system

Abstract

The system according to this embodiment aims to analyze real-time data from the entire city and optimize urban functions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and an optimization 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 optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that real-time data of the entire city has not been sufficiently collected and analyzed effectively to optimize urban functions.

[0005] The system according to the embodiment aims to analyze real-time data of the entire city and optimize urban functions.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an optimization unit. The collection unit collects real-time data from the entire city. The analysis unit analyzes the data collected by the collection unit. The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze real-time data from the entire city and optimize urban functions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] 0As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides for the optimization of urban traffic, public services, and energy management by integrating a next-generation communication network and AI technology. The urban optimization system collects real-time data from across the city using the next-generation communication network. For example, it collects data such as traffic sensors, energy meters, and the usage status of public services. This data is collected from various areas of the city and transmitted to a central data center. Next, the collected data is analyzed by AI. The AI ​​analyzes traffic flow and predicts the occurrence of congestion. It also analyzes energy consumption patterns and proposes efficient ways to use energy. Furthermore, it analyzes the usage status of public services and optimizes the provision of services. Based on the analysis results, the system optimizes each function of the city in real time. For example, if traffic congestion is predicted, the AI ​​adjusts the timing of traffic signals to alleviate congestion. Also, if energy consumption reaches its peak, the AI ​​adjusts the energy supply to achieve efficient energy management. Furthermore, if the demand for public services increases, the AI ​​speeds up the provision of services to achieve efficient operation. This system streamlines urban traffic, public services, and energy management, enabling sustainable urban management. For example, reduced traffic congestion shortens commute times, and more efficient energy use reduces environmental impact. Furthermore, faster delivery of public services improves the quality of life for citizens. In addition, the system automatically collects and analyzes data in various areas of the city, reducing operational costs. For instance, automated installation and maintenance of traffic sensors and energy meters reduces labor costs. Automated AI-driven data analysis improves accuracy and enables more efficient urban management. Thus, a system integrating next-generation communication networks and AI technology optimizes urban traffic, public services, and energy management, leading to sustainable urban operations. In short, this urban optimization system can streamline urban traffic, public services, and energy management, enabling sustainable urban management.

[0029] The urban optimization system according to this embodiment comprises a data collection unit, an analysis unit, and an optimization unit. The data collection unit collects real-time data from the entire city. The data collection unit collects data such as traffic sensors, energy meters, and the usage status of public services. The data collection unit can collect vehicle speed and traffic volume in real time using traffic sensors. For example, camera sensors and loop coil sensors can be used as traffic sensors. The data collection unit can collect energy consumption data in real time using energy meters. For example, smart meters and electricity meters can be used as energy meters. The data collection unit can collect data on the usage status of public services in real time. For example, bus boarding and alighting data and library usage data can be collected. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes traffic flow and predicts the occurrence of congestion. The analysis unit can analyze traffic data and analyze fluctuations in vehicle speed and traffic volume. For example, the analysis unit predicts the occurrence of congestion using traffic simulations and historical data analysis. The analysis unit analyzes energy consumption patterns and proposes efficient energy use methods. The analysis unit can analyze energy data and analyze consumption patterns by time of day and season. For example, the analysis unit can propose peak shifting and the use of energy storage. The analysis unit analyzes the usage status of public services and optimizes service provision. The analysis unit can analyze public service data and analyze usage frequency and user attributes. For example, the analysis unit can propose adjustments to service placement and provision times. The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, the optimization unit can adjust the timing of traffic signals to alleviate congestion. The optimization unit can change the signal cycle and make dynamic adjustments according to traffic volume. For example, the optimization unit can synchronize signals and introduce priority signals. When energy consumption reaches its peak, the optimization unit adjusts the energy supply to perform efficient energy management. The optimization unit can perform demand response and utilize energy storage. For example, the optimization unit adjusts supply based on consumption thresholds and peak time periods.The optimization unit accelerates service delivery and achieves efficient operation when demand for public services increases. The optimization unit can increase staffing levels and automate services. For example, the optimization unit adjusts service delivery based on an increase in the number of users or specific events. As a result, the urban optimization system according to this embodiment can streamline urban transportation, public services, and energy management, enabling sustainable urban management.

[0030] The data collection unit collects real-time data from across the city. Specifically, it collects data from traffic sensors, energy meters, and public service usage. Traffic sensors can use camera sensors and loop coil sensors to collect vehicle speed and traffic volume in real time. Camera sensors photograph vehicles on the road and measure the number and speed of vehicles using image analysis technology. Loop coil sensors detect the metal parts of passing vehicles on coils embedded in the road and measure traffic volume. This allows for real-time understanding of traffic flow and congestion. Energy meters collect energy consumption data in real time using smart meters and electricity meters. Smart meters record detailed electricity consumption in homes and businesses and transmit the data to a central system via wireless communication. This allows for accurate understanding of energy consumption patterns and peak consumption. Regarding public service usage, data such as bus boarding and alighting data and library usage data are collected. Bus boarding and alighting data is collected through sensors installed at bus stops and passenger IC card readers, while library usage data is collected based on borrowing and returning records and the number of visitors. This allows for a detailed understanding of public service usage and provides fundamental data for optimizing service delivery. The data collection unit centrally manages this diverse data and stores it in a real-time updated database. This enables the analysis and optimization units to quickly access the necessary data. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, and understand the situation of the entire city in real time.

[0031] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes traffic flow and predicts congestion. When analyzing traffic data, it analyzes vehicle speed and traffic volume fluctuations in detail and predicts congestion using traffic simulations and historical data analysis. For example, it utilizes AI-based image recognition technology to analyze video data obtained from camera sensors and track the movement of vehicles on the road in real time. This allows for the prediction of congestion at specific intersections and road sections in advance, enabling appropriate countermeasures to be taken. When analyzing energy consumption patterns, it analyzes energy data in detail to understand consumption patterns by time of day and season. For example, it utilizes AI-based data analysis technology to analyze electricity consumption data obtained from smart meters and proposes peak shifting and the use of energy storage. This helps to find efficient ways to use energy and optimize energy management. When analyzing the usage of public services, it analyzes public service data in detail to understand usage frequency and user attributes. For example, it utilizes AI-based data mining technology to analyze bus boarding and alighting data and library usage data to propose adjustments to service placement and provision times. This allows for the optimization of public service provision and improved user satisfaction. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can predict congestion trends in specific areas and time periods based on historical traffic data and formulate future traffic countermeasures. It can also understand seasonal consumption patterns based on energy consumption data and optimize energy supply. In this way, the analytics department can not only grasp the situation in real time but also handle long-term risk management and trend analysis, supporting the efficient operation of the entire city.

[0032] The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. Specifically, it adjusts the timing of traffic signals to alleviate congestion. By changing the signal cycle and making dynamic adjustments according to traffic volume, traffic flow can be made smoother and congestion can be prevented. For example, by utilizing an AI-based traffic control system, traffic conditions are monitored in real time, and signals are synchronized and priority signals are introduced. This optimizes traffic flow at specific intersections and road sections, thereby alleviating congestion. When energy consumption reaches its peak, energy supply is adjusted to ensure efficient energy management. By utilizing demand response and energy storage, the balance between energy supply and consumption can be optimized. For example, by utilizing an AI-based energy management system, supply is adjusted based on consumption thresholds and peak times. This promotes efficient energy use and reduces energy costs. When demand for public services increases, service delivery is accelerated to achieve efficient operation. By increasing staff and automating services, user needs can be responded to quickly. For example, by utilizing an AI-based service management system, service provision is adjusted based on the number of users and specific events. This optimizes the provision of public services and improves user satisfaction. Furthermore, the optimization department reviews the overall management policies of the city based on the analysis results and formulates strategies to achieve sustainable urban management. For example, it reviews urban planning and infrastructure development policies based on traffic flow and energy consumption patterns to improve the efficiency of urban management. It also reviews the methods and placement of public services based on their usage, providing flexible solutions to meet user needs. In this way, the optimization department can play a crucial role in streamlining the overall operation of the city and achieving sustainable urban management.

[0033] The data collection unit can collect data from traffic sensors, energy meters, and public service usage data. For example, the data collection unit can use traffic sensors to collect vehicle speed and traffic volume in real time. The data collection unit can use camera sensors and loop coil sensors. The data collection unit can use energy meters to collect energy consumption data in real time. The data collection unit can use smart meters and electricity meters. The data collection unit can collect public service usage data in real time. The data collection unit can collect bus boarding and alighting data and library usage data. This allows for the collection of diverse data from across the city for analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from traffic sensors and energy meters into an AI and have the AI ​​perform data collection and analysis.

[0034] The analysis unit can analyze collected data, analyze traffic flow, and predict the occurrence of congestion. For example, the analysis unit can analyze traffic data and analyze fluctuations in vehicle speed and traffic volume. The analysis unit can predict the occurrence of congestion using traffic simulations and historical data analysis. By analyzing traffic flow and predicting the occurrence of congestion, the analysis unit can improve the efficiency of traffic management. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input traffic data into AI and have the AI ​​perform congestion prediction.

[0035] The analysis unit can analyze energy consumption patterns and propose efficient energy use methods. For example, the analysis unit can analyze energy data to analyze consumption patterns by time of day or season. The analysis unit can propose peak shifting and the use of energy storage. By analyzing energy consumption patterns, the analysis unit enables efficient energy management. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input energy data into AI and have the AI ​​propose efficient use methods.

[0036] The analysis unit can analyze the usage of public services and optimize service provision. For example, the analysis unit can analyze public service data to analyze usage frequency and user attributes. The analysis unit can propose adjustments to service placement and provision times. The analysis unit can analyze the usage of public services and improve the efficiency of service provision. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input public service data into AI and have the AI ​​perform the optimization of service provision.

[0037] The optimization unit can adjust the timing of traffic signals based on the analysis results to alleviate congestion. For example, the optimization unit can change the signal cycle or make dynamic adjustments according to traffic volume. The optimization unit can synchronize signals or introduce priority signals. By adjusting the timing of traffic signals, the optimization unit can alleviate traffic congestion. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the adjustment of signal timing.

[0038] The optimization unit can adjust energy supply and perform efficient energy management when energy consumption reaches its peak. For example, the optimization unit can utilize demand response and energy storage. The optimization unit can adjust supply based on consumption thresholds and peak time periods. Even when energy consumption reaches its peak, the optimization unit enables efficient energy management. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input energy consumption data into AI and have the AI ​​perform supply adjustments.

[0039] The optimization unit can expedite service delivery and achieve efficient operation when demand for public services increases. For example, the optimization unit can increase staff or automate services. The optimization unit can adjust service delivery based on an increase in the number of users or specific events. The optimization unit enables rapid and efficient service delivery even when demand for public services increases. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input public service data into AI and have the AI ​​expedite service delivery.

[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, when festivals or events are held, the unit can enhance the collection of traffic data to understand congestion levels. In the summer, the unit can enhance the collection of energy consumption data to predict air conditioning demand. In the winter, the unit can collect data on the use of public services to improve heating demand and the efficiency of snow removal operations. This allows for the optimization of data collection according to city events and seasons. 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 changes to data collection according to events or seasons.

[0041] The data collection unit can detect and filter out abnormal values ​​and noise in real time during data collection. For example, the data collection unit can detect and filter out abnormal speed data from traffic sensors. The data collection unit can detect and filter out abnormal consumption data from energy meters. The data collection unit can detect and filter out noise contained in public service usage data in real time. This improves data accuracy by detecting abnormal values ​​and noise 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 have AI perform the detection and filtering of abnormal values ​​and noise.

[0042] The data collection unit can apply different data collection protocols to different areas of the city. For example, in commercial areas, the unit can focus on collecting traffic data and energy consumption data. In residential areas, the unit can focus on collecting data on the use of public services. In industrial areas, the unit can focus on collecting energy consumption data and environmental data. This enables appropriate data collection for each area of ​​the city. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the application of area-specific data collection protocols.

[0043] The data collection unit can perform comparative analysis by referencing data from other cities and regions during data collection. For example, the data collection unit can refer to traffic data from other cities to compare and analyze congestion patterns. The data collection unit can refer to energy consumption data from other regions to compare and analyze efficient energy management methods. The data collection unit can refer to public service utilization data from other cities to compare and analyze methods for optimizing service provision. This makes it possible to collect data with higher accuracy by referencing data from other cities and regions. 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 data from other cities and regions into AI and have the AI ​​perform the comparative analysis.

[0044] The analysis unit can compare historical and current data to predict long-term trends. For example, it can compare historical and current traffic data to predict long-term trends in congestion. It can compare historical and current energy consumption data to predict long-term trends in energy consumption. It can compare historical and current public service usage data to predict long-term trends in service demand. This helps in future urban management by predicting long-term trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input historical and current data into an AI and have the AI ​​perform long-term trend predictions.

[0045] The analysis unit can integrate data from different data sources to perform more accurate analyses. For example, the analysis unit can integrate data from traffic sensors and energy meters to analyze the correlation between traffic and energy consumption. The analysis unit can integrate data on the use of public services and environmental data to optimize service provision. The analysis unit can integrate data from different areas to analyze the efficient operation of an entire city. This improves the accuracy of the analysis by integrating different data sources. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into AI and have the AI ​​perform data integration and analysis.

[0046] The analysis unit can apply different analysis algorithms to different areas of a city. For example, in commercial areas, the analysis unit can apply an algorithm that focuses on analyzing traffic data and energy consumption data. In residential areas, the analysis unit can apply an algorithm that focuses on analyzing data on the use of public services. In industrial areas, the analysis unit can apply an algorithm that focuses on analyzing energy consumption data and environmental data. This enables appropriate analysis for each area of ​​the city. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can have AI perform the application of area-specific analysis algorithms.

[0047] The analysis unit can compare its analysis results with those of other cities and regions to perform benchmarking. For example, it can compare its traffic analysis results with those of other cities to benchmark congestion mitigation. It can compare its energy consumption analysis results with those of other regions to benchmark efficient energy management. It can compare its public service utilization analysis results with those of other cities to benchmark optimization of service delivery. This allows for the adoption of best practices by comparing with other cities and regions. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from other cities and regions into an AI and have the AI ​​perform the benchmarking.

[0048] The optimization unit can dynamically change optimization parameters in response to specific events or seasons in the city. For example, when festivals or events are held, the optimization unit can adjust the timing of traffic signals to alleviate congestion. In the summer, the optimization unit can adjust energy supply to efficiently manage air conditioning demand. In the winter, the optimization unit can adjust the provision of public services to improve the efficiency of heating demand and snow removal operations. By changing optimization parameters in response to city events and seasons, efficient city management becomes possible. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the changes to optimization parameters in response to events and seasons.

[0049] The optimization unit can provide special optimization modes to respond to abnormal situations and emergencies. For example, in the event of a traffic accident, the optimization unit can adjust the timing of traffic signals and provide alternative routes. In the event of an energy shortage, the optimization unit can prioritize supplying energy to critical facilities. In the event of a surge in demand for public services, the optimization unit can expedite service delivery and achieve efficient operation. This enables a rapid response to abnormal situations and emergencies. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the execution of an optimization mode in emergency situations.

[0050] The optimization unit can apply different optimization protocols to different areas of a city. For example, in commercial areas, the optimization unit can adjust the timing of traffic signals to alleviate congestion. In residential areas, the optimization unit can adjust energy supply to enable efficient energy management. In industrial areas, the optimization unit can optimize energy consumption to reduce environmental impact. This enables appropriate optimization for each area of ​​the city. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the application of area-specific optimization protocols.

[0051] The optimization unit can refer to optimization results from other cities and regions and implement best practices. For example, the optimization unit can refer to traffic optimization results from other cities and implement best practices for congestion mitigation. The optimization unit can refer to energy management results from other regions and implement best practices for efficient energy management. The optimization unit can refer to public service optimization results from other cities and implement best practices for service delivery. This enables efficient urban management by implementing best practices from other cities and regions. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input optimization results from other cities and regions into AI and have the AI ​​implement best practices.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The urban optimization system may also include a prediction unit. Based on data obtained from the data collection unit, the prediction unit can forecast future urban demands and problems. For example, it can analyze traffic data to predict congestion during specific times of day or seasons. It can also analyze energy data to predict future peak energy consumption. Furthermore, it can analyze public service usage data to predict increases in service demand during specific events or seasons. This allows urban operators to take proactive measures against future problems. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input collected data into an AI and have the AI ​​perform predictions of future demands and problems.

[0054] The urban optimization system may also include a notification unit. Based on information from the analysis and optimization units, the notification unit can provide real-time notifications to urban operators and citizens. For example, if traffic congestion is predicted, the notification unit can issue a warning to operators and encourage appropriate measures. It can also call on citizens to conserve energy when energy consumption reaches its peak. Furthermore, if the demand for public services increases, it can suggest to operators that they increase staff or automate services. This allows urban operators and citizens to receive information in real time and respond quickly. Some or all of the above-mentioned processes in the notification unit may be performed using AI, or not. For example, the notification unit can input analysis and optimization results into the AI ​​and have the AI ​​execute the content and timing of notifications.

[0055] The urban optimization system may also include a learning unit. The learning unit can learn to improve the overall system performance based on collected data and analysis results. For example, the learning unit can analyze historical traffic data to build a more accurate congestion prediction model. It can also analyze energy data to develop new algorithms for more efficient energy consumption. Furthermore, it can analyze public service usage data to propose new strategies for optimizing service provision. This allows the urban optimization system to continuously learn and improve its performance. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input collected data and analysis results into an AI and have the AI ​​perform the learning.

[0056] The urban optimization system may also include a simulation unit. The simulation unit can perform simulations related to urban management based on collected data and analysis results. For example, the simulation unit can simulate the effects of different traffic management strategies based on traffic data. It can also simulate the effects of different energy management strategies based on energy data. Furthermore, it can simulate the effects of different service delivery strategies based on public service usage data. This allows urban operators to evaluate the effects of different strategies in advance and select the optimal strategy. Some or all of the above-described processes in the simulation unit may be performed using AI, or they may not. For example, the simulation unit can input collected data and analysis results into an AI and have the AI ​​execute the simulation.

[0057] The urban optimization system may also include a feedback unit. The feedback unit can evaluate the overall system performance based on the results of the optimization unit and make adjustments as needed. For example, the feedback unit can evaluate the results of traffic signal adjustments and readjust the signal timing as necessary. It can also evaluate the results of energy supply adjustments and revise energy management strategies. Furthermore, it can evaluate the results of public service delivery and improve the methods of service delivery. This allows the urban optimization system to continuously improve its performance. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the optimization results into an AI and have the AI ​​perform performance evaluation and adjustments.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects real-time data from across the city. The data collection unit collects data from traffic sensors, energy meters, and public service usage. For example, traffic sensors collect vehicle speed and traffic volume in real time, energy meters collect energy consumption data in real time, and public service usage data is also collected in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes traffic flow and predicts congestion. It analyzes energy consumption patterns and proposes efficient usage methods. It analyzes the usage of public services and optimizes service provision. Step 3: The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. It adjusts the timing of traffic signals to alleviate congestion. When energy consumption reaches its peak, it adjusts the energy supply to ensure efficient energy management. When the demand for public services increases, it speeds up the provision of services to achieve efficient operation.

[0060] (Example of form 2) An embodiment of the present invention provides for the optimization of urban traffic, public services, and energy management by integrating a next-generation communication network and AI technology. The urban optimization system collects real-time data from across the city using the next-generation communication network. For example, it collects data such as traffic sensors, energy meters, and the usage status of public services. This data is collected from various areas of the city and transmitted to a central data center. Next, the collected data is analyzed by AI. The AI ​​analyzes traffic flow and predicts the occurrence of congestion. It also analyzes energy consumption patterns and proposes efficient ways to use energy. Furthermore, it analyzes the usage status of public services and optimizes the provision of services. Based on the analysis results, the system optimizes each function of the city in real time. For example, if traffic congestion is predicted, the AI ​​adjusts the timing of traffic signals to alleviate congestion. Also, if energy consumption reaches its peak, the AI ​​adjusts the energy supply to achieve efficient energy management. Furthermore, if the demand for public services increases, the AI ​​speeds up the provision of services to achieve efficient operation. This system streamlines urban traffic, public services, and energy management, enabling sustainable urban management. For example, reduced traffic congestion shortens commute times, and more efficient energy use reduces environmental impact. Furthermore, faster delivery of public services improves the quality of life for citizens. In addition, the system automatically collects and analyzes data in various areas of the city, reducing operational costs. For instance, automated installation and maintenance of traffic sensors and energy meters reduces labor costs. Automated AI-driven data analysis improves accuracy and enables more efficient urban management. Thus, a system integrating next-generation communication networks and AI technology optimizes urban traffic, public services, and energy management, leading to sustainable urban operations. In short, this urban optimization system can streamline urban traffic, public services, and energy management, enabling sustainable urban management.

[0061] The urban optimization system according to this embodiment comprises a data collection unit, an analysis unit, and an optimization unit. The data collection unit collects real-time data from the entire city. The data collection unit collects data such as traffic sensors, energy meters, and the usage status of public services. The data collection unit can collect vehicle speed and traffic volume in real time using traffic sensors. For example, camera sensors and loop coil sensors can be used as traffic sensors. The data collection unit can collect energy consumption data in real time using energy meters. For example, smart meters and electricity meters can be used as energy meters. The data collection unit can collect data on the usage status of public services in real time. For example, bus boarding and alighting data and library usage data can be collected. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes traffic flow and predicts the occurrence of congestion. The analysis unit can analyze traffic data and analyze fluctuations in vehicle speed and traffic volume. For example, the analysis unit predicts the occurrence of congestion using traffic simulations and historical data analysis. The analysis unit analyzes energy consumption patterns and proposes efficient energy use methods. The analysis unit can analyze energy data and analyze consumption patterns by time of day and season. For example, the analysis unit can propose peak shifting and the use of energy storage. The analysis unit analyzes the usage status of public services and optimizes service provision. The analysis unit can analyze public service data and analyze usage frequency and user attributes. For example, the analysis unit can propose adjustments to service placement and provision times. The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. For example, the optimization unit can adjust the timing of traffic signals to alleviate congestion. The optimization unit can change the signal cycle and make dynamic adjustments according to traffic volume. For example, the optimization unit can synchronize signals and introduce priority signals. When energy consumption reaches its peak, the optimization unit adjusts the energy supply to perform efficient energy management. The optimization unit can perform demand response and utilize energy storage. For example, the optimization unit adjusts supply based on consumption thresholds and peak time periods.The optimization unit accelerates service delivery and achieves efficient operation when demand for public services increases. The optimization unit can increase staffing levels and automate services. For example, the optimization unit adjusts service delivery based on an increase in the number of users or specific events. As a result, the urban optimization system according to this embodiment can streamline urban transportation, public services, and energy management, enabling sustainable urban management.

[0062] The data collection unit collects real-time data from across the city. Specifically, it collects data from traffic sensors, energy meters, and public service usage. Traffic sensors can use camera sensors and loop coil sensors to collect vehicle speed and traffic volume in real time. Camera sensors photograph vehicles on the road and measure the number and speed of vehicles using image analysis technology. Loop coil sensors detect the metal parts of passing vehicles on coils embedded in the road and measure traffic volume. This allows for real-time understanding of traffic flow and congestion. Energy meters collect energy consumption data in real time using smart meters and electricity meters. Smart meters record detailed electricity consumption in homes and businesses and transmit the data to a central system via wireless communication. This allows for accurate understanding of energy consumption patterns and peak consumption. Regarding public service usage, data such as bus boarding and alighting data and library usage data are collected. Bus boarding and alighting data is collected through sensors installed at bus stops and passenger IC card readers, while library usage data is collected based on borrowing and returning records and the number of visitors. This allows for a detailed understanding of public service usage and provides fundamental data for optimizing service delivery. The data collection unit centrally manages this diverse data and stores it in a real-time updated database. This enables the analysis and optimization units to quickly access the necessary data. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. As a result, the data collection unit can collect data efficiently and effectively, and understand the situation of the entire city in real time.

[0063] The analysis unit analyzes the data collected by the collection unit. Specifically, it analyzes traffic flow and predicts congestion. When analyzing traffic data, it analyzes vehicle speed and traffic volume fluctuations in detail and predicts congestion using traffic simulations and historical data analysis. For example, it utilizes AI-based image recognition technology to analyze video data obtained from camera sensors and track the movement of vehicles on the road in real time. This allows for the prediction of congestion at specific intersections and road sections in advance, enabling appropriate countermeasures to be taken. When analyzing energy consumption patterns, it analyzes energy data in detail to understand consumption patterns by time of day and season. For example, it utilizes AI-based data analysis technology to analyze electricity consumption data obtained from smart meters and proposes peak shifting and the use of energy storage. This helps to find efficient ways to use energy and optimize energy management. When analyzing the usage of public services, it analyzes public service data in detail to understand usage frequency and user attributes. For example, it utilizes AI-based data mining technology to analyze bus boarding and alighting data and library usage data to propose adjustments to service placement and provision times. This allows for the optimization of public service provision and improved user satisfaction. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term trend analysis and risk assessment. For example, it can predict congestion trends in specific areas and time periods based on historical traffic data and formulate future traffic countermeasures. It can also understand seasonal consumption patterns based on energy consumption data and optimize energy supply. In this way, the analytics department can not only grasp the situation in real time but also handle long-term risk management and trend analysis, supporting the efficient operation of the entire city.

[0064] The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. Specifically, it adjusts the timing of traffic signals to alleviate congestion. By changing the signal cycle and making dynamic adjustments according to traffic volume, traffic flow can be made smoother and congestion can be prevented. For example, by utilizing an AI-based traffic control system, traffic conditions are monitored in real time, and signals are synchronized and priority signals are introduced. This optimizes traffic flow at specific intersections and road sections, thereby alleviating congestion. When energy consumption reaches its peak, energy supply is adjusted to ensure efficient energy management. By utilizing demand response and energy storage, the balance between energy supply and consumption can be optimized. For example, by utilizing an AI-based energy management system, supply is adjusted based on consumption thresholds and peak times. This promotes efficient energy use and reduces energy costs. When demand for public services increases, service delivery is accelerated to achieve efficient operation. By increasing staff and automating services, user needs can be responded to quickly. For example, by utilizing an AI-based service management system, service provision is adjusted based on the number of users and specific events. This optimizes the provision of public services and improves user satisfaction. Furthermore, the optimization department reviews the overall management policies of the city based on the analysis results and formulates strategies to achieve sustainable urban management. For example, it reviews urban planning and infrastructure development policies based on traffic flow and energy consumption patterns to improve the efficiency of urban management. It also reviews the methods and placement of public services based on their usage, providing flexible solutions to meet user needs. In this way, the optimization department can play a crucial role in streamlining the overall operation of the city and achieving sustainable urban management.

[0065] The data collection unit can collect data from traffic sensors, energy meters, and public service usage data. For example, the data collection unit can use traffic sensors to collect vehicle speed and traffic volume in real time. The data collection unit can use camera sensors and loop coil sensors. The data collection unit can use energy meters to collect energy consumption data in real time. The data collection unit can use smart meters and electricity meters. The data collection unit can collect public service usage data in real time. The data collection unit can collect bus boarding and alighting data and library usage data. This allows for the collection of diverse data from across the city for analysis. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from traffic sensors and energy meters into an AI and have the AI ​​perform data collection and analysis.

[0066] The analysis unit can analyze collected data, analyze traffic flow, and predict the occurrence of congestion. For example, the analysis unit can analyze traffic data and analyze fluctuations in vehicle speed and traffic volume. The analysis unit can predict the occurrence of congestion using traffic simulations and historical data analysis. By analyzing traffic flow and predicting the occurrence of congestion, the analysis unit can improve the efficiency of traffic management. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input traffic data into AI and have the AI ​​perform congestion prediction.

[0067] The analysis unit can analyze energy consumption patterns and propose efficient energy use methods. For example, the analysis unit can analyze energy data to analyze consumption patterns by time of day or season. The analysis unit can propose peak shifting and the use of energy storage. By analyzing energy consumption patterns, the analysis unit enables efficient energy management. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input energy data into AI and have the AI ​​propose efficient use methods.

[0068] The analysis unit can analyze the usage of public services and optimize service provision. For example, the analysis unit can analyze public service data to analyze usage frequency and user attributes. The analysis unit can propose adjustments to service placement and provision times. The analysis unit can analyze the usage of public services and improve the efficiency of service provision. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input public service data into AI and have the AI ​​perform the optimization of service provision.

[0069] The optimization unit can adjust the timing of traffic signals based on the analysis results to alleviate congestion. For example, the optimization unit can change the signal cycle or make dynamic adjustments according to traffic volume. The optimization unit can synchronize signals or introduce priority signals. By adjusting the timing of traffic signals, the optimization unit can alleviate traffic congestion. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the adjustment of signal timing.

[0070] The optimization unit can adjust energy supply and perform efficient energy management when energy consumption reaches its peak. For example, the optimization unit can utilize demand response and energy storage. The optimization unit can adjust supply based on consumption thresholds and peak time periods. Even when energy consumption reaches its peak, the optimization unit enables efficient energy management. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input energy consumption data into AI and have the AI ​​perform supply adjustments.

[0071] The optimization unit can expedite service delivery and achieve efficient operation when demand for public services increases. For example, the optimization unit can increase staff or automate services. The optimization unit can adjust service delivery based on an increase in the number of users or specific events. The optimization unit enables rapid and efficient service delivery even when demand for public services increases. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input public service data into AI and have the AI ​​expedite service delivery.

[0072] 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. If the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize collecting only important data and process it quickly. This reduces the system load by adjusting the timing of data collection 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 emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0073] The data collection unit can dynamically change the types of data it collects according to specific events or seasons in the city. For example, when festivals or events are held, the unit can enhance the collection of traffic data to understand congestion levels. In the summer, the unit can enhance the collection of energy consumption data to predict air conditioning demand. In the winter, the unit can collect data on the use of public services to improve heating demand and the efficiency of snow removal operations. This allows for the optimization of data collection according to city events and seasons. 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 changes to data collection according to events or seasons.

[0074] The data collection unit can detect and filter out abnormal values ​​and noise in real time during data collection. For example, the data collection unit can detect and filter out abnormal speed data from traffic sensors. The data collection unit can detect and filter out abnormal consumption data from energy meters. The data collection unit can detect and filter out noise contained in public service usage data in real time. This improves data accuracy by detecting abnormal values ​​and noise 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 have AI perform the detection and filtering of abnormal values ​​and noise.

[0075] The data collection unit can estimate the user's emotions and prioritize the data to be collected 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. If the user is in a hurry, the data collection unit can prioritize collecting data that needs to be processed quickly. 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 emotion data into an AI and have the AI ​​perform data prioritization.

[0076] The data collection unit can apply different data collection protocols to different areas of the city. For example, in commercial areas, the unit can focus on collecting traffic data and energy consumption data. In residential areas, the unit can focus on collecting data on the use of public services. In industrial areas, the unit can focus on collecting energy consumption data and environmental data. This enables appropriate data collection for each area of ​​the city. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the application of area-specific data collection protocols.

[0077] The data collection unit can perform comparative analysis by referencing data from other cities and regions during data collection. For example, the data collection unit can refer to traffic data from other cities to compare and analyze congestion patterns. The data collection unit can refer to energy consumption data from other regions to compare and analyze efficient energy management methods. The data collection unit can refer to public service utilization data from other cities to compare and analyze methods for optimizing service provision. This makes it possible to collect data with higher accuracy by referencing data from other cities and regions. 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 data from other cities and regions into AI and have the AI ​​perform the comparative analysis.

[0078] 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 nervous, 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. 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, visibility is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0079] The analysis unit can compare historical and current data to predict long-term trends. For example, it can compare historical and current traffic data to predict long-term trends in congestion. It can compare historical and current energy consumption data to predict long-term trends in energy consumption. It can compare historical and current public service usage data to predict long-term trends in service demand. This helps in future urban management by predicting long-term trends. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input historical and current data into an AI and have the AI ​​perform long-term trend predictions.

[0080] The analysis unit can integrate data from different data sources to perform more accurate analyses. For example, the analysis unit can integrate data from traffic sensors and energy meters to analyze the correlation between traffic and energy consumption. The analysis unit can integrate data on the use of public services and environmental data to optimize service provision. The analysis unit can integrate data from different areas to analyze the efficient operation of an entire city. This improves the accuracy of the analysis by integrating different data sources. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data sources into AI and have the AI ​​perform data integration and analysis.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize important analyses. If the user is relaxed, the analysis unit will prioritize detailed analyses. If the user is in a hurry, the analysis unit will prioritize analyses that require quick processing. In this way, important analyses can be prioritized by determining the priority of analysis 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 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 AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the determination of analysis priorities.

[0082] The analysis unit can apply different analysis algorithms to different areas of a city. For example, in commercial areas, the analysis unit can apply an algorithm that focuses on analyzing traffic data and energy consumption data. In residential areas, the analysis unit can apply an algorithm that focuses on analyzing data on the use of public services. In industrial areas, the analysis unit can apply an algorithm that focuses on analyzing energy consumption data and environmental data. This enables appropriate analysis for each area of ​​the city. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can have AI perform the application of area-specific analysis algorithms.

[0083] The analysis unit can compare its analysis results with those of other cities and regions to perform benchmarking. For example, it can compare its traffic analysis results with those of other cities to benchmark congestion mitigation. It can compare its energy consumption analysis results with those of other regions to benchmark efficient energy management. It can compare its public service utilization analysis results with those of other cities to benchmark optimization of service delivery. This allows for the adoption of best practices by comparing with other cities and regions. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data from other cities and regions into an AI and have the AI ​​perform the benchmarking.

[0084] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is nervous, the optimization unit can provide a simple and highly visible optimization method. If the user is relaxed, the optimization unit can provide an optimization method that includes detailed information. If the user is in a hurry, the optimization unit can provide a concise optimization method. This allows for more appropriate optimization by adjusting the optimization method 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 optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the optimization method.

[0085] The optimization unit can dynamically change optimization parameters in response to specific events or seasons in the city. For example, when festivals or events are held, the optimization unit can adjust the timing of traffic signals to alleviate congestion. In the summer, the optimization unit can adjust energy supply to efficiently manage air conditioning demand. In the winter, the optimization unit can adjust the provision of public services to improve the efficiency of heating demand and snow removal operations. By changing optimization parameters in response to city events and seasons, efficient city management becomes possible. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the changes to optimization parameters in response to events and seasons.

[0086] The optimization unit can provide special optimization modes to respond to abnormal situations and emergencies. For example, in the event of a traffic accident, the optimization unit can adjust the timing of traffic signals and provide alternative routes. In the event of an energy shortage, the optimization unit can prioritize supplying energy to critical facilities. In the event of a surge in demand for public services, the optimization unit can expedite service delivery and achieve efficient operation. This enables a rapid response to abnormal situations and emergencies. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the execution of an optimization mode in emergency situations.

[0087] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is stressed, the optimization unit will prioritize important optimizations. If the user is relaxed, the optimization unit will prioritize detailed optimizations. If the user is in a hurry, the optimization unit will prioritize optimizations that require quick processing. In this way, important optimizations can be prioritized by determining optimization priorities 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 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 optimization unit may be performed using AI or not. For example, the optimization unit can input user emotion data into an AI and have the AI ​​perform the optimization priority determination.

[0088] The optimization unit can apply different optimization protocols to different areas of a city. For example, in commercial areas, the optimization unit can adjust the timing of traffic signals to alleviate congestion. In residential areas, the optimization unit can adjust energy supply to enable efficient energy management. In industrial areas, the optimization unit can optimize energy consumption to reduce environmental impact. This enables appropriate optimization for each area of ​​the city. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can have AI perform the application of area-specific optimization protocols.

[0089] The optimization unit can refer to optimization results from other cities and regions and implement best practices. For example, the optimization unit can refer to traffic optimization results from other cities and implement best practices for congestion mitigation. The optimization unit can refer to energy management results from other regions and implement best practices for efficient energy management. The optimization unit can refer to public service optimization results from other cities and implement best practices for service delivery. This enables efficient urban management by implementing best practices from other cities and regions. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, the optimization unit can input optimization results from other cities and regions into AI and have the AI ​​implement best practices.

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

[0091] The urban optimization system may also include a prediction unit. Based on data obtained from the data collection unit, the prediction unit can forecast future urban demands and problems. For example, it can analyze traffic data to predict congestion during specific times of day or seasons. It can also analyze energy data to predict future peak energy consumption. Furthermore, it can analyze public service usage data to predict increases in service demand during specific events or seasons. This allows urban operators to take proactive measures against future problems. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can input collected data into an AI and have the AI ​​perform predictions of future demands and problems.

[0092] The urban optimization system may also include a notification unit. Based on information from the analysis and optimization units, the notification unit can provide real-time notifications to urban operators and citizens. For example, if traffic congestion is predicted, the notification unit can issue a warning to operators and encourage appropriate measures. It can also call on citizens to conserve energy when energy consumption reaches its peak. Furthermore, if the demand for public services increases, it can suggest to operators that they increase staff or automate services. This allows urban operators and citizens to receive information in real time and respond quickly. Some or all of the above-mentioned processes in the notification unit may be performed using AI, or not. For example, the notification unit can input analysis and optimization results into the AI ​​and have the AI ​​execute the content and timing of notifications.

[0093] The urban optimization system may also include a learning unit. The learning unit can learn to improve the overall system performance based on collected data and analysis results. For example, the learning unit can analyze historical traffic data to build a more accurate congestion prediction model. It can also analyze energy data to develop new algorithms for more efficient energy consumption. Furthermore, it can analyze public service usage data to propose new strategies for optimizing service provision. This allows the urban optimization system to continuously learn and improve its performance. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input collected data and analysis results into an AI and have the AI ​​perform the learning.

[0094] The urban optimization system may also include a simulation unit. The simulation unit can perform simulations related to urban management based on collected data and analysis results. For example, the simulation unit can simulate the effects of different traffic management strategies based on traffic data. It can also simulate the effects of different energy management strategies based on energy data. Furthermore, it can simulate the effects of different service delivery strategies based on public service usage data. This allows urban operators to evaluate the effects of different strategies in advance and select the optimal strategy. Some or all of the above-described processes in the simulation unit may be performed using AI, or they may not. For example, the simulation unit can input collected data and analysis results into an AI and have the AI ​​execute the simulation.

[0095] The urban optimization system may also include a feedback unit. The feedback unit can evaluate the overall system performance based on the results of the optimization unit and make adjustments as needed. For example, the feedback unit can evaluate the results of traffic signal adjustments and readjust the signal timing as necessary. It can also evaluate the results of energy supply adjustments and revise energy management strategies. Furthermore, it can evaluate the results of public service delivery and improve the methods of service delivery. This allows the urban optimization system to continuously improve its performance. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit can input the optimization results into an AI and have the AI ​​perform performance evaluation and adjustments.

[0096] The urban optimization system may further include an emotion estimation unit. This unit can estimate the user's emotions and adjust the system's operation based on the estimated emotions. For example, if the user is stressed, the emotion estimation unit can reduce the frequency of system notifications, thereby easing the user's burden. If the user is relaxed, it can provide detailed information to deepen the user's understanding. Furthermore, if the user is in a hurry, it can prioritize providing only essential information to support a quick response. This improves the user experience by adjusting the system's operation according to the user's emotions. Some or all of the above processing in the emotion estimation unit may be performed using AI or not. For example, the emotion estimation unit can input user emotion data into an AI and have the AI ​​perform the system operation adjustments.

[0097] The urban optimization system may further include an emotional feedback unit. This emotional feedback unit can monitor the user's emotions in real time and provide feedback on the system's operation. For example, if the user is stressed by system notifications, the emotional feedback unit can adjust the content and frequency of those notifications. If the user is satisfied with the information provided by the system, it can continue providing similar information. Furthermore, if the user is dissatisfied with the system's operation, it can provide feedback to improve the operation method. This allows for continuous improvement of the system's operation based on the user's emotions. Some or all of the above processing in the emotional feedback unit may be performed using AI or not. For example, the emotional feedback unit can input user emotion data into an AI and have the AI ​​provide the feedback.

[0098] The urban optimization system may further include an emotion analysis unit. The emotion analysis unit can analyze user emotion data and identify areas for improvement in the system. For example, if a user is stressed by a particular function, the emotion analysis unit can identify areas for improvement in that function. Also, if a user is satisfied with a particular information provision, that information provision method can be applied to other functions. Furthermore, if a user is dissatisfied with a particular operation, the emotion analysis unit can make specific suggestions for improving that operation method. This allows for the identification of areas for improvement in the system based on user emotions and enables continuous improvement. Some or all of the above processing in the emotion analysis unit may be performed using AI or not. For example, the emotion analysis unit can input user emotion data into an AI and have the AI ​​identify areas for improvement.

[0099] The urban optimization system may also include an emotion prediction unit. This unit can predict future emotions based on the user's past emotional data. For example, if the emotion prediction unit tends to feel stressed during certain times or situations, it can adjust the system's operation during those times or situations. It can also enhance information provision if the user tends to be satisfied with it. Furthermore, if the user tends to be dissatisfied with certain operations, the system can proactively improve those operations. This allows the system to predict future emotions based on the user's past emotional data and proactively adjust its operation. Some or all of the above-described processes in the emotion prediction unit may be performed using AI or not. For example, the emotion prediction unit can input the user's past emotional data into an AI and have the AI ​​perform future emotion predictions.

[0100] The urban optimization system may also include an emotion-responsive unit. This unit can dynamically change the system's interface and operation methods in response to the user's emotions. For example, if the user is stressed, the emotion-responsive unit can provide a simple and intuitive interface. If the user is relaxed, it can provide an interface with detailed information. Furthermore, if the user is in a hurry, it can provide a quickly operable interface. This allows for an improved user experience by dynamically changing the system's interface and operation methods in response to the user's emotions. Some or all of the above processing in the emotion-responsive unit may be performed using AI or not. For example, the emotion-responsive unit can input user emotion data into an AI and have the AI ​​perform the necessary changes to the interface and operation methods.

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

[0102] Step 1: The data collection unit collects real-time data from across the city. The data collection unit collects data from traffic sensors, energy meters, and public service usage. For example, traffic sensors collect vehicle speed and traffic volume in real time, energy meters collect energy consumption data in real time, and public service usage data is also collected in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes traffic flow and predicts congestion. It analyzes energy consumption patterns and proposes efficient usage methods. It analyzes the usage of public services and optimizes service provision. Step 3: The optimization unit optimizes urban functions based on the analysis results obtained by the analysis unit. It adjusts the timing of traffic signals to alleviate congestion. When energy consumption reaches its peak, it adjusts the energy supply to ensure efficient energy management. When the demand for public services increases, it speeds up the provision of services to achieve efficient operation.

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

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include 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.

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

[0106] Each of the multiple elements described above, including the data collection unit, analysis unit, and optimization unit, 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 the camera 42 and energy meter of the smart device 14. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing unit 12 to analyze traffic flow and energy consumption patterns. The optimization unit optimizes urban functions based on the analysis results by the specific processing unit 290 of the data processing unit 12, adjusting signal timing and energy supply. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the data collection unit, analysis unit, and optimization unit, is implemented, for example, by 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 camera 42 and energy meter of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze traffic flow and energy consumption patterns. The optimization unit optimizes urban functions based on the analysis results by the specific processing unit 290 of the data processing unit 12, adjusting signal timing and energy supply. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

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

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

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the data collection unit, analysis unit, and optimization unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit collects real-time data from the entire city using the camera 42 and energy meter of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to analyze traffic flow and energy consumption patterns. The optimization unit optimizes urban functions based on the analysis results using the specific processing unit 290 of the data processing unit 12, adjusting signal timing and energy supply. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the data collection unit, analysis unit, and optimization unit, is implemented in, for example, 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 camera 42 and energy meter of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze traffic flow and energy consumption patterns. The optimization unit optimizes urban functions based on the analysis results by the specific processing unit 290 of the data processing unit 12, adjusting signal timing and energy supply. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

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

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0174] (Note 1) A data collection unit that collects real-time data from the entire city, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes an optimization unit that optimizes urban functions based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as traffic sensors, energy meters, and usage data of public services. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing collected data, we analyze traffic flow and predict the occurrence of congestion. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We analyze energy consumption patterns and propose efficient energy utilization methods. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze the usage of public services and optimize service delivery. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, Based on the analysis results, the timing of traffic signals is adjusted to alleviate congestion. The system described in Appendix 1, characterized by the features described herein. (Note 7) The optimization unit, When energy consumption reaches its peak, the energy supply is adjusted to ensure efficient energy management. The system described in Appendix 1, characterized by the features described herein. (Note 8) The optimization unit, When demand for public services increases, the delivery of services will be expedited and operations will be made more efficient. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) The aforementioned collection unit is During data collection, anomalies and noise are detected and filtered in real time. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is Apply different data collection protocols to different areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting data, we will also refer to and compare data from other cities and regions. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, By comparing historical and current data, we can predict long-term trends. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Integrate data from different data sources to perform more accurate analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Apply different analysis algorithms to different areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, The analysis results are compared with those of other cities and regions to perform benchmarking. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, Dynamically change optimization parameters based on specific events or seasons in the city. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, It provides a special optimization mode to respond to abnormal situations and emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, Apply different optimization protocols to different areas of the city. The system described in Appendix 1, characterized by the features described herein. (Note 26) The optimization unit, Refer to optimization results from other cities and regions and implement best practices. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A data collection unit that collects real-time data from the entire city, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes an optimization unit that optimizes urban functions based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as traffic sensors, energy meters, and usage data of public services. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing collected data, we analyze traffic flow and predict the occurrence of congestion. The system according to feature 1.

4. The aforementioned analysis unit, We analyze energy consumption patterns and propose efficient energy utilization methods. The system according to feature 1.

5. The aforementioned analysis unit, Analyze the usage of public services and optimize service delivery. The system according to feature 1.

6. The optimization unit, Based on the analysis results, the timing of traffic signals is adjusted to alleviate congestion. The system according to feature 1.

7. The optimization unit, When energy consumption reaches its peak, the energy supply is adjusted to ensure efficient energy management. The system according to feature 1.

8. The optimization unit, When demand for public services increases, the delivery of services will be expedited and operations will be made more efficient. The system according to feature 1.

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