system

Abstract

The system according to this embodiment aims to optimally manage the water level of a dam and predict future water level fluctuations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an operation unit, and a prediction unit. The collection unit collects water level data. The analysis unit analyzes the data collected by the collection unit. The operation unit performs operations based on the analysis results obtained by the analysis unit. The prediction unit predicts future water level fluctuations.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the water level management and prediction of dams have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to optimally manage the water level of a dam and predict future water level fluctuations.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an operation unit, and a prediction unit. The collection unit collects water level data. The analysis unit analyzes the data collected by the collection unit. The operation unit performs an operation based on the analysis result obtained by the analysis unit. The prediction unit predicts future water level fluctuations.

Effects of the Invention

[0007] The system according to this embodiment can optimally manage the water level of a dam and predict future water level fluctuations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The multifunctional dam system according to an embodiment of the present invention is a system for enhancing and adding hydroelectric power generation capabilities to various types of dams, such as flood control dams, power generation dams, water supply dams, industrial water dams, and agricultural water dams. This multifunctional dam system uses AI to perform optimal water level management and prediction, thereby realizing the multifunctionality of the dam. This makes it possible to achieve multiple purposes with a single dam and to make effective use of the dam. For example, water level data from each dam is collected and analyzed by AI. In flood control dams, rainfall and inflow data are collected and analyzed by AI to enable optimal water level management. This makes it possible to maximize power generation efficiency while reducing the risk of flooding. Next, based on the data analyzed by the AI, the system performs optimal operations to enhance the hydroelectric power generation function. In power generation dams, the AI ​​analyzes water level data and proposes operations to maintain the optimal water level for power generation. This makes it possible to improve power generation efficiency. Furthermore, the AI ​​predicts future water level fluctuations and optimizes the dam's operation plan. In water supply dams, the AI ​​predicts future rainfall and demand and performs optimal water level management to realize a stable water supply. This mechanism allows various types of dams to be transformed into multi-functional, multi-purpose dams. In agricultural irrigation dams, AI can analyze water level data to optimize both the supply of agricultural water and power generation. This leads to more effective use of dams, making it possible to achieve multiple purposes with a single dam. Thus, the multi-functional dam system realizes the multi-functionality of dams, enabling a single dam to achieve multiple purposes.

[0029] The multifunctional dam system according to this embodiment comprises a data collection unit, an analysis unit, an operation unit, and a prediction unit. The data collection unit collects water level data from various dams. The data collection unit can collect water level data from, for example, flood control dams, power generation dams, water supply dams, industrial water dams, agricultural water dams, etc. The data collection unit collects water level data using, for example, sensors. The data collection unit can also set the frequency and range of data collection. For example, the data collection unit can collect water level data at a fixed time every day. The analysis unit analyzes the collected data using AI. The analysis unit can analyze the data using, for example, machine learning or deep learning. Based on the collected data, the analysis unit can analyze fluctuations in the water level of the dam. For example, the analysis unit can predict future water level fluctuations based on past data. The operation unit performs operations to enhance the hydroelectric power generation function based on the analysis results. The operation unit can perform, for example, water level adjustment and turbine control. Based on the analysis results, the operation unit can propose operations to maintain the optimal water level for power generation. The forecasting unit predicts future rainfall and demand. For example, the forecasting unit can predict future rainfall based on meteorological data and past rainfall data. The forecasting unit can predict future water level fluctuations and optimize the dam's operation plan. As a result, the multi-functional dam system according to this embodiment realizes multi-functionality of the dam and can achieve multiple purposes with a single dam.

[0030] The data collection unit can collect water level data from various types of dams. For example, it can collect water level data from flood control dams, power generation dams, water supply dams, industrial water dams, agricultural water dams, and so on. The data collection unit collects water level data using sensors, for example. For example, the data collection unit can install water level sensors to measure the water level of a dam and collect data in real time. The data collection unit can also set the frequency and range of data collection. For example, the data collection unit can collect water level data at a fixed time every day. Furthermore, the data collection unit can transmit the collected data to the cloud and use it for analysis by the analysis unit. In this way, by collecting water level data from various types of dams, it is possible to support the multi-functionalization of dams.

[0031] The analysis unit can analyze the collected data using AI. The analysis unit can analyze the data using, for example, machine learning or deep learning. For example, the analysis unit can analyze the water level fluctuations of a dam based on the collected water level data. The analysis unit can also predict future water level fluctuations based on past data. For example, the analysis unit can predict future rainfall amounts and analyze water level fluctuations based on past rainfall data. Furthermore, the analysis unit can optimize the dam's operation plan based on the analysis results. This allows for highly accurate analysis results to be obtained through AI-based data analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0032] The control unit can perform operations to enhance the hydroelectric power generation function based on the analysis results. For example, the control unit can adjust the water level and control the turbine. For example, based on the analysis results, the control unit can adjust the water level of the dam to maximize power generation efficiency. The control unit can also control the rotation speed of the turbine to optimize power generation. Furthermore, based on the analysis results, the control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the discharge rate of the dam to maintain the optimal water level for power generation. In this way, the hydroelectric power generation function can be enhanced through operations based on the analysis results. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the analysis results into a generating AI and have the generating AI execute the optimal operations for power generation.

[0033] The forecasting unit can predict future rainfall and demand. For example, the forecasting unit can predict future rainfall based on meteorological data and historical rainfall data. For example, the forecasting unit can collect meteorological data and build a model for predicting future rainfall. The forecasting unit can also predict future rainfall based on historical rainfall data. Furthermore, the forecasting unit can predict future water level fluctuations and optimize dam operation plans. For example, the forecasting unit can predict future rainfall and demand and optimize dam water level management. This allows for the optimization of dam operation plans by predicting future rainfall and demand. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input meteorological data into a generating AI and have the generating AI perform predictions of future rainfall.

[0034] The control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the dam's discharge rate to maintain the optimal water level for power generation. For example, based on the analysis results, the control unit can adjust the dam's discharge rate to maximize power generation efficiency. The control unit can also control the turbine's rotation speed to optimize power generation. Furthermore, the control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the dam's water level to maintain the optimal water level for power generation. By maintaining the optimal water level for power generation, power generation efficiency can be improved. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the analysis results into a generating AI and have the generating AI execute the optimal operations for power generation.

[0035] The data collection unit can analyze past water level data for each dam and select the optimal collection method. For example, the data collection unit can increase the collection frequency during periods of heavy rainfall based on past data. It can also decrease the collection frequency during the dry season based on past data. Furthermore, it can concentrate collection during specific time periods based on past data. In this way, the optimal collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past water level data into a generating AI and have the generating AI select the optimal collection method.

[0036] The data collection unit can filter water level data based on the type and purpose of the dam. For example, the data collection unit can prioritize the collection of rainfall data for flood control dams. It can also prioritize the collection of water level fluctuation data for power generation dams. Furthermore, it can prioritize the collection of seasonal water demand data for agricultural water dams. This enables efficient data management through data collection tailored to the type and purpose of the dam. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input criteria for filtering based on the type and purpose of the dam into a generating AI and leave the execution of the filtering to the generating AI.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of dams when collecting water level data. For example, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location of dams when collecting water level data. For example, the data collection unit can prioritize the collection of data from dams that are geographically close. Furthermore, the data collection unit can prioritize the collection of data from dams in the same watershed. In addition, the data collection unit can prioritize the collection of data from dams in areas with similar weather conditions. This allows for the efficient collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of dams into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0038] The data collection unit can simultaneously collect environmental data around the dam when collecting water level data. For example, the data collection unit can simultaneously collect environmental data around the dam (e.g., temperature and humidity) when collecting water level data. For example, the data collection unit can collect temperature data along with water level data and use it for analysis. The data collection unit can also collect humidity data along with water level data and use it for analysis. Furthermore, the data collection unit can collect wind speed data along with water level data and use it for analysis. By simultaneously collecting environmental data, the accuracy of the analysis can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input environmental data around the dam into a generating AI and have the generating AI perform the collection of environmental data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the water level data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a normal analysis on general data. Furthermore, it can perform a simplified analysis on less important data. By adjusting the level of detail of the analysis according to the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the water level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the type and purpose of the dam during the analysis. For example, the analysis unit can apply a rainfall analysis algorithm to flood control dams. It can also apply a water level fluctuation analysis algorithm to power generation dams. Furthermore, it can apply a seasonal water demand analysis algorithm to agricultural water dams. By applying analysis algorithms appropriate to the type and purpose of the dam, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input analysis algorithms appropriate to the type and purpose of the dam into a generating AI and leave the execution of the analysis to the generating AI.

[0041] The analysis unit can determine the priority of analysis based on the dam's operational history during the analysis. For example, the analysis unit can prioritize the analysis of important data from past operational history. It can also prioritize the analysis of data where problems frequently occurred from past operational history. Furthermore, it can prioritize the analysis of data related to a specific season from past operational history. In this way, by determining the priority of analysis based on operational history, important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dam's operational history into a generating AI and have the generating AI perform the determination of the analysis priority.

[0042] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and past research data on dams during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of the analysis results by referring to past research data. Furthermore, the analysis unit can improve the accuracy of the analysis by combining relevant literature and past research data. In this way, the accuracy of the analysis can be improved by referring to relevant literature and past research data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and past research data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0043] The control unit can adjust the level of detail of operations based on the importance of the analysis results during operation. For example, the control unit can perform detailed operations on important analysis results. It can also perform normal operations on general analysis results. Furthermore, it can perform simplified operations on analysis results of low importance. By adjusting the level of detail of operations according to the importance of the analysis results, efficient operation becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of operations.

[0044] The control unit can apply different operation algorithms depending on the type and purpose of the dam during operation. For example, the control unit can apply an operation algorithm based on rainfall to flood control dams. It can also apply an operation algorithm based on water level fluctuations to power generation dams. Furthermore, it can apply an operation algorithm based on seasonal water demand to agricultural water dams. This allows for improved accuracy of operation by applying an operation algorithm appropriate to the type and purpose of the dam. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input an operation algorithm appropriate to the type and purpose of the dam into a generating AI and entrust the execution of the operation to the generating AI.

[0045] The control unit can select the optimal operating method during operation, taking into account the geographical location information of the dam. For example, the control unit can refer to the operating methods of geographically close dams. It can also refer to the operating methods of dams in the same river basin. Furthermore, it can refer to the operating methods of dams in areas with similar weather conditions. In this way, the optimal operating method can be selected by taking geographical location information into account. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input the geographical location information of the dam into a generating AI and have the generating AI select the optimal operating method.

[0046] The control unit can improve the accuracy of its operations by referring to relevant literature and past research data on dams during operation. For example, the control unit can improve the accuracy of its operations by referring to relevant literature and past research data on dams during operation. For example, the control unit can optimize the operation algorithm by referring to relevant literature. The control unit can also improve the accuracy of its operations by referring to past research data. Furthermore, the control unit can improve the accuracy of its operations by combining relevant literature and past research data. In this way, the accuracy of operations can be improved by referring to relevant literature and past research data. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input relevant literature and past research data into a generating AI and have the generating AI perform the operation accuracy improvement.

[0047] The forecasting unit can optimize its forecasting algorithm by referring to past rainfall and demand data during forecasting. For example, the forecasting unit can optimize its forecasting algorithm based on past rainfall data. The forecasting unit can also optimize its forecasting algorithm based on past demand data. Furthermore, the forecasting unit can optimize its forecasting algorithm by combining past rainfall and demand data. In this way, the forecasting algorithm can be optimized by referring to past data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input past rainfall and demand data into a generating AI and have the generating AI perform the optimization of the forecasting algorithm.

[0048] The prediction unit can apply different prediction methods depending on the type and purpose of the dam during the prediction process. For example, the prediction unit can apply a rainfall prediction method to flood control dams. It can also apply a water level fluctuation prediction method to power generation dams. Furthermore, it can apply a seasonal water demand prediction method to agricultural water dams. By applying a prediction method appropriate to the type and purpose of the dam, the accuracy of the prediction can be improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input a prediction method appropriate to the type and purpose of the dam into a generating AI and leave the execution of the prediction to the generating AI.

[0049] The prediction unit can improve the accuracy of its predictions by considering the geographical location information of the dams during the prediction process. For example, the prediction unit can improve its prediction accuracy by referring to data from geographically close dams. It can also improve its prediction accuracy by referring to data from dams in the same river basin. Furthermore, it can improve its prediction accuracy by referring to data from dams in areas with similar weather conditions. In this way, the accuracy of predictions can be improved by considering geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical location information of the dams into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0050] The prediction unit can improve the accuracy of its predictions by referring to relevant market data and environmental data related to dams during the prediction process. For example, the prediction unit can optimize its prediction algorithm by referring to relevant market data. It can also optimize its prediction algorithm by referring to environmental data. Furthermore, the prediction unit can improve the accuracy of its predictions by combining relevant market data and environmental data. In this way, the accuracy of predictions can be improved by referring to relevant market data and environmental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant market data and environmental data into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] A multi-functional dam system can be equipped with an energy optimization unit to further improve energy efficiency. Based on data obtained from the data collection unit, the energy optimization unit can propose the optimal operating method to minimize the dam's energy consumption. For example, in a power generation dam, the energy optimization unit can optimize the turbine rotation speed to reduce energy consumption. In a water supply dam, the energy optimization unit can optimize the pump operating schedule to improve energy efficiency. Furthermore, in an agricultural water dam, the energy optimization unit can optimize the operation of the irrigation system to reduce energy consumption. This improves the energy efficiency of the dam and enables sustainable operation.

[0053] The multi-functional dam system can also be equipped with an environmental monitoring unit. This unit can monitor the ecosystem and water quality around the dam and collect data for environmental protection. For example, the environmental monitoring unit can use water quality sensors to collect water quality data from the dam in real time, which can then be used for analysis in the analysis unit. Furthermore, the environmental monitoring unit can collect ecosystem data around the dam and monitor changes in the ecosystem. In addition, the environmental monitoring unit can collect meteorological data and evaluate the environmental impact of dam operations. This allows for minimizing the environmental impact of dam operations and achieving sustainable operation.

[0054] A multi-functional dam system can also be equipped with a user feedback unit. This unit can collect user feedback and incorporate it into the system's operation. For example, it can provide an interface where users can input opinions and requests regarding dam operation. Furthermore, it can analyze the collected feedback to identify areas for system improvement. Additionally, it can adjust the system's operation methods based on the feedback, thereby improving user satisfaction. This enables operation that reflects user opinions, improving system reliability and user satisfaction.

[0055] A multi-functional dam system can also be equipped with an emergency response unit. This unit can provide functions for rapid response in the event of an emergency. For example, in the event of a natural disaster such as a flood or earthquake, the emergency response unit can automatically adjust dam operations to minimize damage. Furthermore, the emergency response unit can quickly notify relevant parties and encourage appropriate responses. In addition, the emergency response unit can monitor dam operation data in real time during an emergency and provide information to enable appropriate responses. This allows for a rapid response to emergencies and minimizes damage.

[0056] The multi-functional dam system can further improve the accuracy of its predictions by referencing relevant market and environmental data related to the dam during the prediction process. For example, the prediction unit can optimize its prediction algorithm by referencing relevant market data. It can also optimize its prediction algorithm by referencing environmental data. Furthermore, the prediction unit can improve the accuracy of its predictions by combining relevant market and environmental data. Thus, by referencing relevant market and environmental data, the accuracy of predictions can be improved.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The collection unit collects water level data from various dams. The collection unit can collect water level data from, for example, flood control dams, power generation dams, water supply dams, industrial water dams, and agricultural water dams. The collection unit collects water level data using sensors and can set the frequency and range of data collection. For example, the collection unit can collect water level data at a fixed time every day. Step 2: The analysis unit analyzes the collected data using AI. The analysis unit uses machine learning and deep learning to analyze the data and can analyze the water level fluctuations of the dam based on the collected data. For example, the analysis unit can predict future water level fluctuations based on past data. Step 3: The control unit performs operations to enhance the hydroelectric power generation function based on the analysis results. The control unit can adjust the water level and control the turbine, and can propose operations to maintain the optimal water level for power generation. Step 4: The forecasting unit predicts future rainfall and demand. Based on meteorological data and historical rainfall data, the forecasting unit can predict future rainfall and future water level fluctuations to optimize the dam's operational plan.

[0059] (Example of form 2) The multifunctional dam system according to an embodiment of the present invention is a system for enhancing and adding hydroelectric power generation capabilities to various types of dams, such as flood control dams, power generation dams, water supply dams, industrial water dams, and agricultural water dams. This multifunctional dam system uses AI to perform optimal water level management and prediction, thereby realizing the multifunctionality of the dam. This makes it possible to achieve multiple purposes with a single dam and to make effective use of the dam. For example, water level data from each dam is collected and analyzed by AI. In flood control dams, rainfall and inflow data are collected and analyzed by AI to enable optimal water level management. This makes it possible to maximize power generation efficiency while reducing the risk of flooding. Next, based on the data analyzed by the AI, the system performs optimal operations to enhance the hydroelectric power generation function. In power generation dams, the AI ​​analyzes water level data and proposes operations to maintain the optimal water level for power generation. This makes it possible to improve power generation efficiency. Furthermore, the AI ​​predicts future water level fluctuations and optimizes the dam's operation plan. In water supply dams, the AI ​​predicts future rainfall and demand and performs optimal water level management to realize a stable water supply. This mechanism allows various types of dams to be transformed into multi-functional, multi-purpose dams. In agricultural irrigation dams, AI can analyze water level data to optimize both the supply of agricultural water and power generation. This leads to more effective use of dams, making it possible to achieve multiple purposes with a single dam. Thus, the multi-functional dam system realizes the multi-functionality of dams, enabling a single dam to achieve multiple purposes.

[0060] The multifunctional dam system according to this embodiment comprises a data collection unit, an analysis unit, an operation unit, and a prediction unit. The data collection unit collects water level data from various dams. The data collection unit can collect water level data from, for example, flood control dams, power generation dams, water supply dams, industrial water dams, agricultural water dams, etc. The data collection unit collects water level data using, for example, sensors. The data collection unit can also set the frequency and range of data collection. For example, the data collection unit can collect water level data at a fixed time every day. The analysis unit analyzes the collected data using AI. The analysis unit can analyze the data using, for example, machine learning or deep learning. Based on the collected data, the analysis unit can analyze fluctuations in the water level of the dam. For example, the analysis unit can predict future water level fluctuations based on past data. The operation unit performs operations to enhance the hydroelectric power generation function based on the analysis results. The operation unit can perform, for example, water level adjustment and turbine control. Based on the analysis results, the operation unit can propose operations to maintain the optimal water level for power generation. The forecasting unit predicts future rainfall and demand. For example, the forecasting unit can predict future rainfall based on meteorological data and past rainfall data. The forecasting unit can predict future water level fluctuations and optimize the dam's operation plan. As a result, the multi-functional dam system according to this embodiment realizes multi-functionality of the dam and can achieve multiple purposes with a single dam.

[0061] The data collection unit can collect water level data from various types of dams. For example, it can collect water level data from flood control dams, power generation dams, water supply dams, industrial water dams, agricultural water dams, and so on. The data collection unit collects water level data using sensors, for example. For example, the data collection unit can install water level sensors to measure the water level of a dam and collect data in real time. The data collection unit can also set the frequency and range of data collection. For example, the data collection unit can collect water level data at a fixed time every day. Furthermore, the data collection unit can transmit the collected data to the cloud and use it for analysis by the analysis unit. In this way, by collecting water level data from various types of dams, it is possible to support the multi-functionalization of dams.

[0062] The analysis unit can analyze the collected data using AI. The analysis unit can analyze the data using, for example, machine learning or deep learning. For example, the analysis unit can analyze the water level fluctuations of a dam based on the collected water level data. The analysis unit can also predict future water level fluctuations based on past data. For example, the analysis unit can predict future rainfall amounts and analyze water level fluctuations based on past rainfall data. Furthermore, the analysis unit can optimize the dam's operation plan based on the analysis results. This allows for highly accurate analysis results to be obtained through AI-based data analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0063] The control unit can perform operations to enhance the hydroelectric power generation function based on the analysis results. For example, the control unit can adjust the water level and control the turbine. For example, based on the analysis results, the control unit can adjust the water level of the dam to maximize power generation efficiency. The control unit can also control the rotation speed of the turbine to optimize power generation. Furthermore, based on the analysis results, the control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the discharge rate of the dam to maintain the optimal water level for power generation. In this way, the hydroelectric power generation function can be enhanced through operations based on the analysis results. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the analysis results into a generating AI and have the generating AI execute the optimal operations for power generation.

[0064] The forecasting unit can predict future rainfall and demand. For example, the forecasting unit can predict future rainfall based on meteorological data and historical rainfall data. For example, the forecasting unit can collect meteorological data and build a model for predicting future rainfall. The forecasting unit can also predict future rainfall based on historical rainfall data. Furthermore, the forecasting unit can predict future water level fluctuations and optimize dam operation plans. For example, the forecasting unit can predict future rainfall and demand and optimize dam water level management. This allows for the optimization of dam operation plans by predicting future rainfall and demand. Some or all of the above-described processes in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input meteorological data into a generating AI and have the generating AI perform predictions of future rainfall.

[0065] The control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the dam's discharge rate to maintain the optimal water level for power generation. For example, based on the analysis results, the control unit can adjust the dam's discharge rate to maximize power generation efficiency. The control unit can also control the turbine's rotation speed to optimize power generation. Furthermore, the control unit can propose operations to maintain the optimal water level for power generation. For example, the control unit can adjust the dam's water level to maintain the optimal water level for power generation. By maintaining the optimal water level for power generation, power generation efficiency can be improved. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the analysis results into a generating AI and have the generating AI execute the optimal operations for power generation.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of water level data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can increase the collection frequency to provide reassurance. If the user is relaxed, the data collection unit can set the collection frequency to normal. Furthermore, if the user is in a hurry, the data collection unit can shorten the collection frequency to provide data quickly. In this way, by adjusting the collection timing according to the user's emotions, a sense of reassurance can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0067] The data collection unit can analyze past water level data for each dam and select the optimal collection method. For example, the data collection unit can increase the collection frequency during periods of heavy rainfall based on past data. It can also decrease the collection frequency during the dry season based on past data. Furthermore, it can concentrate collection during specific time periods based on past data. In this way, the optimal collection method can be selected by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past water level data into a generating AI and have the generating AI select the optimal collection method.

[0068] The data collection unit can filter water level data based on the type and purpose of the dam. For example, the data collection unit can prioritize the collection of rainfall data for flood control dams. It can also prioritize the collection of water level fluctuation data for power generation dams. Furthermore, it can prioritize the collection of seasonal water demand data for agricultural water dams. This enables efficient data management through data collection tailored to the type and purpose of the dam. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input criteria for filtering based on the type and purpose of the dam into a generating AI and leave the execution of the filtering to the generating AI.

[0069] The data collection unit can estimate the user's emotions and determine the priority of water level data to collect based on the estimated user emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting important data. If the user is relaxed, the data collection unit can collect data with normal priority. Furthermore, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows for the priority collection of important data by determining data priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of water level data to collect.

[0070] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location of dams when collecting water level data. For example, the data collection unit can prioritize the collection of highly relevant data by considering the geographical location of dams when collecting water level data. For example, the data collection unit can prioritize the collection of data from dams that are geographically close. Furthermore, the data collection unit can prioritize the collection of data from dams in the same watershed. In addition, the data collection unit can prioritize the collection of data from dams in areas with similar weather conditions. This allows for the efficient collection of highly relevant data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location of dams into a generating AI and have the generating AI perform the priority collection of highly relevant data.

[0071] The data collection unit can simultaneously collect environmental data around the dam when collecting water level data. For example, the data collection unit can simultaneously collect environmental data around the dam (e.g., temperature and humidity) when collecting water level data. For example, the data collection unit can collect temperature data along with water level data and use it for analysis. The data collection unit can also collect humidity data along with water level data and use it for analysis. Furthermore, the data collection unit can collect wind speed data along with water level data and use it for analysis. By simultaneously collecting environmental data, the accuracy of the analysis can be improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input environmental data around the dam into a generating AI and have the generating AI perform the collection of environmental data.

[0072] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the water level data during the analysis. For example, the analysis unit can perform a detailed analysis on important data. It can also perform a normal analysis on general data. Furthermore, it can perform a simplified analysis on less important data. By adjusting the level of detail of the analysis according to the importance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the water level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the type and purpose of the dam during the analysis. For example, the analysis unit can apply a rainfall analysis algorithm to flood control dams. It can also apply a water level fluctuation analysis algorithm to power generation dams. Furthermore, it can apply a seasonal water demand analysis algorithm to agricultural water dams. By applying analysis algorithms appropriate to the type and purpose of the dam, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input analysis algorithms appropriate to the type and purpose of the dam into a generating AI and leave the execution of the analysis to the generating AI.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can determine the priority of analysis based on the dam's operational history during the analysis. For example, the analysis unit can prioritize the analysis of important data from past operational history. It can also prioritize the analysis of data where problems frequently occurred from past operational history. Furthermore, it can prioritize the analysis of data related to a specific season from past operational history. In this way, by determining the priority of analysis based on operational history, important data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the dam's operational history into a generating AI and have the generating AI perform the determination of the analysis priority.

[0077] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and past research data on dams during the analysis. For example, the analysis unit can optimize the analysis algorithm by referring to relevant literature. The analysis unit can also improve the accuracy of the analysis results by referring to past research data. Furthermore, the analysis unit can improve the accuracy of the analysis by combining relevant literature and past research data. In this way, the accuracy of the analysis can be improved by referring to relevant literature and past research data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature and past research data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0078] The control unit can estimate the user's emotions and adjust the operation method based on the estimated emotions. For example, the control unit can provide a simple and highly visible operation method when the user is tense. It can also provide a more detailed operation method when the user is relaxed. Furthermore, it can provide a quick operation method when the user is in a hurry. By adjusting the operation method according to the user's emotions, a user-friendly operation method can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI or not. For example, the control unit can input user emotion data into the generative AI and have the generative AI adjust the operation method.

[0079] The control unit can adjust the level of detail of operations based on the importance of the analysis results during operation. For example, the control unit can perform detailed operations on important analysis results. It can also perform normal operations on general analysis results. Furthermore, it can perform simplified operations on analysis results of low importance. By adjusting the level of detail of operations according to the importance of the analysis results, efficient operation becomes possible. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of operations.

[0080] The control unit can apply different operation algorithms depending on the type and purpose of the dam during operation. For example, the control unit can apply an operation algorithm based on rainfall to flood control dams. It can also apply an operation algorithm based on water level fluctuations to power generation dams. Furthermore, it can apply an operation algorithm based on seasonal water demand to agricultural water dams. This allows for improved accuracy of operation by applying an operation algorithm appropriate to the type and purpose of the dam. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input an operation algorithm appropriate to the type and purpose of the dam into a generating AI and entrust the execution of the operation to the generating AI.

[0081] The control unit can estimate the user's emotions and determine the priority of operations based on the estimated emotions. For example, if the user is feeling anxious, the control unit can prioritize important operations. If the user is relaxed, the control unit can perform operations with normal priority. Furthermore, if the user is in a hurry, the control unit can prioritize operations that can be performed quickly. In this way, by determining the priority of operations according to the user's emotions, important operations can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine the priority of operations.

[0082] The control unit can select the optimal operating method during operation, taking into account the geographical location information of the dam. For example, the control unit can refer to the operating methods of geographically close dams. It can also refer to the operating methods of dams in the same river basin. Furthermore, it can refer to the operating methods of dams in areas with similar weather conditions. In this way, the optimal operating method can be selected by taking geographical location information into account. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input the geographical location information of the dam into a generating AI and have the generating AI select the optimal operating method.

[0083] The control unit can improve the accuracy of its operations by referring to relevant literature and past research data on dams during operation. For example, the control unit can improve the accuracy of its operations by referring to relevant literature and past research data on dams during operation. For example, the control unit can optimize the operation algorithm by referring to relevant literature. The control unit can also improve the accuracy of its operations by referring to past research data. Furthermore, the control unit can improve the accuracy of its operations by combining relevant literature and past research data. In this way, the accuracy of operations can be improved by referring to relevant literature and past research data. Some or all of the above processing in the control unit may be performed using AI, for example, or without using AI. For example, the control unit can input relevant literature and past research data into a generating AI and have the generating AI perform the operation accuracy improvement.

[0084] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-understand prediction method. If the user is relaxed, the prediction unit can provide a more detailed prediction method. Furthermore, if the user is in a hurry, the prediction unit can provide a concise prediction method. By adjusting the prediction method according to the user's emotions, the system can provide prediction results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the prediction method.

[0085] The forecasting unit can optimize its forecasting algorithm by referring to past rainfall and demand data during forecasting. For example, the forecasting unit can optimize its forecasting algorithm based on past rainfall data. The forecasting unit can also optimize its forecasting algorithm based on past demand data. Furthermore, the forecasting unit can optimize its forecasting algorithm by combining past rainfall and demand data. In this way, the forecasting algorithm can be optimized by referring to past data. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without using AI. For example, the forecasting unit can input past rainfall and demand data into a generating AI and have the generating AI perform the optimization of the forecasting algorithm.

[0086] The prediction unit can apply different prediction methods depending on the type and purpose of the dam during the prediction process. For example, the prediction unit can apply a rainfall prediction method to flood control dams. It can also apply a water level fluctuation prediction method to power generation dams. Furthermore, it can apply a seasonal water demand prediction method to agricultural water dams. By applying a prediction method appropriate to the type and purpose of the dam, the accuracy of the prediction can be improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input a prediction method appropriate to the type and purpose of the dam into a generating AI and leave the execution of the prediction to the generating AI.

[0087] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. If the user is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. By adjusting the display method of the prediction results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the prediction results.

[0088] The prediction unit can improve the accuracy of its predictions by considering the geographical location information of the dams during the prediction process. For example, the prediction unit can improve its prediction accuracy by referring to data from geographically close dams. It can also improve its prediction accuracy by referring to data from dams in the same river basin. Furthermore, it can improve its prediction accuracy by referring to data from dams in areas with similar weather conditions. In this way, the accuracy of predictions can be improved by considering geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the geographical location information of the dams into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0089] The prediction unit can improve the accuracy of its predictions by referring to relevant market data and environmental data related to dams during the prediction process. For example, the prediction unit can optimize its prediction algorithm by referring to relevant market data. It can also optimize its prediction algorithm by referring to environmental data. Furthermore, the prediction unit can improve the accuracy of its predictions by combining relevant market data and environmental data. In this way, the accuracy of predictions can be improved by referring to relevant market data and environmental data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input relevant market data and environmental data into a generating AI and have the generating AI perform the prediction accuracy improvement. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and prediction unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects water level data from various dams using the sensors of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The operation unit is implemented, for example, by the control unit 46A of the smart device 14, and performs operations to enhance the hydroelectric power generation function based on the analysis results. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts future rainfall and demand. === Hard Collateral 1-2 === Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and prediction unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects water level data for various dams using the sensors of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The operation unit is implemented, for example, in the control unit 46A of the smart glasses 214, and performs operations to enhance the hydroelectric power generation function based on the analysis results. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and predicts future rainfall and demand. === Hard Collateral 1-3 === Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and prediction unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit collects water level data for various dams using the sensors of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The operation unit is implemented in the control unit 46A of the headset terminal 314 and performs operations to enhance the hydroelectric power generation function based on the analysis results. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future rainfall and demand. === Hard Collateral 1-4 === Each of the multiple elements described above, including the data collection unit, analysis unit, operation unit, and prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects water level data for various dams using the sensors of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The operation unit is implemented, for example, by the control unit 46A of the robot 414, and performs operations to enhance the hydroelectric power generation function based on the analysis results. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts future rainfall and demand.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] A multi-functional dam system can be equipped with an energy optimization unit to further improve energy efficiency. Based on data obtained from the data collection unit, the energy optimization unit can propose the optimal operating method to minimize the dam's energy consumption. For example, in a power generation dam, the energy optimization unit can optimize the turbine rotation speed to reduce energy consumption. In a water supply dam, the energy optimization unit can optimize the pump operating schedule to improve energy efficiency. Furthermore, in an agricultural water dam, the energy optimization unit can optimize the operation of the irrigation system to reduce energy consumption. This improves the energy efficiency of the dam and enables sustainable operation.

[0092] The multi-functional dam system can also be equipped with an environmental monitoring unit. This unit can monitor the ecosystem and water quality around the dam and collect data for environmental protection. For example, the environmental monitoring unit can use water quality sensors to collect water quality data from the dam in real time, which can then be used for analysis in the analysis unit. Furthermore, the environmental monitoring unit can collect ecosystem data around the dam and monitor changes in the ecosystem. In addition, the environmental monitoring unit can collect meteorological data and evaluate the environmental impact of dam operations. This allows for minimizing the environmental impact of dam operations and achieving sustainable operation.

[0093] A multi-functional dam system can also be equipped with a user feedback unit. This unit can collect user feedback and incorporate it into the system's operation. For example, it can provide an interface where users can input opinions and requests regarding dam operation. Furthermore, it can analyze the collected feedback to identify areas for system improvement. Additionally, it can adjust the system's operation methods based on the feedback, thereby improving user satisfaction. This enables operation that reflects user opinions, improving system reliability and user satisfaction.

[0094] A multi-functional dam system can also be equipped with an emergency response unit. This unit can provide functions for rapid response in the event of an emergency. For example, in the event of a natural disaster such as a flood or earthquake, the emergency response unit can automatically adjust dam operations to minimize damage. Furthermore, the emergency response unit can quickly notify relevant parties and encourage appropriate responses. In addition, the emergency response unit can monitor dam operation data in real time during an emergency and provide information to enable appropriate responses. This allows for a rapid response to emergencies and minimizes damage.

[0095] The multi-functional dam system can further estimate the user's emotions in its prediction unit and adjust the reliability of the prediction results based on the estimated user emotions. For example, if the user is feeling anxious, the prediction unit can use more detailed data to increase the reliability of the prediction results. If the user is relaxed, the prediction unit can use normal data to make predictions. Furthermore, if the user is in a hurry, the prediction unit can use simplified data to provide prediction results quickly. In this way, by adjusting the reliability of the prediction results according to the user's emotions, the system can provide users with highly reliable prediction results.

[0096] The multi-functional dam system can further estimate the user's emotions in its analysis unit and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and highly visual analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the level of detail in the analysis results according to the user's emotions, the system can provide analysis results that are easy for the user to understand.

[0097] The multi-functional dam system can further estimate the user's emotions at the control panel and determine the priority of operations based on those emotions. For example, if the user is feeling anxious, the control panel can prioritize important operations. If the user is relaxed, the control panel can perform operations with normal priority. Furthermore, if the user is in a hurry, the control panel can prioritize operations that can be performed quickly. In this way, by determining the priority of operations according to the user's emotions, important operations can be prioritized.

[0098] The multi-functional dam system can further estimate the user's emotions in its data collection unit and determine the priority of data to collect based on those emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting important data. If the user is relaxed, the data collection unit can collect data with normal priority. Furthermore, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. In this way, by prioritizing data according to the user's emotions, important data can be collected preferentially.

[0099] The multi-functional dam system can further estimate the user's emotions at the control panel and adjust the operating method based on those emotions. For example, if the user is tense, the control panel can provide a simple and highly visible operating method. If the user is relaxed, the control panel can provide a detailed operating method. Furthermore, if the user is in a hurry, the control panel can provide a method that allows for quick operation. In this way, by adjusting the operating method according to the user's emotions, a user-friendly operating method can be provided.

[0100] The multi-functional dam system can further improve the accuracy of its predictions by referencing relevant market and environmental data related to the dam during the prediction process. For example, the prediction unit can optimize its prediction algorithm by referencing relevant market data. It can also optimize its prediction algorithm by referencing environmental data. Furthermore, the prediction unit can improve the accuracy of its predictions by combining relevant market and environmental data. Thus, by referencing relevant market and environmental data, the accuracy of predictions can be improved.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The collection unit collects water level data from various dams. The collection unit can collect water level data from, for example, flood control dams, power generation dams, water supply dams, industrial water dams, and agricultural water dams. The collection unit collects water level data using sensors and can set the frequency and range of data collection. For example, the collection unit can collect water level data at a fixed time every day. Step 2: The analysis unit analyzes the collected data using AI. The analysis unit uses machine learning and deep learning to analyze the data and can analyze the water level fluctuations of the dam based on the collected data. For example, the analysis unit can predict future water level fluctuations based on past data. Step 3: The control unit performs operations to enhance the hydroelectric power generation function based on the analysis results. The control unit can adjust the water level and control the turbine, and can propose operations to maintain the optimal water level for power generation. Step 4: The forecasting unit predicts future rainfall and demand. Based on meteorological data and historical rainfall data, the forecasting unit can predict future rainfall and future water level fluctuations to optimize the dam's operational plan.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the 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.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects water level data, An analysis unit analyzes the data collected by the aforementioned collection unit, An operating unit that performs operations based on the analysis results obtained by the aforementioned analysis unit, It includes a prediction unit that predicts future water level fluctuations. A system characterized by the following features.

2. The aforementioned collection unit is Collect water level data from various dams. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed using AI. The system according to feature 1.

4. The aforementioned operating unit is Based on the analysis results, operations will be carried out to enhance the hydroelectric power generation function. The system according to feature 1.

5. The prediction unit, Predicting future rainfall and demand The system according to feature 1.

6. The aforementioned operating unit is We propose operations to maintain the optimal water level for power generation. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of water level data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past water level data for each dam and select the optimal data collection method. The system according to feature 1.

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