execution by referring to related literature on wind speed countermeasures. For example, the execution unit improves the accuracy of execution by referring to related literature on wind speed countermeasures. Furthermore, the execution unit can also perform execution by referring to related literature on temperature countermeasures. For example, the execution unit improves the accuracy of execution by referring to related literature on temperature countermeasures. As a result, the accuracy of execution is improved by referring to the related literature. A part or all of the above-mentioned processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit can input related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of execution.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The collection unit can monitor the growth status of a specific plant when collecting weather data. For example, the collection unit can capture the color and growth rate of the plant's leaves with a camera and evaluate the growth status using image analysis technology. The collection unit can also measure the soil moisture and nutrient levels with a sensor to grasp the health status of the plant. Furthermore, the collection unit monitors meteorological conditions that affect the growth of the plant (e.g., sunshine hours and wind speed) in real time and collects data. This allows for integrated management of meteorological data and the growth status of the plant, enabling more accurate weather forecasting and crop protection. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input plant growth data to the generation AI and have the generation AI select the optimal collection method.

[0100] The analysis unit can take into account the history of pesticide use when analyzing weather data. For example, the analysis unit can evaluate the effectiveness of pesticides under specific weather conditions based on past pesticide use data. The analysis unit can also analyze the frequency and amount of pesticide use to clarify correlations with weather conditions. Furthermore, the analysis unit can propose an optimal pesticide use plan according to future weather conditions based on the history of pesticide use. This enables more effective crop protection by integrating the analysis of weather data and pesticide use data. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input pesticide use data to the generation AI and cause the generation AI to select the optimal analysis method.

[0101] The judgment unit can optimize the harvest time of the crop based on the analysis result of the weather data. For example, the judgment unit performs an integrated analysis of the weather data and the crop growth data to propose an optimal harvest time. The judgment unit can also evaluate the relationship between weather conditions and the harvest amount based on past harvest data and adjust the harvest time. Furthermore, the judgment unit can optimize the harvest work schedule based on the weather data. This makes it possible to improve the efficiency of the harvest by managing the weather data and the crop growth data in an integrated manner. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the harvest data to the generation AI and cause the generation AI to execute a proposal for an optimal harvest time.

[0102] The execution unit can automatically control the irrigation system based on weather data. For example, the execution unit can adjust the timing and amount of irrigation based on precipitation data. The execution unit can also monitor soil moisture data in real time and irrigate as necessary. Furthermore, the execution unit can predict future precipitation based on weather forecast data and optimize irrigation plans. This enables efficient use of water resources by managing the weather data and the irrigation system in an integrated manner. Some or all of the above-mentioned processing in the execution unit can be performed using, for example, AI, or can be performed without using AI. For example, the execution unit can input irrigation data to a generation AI and have the generation AI execute a proposal for an optimal irrigation plan.

[0103] The collection unit can monitor the occurrence of pests in crops when collecting weather data. For example, the collection unit can take pictures of the leaves and stems of crops with a camera and detect the occurrence of pests using image analysis technology. The collection unit can also monitor the state of the soil and weather conditions to evaluate the risk of pests occurring. Furthermore, the collection unit can predict the risk of pests occurring under specific weather conditions based on past pest occurrence data. This makes it possible to take early measures against pests by managing weather data and pest occurrence data in an integrated manner. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input pest occurrence data to the generation AI and cause the generation AI to select the optimal monitoring method.

[0104] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis result based on the estimated user's emotions. For example, when the user feels anxious, the analysis unit can notify the user of a concise summary of the analysis result. In addition, when the user feels relieved, the analysis unit can also provide a detailed analysis result. Furthermore, the analysis unit can adjust the timing of the notification according to the user's emotions. This makes it possible to notify the user of an appropriate analysis result according to the user's emotions. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the notification method.

[0105] The judgment unit can estimate the user's emotions and determine the priority of judgments based on the estimated user's emotions. For example, when the user feels anxious, the judgment unit prioritizes important judgments. Also, when the user feels relieved, the judgment unit can make normal judgments. Furthermore, the judgment unit can adjust the judgment criteria according to the user's emotions. This enables appropriate judgments according to the user's emotions. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of judgments.

[0106] The execution unit can estimate the user's emotions and adjust the speed of execution based on the estimated user's emotions. For example, the execution unit responds quickly when the user feels anxious. Also, the execution unit can respond at a normal speed when the user feels relieved. Furthermore, the execution unit can adjust the method of execution according to the user's emotions. This enables appropriate responses according to the user's emotions. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the speed of execution.

[0107] The collection unit can estimate the user's emotions and adjust the type of weather data to be collected based on the estimated user's emotions. For example, if the user feels anxious, the collection unit preferentially collects data on heavy rain and storms. Also, if the user feels relieved, the collection unit can collect normal weather data. Furthermore, the collection unit can adjust the frequency of data to be collected according to the user's emotions. This makes it possible to collect appropriate weather data according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the type of data to be collected.

[0108] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit can provide a detailed analysis result when the user feels anxious. The analysis unit can also provide a simplified analysis result when the user feels relieved. Furthermore, the analysis unit can adjust the frequency of analysis according to the user's emotions. This makes it possible to provide an appropriate analysis result according to the user's emotions. A part or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or can be performed without using AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects weather data. The weather data includes temperature, humidity, precipitation, wind speed, etc. The collection unit collects weather information using weather sensors and satellite data. The weather sensors include temperature sensors, humidity sensors, precipitation sensors, wind speed sensors, etc. The satellite data is collected based on the type of satellite and the frequency of data acquisition. Step 2: The analysis unit analyzes the data collected by the collection unit and predicts torrential rains and local climate changes. The analysis is performed based on the algorithm used and the accuracy of the analysis. The analysis unit predicts future weather changes based on past weather data and current weather conditions. The prediction is performed based on the prediction model and the accuracy of the prediction. Step 3: The judgment unit determines the specific response based on the prediction results obtained by the analysis unit. Specific responses include evacuation orders and protection of facilities. The judgment unit can also use AI to make decisions. Step 4: The execution unit executes the response determined by the judgment unit. The execution unit is equipped with a system for automatically stacking sandbags. It includes a robotic arm that stacks the sandbags and an automatic stacking device. Some or all of the processing in the execution unit may be performed using AI. For example, the execution unit uses AI to control the operation of the robotic arm that stacks the sandbags and executes the optimal stacking procedure.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, judgment unit, and execution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects weather information using a weather sensor of the smart device 14 and satellite data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict torrential rain and local climate changes. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a specific response based on the analysis result. The execution unit is realized, for example, by the control unit 46A of the smart device 14, and controls a system that automatically piles up sandbags. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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, the RAM 30, and the 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, judgment unit, and execution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects weather information using a weather sensor of the smart glasses 214 or satellite data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict torrential rain and local climate changes. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a specific response based on the analysis result. The execution unit is realized, for example, by the control unit 46A of the smart glasses 214, and controls a system that automatically piles up sandbags. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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, the RAM 30, and the 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, judgment unit, and execution unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects weather information using a weather sensor of the headset type terminal 314 and satellite data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict torrential rain and local climate changes. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines a specific response based on the analysis result. The execution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and controls a system that automatically piles up sandbags. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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, the RAM 30, and the 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.

[0150] 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.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] 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.

[0157] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, judgment unit, and execution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects weather information using a weather sensor of the robot 414 or satellite data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to predict torrential rains and local climate changes. The judgment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and judges a specific response based on the analysis result. The execution unit is realized, for example, by the control unit 46A of the robot 414, and controls a system that automatically piles up sandbags. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects meteorological data; An analysis unit that analyzes the data collected by the collection unit and predicts torrential rain or local climate change; a determination unit that determines a specific response based on the prediction result obtained by the analysis unit; An execution unit that executes the response determined by the determination unit. A system characterized by: (Appendix 2) The execution unit is Equipped with an automatic sandbag stacking system 2. The system of claim 1, (Appendix 3) The execution unit is Equipped with a system that automatically controls the drainage system 2. The system of claim 1, (Appendix 4) The execution unit is Equipped with a system that automatically deploys crop protection sheets 2. The system of claim 1, (Appendix 5) The collecting unit includes: Collect weather information using weather sensors or satellite data 2. The system of claim 1, (Appendix 6) The analysis unit is Predict future weather changes based on past weather data or current weather conditions 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting weather data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past weather data and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filtering weather data collection based on specific regions and conditions 2. The system of claim 1, (Appendix 10) The collecting unit includes: To estimate a user's emotion and determine a priority of weather data to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting weather data, take geographic location information into account to prioritize collection of relevant data 2. The system of claim 1, (Appendix 12) The collecting unit includes: Analyze social media activity and collect relevant data when collecting weather data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the weather data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of weather data. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the weather data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of weather data 2. The system of claim 1, (Appendix 19) The determination unit is Infer the user's emotions and adjust the decision criteria based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The determination unit is When making a decision, the decision algorithm is optimized by referring to past decision data. 2. The system of claim 1, (Appendix 21) The determination unit is When making decisions, the accuracy of the decisions will be improved by taking into account the correlation of weather data. 2. The system of claim 1, (Appendix 22) The determination unit is Inferring user emotions and adjusting the order in which judgment results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The determination unit is When making a decision, consider geographical distribution. 2. The system of claim 1, (Appendix 24) The determination unit is When making a decision, refer to related literature to improve the accuracy of the decision. 2. The system of claim 1, (Appendix 25) The execution unit is Inferring a user's emotions and adjusting the execution method based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The execution unit is At run time, the execution algorithm is optimized by referencing past execution data. 2. The system of claim 1, (Appendix 27) The execution unit is At run time, weather data correlations are taken into account to improve execution accuracy 2. The system of claim 1, (Appendix 28) The execution unit is Inferring user emotions and determining execution priorities based on the inferred user emotions 2. The system of claim 1, (Appendix 29) The execution unit is At run time, the execution takes into account geographic distribution 2. The system of claim 1, (Appendix 30) The execution unit is At run time, the accuracy of the execution is improved by consulting related literature. 2. The system of claim 1, [Explanation of symbols]

[0183] 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 meteorological data; An analysis unit that analyzes the data collected by the collection unit and predicts torrential rain or local climate change; A determination unit that determines a specific response based on the prediction result obtained by the analysis unit; an execution unit that executes the response determined by the determination unit; A system characterized by:

2. The execution unit is Equipped with an automatic sandbag stacking system 2. The system of claim 1 .

3. The execution unit is Equipped with a system that automatically controls the drainage system 2. The system of claim 1 .

4. The execution unit is Equipped with a system that automatically deploys crop protection sheets 2. The system of claim 1 .

5. The collecting unit includes: Collect weather information using weather sensors or satellite data 2. The system of claim 1 .

6. The analysis unit is Predict future weather changes based on past weather data or current weather conditions 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting weather data based on the estimated user's emotion.

2. The system of claim 1 .

8. The collecting unit includes: Analyze past weather data and select the optimal collection method 2. The system of claim 1 .

9. The collecting unit includes: Filtering weather data collection based on specific regions and conditions 2. The system of claim 1 .

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