system

Abstract

The system according to this embodiment aims to comprehensively manage and control data for the entire city. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a control unit. The data collection unit collects real-time data from the entire city. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal city management plan based on the analysis results obtained by the analysis unit. The control unit controls city functions based on the plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the data of the entire city has not been sufficiently managed and controlled integrally, and there is room for improvement.

[0005] The system according to the embodiment aims to integrally manage and control the data of the entire city.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a control unit. The data collection unit collects real-time data from the entire city. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal city management plan based on the analysis results obtained by the analysis unit. The control unit controls city functions based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage and control data across the entire city in an integrated manner. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 AIDriven Urban Harmony system according to an embodiment of the present invention is a system that reproduces the entire city as a digital twin and optimizes urban functions using CPS and AI. This system combines real-time data and predictive models to comprehensively manage and control various urban functions such as traffic, energy, environment, and disaster prevention. First, the entire city is reproduced as a digital twin. A digital twin is a digital reproduction of the entire physical city, representing all elements of the city as digital data. This allows for real-time understanding of the city's current state. Next, urban functions are optimized using CPS and AI. CPS is a system that links the physical city with the digital twin, monitoring and controlling each element of the city in real time. AI predicts traffic flow, energy demand, environmental impact, disaster risk, etc., based on past data and current conditions, and proposes an optimal urban management plan. Specific examples of its use include in the fields of traffic management, energy management, environmental management, and disaster prevention. In traffic management, AI predicts traffic flow and optimizes signal control and park-and-ride systems. In energy management, it combines renewable energy generation forecasts with demand forecasts to improve the efficiency of power supply. In environmental management, the system monitors air quality and noise levels, proposing traffic restrictions and green space development as needed. For disaster prevention, it combines meteorological and urban structure data to predict flood and earthquake risks and propose preventative measures. Furthermore, by utilizing generative AI, it enables functions such as pattern recognition and prediction, multimodal optimization, scenario simulation, natural language processing, and computer vision. This significantly improves urban efficiency and sustainability, enhancing the quality of life for citizens. It also has the potential to reduce urban management costs and create new industries. Thus, the AIDriven Urban Harmony system can optimize urban functions by collecting and analyzing real-time data across the entire city, proposing optimal urban management plans, and controlling urban functions.

[0029] The AIDriven Urban Harmony system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a control unit. The data collection unit collects real-time data from the entire city. The data collection unit can collect, for example, traffic data, energy data, environmental data, disaster prevention data, etc. The data collection unit collects data from various locations in the city using sensors and IoT devices. For example, traffic data is collected from cameras and sensors installed on roads. Energy data is collected from power meters and energy management systems. Environmental data is collected from air quality sensors and noise sensors. Disaster prevention data is collected from weather sensors and seismometers. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict traffic flow, energy demand, environmental impact, disaster risk, etc., based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on past and current traffic data. The generative AI also predicts peak energy demand based on past and current energy data. Furthermore, the generative AI predicts the occurrence of environmental pollution based on past and current environmental data. Furthermore, the generating AI predicts disaster risks based on past and current disaster prevention data. The proposal department proposes an optimal urban management plan based on the analysis results obtained by the analysis department. The proposal department uses the generating AI to propose plans that optimize signal control and park-and-ride systems based on the analysis results. For example, the generating AI proposes a plan to adjust signal timing to avoid traffic congestion. It also proposes a plan to promote the use of park-and-ride systems. Furthermore, the generating AI proposes plans to improve the efficiency of energy supply. For example, it proposes a plan to optimize power supply by combining renewable energy generation forecasts and demand forecasts. Furthermore, the generating AI proposes plans to mitigate environmental pollution. For example, it monitors air quality and noise levels and proposes traffic regulations and green space development as needed. Furthermore, the generating AI proposes plans to strengthen disaster prevention measures. For example, it combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures.The control unit controls urban functions based on the plan proposed by the proposal unit. Using AI, the control unit executes traffic signal control, energy supply, traffic regulation, green space development, and disaster prevention measures based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. It also optimizes power supply to reduce energy waste. Furthermore, it implements traffic regulations to reduce environmental pollution. Furthermore, it improves the urban environment by developing green spaces. Furthermore, it implements disaster prevention measures to reduce disaster risk. As a result, the AIDriven Urban Harmony system according to this embodiment can optimize urban functions by collecting and analyzing real-time data from the entire city, proposing an optimal urban management plan, and controlling urban functions.

[0030] The data collection unit collects real-time data from across the entire city. For example, it can collect traffic data, energy data, environmental data, and disaster prevention data. The unit uses sensors and IoT devices to collect data from various locations throughout the city. For instance, traffic data is collected from cameras and sensors installed along roads. These cameras monitor vehicle flow, speed, and volume in real time, while sensors detect vehicle movement. Energy data is collected from electricity meters and energy management systems. Electricity meters measure the electricity consumption of individual homes and buildings, and energy management systems monitor the operation of power plants and the power grid. Environmental data is collected from air quality sensors and noise sensors. Air quality sensors measure the concentration of harmful substances such as PM2.5 and NOx, and noise sensors monitor the noise level of the city. Disaster prevention data is collected from weather sensors and seismometers. Weather sensors collect meteorological data such as temperature, humidity, precipitation, and wind speed, while seismometers detect earthquakes and identify their intensity and epicenter. This allows the data collection unit to gather a wide range of data from diverse data sources and understand the urban situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data collected by the data collection unit. Using generative AI, the analysis unit predicts traffic flow, energy demand, environmental impact, and disaster risk based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on historical and current traffic data. Specifically, the generative AI analyzes vehicle flow, speed, and traffic volume fluctuation patterns to calculate the probability of congestion occurring at specific times and locations. The generative AI also predicts peak energy demand based on historical and current energy data. This enables optimization of energy supply and adjustment of demand. Furthermore, the generative AI predicts the occurrence of environmental pollution based on historical and current environmental data. For example, it analyzes data from air quality sensors to predict increases in the concentration of hazardous substances in specific areas and times. In addition, the generative AI predicts disaster risk based on historical and current disaster prevention data. For example, it analyzes weather data and earthquake data to calculate the probability of floods and earthquakes occurring in specific areas. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp the risk situation of the city in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in congestion in specific areas and time periods based on past traffic congestion data, and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department proposes optimal urban management plans based on the analysis results obtained by the analysis department. Using generative AI, the proposal department proposes plans to optimize signal control and park-and-ride systems based on the analysis results. For example, the generative AI proposes plans to adjust signal timing to avoid traffic congestion. Specifically, it analyzes traffic flow data and optimizes the timing of signal changes at specific intersections and time periods. The generative AI also proposes plans to promote the use of park-and-ride systems. For example, it proposes optimal parking locations and transfer routes based on parking availability and public transport operation status. Furthermore, the generative AI proposes plans to improve the efficiency of energy supply. For example, it proposes plans to optimize power supply by combining renewable energy generation forecasts and demand forecasts. This reduces energy waste and realizes sustainable urban management. Furthermore, the generative AI proposes plans to mitigate environmental pollution. For example, it monitors air quality and noise levels and proposes traffic regulations and green space development as needed. This improves the environmental quality of the city. Furthermore, the generative AI proposes plans to strengthen disaster prevention measures. For example, it combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures. This will improve urban safety and minimize disaster risks. The proposals department will provide these plans to various departments of urban management and evaluate their feasibility and effectiveness. This will enable the proposals department to provide concrete measures to optimize overall urban management and create a sustainable and safe urban environment.

[0033] The control unit controls urban functions based on the plan proposed by the proposal unit. Using AI, the control unit executes traffic signal control, energy supply, traffic regulations, green space development, and disaster prevention measures based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. Specifically, it monitors traffic flow data in real time and dynamically adjusts the timing of signal changes. The control unit also optimizes power supply to reduce energy waste. For example, it balances power supply and demand based on renewable energy generation and demand forecasts. Furthermore, the control unit implements traffic regulations to reduce environmental pollution. For example, it restricts vehicle traffic in specific areas or time periods to improve air quality and noise levels. Furthermore, the control unit improves the urban environment by developing green spaces. For example, it increases the area of ​​green space in the city to mitigate the urban heat island effect. Furthermore, the control unit implements disaster prevention measures to reduce disaster risk. For example, it secures evacuation routes and sets up evacuation shelters based on weather and earthquake data. In this way, the control unit can efficiently and effectively control the functions of the entire city, improving urban safety and sustainability. Furthermore, the control unit monitors the effectiveness of the implemented plan and makes adjustments as needed. For example, it evaluates the effectiveness of signal control and readjusts the timing of signals according to traffic congestion levels. It also monitors the energy supply situation and modifies the supply plan in response to fluctuations in demand. This allows the control unit to constantly respond to the latest conditions and continuously optimize urban functions.

[0034] The data collection unit can collect data related to traffic, energy, environment, disaster prevention, and more. For example, the data collection unit collects traffic volume data, energy consumption data, air quality data, and disaster prediction data. The data collection unit uses sensors and IoT devices to collect data from various locations in the city. For example, traffic volume data is collected from cameras and sensors installed on roads. Energy consumption data is collected from electricity meters and energy management systems. Air quality data is collected from air quality sensors. Disaster prediction data is collected from weather sensors and seismometers. By collecting data related to traffic, energy, environment, and disaster prevention, it is possible to understand the situation of the entire city. Some or all of the above-described processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data acquired from sensors into AI and have the AI ​​perform data collection and analysis.

[0035] The analysis unit can predict traffic flow, energy demand, environmental impact, disaster risk, and more based on the collected data. For example, the analysis unit can predict traffic congestion, peak energy consumption, environmental pollution, and disaster risk. The analysis unit uses generative AI to make predictions based on the collected data. For instance, the generative AI predicts traffic congestion based on past and current traffic data. It also predicts peak energy demand based on past and current energy data. Furthermore, it predicts environmental pollution based on past and current environmental data. Finally, it predicts disaster risk based on past and current disaster prevention data. This allows for the optimization of urban functions by making predictions based on the collected data. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the AI ​​perform predictions of traffic flow, energy demand, environmental impact, and disaster risk.

[0036] The proposal unit can propose plans to optimize traffic signal control and park-and-ride systems based on prediction results. For example, the proposal unit can propose adjustments to traffic signal timing and measures to promote the use of park-and-ride systems. The proposal unit uses generative AI to propose the optimal plan based on the prediction results. For example, the generative AI proposes a plan to adjust traffic signal timing to avoid traffic congestion. The generative AI also proposes a plan to promote the use of park-and-ride systems. This improves traffic flow by optimizing traffic signal control and park-and-ride systems. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit can input prediction results into the generative AI and have the generative AI execute optimization plans for traffic signal control and park-and-ride systems.

[0037] The control unit can optimize power supply based on the proposed plan. The control unit can perform actions such as peak shifting and promoting the use of renewable energy. The control unit uses AI to optimize power supply based on the proposed plan. For example, the control unit optimizes power supply by combining renewable energy generation forecasts and demand forecasts. This reduces energy waste by optimizing power supply. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input generation forecast and demand forecast data into the AI ​​and have the AI ​​perform the optimization of power supply.

[0038] The control unit can implement traffic regulations and green space development based on the proposed plan. For example, the control unit can set up areas where vehicle traffic is prohibited and install new green spaces. The control unit uses AI to execute traffic regulations and green space development based on the proposed plan. For example, the control unit sets up areas where vehicle traffic is prohibited based on traffic volume data. The control unit also installs new green spaces based on environmental data. As a result, improvements to the environment can be expected by implementing traffic regulations and green space development. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input traffic volume data and environmental data into the AI ​​and have the AI ​​perform optimization of traffic regulations and green space development.

[0039] The control unit can take proactive measures against flood and earthquake risks based on the proposed plan. For example, the control unit can reinforce levees and secure evacuation routes. The control unit uses AI to execute proactive measures against flood and earthquake risks based on the proposed plan. For example, the control unit reinforces levees based on meteorological data and urban structure data. The control unit also secures evacuation routes based on disaster risk data. By taking proactive measures against flood and earthquake risks, disaster damage can be mitigated. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input meteorological data and disaster risk data into the AI ​​and have the AI ​​execute proactive measures against flood and earthquake risks.

[0040] The data collection unit can dynamically change the types of data it collects according to specific events or seasons in the city. For example, in the summer, the unit can focus on collecting energy consumption data to predict air conditioning demand. In winter, it can monitor road freezing conditions to strengthen traffic safety measures. Furthermore, during large-scale events, the unit can focus on collecting traffic flow data to predict congestion. By dynamically changing data collection according to specific events or seasons in the city, more appropriate data can be collected. 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 settings for data collection according to events and seasons into the AI ​​and have the AI ​​execute the dynamic changes in data collection.

[0041] The data collection unit can detect anomalies in specific areas of a city during data collection and increase the collection frequency. For example, the data collection unit can collect detailed environmental data in areas where air quality has rapidly deteriorated. It can also frequently collect traffic flow data in areas where traffic accidents are frequent. Furthermore, it can collect detailed energy data in areas where energy consumption is abnormally high. This allows for the rapid identification of abnormal situations by detecting anomalies and increasing the collection frequency. 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 have AI perform the detection of anomalies and the adjustment of the collection frequency.

[0042] The data collection unit can select data to be collected by referring to the city's historical data during data collection. For example, the data collection unit can collect data from areas where congestion is predicted based on past traffic congestion data. It can also collect data from areas with high energy consumption based on past energy consumption data. Furthermore, it can collect data from areas with significant environmental impact based on past environmental data. This allows for more accurate data collection by referring to historical data. 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 historical data into AI and have the AI ​​select the data to be collected.

[0043] The data collection unit can incorporate feedback from urban residents in real time during data collection. For example, the unit can collect data on congested areas based on traffic congestion reports from residents. It can also collect data on polluted areas based on environmental pollution reports from residents. Furthermore, it can collect data on areas with high energy consumption based on energy consumption reports from residents. This allows for more appropriate data collection by incorporating resident feedback in real time. 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 resident feedback into AI and have the AI ​​adjust the data collection.

[0044] The analysis unit can improve the accuracy of its analysis by weighting historical data for specific areas of a city during the analysis process. For example, the analysis unit can prioritize historical data from areas where traffic congestion frequently occurs. It can also prioritize historical data from areas with high energy consumption. Furthermore, it can prioritize historical data from areas with severe environmental pollution. By weighting historical data in this way, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform a weighted analysis.

[0045] The analysis unit can automatically filter out abnormal data using an anomaly detection algorithm during analysis. For example, the analysis unit can detect and filter out abnormal values ​​in traffic data. It can also detect and filter out abnormal values ​​in energy data. Furthermore, it can detect and filter out abnormal values ​​in environmental data. By filtering out abnormal data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can have a generation AI execute an anomaly detection algorithm to filter out abnormal data.

[0046] The analysis unit can adjust its analysis algorithm to reflect the opinions of urban residents during the analysis process. For example, the analysis unit can adjust its analysis algorithm based on traffic congestion reports from residents. It can also adjust its analysis algorithm based on environmental pollution reports from residents. Furthermore, it can adjust its analysis algorithm based on energy consumption reports from residents. This allows for more appropriate analysis by reflecting the opinions of residents. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input residents' opinions into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0047] The analysis unit can display analysis results by region, taking into account the geographical characteristics of the city during the analysis. For example, the analysis unit can prioritize displaying analysis results for areas where traffic congestion frequently occurs. It can also prioritize displaying analysis results for areas with high energy consumption. Furthermore, it can prioritize displaying analysis results for areas with severe environmental pollution. In this way, by considering geographical characteristics, detailed analysis results can be provided for each region. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input geographical characteristics into a generation AI and have the generation AI perform the display of analysis results for each region.

[0048] The proposal function can customize its proposals by referencing past success stories in specific areas of a city. For example, it can customize proposals based on successful examples of areas where traffic congestion has been resolved. It can also customize proposals based on successful examples of areas where energy consumption has been made more efficient. Furthermore, it can customize proposals based on successful examples of areas where environmental pollution has been improved. This allows for more effective proposals by referencing past success stories. Some or all of the above processing in the proposal function may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal function can input past success stories into a generative AI and have the generative AI perform the customization of the proposal.

[0049] The proposal department can adjust its proposals to reflect the opinions of residents in specific areas of the city. For example, it can adjust proposals based on residents' opinions on alleviating traffic congestion. It can also adjust proposals based on residents' opinions on improving environmental pollution. Furthermore, it can adjust proposals based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input residents' opinions into a generation AI and have the generation AI perform the adjustments to the proposal.

[0050] The proposal unit can adjust its proposals by referring to environmental data for specific areas of a city. For example, it can adjust its proposals based on environmental data from areas with poor air quality, high noise levels, and water quality. By referring to environmental data, it can make more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input environmental data into a generation AI and have the generation AI perform the adjustment of the proposals.

[0051] The proposal unit can adjust its proposals by referring to traffic data for specific areas of a city. For example, it can adjust its proposals based on traffic data from areas where traffic congestion frequently occurs. It can also adjust its proposals based on traffic data from areas where traffic accidents frequently occur. Furthermore, it can adjust its proposals based on traffic data from areas where public transportation is poorly operated. This allows for more appropriate proposals by referring to traffic data. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input traffic data into a generation AI and have the generation AI perform the adjustment of the proposals.

[0052] The control unit can optimize its control algorithm by referring to historical data from specific areas of a city during control. For example, the control unit can optimize its signal control algorithm based on historical data from areas with frequent traffic congestion. It can also optimize its power supply algorithm based on historical data from areas with high energy consumption. Furthermore, it can optimize its environmental control algorithm based on historical data from areas with severe environmental pollution. By referring to historical data, the accuracy of the control algorithm is improved. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input historical data into an AI and have the AI ​​perform the optimization of the control algorithm.

[0053] The control unit can adjust its control methods during operation to reflect the opinions of residents in a specific area of ​​the city. For example, the control unit can adjust the signal control method based on residents' opinions on alleviating traffic congestion. It can also adjust the environmental control method based on residents' opinions on improving environmental pollution. Furthermore, it can adjust the power supply method based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate control becomes possible. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input residents' opinions into the AI ​​and have the AI ​​perform the adjustment of the control method.

[0054] The control unit can adjust its control method by referring to environmental data in a specific area of ​​the city during control. For example, the control unit can implement traffic regulations based on environmental data of areas with poor air quality. It can also carry out green space development based on environmental data of areas with high noise levels. Furthermore, it can manage drainage based on environmental data of areas with poor water quality. This allows for more appropriate control by referring to environmental data. Some or all of the above-described processes in the control unit may be performed using AI, or they may not. For example, the control unit can input environmental data into the AI ​​and have the AI ​​adjust the control method.

[0055] The control unit can adjust the control method by referring to traffic data in a specific area of ​​the city during control. For example, the control unit can perform signal control based on traffic data from areas where traffic congestion frequently occurs. It can also implement traffic safety measures based on traffic data from areas where traffic accidents frequently occur. Furthermore, the control unit can adjust the operating schedule based on traffic data from areas where public transportation is underperforming. This allows for more appropriate control by referring to traffic data. Some or all of the above-described processes in the control unit may be performed using AI, or they may not. For example, the control unit can input traffic data into an AI and have the AI ​​adjust the control method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit can reflect resident feedback in specific areas of a city in real time. For example, the unit can collect data on congested areas based on traffic congestion reports from residents. It can also collect data on polluted areas based on environmental pollution reports from residents. Furthermore, it can collect data on areas with high energy consumption based on energy consumption reports from residents. This allows for more appropriate data collection by reflecting resident feedback in real time. 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 resident feedback into AI and have the AI ​​adjust the data collection.

[0058] The proposal function can customize its proposals by referencing past success stories in specific areas of a city. For example, it can customize proposals based on successful examples of areas where traffic congestion has been resolved. It can also customize proposals based on successful examples of areas where energy consumption has been made more efficient. Furthermore, it can customize proposals based on successful examples of areas where environmental pollution has been improved. This allows for more effective proposals by referencing past success stories. Some or all of the above processing in the proposal function may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal function can input past success stories into a generative AI and have the generative AI perform the customization of the proposal.

[0059] The data collection unit can detect anomalies in specific areas of a city during data collection and increase the collection frequency. For example, the data collection unit can collect detailed environmental data in areas where air quality has rapidly deteriorated. It can also frequently collect traffic flow data in areas where traffic accidents are frequent. Furthermore, it can collect detailed energy data in areas where energy consumption is abnormally high. This allows for the rapid identification of abnormal situations by detecting anomalies and increasing the collection frequency. 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 have AI perform the detection of anomalies and the adjustment of the collection frequency.

[0060] The analysis unit can improve the accuracy of its analysis by weighting historical data for specific areas of a city during the analysis process. For example, the analysis unit can prioritize historical data from areas where traffic congestion frequently occurs. It can also prioritize historical data from areas with high energy consumption. Furthermore, it can prioritize historical data from areas with severe environmental pollution. By weighting historical data in this way, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform a weighted analysis.

[0061] The control unit can adjust its control methods during operation to reflect the opinions of residents in a specific area of ​​the city. For example, the control unit can adjust the signal control method based on residents' opinions on alleviating traffic congestion. It can also adjust the environmental control method based on residents' opinions on improving environmental pollution. Furthermore, it can adjust the power supply method based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate control becomes possible. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input residents' opinions into the AI ​​and have the AI ​​perform the adjustment of the control method.

[0062] The analysis unit can automatically filter out abnormal data using an anomaly detection algorithm during analysis. For example, the analysis unit can detect and filter out abnormal values ​​in traffic data. It can also detect and filter out abnormal values ​​in energy data. Furthermore, it can detect and filter out abnormal values ​​in environmental data. By filtering out abnormal data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can have a generation AI execute an anomaly detection algorithm to filter out abnormal data.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects real-time data from across the city. The data collection unit can collect, for example, traffic data, energy data, environmental data, and disaster prevention data. The data collection unit uses sensors and IoT devices to collect data from various locations in the city. For example, traffic data is collected from cameras and sensors installed on roads. Energy data is collected from electricity meters and energy management systems. Environmental data is collected from air quality sensors and noise sensors. Disaster prevention data is collected from weather sensors and seismometers. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to predict traffic flow, energy demand, environmental impact, disaster risk, etc., based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on past and current traffic data. The generative AI also predicts peak energy demand based on past and current energy data. Furthermore, the generative AI predicts the occurrence of environmental pollution based on past and current environmental data. In addition, the generative AI predicts disaster risk based on past and current disaster prevention data. Step 3: The proposal department proposes an optimal urban management plan based on the analysis results obtained by the analysis department. The proposal department uses generative AI to propose plans that optimize signal control and park-and-ride systems based on the analysis results. For example, the generative AI proposes a plan to adjust the timing of signals to avoid traffic congestion. The generative AI also proposes a plan to promote the use of park-and-ride systems. Furthermore, the generative AI proposes a plan to make energy supply more efficient. For example, the generative AI proposes a plan to optimize power supply by combining renewable energy generation forecasts and demand forecasts. Furthermore, the generative AI proposes a plan to reduce environmental pollution. For example, the generative AI monitors air quality and noise levels and proposes traffic regulations and green space development as needed. Furthermore, the generative AI proposes a plan to strengthen disaster prevention measures. For example, the generative AI combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures. Step 4: The control unit controls urban functions based on the plan proposed by the proposal unit. The control unit uses AI to implement traffic signal control, energy supply, traffic regulation, green space development, disaster prevention measures, etc., based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. The control unit also optimizes power supply to reduce energy waste. Furthermore, the control unit implements traffic regulations to reduce environmental pollution. Furthermore, the control unit improves the urban environment by developing green spaces. Furthermore, the control unit implements disaster prevention measures to reduce disaster risk.

[0065] (Example of form 2) The AIDriven Urban Harmony system according to an embodiment of the present invention is a system that reproduces the entire city as a digital twin and optimizes urban functions using CPS and AI. This system combines real-time data and predictive models to comprehensively manage and control various urban functions such as traffic, energy, environment, and disaster prevention. First, the entire city is reproduced as a digital twin. A digital twin is a digital reproduction of the entire physical city, representing all elements of the city as digital data. This allows for real-time understanding of the city's current state. Next, urban functions are optimized using CPS and AI. CPS is a system that links the physical city with the digital twin, monitoring and controlling each element of the city in real time. AI predicts traffic flow, energy demand, environmental impact, disaster risk, etc., based on past data and current conditions, and proposes an optimal urban management plan. Specific examples of its use include in the fields of traffic management, energy management, environmental management, and disaster prevention. In traffic management, AI predicts traffic flow and optimizes signal control and park-and-ride systems. In energy management, it combines renewable energy generation forecasts with demand forecasts to improve the efficiency of power supply. In environmental management, the system monitors air quality and noise levels, proposing traffic restrictions and green space development as needed. For disaster prevention, it combines meteorological and urban structure data to predict flood and earthquake risks and propose preventative measures. Furthermore, by utilizing generative AI, it enables functions such as pattern recognition and prediction, multimodal optimization, scenario simulation, natural language processing, and computer vision. This significantly improves urban efficiency and sustainability, enhancing the quality of life for citizens. It also has the potential to reduce urban management costs and create new industries. Thus, the AIDriven Urban Harmony system can optimize urban functions by collecting and analyzing real-time data across the entire city, proposing optimal urban management plans, and controlling urban functions.

[0066] The AIDriven Urban Harmony system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a control unit. The data collection unit collects real-time data from the entire city. The data collection unit can collect, for example, traffic data, energy data, environmental data, disaster prevention data, etc. The data collection unit collects data from various locations in the city using sensors and IoT devices. For example, traffic data is collected from cameras and sensors installed on roads. Energy data is collected from power meters and energy management systems. Environmental data is collected from air quality sensors and noise sensors. Disaster prevention data is collected from weather sensors and seismometers. The analysis unit analyzes the data collected by the data collection unit. The analysis unit uses generative AI to predict traffic flow, energy demand, environmental impact, disaster risk, etc., based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on past and current traffic data. The generative AI also predicts peak energy demand based on past and current energy data. Furthermore, the generative AI predicts the occurrence of environmental pollution based on past and current environmental data. Furthermore, the generating AI predicts disaster risks based on past and current disaster prevention data. The proposal department proposes an optimal urban management plan based on the analysis results obtained by the analysis department. The proposal department uses the generating AI to propose plans that optimize signal control and park-and-ride systems based on the analysis results. For example, the generating AI proposes a plan to adjust signal timing to avoid traffic congestion. It also proposes a plan to promote the use of park-and-ride systems. Furthermore, the generating AI proposes plans to improve the efficiency of energy supply. For example, it proposes a plan to optimize power supply by combining renewable energy generation forecasts and demand forecasts. Furthermore, the generating AI proposes plans to mitigate environmental pollution. For example, it monitors air quality and noise levels and proposes traffic regulations and green space development as needed. Furthermore, the generating AI proposes plans to strengthen disaster prevention measures. For example, it combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures.The control unit controls urban functions based on the plan proposed by the proposal unit. Using AI, the control unit executes traffic signal control, energy supply, traffic regulation, green space development, and disaster prevention measures based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. It also optimizes power supply to reduce energy waste. Furthermore, it implements traffic regulations to reduce environmental pollution. Furthermore, it improves the urban environment by developing green spaces. Furthermore, it implements disaster prevention measures to reduce disaster risk. As a result, the AIDriven Urban Harmony system according to this embodiment can optimize urban functions by collecting and analyzing real-time data from the entire city, proposing an optimal urban management plan, and controlling urban functions.

[0067] The data collection unit collects real-time data from across the entire city. For example, it can collect traffic data, energy data, environmental data, and disaster prevention data. The unit uses sensors and IoT devices to collect data from various locations throughout the city. For instance, traffic data is collected from cameras and sensors installed along roads. These cameras monitor vehicle flow, speed, and volume in real time, while sensors detect vehicle movement. Energy data is collected from electricity meters and energy management systems. Electricity meters measure the electricity consumption of individual homes and buildings, and energy management systems monitor the operation of power plants and the power grid. Environmental data is collected from air quality sensors and noise sensors. Air quality sensors measure the concentration of harmful substances such as PM2.5 and NOx, and noise sensors monitor the noise level of the city. Disaster prevention data is collected from weather sensors and seismometers. Weather sensors collect meteorological data such as temperature, humidity, precipitation, and wind speed, while seismometers detect earthquakes and identify their intensity and epicenter. This allows the data collection unit to gather a wide range of data from diverse data sources and understand the urban situation in real time. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the data collection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0068] The analysis unit analyzes the data collected by the data collection unit. Using generative AI, the analysis unit predicts traffic flow, energy demand, environmental impact, and disaster risk based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on historical and current traffic data. Specifically, the generative AI analyzes vehicle flow, speed, and traffic volume fluctuation patterns to calculate the probability of congestion occurring at specific times and locations. The generative AI also predicts peak energy demand based on historical and current energy data. This enables optimization of energy supply and adjustment of demand. Furthermore, the generative AI predicts the occurrence of environmental pollution based on historical and current environmental data. For example, it analyzes data from air quality sensors to predict increases in the concentration of hazardous substances in specific areas and times. In addition, the generative AI predicts disaster risk based on historical and current disaster prevention data. For example, it analyzes weather data and earthquake data to calculate the probability of floods and earthquakes occurring in specific areas. As a result, the analysis unit can quickly and accurately analyze the collected data and grasp the risk situation of the city in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict fluctuations in congestion in specific areas and time periods based on past traffic congestion data, and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0069] The proposal department proposes optimal urban management plans based on the analysis results obtained by the analysis department. Using generative AI, the proposal department proposes plans to optimize signal control and park-and-ride systems based on the analysis results. For example, the generative AI proposes plans to adjust signal timing to avoid traffic congestion. Specifically, it analyzes traffic flow data and optimizes the timing of signal changes at specific intersections and time periods. The generative AI also proposes plans to promote the use of park-and-ride systems. For example, it proposes optimal parking locations and transfer routes based on parking availability and public transport operation status. Furthermore, the generative AI proposes plans to improve the efficiency of energy supply. For example, it proposes plans to optimize power supply by combining renewable energy generation forecasts and demand forecasts. This reduces energy waste and realizes sustainable urban management. Furthermore, the generative AI proposes plans to mitigate environmental pollution. For example, it monitors air quality and noise levels and proposes traffic regulations and green space development as needed. This improves the environmental quality of the city. Furthermore, the generative AI proposes plans to strengthen disaster prevention measures. For example, it combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures. This will improve urban safety and minimize disaster risks. The proposals department will provide these plans to various departments of urban management and evaluate their feasibility and effectiveness. This will enable the proposals department to provide concrete measures to optimize overall urban management and create a sustainable and safe urban environment.

[0070] The control unit controls urban functions based on the plan proposed by the proposal unit. Using AI, the control unit executes traffic signal control, energy supply, traffic regulations, green space development, and disaster prevention measures based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. Specifically, it monitors traffic flow data in real time and dynamically adjusts the timing of signal changes. The control unit also optimizes power supply to reduce energy waste. For example, it balances power supply and demand based on renewable energy generation and demand forecasts. Furthermore, the control unit implements traffic regulations to reduce environmental pollution. For example, it restricts vehicle traffic in specific areas or time periods to improve air quality and noise levels. Furthermore, the control unit improves the urban environment by developing green spaces. For example, it increases the area of ​​green space in the city to mitigate the urban heat island effect. Furthermore, the control unit implements disaster prevention measures to reduce disaster risk. For example, it secures evacuation routes and sets up evacuation shelters based on weather and earthquake data. In this way, the control unit can efficiently and effectively control the functions of the entire city, improving urban safety and sustainability. Furthermore, the control unit monitors the effectiveness of the implemented plan and makes adjustments as needed. For example, it evaluates the effectiveness of signal control and readjusts the timing of signals according to traffic congestion levels. It also monitors the energy supply situation and modifies the supply plan in response to fluctuations in demand. This allows the control unit to constantly respond to the latest conditions and continuously optimize urban functions.

[0071] The data collection unit can collect data related to traffic, energy, environment, disaster prevention, and more. For example, the data collection unit collects traffic volume data, energy consumption data, air quality data, and disaster prediction data. The data collection unit uses sensors and IoT devices to collect data from various locations in the city. For example, traffic volume data is collected from cameras and sensors installed on roads. Energy consumption data is collected from electricity meters and energy management systems. Air quality data is collected from air quality sensors. Disaster prediction data is collected from weather sensors and seismometers. By collecting data related to traffic, energy, environment, and disaster prevention, it is possible to understand the situation of the entire city. Some or all of the above-described processing in the data collection unit may be performed using AI, or it may be performed without AI. For example, the data collection unit can input data acquired from sensors into AI and have the AI ​​perform data collection and analysis.

[0072] The analysis unit can predict traffic flow, energy demand, environmental impact, disaster risk, and more based on the collected data. For example, the analysis unit can predict traffic congestion, peak energy consumption, environmental pollution, and disaster risk. The analysis unit uses generative AI to make predictions based on the collected data. For instance, the generative AI predicts traffic congestion based on past and current traffic data. It also predicts peak energy demand based on past and current energy data. Furthermore, it predicts environmental pollution based on past and current environmental data. Finally, it predicts disaster risk based on past and current disaster prevention data. This allows for the optimization of urban functions by making predictions based on the collected data. Some or all of the above-described processes in the analysis unit are performed using generative AI. For example, the analysis unit can input collected data into the generative AI and have the AI ​​perform predictions of traffic flow, energy demand, environmental impact, and disaster risk.

[0073] The proposal unit can propose plans to optimize traffic signal control and park-and-ride systems based on prediction results. For example, the proposal unit can propose adjustments to traffic signal timing and measures to promote the use of park-and-ride systems. The proposal unit uses generative AI to propose the optimal plan based on the prediction results. For example, the generative AI proposes a plan to adjust traffic signal timing to avoid traffic congestion. The generative AI also proposes a plan to promote the use of park-and-ride systems. This improves traffic flow by optimizing traffic signal control and park-and-ride systems. Some or all of the above processing in the proposal unit is performed using generative AI. For example, the proposal unit can input prediction results into the generative AI and have the generative AI execute optimization plans for traffic signal control and park-and-ride systems.

[0074] The control unit can optimize power supply based on the proposed plan. The control unit can perform actions such as peak shifting and promoting the use of renewable energy. The control unit uses AI to optimize power supply based on the proposed plan. For example, the control unit optimizes power supply by combining renewable energy generation forecasts and demand forecasts. This reduces energy waste by optimizing power supply. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input generation forecast and demand forecast data into the AI ​​and have the AI ​​perform the optimization of power supply.

[0075] The control unit can implement traffic regulations and green space development based on the proposed plan. For example, the control unit can set up areas where vehicle traffic is prohibited and install new green spaces. The control unit uses AI to execute traffic regulations and green space development based on the proposed plan. For example, the control unit sets up areas where vehicle traffic is prohibited based on traffic volume data. The control unit also installs new green spaces based on environmental data. As a result, improvements to the environment can be expected by implementing traffic regulations and green space development. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input traffic volume data and environmental data into the AI ​​and have the AI ​​perform optimization of traffic regulations and green space development.

[0076] The control unit can take proactive measures against flood and earthquake risks based on the proposed plan. For example, the control unit can reinforce levees and secure evacuation routes. The control unit uses AI to execute proactive measures against flood and earthquake risks based on the proposed plan. For example, the control unit reinforces levees based on meteorological data and urban structure data. The control unit also secures evacuation routes based on disaster risk data. By taking proactive measures against flood and earthquake risks, disaster damage can be mitigated. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input meteorological data and disaster risk data into the AI ​​and have the AI ​​execute proactive measures against flood and earthquake risks.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the system load. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting only important data. By adjusting the timing of data collection according to the user's emotions, the system load is reduced and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can dynamically change the types of data it collects according to specific events or seasons in the city. For example, in the summer, the unit can focus on collecting energy consumption data to predict air conditioning demand. In winter, it can monitor road freezing conditions to strengthen traffic safety measures. Furthermore, during large-scale events, the unit can focus on collecting traffic flow data to predict congestion. By dynamically changing data collection according to specific events or seasons in the city, more appropriate data can be collected. 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 settings for data collection according to events and seasons into the AI ​​and have the AI ​​execute the dynamic changes in data collection.

[0079] The data collection unit can detect anomalies in specific areas of a city during data collection and increase the collection frequency. For example, the data collection unit can collect detailed environmental data in areas where air quality has rapidly deteriorated. It can also frequently collect traffic flow data in areas where traffic accidents are frequent. Furthermore, it can collect detailed energy data in areas where energy consumption is abnormally high. This allows for the rapid identification of abnormal situations by detecting anomalies and increasing the collection frequency. 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 have AI perform the detection of anomalies and the adjustment of the collection frequency.

[0080] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting real-time data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can select data to be collected by referring to the city's historical data during data collection. For example, the data collection unit can collect data from areas where congestion is predicted based on past traffic congestion data. It can also collect data from areas with high energy consumption based on past energy consumption data. Furthermore, it can collect data from areas with significant environmental impact based on past environmental data. This allows for more accurate data collection by referring to historical data. 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 historical data into AI and have the AI ​​select the data to be collected.

[0082] The data collection unit can incorporate feedback from urban residents in real time during data collection. For example, the unit can collect data on congested areas based on traffic congestion reports from residents. It can also collect data on polluted areas based on environmental pollution reports from residents. Furthermore, it can collect data on areas with high energy consumption based on energy consumption reports from residents. This allows for more appropriate data collection by incorporating resident feedback in real time. 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 resident feedback into AI and have the AI ​​adjust the data collection.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user 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 these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not using the generative AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of its analysis by weighting historical data for specific areas of a city during the analysis process. For example, the analysis unit can prioritize historical data from areas where traffic congestion frequently occurs. It can also prioritize historical data from areas with high energy consumption. Furthermore, it can prioritize historical data from areas with severe environmental pollution. By weighting historical data in this way, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform a weighted analysis.

[0085] The analysis unit can automatically filter out abnormal data using an anomaly detection algorithm during analysis. For example, the analysis unit can detect and filter out abnormal values ​​in traffic data. It can also detect and filter out abnormal values ​​in energy data. Furthermore, it can detect and filter out abnormal values ​​in environmental data. By filtering out abnormal data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can have a generation AI execute an anomaly detection algorithm to filter out abnormal data.

[0086] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying only the most important analysis results. If the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying real-time analysis results. In this way, by prioritizing the analysis results according to the user's emotions, important analysis results can be displayed preferentially. 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 analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can adjust its analysis algorithm to reflect the opinions of urban residents during the analysis process. For example, the analysis unit can adjust its analysis algorithm based on traffic congestion reports from residents. It can also adjust its analysis algorithm based on environmental pollution reports from residents. Furthermore, it can adjust its analysis algorithm based on energy consumption reports from residents. This allows for more appropriate analysis by reflecting the opinions of residents. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input residents' opinions into a generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0088] The analysis unit can display analysis results by region, taking into account the geographical characteristics of the city during the analysis. For example, the analysis unit can prioritize displaying analysis results for areas where traffic congestion frequently occurs. It can also prioritize displaying analysis results for areas with high energy consumption. Furthermore, it can prioritize displaying analysis results for areas with severe environmental pollution. In this way, by considering geographical characteristics, detailed analysis results can be provided for each region. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input geographical characteristics into a generation AI and have the generation AI perform the display of analysis results for each region.

[0089] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. By adjusting the way the suggestion is presented according to the user's emotions, it becomes possible to provide suggestions 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 processing in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The proposal function can customize its proposals by referencing past success stories in specific areas of a city. For example, it can customize proposals based on successful examples of areas where traffic congestion has been resolved. It can also customize proposals based on successful examples of areas where energy consumption has been made more efficient. Furthermore, it can customize proposals based on successful examples of areas where environmental pollution has been improved. This allows for more effective proposals by referencing past success stories. Some or all of the above processing in the proposal function may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal function can input past success stories into a generative AI and have the generative AI perform the customization of the proposal.

[0091] The proposal department can adjust its proposals to reflect the opinions of residents in specific areas of the city. For example, it can adjust proposals based on residents' opinions on alleviating traffic congestion. It can also adjust proposals based on residents' opinions on improving environmental pollution. Furthermore, it can adjust proposals based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can input residents' opinions into a generation AI and have the generation AI perform the adjustments to the proposal.

[0092] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying only important suggestions. If the user is relaxed, the suggestion unit can prioritize displaying detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying real-time suggestions. This allows for prioritizing important suggestions based on the user's emotions. 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 processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The proposal unit can adjust its proposals by referring to environmental data for specific areas of a city. For example, it can adjust its proposals based on environmental data from areas with poor air quality, high noise levels, and water quality. By referring to environmental data, it can make more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input environmental data into a generation AI and have the generation AI perform the adjustment of the proposals.

[0094] The proposal unit can adjust its proposals by referring to traffic data for specific areas of a city. For example, it can adjust its proposals based on traffic data from areas where traffic congestion frequently occurs. It can also adjust its proposals based on traffic data from areas where traffic accidents frequently occur. Furthermore, it can adjust its proposals based on traffic data from areas where public transportation is poorly operated. This allows for more appropriate proposals by referring to traffic data. Some or all of the above processing in the proposal unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal unit can input traffic data into a generation AI and have the generation AI perform the adjustment of the proposals.

[0095] The control unit can estimate the user's emotions and adjust the control method based on the estimated emotions. For example, if the user is tense, the control unit can provide a simple and highly visible control method. If the user is relaxed, the control unit can provide a control method that includes detailed information. Furthermore, if the user is in a hurry, the control unit can provide a concise control method. By adjusting the control method according to the user's emotions, optimal control for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 or not using AI. For example, the control unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0096] The control unit can optimize its control algorithm by referring to historical data from specific areas of a city during control. For example, the control unit can optimize its signal control algorithm based on historical data from areas with frequent traffic congestion. It can also optimize its power supply algorithm based on historical data from areas with high energy consumption. Furthermore, it can optimize its environmental control algorithm based on historical data from areas with severe environmental pollution. By referring to historical data, the accuracy of the control algorithm is improved. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input historical data into an AI and have the AI ​​perform the optimization of the control algorithm.

[0097] The control unit can adjust its control methods during operation to reflect the opinions of residents in a specific area of ​​the city. For example, the control unit can adjust the signal control method based on residents' opinions on alleviating traffic congestion. It can also adjust the environmental control method based on residents' opinions on improving environmental pollution. Furthermore, it can adjust the power supply method based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate control becomes possible. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input residents' opinions into the AI ​​and have the AI ​​perform the adjustment of the control method.

[0098] The control unit can estimate the user's emotions and determine control priorities based on the estimated emotions. For example, if the user is stressed, the control unit will prioritize only important controls. If the user is relaxed, the control unit will prioritize detailed controls. Furthermore, if the user is in a hurry, the control unit will prioritize real-time controls. This allows for prioritizing important controls by determining control priorities 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 may be, 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 or not. For example, the control unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The control unit can adjust its control method by referring to environmental data in a specific area of ​​the city during control. For example, the control unit can implement traffic regulations based on environmental data of areas with poor air quality. It can also carry out green space development based on environmental data of areas with high noise levels. Furthermore, it can manage drainage based on environmental data of areas with poor water quality. This allows for more appropriate control by referring to environmental data. Some or all of the above-described processes in the control unit may be performed using AI, or they may not. For example, the control unit can input environmental data into the AI ​​and have the AI ​​adjust the control method.

[0100] The control unit can adjust the control method by referring to traffic data in a specific area of ​​the city during control. For example, the control unit can perform signal control based on traffic data from areas where traffic congestion frequently occurs. It can also implement traffic safety measures based on traffic data from areas where traffic accidents frequently occur. Furthermore, the control unit can adjust the operating schedule based on traffic data from areas where public transportation is underperforming. This allows for more appropriate control by referring to traffic data. Some or all of the above-described processes in the control unit may be performed using AI, or they may not. For example, the control unit can input traffic data into an AI and have the AI ​​adjust the control method.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit can reflect resident feedback in specific areas of a city in real time. For example, the unit can collect data on congested areas based on traffic congestion reports from residents. It can also collect data on polluted areas based on environmental pollution reports from residents. Furthermore, it can collect data on areas with high energy consumption based on energy consumption reports from residents. This allows for more appropriate data collection by reflecting resident feedback in real time. 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 resident feedback into AI and have the AI ​​adjust the data collection.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user 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 these examples. Some or all of the above processing in the analysis unit may be performed using the generative AI or not using the generative AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0104] The proposal function can customize its proposals by referencing past success stories in specific areas of a city. For example, it can customize proposals based on successful examples of areas where traffic congestion has been resolved. It can also customize proposals based on successful examples of areas where energy consumption has been made more efficient. Furthermore, it can customize proposals based on successful examples of areas where environmental pollution has been improved. This allows for more effective proposals by referencing past success stories. Some or all of the above processing in the proposal function may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal function can input past success stories into a generative AI and have the generative AI perform the customization of the proposal.

[0105] The control unit can estimate the user's emotions and adjust the control method based on the estimated emotions. For example, if the user is tense, the control unit can provide a simple and highly visible control method. If the user is relaxed, the control unit can provide a control method that includes detailed information. Furthermore, if the user is in a hurry, the control unit can provide a concise control method. By adjusting the control method according to the user's emotions, optimal control for the user becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 or not using AI. For example, the control unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The data collection unit can detect anomalies in specific areas of a city during data collection and increase the collection frequency. For example, the data collection unit can collect detailed environmental data in areas where air quality has rapidly deteriorated. It can also frequently collect traffic flow data in areas where traffic accidents are frequent. Furthermore, it can collect detailed energy data in areas where energy consumption is abnormally high. This allows for the rapid identification of abnormal situations by detecting anomalies and increasing the collection frequency. 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 have AI perform the detection of anomalies and the adjustment of the collection frequency.

[0107] The analysis unit can improve the accuracy of its analysis by weighting historical data for specific areas of a city during the analysis process. For example, the analysis unit can prioritize historical data from areas where traffic congestion frequently occurs. It can also prioritize historical data from areas with high energy consumption. Furthermore, it can prioritize historical data from areas with severe environmental pollution. By weighting historical data in this way, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input historical data into a generative AI and have the generative AI perform a weighted analysis.

[0108] The suggestion unit can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize displaying only important suggestions. If the user is relaxed, the suggestion unit can prioritize displaying detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can prioritize displaying real-time suggestions. This allows for prioritizing important suggestions based on the user's emotions. 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 processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The control unit can adjust its control methods during operation to reflect the opinions of residents in a specific area of ​​the city. For example, the control unit can adjust the signal control method based on residents' opinions on alleviating traffic congestion. It can also adjust the environmental control method based on residents' opinions on improving environmental pollution. Furthermore, it can adjust the power supply method based on residents' opinions on improving energy efficiency. By reflecting residents' opinions, more appropriate control becomes possible. Some or all of the above processing in the control unit may be performed using AI or not. For example, the control unit can input residents' opinions into the AI ​​and have the AI ​​perform the adjustment of the control method.

[0110] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting real-time data. This allows for the priority collection of important data by prioritizing data according to the user's emotions. 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The analysis unit can automatically filter out abnormal data using an anomaly detection algorithm during analysis. For example, the analysis unit can detect and filter out abnormal values ​​in traffic data. It can also detect and filter out abnormal values ​​in energy data. Furthermore, it can detect and filter out abnormal values ​​in environmental data. By filtering out abnormal data, the accuracy of the analysis results is improved. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can have a generation AI execute an anomaly detection algorithm to filter out abnormal data.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects real-time data from across the city. The data collection unit can collect, for example, traffic data, energy data, environmental data, and disaster prevention data. The data collection unit uses sensors and IoT devices to collect data from various locations in the city. For example, traffic data is collected from cameras and sensors installed on roads. Energy data is collected from electricity meters and energy management systems. Environmental data is collected from air quality sensors and noise sensors. Disaster prevention data is collected from weather sensors and seismometers. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses generative AI to predict traffic flow, energy demand, environmental impact, disaster risk, etc., based on the collected data. For example, the generative AI predicts the occurrence of traffic congestion based on past and current traffic data. The generative AI also predicts peak energy demand based on past and current energy data. Furthermore, the generative AI predicts the occurrence of environmental pollution based on past and current environmental data. In addition, the generative AI predicts disaster risk based on past and current disaster prevention data. Step 3: The proposal department proposes an optimal urban management plan based on the analysis results obtained by the analysis department. The proposal department uses generative AI to propose plans that optimize signal control and park-and-ride systems based on the analysis results. For example, the generative AI proposes a plan to adjust the timing of signals to avoid traffic congestion. The generative AI also proposes a plan to promote the use of park-and-ride systems. Furthermore, the generative AI proposes a plan to make energy supply more efficient. For example, the generative AI proposes a plan to optimize power supply by combining renewable energy generation forecasts and demand forecasts. Furthermore, the generative AI proposes a plan to reduce environmental pollution. For example, the generative AI monitors air quality and noise levels and proposes traffic regulations and green space development as needed. Furthermore, the generative AI proposes a plan to strengthen disaster prevention measures. For example, the generative AI combines weather data and urban structure data to predict the risk of floods and earthquakes and proposes preventative measures. Step 4: The control unit controls urban functions based on the plan proposed by the proposal unit. The control unit uses AI to implement traffic signal control, energy supply, traffic regulation, green space development, disaster prevention measures, etc., based on the proposed plan. For example, the control unit adjusts the timing of traffic signals to avoid traffic congestion. The control unit also optimizes power supply to reduce energy waste. Furthermore, the control unit implements traffic regulations to reduce environmental pollution. Furthermore, the control unit improves the urban environment by developing green spaces. Furthermore, the control unit implements disaster prevention measures to reduce disaster risk.

[0114] 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.

[0115] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects real-time data from the entire city using sensors and IoT devices of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts traffic flow, energy demand, environmental impact, disaster risk, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an optimal city management plan based on the analysis results. The control unit is implemented by the control unit 46A of the smart device 14, which controls city functions based on the proposed plan. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects real-time data from the entire city using the sensors and IoT devices of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts traffic flow, energy demand, environmental impact, disaster risk, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an optimal city management plan based on the analysis results. The control unit is implemented by the control unit 46A of the smart glasses 214, which controls city functions based on the proposed plan. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects real-time data from the entire city using sensors and IoT devices of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts traffic flow, energy demand, environmental impact, disaster risk, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an optimal city management plan based on the analysis results. The control unit is implemented by the control unit 46A of the headset terminal 314, which controls city functions based on the proposed plan. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, control unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects real-time data from the entire city using the sensors and IoT devices of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and predicts traffic flow, energy demand, environmental impact, disaster risk, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12, which proposes an optimal city management plan based on the analysis results. The control unit is implemented by the control unit 46A of the robot 414, which controls city functions based on the proposed plan. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12, which estimates the user's emotions and adjusts the timing of data collection. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A data collection unit that collects real-time data for the entire city, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal urban management plan. The system includes a control unit that controls urban functions based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on transportation, energy, environment, disaster prevention, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we predict traffic flow, energy demand, environmental impact, disaster risk, and more. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose a plan to optimize traffic signal control and park-and-ride systems based on the prediction results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The control unit, Optimize power supply based on the proposed plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, Traffic regulations and green space development will be implemented based on the proposed plan. The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, Based on the proposed plan, proactive measures will be taken against the risks of floods and earthquakes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The types of data collected are dynamically changed according to specific events or seasons in the city. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, detect anomalies in specific areas of the city and increase the collection frequency. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the subjects to be collected are selected by referring to the city's historical data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting data, incorporate feedback from urban residents in real time. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, historical data from specific areas of the city is weighted to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, anomaly detection algorithms are used to automatically filter out abnormal data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, we adjust the analysis algorithm to reflect the opinions of the city's residents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the results are displayed separately for each region, taking into account the geographical characteristics of the city. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we customize it by referring to past success stories in specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we adjust the proposal to reflect the opinions of residents in specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we will adjust the proposal by referring to environmental data for specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we adjust the proposal by referring to traffic data for specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, It estimates the user's emotions and adjusts the control method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The control unit, During control, the control algorithm is optimized by referencing historical data from specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 28) The control unit, During control, the control method is adjusted to reflect the opinions of residents in specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 29) The control unit, It estimates the user's emotions and determines the priority of control based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The control unit, During control, the control method is adjusted by referring to environmental data in specific areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 31) The control unit, During control, the control method is adjusted by referring to traffic data in specific areas of the city. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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 data collection unit that collects real-time data for the entire city, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an optimal urban management plan. The system includes a control unit that controls urban functions based on the plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on transportation, energy, environment, disaster prevention, and more. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we predict traffic flow, energy demand, environmental impact, disaster risk, and more. The system according to feature 1.

4. The aforementioned proposal section is, We propose a plan to optimize traffic signal control and park-and-ride systems based on the prediction results. The system according to feature 1.

5. The control unit, Optimize power supply based on the proposed plan. The system according to feature 1.

6. The control unit, Traffic regulations and green space development will be implemented based on the proposed plan. The system according to feature 1.

7. The control unit, Based on the proposed plan, proactive measures will be taken against the risks of floods and earthquakes. The system according to feature 1.

8. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

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