system

Abstract

We provide the system. [Solution] A means of collecting data in real time from a data generation device, A means for predicting future states using a prediction device based on the aforementioned data, A means for analyzing the aforementioned predicted data and providing optimal information to the user, A system that includes this.

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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern cities, there are a variety of sustainability-related problems such as traffic congestion, peak energy consumption, and increasing environmental burdens. These problems impose a heavy load on the urban infrastructure and may reduce the quality of life of residents. In contrast, there is a demand for comprehensive information provision that utilizes real-time data collection and future prediction, but the current technology has not achieved sufficient accuracy and speed. The purpose of the present invention is to solve these problems and realize more efficient and sustainable urban operation. 【Means for Solving the Problems】 【0005】 This invention provides a system comprising means for collecting data in real time from a data generation device, means for predicting future states using a prediction device based on the collected data, and means for analyzing the predicted data and providing optimal information to users. Furthermore, by including means for detecting emergencies using an anomaly detection device and quickly formulating response plans, and a communication device for notifying users of recommended actions, the system supports the sustainable operation of cities. This makes it possible to achieve efficient operation of urban infrastructure and reduce environmental impact. 【0006】 A "data generation device" is a device that uses sensors, IoT devices, etc., to measure the state of the physical environment and infrastructure, and generates the measurement results as digital data. 【0007】 "Means of collecting data in real time" refers to methods for continuously acquiring data from various sensors and devices, and for immediately processing or recording that acquired data. 【0008】 A "predictive device" is a device that uses statistical methods and machine learning models based on collected data to derive future states and trends. 【0009】 "Means of predicting future conditions" refer to methods and techniques used to estimate future situations by analyzing current data and past patterns. 【0010】 "Means of analyzing data" refer to methods for evaluating and examining collected or predicted data to extract useful information and patterns. 【0011】 "Means of providing users with the most relevant information" refers to a method of selecting beneficial action guidelines and information for users based on the results of analyzed data, and communicating them in an appropriate format. 【0012】 An "anomaly detection device" is a device used to detect unusual events or abnormal signals and to issue warnings or take countermeasures. 【0013】 "Means of detecting emergencies" are methods used to identify and immediately grasp emergency events that deviate from normal circumstances. 【0014】 "Means for rapidly formulating response plans" refers to methods that enable the prompt planning and implementation of appropriate countermeasures in response to detected anomalies or emergencies. 【0015】 "Communication equipment" refers to devices used to transmit or exchange information, and includes network infrastructure and wireless communication devices. 【0016】 A "communication device for notifying recommended actions" is a device that sends notifications to users based on analyzed information, urging them to take specific actions. [Brief explanation of the drawing] 【0017】 [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] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0019】 First, the language used in the following description will be described. 【0020】 In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0021】 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. 【0022】 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. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. 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). 【0024】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 As shown in Figure 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. 【0028】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0029】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0030】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0031】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0032】 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. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 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. 【0035】 The 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. 【0036】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0037】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0038】 This invention is an integrated management system for supporting the sustainable development of cities, which uses AI agents to monitor urban infrastructure in real time and optimize various resources. This system has a complex configuration that includes data generation devices, prediction devices, and anomaly detection devices. 【0039】 In this system, a server collects data in real time from various IoT sensors installed throughout the city. This data covers various aspects of urban infrastructure, such as traffic conditions, energy consumption, water resource usage, and air quality. The server stores the data in a database for continuous analysis. 【0040】 Next, the server applies an AI model to analyze the collected data and predict future demand and possible anomalies. In particular, by predicting traffic flow and energy consumption trends, it becomes possible to manage cities and reallocate resources efficiently. 【0041】 Anomaly detection devices are used to immediately detect events that deviate from normal conditions or emergencies by having the server perform real-time data analysis. After detection, a response plan is automatically generated quickly and notified to the relevant organizations to facilitate the early resolution of the problem. 【0042】 Furthermore, this system is equipped with communication devices and can provide important information to users. Users are expected to make better choices based on the information provided. For example, if traffic congestion is predicted, the server can send a notification to the user's smartphone app to encourage the use of public transportation. It may also recommend adjusting the usage time of home appliances to avoid peak energy consumption. 【0043】 As a concrete example, consider the system operation on a typical day. A server receives traffic data from sensors before the morning rush hour, and an advanced AI model predicts the day's traffic patterns. As a result, if higher-than-usual congestion is expected on certain roads or intersections, the server suggests alternative routes to the user. Based on this information, the user may be able to significantly reduce their commute time. 【0044】 Furthermore, if weather conditions worsen and a surge in electricity demand is expected, the server will use an anomaly detection device to issue a warning in advance and propose energy-saving measures to power companies and residents. In this way, the present invention will benefit a large number of users and enable the rationalization of urban management and improvement of sustainability. 【0045】 The following describes the processing flow. 【0046】 Step 1: 【0047】 The server establishes connections with each IoT sensor placed throughout the city. The server collects data such as traffic conditions, energy consumption, water resource usage, and air quality in real time and stores the collected data in a database. 【0048】 Step 2: 【0049】 The server retrieves the latest sensor data stored in the database. Based on this data, the server uses an AI model to predict future conditions such as traffic patterns and energy demand. The server evaluates the prediction results and identifies anomalies and necessary interventions. 【0050】 Step 3: 【0051】 The server uses an anomaly detection device to detect events that exceed the normal range in the collected data and predictive information. If an anomaly is detected, the server quickly and automatically generates a countermeasure plan and prepares to notify relevant organizations. 【0052】 Step 4: 【0053】 The server generates useful information for the user based on prediction and anomaly detection results. The terminal presents the generated information to the user, for example, recommending alternative routes in response to changes in traffic conditions or prompting optimization of appliance usage for energy saving. 【0054】 Step 5: 【0055】 Users can review information received through their devices and adjust their actions as needed. By following the notified recommendations, users can improve convenience and efficiency by changing their commute routes or adjusting their power usage. 【0056】 (Example 1) 【0057】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0058】 In urban areas, there is a need for optimized infrastructure operations and immediate response to emergencies. Currently, real-time data analysis and forecasting are not sufficiently performed, resulting in challenges in efficient resource management and rapid response. 【0059】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0060】 In this invention, the server includes means for collecting information in a time series from an information acquisition device, means for predicting future states using a prediction device based on the information, and means for storing the collected information in an information aggregate and performing preprocessing as necessary. This enables efficient resource management and rapid response to anomalies based on real-time data analysis and future prediction. 【0061】 An "information acquisition device" is a device used to collect data from sensors and devices installed within a city. 【0062】 "Methods for collecting information in a time series" refers to the process of continuously acquiring data along a time axis and processing it in real time. 【0063】 A "predictive device" is a device or system used to predict future states based on collected data. 【0064】 An "information collection" is a database or storage system that stores collected data and allows it to be retrieved and used when needed. 【0065】 "Preprocessing" refers to processes such as data normalization and cleaning that are performed to prepare data before data analysis or prediction. 【0066】 An "artificial intelligence model" is an algorithm or learning model used to analyze large amounts of data and identify specific patterns or trends. 【0067】 An "anomaly detection algorithm" is a computational method for detecting patterns or values ​​that are different from the norm within data and determining whether an anomaly exists. 【0068】 A "means for detecting anomalies in real time" refers to a device or algorithm for analyzing data in real time and identifying anomalies on the spot. 【0069】 An "emergency situation" refers to a situation or event that disrupts normal infrastructure operations or requires immediate attention. 【0070】 A "response plan" is a plan of specific actions and procedures to be taken in response to detected anomalies or emergencies. 【0071】 A "communication device" is a combination of hardware and software used to transmit information to users or other systems. 【0072】 "Improvement actions" refer to specific actions taken by users based on information and notifications from the system, aimed at improving the situation or increasing efficiency. 【0073】 This invention is an integrated management system aimed at the efficient and safe operation of infrastructure in urban areas. This system optimizes urban management by predicting future conditions based on data obtained from information acquisition devices and detecting anomalies in real time. The following specifically describes embodiments for carrying out this invention. 【0074】 The server first acquires information through numerous sensors. These sensors provide real-time data on various environmental indicators within the city, such as traffic, energy, water resources, and air quality. This information is then stored in a database in JSON format. The database utilizes a distributed database system to provide high-speed information access. 【0075】 For data analysis, the server utilizes AI frameworks such as TENSORFLOW® and PyTorch. These frameworks allow the server to generate machine learning models based on collected data to predict traffic patterns and energy consumption trends. For example, the server can analyze past trends to predict traffic congestion before the morning rush hour and suggest alternative routes on specific roads to the user. 【0076】 In anomaly detection, the server uses an anomaly identification algorithm. This allows the server to detect data that deviates significantly from normal patterns and immediately notify the system administrator. For example, if the server detects a sudden increase in energy consumption, it can issue a warning to the power company and instruct them to take appropriate energy-saving measures. 【0077】 Furthermore, the server provides information to the user's terminal via communication equipment. The system utilizes a smartphone app to facilitate smooth communication. Users can receive traffic information on their devices and change their commute route based on that information. In particular, notifications encouraging the use of public transportation are sent to users, supporting efficient travel. 【0078】 Specific examples include servers using prompts such as "Please tell me the predicted traffic congestion patterns in your area for the next 24 hours" or "Please predict the energy consumption trends based on this week's weather and suggest measures to avoid peaks," to provide users with the necessary information. 【0079】 In this way, the present invention supports the sustainable development of cities by combining data-driven infrastructure management with real-time anomaly response. 【0080】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0081】 Step 1: 【0082】 The server collects data from various sensors. The input is real-time data from various urban infrastructure sensors. The server receives this data in JSON format and stores it in a database. Specifically, the server retrieves data using HTTP requests and automatically stores it in the database. 【0083】 Step 2: 【0084】 The server performs preprocessing on the collected data. The input is raw data collected from sensors. The server cleans this data, removes missing values, and performs unification processes such as normalization. The output is clean data prepared in an analyzable format. Specifically, the server unifies the data format and filters out unnecessary data. 【0085】 Step 3: 【0086】 The server uses an AI model to perform predictive analysis. The input is pre-processed data. Based on this, the server executes machine learning algorithms to predict trends in traffic volume and energy consumption. The output is predicted future data. Specifically, the server uses TensorFlow to build a neural network model and perform predictions. 【0087】 Step 4: 【0088】 The server applies anomaly detection algorithms to detect anomalies in real time. The input is the latest data, which is continuously updated. The server identifies data that deviates from normal patterns and generates alerts. The output is a notification that an anomaly has been detected. Specifically, the server uses statistical methods to extract outliers and sends alerts to relevant organizations. 【0089】 Step 5: 【0090】 The server performs communication processing to provide information to the user's device. The input consists of anomaly detection results and prediction data. Based on this, the server generates notifications for the user and sends them via smartphone apps or email. The output is the sent notification. Specifically, the server configures push notifications via an API to deliver important information to the user in real time. 【0091】 Step 6: 【0092】 The user selects an action based on the information received. The input is notification information sent from the server. Based on this information, the user selects a specific action, such as changing the route or saving energy. The output is the user's actual change in behavior. This action might involve displaying the suggested route on the device and starting navigation. 【0093】 (Application Example 1) 【0094】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0095】 Modern urban life presents numerous problems, including traffic congestion, energy waste, and environmental pollution, which hinder the sustainable development of cities. To address these issues, there is a need for a system that collects and efficiently analyzes information in real time, providing users with concrete and immediate actionable insights. Furthermore, the ability to quickly implement countermeasures in emergency situations is essential. 【0096】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0097】 In this invention, the server includes means for collecting information in real time from data generation means, means for predicting future states based on the collected information using prediction means, and means for analyzing the predicted information and proposing the optimal action to the user. This makes it possible to reduce various risks and waste in the urban environment and improve urban functions so that residents can live safely and comfortably. 【0098】 A "data generation means" is a device for generating and collecting various types of information within a city in real time. 【0099】 "Means of collecting information in real time" refers to methods of instantly gathering the latest data from across an entire city using various sensors and communication devices. 【0100】 "Predictive tools" refer to algorithms and computer devices that analyze collected data and use that information to scientifically predict future states. 【0101】 A "means for suggesting optimal actions" refers to a function that, based on predicted information, suggests the most rational and efficient course of action to the user. 【0102】 An "anomaly detection method" is a technology for analyzing and identifying situations that deviate from normal conditions, and a device for quickly detecting such anomalies. 【0103】 "Means for formulating countermeasures and distributing those countermeasures" refers to a system that quickly and automatically devises countermeasures when an anomaly is detected and transmits the necessary information to relevant organizations and users. 【0104】 "Communication means" refers to infrastructure for exchanging information between users and systems, which enables the rapid dissemination of recommended actions. 【0105】 In this embodiment of the invention, the server collects information in real time from a wide variety of sensors placed within the city. This includes traffic sensors, environmental sensors, energy consumption sensors, and the like. This information is first stored in a database system, which serves as the basis for continuous analysis. 【0106】 The server then uses machine learning frameworks such as TensorFlow and PyTorch to process the data and predict future states. These predictions include, for example, peak traffic congestion and increases or decreases in energy consumption. 【0107】 The analyzed information is transmitted to the user's device in real time. This is done using a smartphone app or a web interface. Based on the information provided on the app, users can decide on the optimal course of action. Specifically, this includes suggestions for alternative routes to avoid congestion and suggestions for optimizing energy use. 【0108】 If an abnormal condition is detected, the anomaly detection system will identify it and promptly formulate countermeasures. These countermeasures will then be notified to the relevant organizations and users via communication channels. 【0109】 As a concrete example, one morning, the server analyzes traffic data and predicts congestion on a specific road. The user's smartphone then displays alternative routes to avoid congestion, potentially shortening their commute time. Additionally, if a peak in electricity demand is predicted, the user receives advice on energy conservation. 【0110】 An example of a prompt message is, "How can I generate recommendations that predict and inform the user of the peak period for energy consumption next week?" This allows the user to take rational action based on the information provided. 【0111】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0112】 Step 1: 【0113】 The server collects data in real time from sensors placed throughout the city. This data includes traffic information, environmental parameters, and energy consumption. The input is raw data from the sensors, and the output is structured data for storage in the database. The structured data is processed based on a schema to ensure consistency. 【0114】 Step 2: 【0115】 The server stores the collected structured data in a database. For example, Amazon RDS is used as the database system. The input is the structured data obtained in step 1, and the output is the data properly stored in the database for long-term storage and analysis. 【0116】 Step 3: 【0117】 The server provides stored data to a machine learning model to predict future states. TensorFlow is used to analyze traffic flow and energy consumption trends. The input consists of historical and real-time data retrieved from a database, while the output is a prediction of future conditions generated by the predictive model. 【0118】 Step 4: 【0119】 The output of the predictive model is analyzed to create data for suggesting the optimal action for the user. A generative AI model is used in this process. The input is the predicted value obtained in step 3, and the output is specific action suggestions for the user. These suggestions are generated considering the user's behavioral patterns. 【0120】 Step 5: 【0121】 The system notifies the user's device, i.e., their smartphone or web platform, of suggestions from the server. This allows the user to receive information in real time and take appropriate action. The input is the action suggestion generated in step 4, and the output is the specific action suggestion displayed on the user's device. 【0122】 Step 6: 【0123】 When anomaly detection is required, the server analyzes real-time data and detects the anomaly. The anomaly detection algorithm operates to determine whether or not an emergency situation exists. The input is the latest data from the sensor, and the output is the presence or absence and nature of the anomaly. 【0124】 Step 7: 【0125】 If an anomaly is detected, the server quickly develops countermeasures and sends notifications to relevant organizations and users. Communication APIs such as Twilio may be used for these notifications. The input is the anomaly information detected in step 6, and the output is the countermeasures and related notification text. 【0126】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0127】 This invention is an integrated management system that supports the sustainable development of cities and has the function of recognizing user emotions and providing appropriate information. This system has a complex configuration that includes a data generation device, a prediction device, an anomaly detection device, and an emotion engine. 【0128】 First, the server collects environmental data in real time from various IoT sensors deployed throughout the city. This data encompasses a wide range of infrastructure information, including traffic conditions, energy consumption, and air quality. The server stores this data in a database and analyzes it as needed. 【0129】 Next, an AI predictive model is applied based on the data collected by the server to forecast future demand and unusual events. This includes providing information to optimize resources according to the predicted situation. Anomaly detection devices allow the server to immediately identify abnormal situations and quickly formulate response plans. 【0130】 Furthermore, a distinctive element of this invention, the emotion engine, analyzes the user's emotional state through the terminal. The emotion engine analyzes the user's input and behavioral patterns to recognize the current emotion. The server can then provide information optimized for the user, taking this emotional data into consideration. For example, if the user is feeling stressed, the server can adjust the format and frequency of the information it notifies to reduce the emotional burden. 【0131】 As a concrete example, suppose the server analyzes traffic data before the morning rush hour and determines that congestion is expected to be greater than usual. If the emotion engine recognizes that the user is busy and stressed, the server will provide an encouraging message to help the user cope with the situation, along with suggestions for alternative routes. In this way, the system functions to support a better life experience by providing flexible information that takes the user's emotions into consideration. 【0132】 This invention enables efficient and emotionally considerate responses to various fluctuating factors in urban life, thereby contributing to an improvement in the quality of life for residents. 【0133】 The following describes the processing flow. 【0134】 Step 1: 【0135】 The server collects data in real time from a group of IoT sensors installed throughout the city. This data includes information such as traffic volume, energy consumption, and air quality, and the server stores this data in a database. 【0136】 Step 2: 【0137】 The server analyzes the stored sensor data. Here, an AI predictive model is used to forecast traffic congestion, energy consumption peaks, and other factors. This enables the provision of information based on future conditions. 【0138】 Step 3: 【0139】 The server uses anomaly detection devices to detect data that deviates from normal conditions or abnormal situations. For example, large-scale traffic congestion or a sudden increase in energy consumption may be detected. Based on this, the server develops a rapid response plan and prepares to notify relevant organizations. 【0140】 Step 4: 【0141】 The device analyzes user input and behavioral patterns through an emotion engine to recognize the user's emotional state. This allows it to understand the user's stress levels and emotional trends. 【0142】 Step 5: 【0143】 The server considers aggregated emotional data to provide users with the most relevant information. For example, if a user is feeling stressed, the tone of notification messages will be gentler, and action suggestions will be more flexible. 【0144】 Step 6: 【0145】 Users can review information and recommended actions provided by their devices and adjust their daily schedules and routes as needed. This supports them in making emotionally conscious choices and improves their quality of life. 【0146】 (Example 2) 【0147】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0148】 In today's urban environment, there is a need to achieve sustainable development while accurately managing diverse environmental information in real time and providing information that is sensitive to users' feelings. However, achieving this with conventional systems is difficult, and they particularly lack the ability to respond quickly to abnormal situations and provide flexible information that responds to users' emotions. 【0149】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0150】 In this invention, the server includes means for aggregating data from an information gathering device for acquiring environmental information, means for estimating future conditions based on the data using a prediction function, optimization means for analyzing information based on the predicted data and providing information according to the user's situation, and means for identifying the user's emotions using an emotion analysis function and adjusting the information provision based on those emotions. This not only enables a rapid and appropriate response to changes in the urban environment, but also reduces the emotional burden on users and improves their quality of life. 【0151】 "Environmental information" refers to a variety of urban data related to infrastructure, such as traffic conditions, energy consumption, and air quality. 【0152】 "Information gathering devices" refer to various sensors and equipment used to collect environmental data within a city. 【0153】 A "predictive function" refers to a function that calculates future states and conditions based on past data. 【0154】 "Optimization methods" refer to the process of analyzing and adjusting data in order to provide users with appropriate and effective information. 【0155】 The "emotion analysis function" refers to a function that analyzes the user's emotional state through their input and behavioral patterns. 【0156】 "Anomaly response function" refers to the process by which a system identifies potential emergencies and quickly develops appropriate countermeasures. 【0157】 "Communication function" refers to the technology within a system used to transmit information and recommended actions to users. 【0158】 This invention is a system for supporting the sustainable development of cities and providing information optimized according to the user's emotions. This system consists primarily of a server, terminals, and users, which work together to achieve its functions. 【0159】 The server collects environmental information in real time from a wide variety of information gathering devices installed throughout the city. This includes traffic conditions, energy consumption, and air quality, and this data is stored in a database. The server has a predictive function that utilizes advanced machine learning algorithms to estimate future infrastructure demand and abnormal events based on past data. Based on these prediction results, information is provided to users through optimized means. 【0160】 The device analyzes the user's input and behavior patterns through its emotion analysis function to identify their emotional state. This analysis uses natural language processing technology, and the user's emotional data is sent to a server. 【0161】 A concrete example is when a server analyzes traffic data during the morning commute and determines that congestion is expected. In this case, if the sentiment analysis function recognizes that the user is busy and stressed, the server will provide the user with an encouraging message along with guidance on alternative routes. This kind of information provision allows users to travel efficiently while feeling reassured. 【0162】 An example of a prompt for a generated AI model is, "Please explain how to detect anomalies based on data collected from IoT sensors and provide prompt countermeasures." This invention enables efficient management of urban environments and the provision of services that take into account the feelings of users. 【0163】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0164】 Step 1: 【0165】 The server acquires environmental information in real time from information gathering devices within the city. This input data includes traffic conditions, energy consumption, and air quality. The server stores this acquired data in a database and uses it for future analysis and prediction. Specifically, it includes the function of aggregating data collected from sensors via the existing network. 【0166】 Step 2: 【0167】 The server analyzes the acquired environmental information using machine learning algorithms. The input is historical environmental data, from which trends and patterns are extracted. This allows for predictions of traffic volume increases and energy consumption peaks, and estimates future demand. The output includes, for example, forecasts of traffic congestion and peak energy usage times for the following week. 【0168】 Step 3: 【0169】 The device processes user input and behavioral data using sentiment analysis to identify the user's emotional state. Input includes text messages and activity logs provided by the user via smartphone or computer. This data is analyzed using natural language processing technology to evaluate the user's mental state. The output generates data indicating the user's current emotional state. 【0170】 Step 4: 【0171】 The server optimizes the information it provides to users based on predicted needs and emotional data. It takes emotional state and predicted data as input and determines what information to provide, how often, and in what format. For example, a scenario might involve providing users with high stress levels with encouraging messages along with recommended routes to avoid congestion. 【0172】 Step 5: 【0173】 Users receive information provided by the server via their devices and use it as a basis for their daily choices. The input consists of recommendations from the server, which the user uses to decide on their actions. The output generates new behavioral patterns and feedback from the user, which then serve as input for the next processing cycle. 【0174】 (Application Example 2) 【0175】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0176】 In modern urban life, residents often experience stress due to traffic congestion and environmental changes. Furthermore, real-time information provided often fails to consider the emotional state of users, leading to an emotional burden from excessive information. Therefore, there is a need for information provision that takes into account the emotional state of urban dwellers, as well as for swift and accurate responses to abnormal situations. 【0177】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0178】 In this invention, the server includes means for collecting information in real time from a data generation device, means for predicting future states using a prediction device based on the information, means for analyzing the predicted information and providing optimized information based on the user's emotional state, and means equipped with an emotion analysis device that analyzes the user's emotions and determines appropriate recommended actions based on the same. This enables flexible information provision that takes into account the user's emotional state and a rapid and efficient response to abnormal situations. 【0179】 A "data generation device" is a device that collects information in real time from various sensors and devices within a city. 【0180】 A "prediction device" is a device that predicts future states or events based on collected information. 【0181】 An "emotion analysis device" is a device that analyzes a user's input and behavioral patterns to identify their emotional state. 【0182】 "Optimized information" refers to information that has been adjusted to be necessary and appropriate, taking into account the user's current emotional state. 【0183】 "Recommended actions" are specific actions suggested based on the user's emotional state and anticipated future circumstances. 【0184】 The system that realizes this invention mainly consists of a server and terminals for each user. The server collects information in real time from multiple data generation devices located within the city. This includes urban infrastructure data such as traffic conditions, energy consumption, and air quality. More accurate information can be obtained by having various sensors and devices work together for data collection. The collected data is stored in a database (e.g., MySQL®). 【0185】 The server then uses a machine learning platform (e.g., TensorFlow) to analyze the data collected by the predictor and forecast future states. These forecasts include predicting traffic congestion and assessing the likelihood of abnormal events occurring. Based on this information, the server optimizes resources for efficient use. 【0186】 Furthermore, the device is equipped with an emotion analysis device that analyzes the user's emotional state in real time. This analysis uses technologies that analyze facial expressions and voice (e.g., Microsoft® Azure® Face API). The data obtained from the emotion analysis is received by a server, which then provides optimized information based on a profile that includes the user's behavioral patterns. 【0187】 As a concrete example, consider a scenario where a server predicts morning traffic congestion based on information from a data generation device, and a user's device detects that the user is experiencing stress through emotion analysis. In this case, the server suggests a relatively less congested alternative route. It also provides a link to a service that streams relaxing music. 【0188】 For better results when inputting prompts to the generating AI model, consider the following example: "Suggest appropriate relaxation methods when the user is feeling stressed. Specifically, this includes alternative routes to avoid crowds and rest spots within the city." 【0189】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0190】 Step 1: 【0191】 The server collects information in real time from data generation devices located throughout the city. Input data includes traffic conditions, energy consumption, and air quality. This information is stored in a database, making it readily accessible when needed. 【0192】 Step 2: 【0193】 The server uses a prediction device to forecast future conditions based on the collected data. It leverages a machine learning platform to predict traffic congestion and assess the likelihood of abnormal events. The input is the data acquired in step 1, and the output is the future prediction data as a result of the analysis. 【0194】 Step 3: 【0195】 The device uses an emotion analysis device to analyze the user's emotional state in real time. Input consists of the user's facial expressions and voice data, which are processed to identify the emotional state. The output is the user's emotional state. 【0196】 Step 4: 【0197】 The server integrates the predictive data from step 2 and the sentiment data from step 3 to generate user-optimized information. This step takes the user's emotional state into account when creating appropriate route suggestions and encouraging messages. The output is a customized set of action suggestions for the user. 【0198】 Step 5: 【0199】 The terminal notifies the user of customized information provided by the server. The input is the output data from step 4. The information the user receives includes suggestions for alternative routes and relaxing activities. 【0200】 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. 【0201】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0202】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0203】 [Second Embodiment] 【0204】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0205】 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. 【0206】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0207】 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. 【0208】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0209】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0210】 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. 【0211】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0212】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0213】 The 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. 【0214】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0215】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0216】 This invention is an integrated management system for supporting the sustainable development of cities, which uses AI agents to monitor urban infrastructure in real time and optimize various resources. This system has a complex configuration that includes data generation devices, prediction devices, and anomaly detection devices. 【0217】 In this system, a server collects data in real time from various IoT sensors installed throughout the city. This data covers various aspects of urban infrastructure, such as traffic conditions, energy consumption, water resource usage, and air quality. The server stores the data in a database for continuous analysis. 【0218】 Next, the server applies an AI model to analyze the collected data and predict future demand and possible anomalies. In particular, by predicting traffic flow and energy consumption trends, it becomes possible to manage cities and reallocate resources efficiently. 【0219】 Anomaly detection devices are used to immediately detect events that deviate from normal conditions or emergencies by having the server perform real-time data analysis. After detection, a response plan is automatically generated quickly and notified to the relevant organizations to facilitate the early resolution of the problem. 【0220】 Furthermore, this system is equipped with communication devices and can provide important information to users. Users are expected to make better choices based on the information provided. For example, if traffic congestion is predicted, the server can send a notification to the user's smartphone app to encourage the use of public transportation. It may also recommend adjusting the usage time of home appliances to avoid peak energy consumption. 【0221】 As a concrete example, consider the system operation on a typical day. A server receives traffic data from sensors before the morning rush hour, and an advanced AI model predicts the day's traffic patterns. As a result, if higher-than-usual congestion is expected on certain roads or intersections, the server suggests alternative routes to the user. Based on this information, the user may be able to significantly reduce their commute time. 【0222】 Furthermore, if weather conditions worsen and a surge in electricity demand is expected, the server will use an anomaly detection device to issue a warning in advance and propose energy-saving measures to power companies and residents. In this way, the present invention will benefit a large number of users and enable the rationalization of urban management and improvement of sustainability. 【0223】 The following describes the processing flow. 【0224】 Step 1: 【0225】 The server establishes connections with each IoT sensor placed throughout the city. The server collects data such as traffic conditions, energy consumption, water resource usage, and air quality in real time and stores the collected data in a database. 【0226】 Step 2: 【0227】 The server retrieves the latest sensor data stored in the database. Based on this data, the server uses an AI model to predict future conditions such as traffic patterns and energy demand. The server evaluates the prediction results and identifies anomalies and necessary interventions. 【0228】 Step 3: 【0229】 The server uses an anomaly detection device to detect events that exceed the normal range in the collected data and predictive information. If an anomaly is detected, the server quickly and automatically generates a countermeasure plan and prepares to notify relevant organizations. 【0230】 Step 4: 【0231】 The server generates useful information for the user based on prediction and anomaly detection results. The terminal presents the generated information to the user, for example, recommending alternative routes in response to changes in traffic conditions or prompting optimization of appliance usage for energy saving. 【0232】 Step 5: 【0233】 Users can review information received through their devices and adjust their actions as needed. By following the notified recommendations, users can improve convenience and efficiency by changing their commute routes or adjusting their power usage. 【0234】 (Example 1) 【0235】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0236】 In urban areas, there is a need for optimized infrastructure operations and immediate response to emergencies. Currently, real-time data analysis and forecasting are not sufficiently performed, resulting in challenges in efficient resource management and rapid response. 【0237】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0238】 In this invention, the server includes means for collecting information in a time series from an information acquisition device, means for predicting future states using a prediction device based on the information, and means for storing the collected information in an information aggregate and performing preprocessing as necessary. This enables efficient resource management and rapid response to anomalies based on real-time data analysis and future prediction. 【0239】 An "information acquisition device" is a device used to collect data from sensors and devices installed within a city. 【0240】 "Methods for collecting information in a time series" refers to the process of continuously acquiring data along a time axis and processing it in real time. 【0241】 A "predictive device" is a device or system used to predict future states based on collected data. 【0242】 An "information collection" is a database or storage system that stores collected data and allows it to be retrieved and used when needed. 【0243】 "Preprocessing" refers to processes such as data normalization and cleaning that are performed to prepare data before data analysis or prediction. 【0244】 An "artificial intelligence model" is an algorithm or learning model used to analyze large amounts of data and identify specific patterns or trends. 【0245】 An "anomaly detection algorithm" is a computational method for detecting patterns or values ​​that are different from the norm within data and determining whether an anomaly exists. 【0246】 A "means for detecting anomalies in real time" refers to a device or algorithm for analyzing data in real time and identifying anomalies on the spot. 【0247】 An "emergency situation" refers to a situation or event that disrupts normal infrastructure operations or requires immediate attention. 【0248】 A "response plan" is a plan of specific actions and procedures to be taken in response to detected anomalies or emergencies. 【0249】 A "communication device" is a combination of hardware and software used to transmit information to users or other systems. 【0250】 "Improvement actions" refer to specific actions taken by users based on information and notifications from the system, aimed at improving the situation or increasing efficiency. 【0251】 This invention is an integrated management system aimed at the efficient and safe operation of infrastructure in urban areas. This system optimizes urban management by predicting future conditions based on data obtained from information acquisition devices and detecting anomalies in real time. The following specifically describes embodiments for carrying out this invention. 【0252】 The server first acquires information through numerous sensors. These sensors provide real-time data on various environmental indicators within the city, such as traffic, energy, water resources, and air quality. This information is then stored in a database in JSON format. The database utilizes a distributed database system to provide high-speed information access. 【0253】 In data analysis, the server utilizes AI frameworks such as TensorFlow and PyTorch. These frameworks allow the server to generate machine learning models based on collected data to predict traffic patterns and energy consumption trends. For example, the server can analyze past trends to predict traffic congestion before the morning rush hour and suggest alternative routes to the user on specific roads. 【0254】 In anomaly detection, the server uses an anomaly identification algorithm. This allows the server to detect data that deviates significantly from normal patterns and immediately notify the system administrator. For example, if the server detects a sudden increase in energy consumption, it can issue a warning to the power company and instruct them to take appropriate energy-saving measures. 【0255】 Furthermore, the server provides information to the user's terminal via communication equipment. The system utilizes a smartphone app to facilitate smooth communication. Users can receive traffic information on their devices and change their commute route based on that information. In particular, notifications encouraging the use of public transportation are sent to users, supporting efficient travel. 【0256】 Specific examples include servers using prompts such as "Please tell me the predicted traffic congestion patterns in your area for the next 24 hours" or "Please predict the energy consumption trends based on this week's weather and suggest measures to avoid peaks," to provide users with the necessary information. 【0257】 In this way, the present invention supports the sustainable development of cities by combining data-driven infrastructure management with real-time anomaly response. 【0258】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0259】 Step 1: 【0260】 The server collects data from various sensors. The input is real-time data from various urban infrastructure sensors. The server receives this data in JSON format and stores it in a database. Specifically, the server retrieves data using HTTP requests and automatically stores it in the database. 【0261】 Step 2: 【0262】 The server performs preprocessing on the collected data. The input is raw data collected from sensors. The server cleans this data, removes missing values, and performs unification processes such as normalization. The output is clean data prepared in an analyzable format. Specifically, the server unifies the data format and filters out unnecessary data. 【0263】 Step 3: 【0264】 The server uses an AI model to perform predictive analysis. The input is pre-processed data. Based on this, the server executes machine learning algorithms to predict trends in traffic volume and energy consumption. The output is predicted future data. Specifically, the server uses TensorFlow to build a neural network model and perform predictions. 【0265】 Step 4: 【0266】 The server applies anomaly detection algorithms to detect anomalies in real time. The input is the latest data, which is continuously updated. The server identifies data that deviates from normal patterns and generates alerts. The output is a notification that an anomaly has been detected. Specifically, the server uses statistical methods to extract outliers and sends alerts to relevant organizations. 【0267】 Step 5: 【0268】 The server performs communication processing to provide information to the user's device. The input consists of anomaly detection results and prediction data. Based on this, the server generates notifications for the user and sends them via smartphone apps or email. The output is the sent notification. Specifically, the server configures push notifications via an API to deliver important information to the user in real time. 【0269】 Step 6: 【0270】 The user selects an action based on the information received. The input is notification information sent from the server. Based on this information, the user selects a specific action, such as changing the route or saving energy. The output is the user's actual change in behavior. This action might involve displaying the suggested route on the device and starting navigation. 【0271】 (Application Example 1) 【0272】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0273】 Modern urban life presents numerous problems, including traffic congestion, energy waste, and environmental pollution, which hinder the sustainable development of cities. To address these issues, there is a need for a system that collects and efficiently analyzes information in real time, providing users with concrete and immediate actionable insights. Furthermore, the ability to quickly implement countermeasures in emergency situations is essential. 【0274】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0275】 In this invention, the server includes means for collecting information in real time from data generation means, means for predicting future states based on the collected information using prediction means, and means for analyzing the predicted information and proposing the optimal action to the user. This makes it possible to reduce various risks and waste in the urban environment and improve urban functions so that residents can live safely and comfortably. 【0276】 A "data generation means" is a device for generating and collecting various types of information within a city in real time. 【0277】 "Means of collecting information in real time" refers to methods of instantly gathering the latest data from across an entire city using various sensors and communication devices. 【0278】 "Predictive tools" refer to algorithms and computer devices that analyze collected data and use that information to scientifically predict future states. 【0279】 A "means for suggesting optimal actions" refers to a function that, based on predicted information, suggests the most rational and efficient course of action to the user. 【0280】 An "anomaly detection method" is a technology for analyzing and identifying situations that deviate from normal conditions, and a device for quickly detecting such anomalies. 【0281】 "Means for formulating countermeasures and distributing those countermeasures" refers to a system that quickly and automatically devises countermeasures when an anomaly is detected and transmits the necessary information to relevant organizations and users. 【0282】 "Communication means" refers to infrastructure for exchanging information between users and systems, which enables the rapid dissemination of recommended actions. 【0283】 In an embodiment of this invention, the server collects information in real time from a variety of sensors deployed within the city. This includes traffic sensors, environmental sensors, energy consumption sensors, and the like. This information is first stored in a database system, serving as a basis for continuous analysis. 【0284】 Next, as a prediction means, the server utilizes machine learning frameworks such as TensorFlow or PyTorch to process the data and predict future states. This prediction includes, for example, traffic congestion peaks and increases or decreases in energy consumption. 【0285】 The analyzed information is transmitted to the user terminal in real time. This involves the use of smartphone apps or web interfaces. Based on the information provided on the app, users can determine optimal actions. Specifically, this includes proposals for alternative routes to avoid congestion and suggestions for timing to optimize energy usage. 【0286】 When an abnormal state is detected, the anomaly detection means identifies this and promptly formulates countermeasures. Subsequently, the countermeasures are notified to relevant institutions and users via communication means. 【0287】 As a specific example, one morning, based on the analysis results of traffic data, the server predicts congestion on a specific road. The user's smartphone is shown an alternative route to avoid congestion, resulting in a possible reduction in commuting time. Also, when a peak in electricity demand is predicted, advice on energy conservation is provided to the user. 【0288】 An example of a prompt sentence is "Please teach me the method of generating recommendations to predict the peak energy consumption period next week and notify the user." Thus, users can take reasonable actions based on the provided information. 【0289】 The flow of specific processing in Application Example 1 will be described using FIG. 12. 【0290】 Step 1: 【0291】 The server collects data in real time from sensors placed throughout the city. This data includes traffic information, environmental parameters, and energy consumption. The input is raw data from the sensors, and the output is structured data for storage in the database. The structured data is processed based on a schema to ensure consistency. 【0292】 Step 2: 【0293】 The server stores the collected structured data in a database. For example, Amazon RDS is used as the database system. The input is the structured data obtained in step 1, and the output is the data properly stored in the database for long-term storage and analysis. 【0294】 Step 3: 【0295】 The server provides stored data to a machine learning model to predict future states. TensorFlow is used to analyze traffic flow and energy consumption trends. The input consists of historical and real-time data retrieved from a database, while the output is a prediction of future conditions generated by the predictive model. 【0296】 Step 4: 【0297】 The output of the predictive model is analyzed to create data for suggesting the optimal action for the user. A generative AI model is used in this process. The input is the predicted value obtained in step 3, and the output is specific action suggestions for the user. These suggestions are generated considering the user's behavioral patterns. 【0298】 Step 5: 【0299】 The system notifies the user's device, i.e., their smartphone or web platform, of suggestions from the server. This allows the user to receive information in real time and take appropriate action. The input is the action suggestion generated in step 4, and the output is the specific action suggestion displayed on the user's device. 【0300】 Step 6: 【0301】 When anomaly detection is required, the server analyzes real-time data and detects the anomaly. The anomaly detection algorithm operates to determine whether or not an emergency situation exists. The input is the latest data from the sensor, and the output is the presence or absence and nature of the anomaly. 【0302】 Step 7: 【0303】 If an anomaly is detected, the server quickly develops countermeasures and sends notifications to relevant organizations and users. Communication APIs such as Twilio may be used for these notifications. The input is the anomaly information detected in step 6, and the output is the countermeasures and related notification text. 【0304】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0305】 This invention is an integrated management system that supports the sustainable development of cities and has the function of recognizing user emotions and providing appropriate information. This system has a complex configuration that includes a data generation device, a prediction device, an anomaly detection device, and an emotion engine. 【0306】 First, the server collects environmental data in real time from various IoT sensors deployed throughout the city. This data encompasses a wide range of infrastructure information, including traffic conditions, energy consumption, and air quality. The server stores this data in a database and analyzes it as needed. 【0307】 Next, an AI prediction model is applied based on the data collected by the server to predict future demand and abnormal events. This includes providing information for optimizing resources according to the predicted situation. The anomaly detection device enables the server to immediately grasp the abnormal situation and quickly formulate a response plan. 【0308】 Furthermore, the emotion engine, which is a characteristic element of the present invention, analyzes the user's emotional state through the terminal. The emotion engine analyzes the user's input and behavior patterns to recognize the current emotion. The server can provide information optimized for the user in consideration of this emotion data. For example, when the user is feeling stressed, the server adjusts the format and frequency of the information to be notified and designs it to reduce the emotional burden. 【0309】 As a specific example, assume that the server analyzes traffic data before the morning commuting rush hour and determines that congestion is expected to be worse than usual. When the emotion engine recognizes that the user is busy and stressed, the server provides an encouraging message to cope with the current situation along with a proposal for a detour. By providing flexible information considering the user's emotions in this way, it functions as a system that supports a better life experience. 【0310】 According to the present invention, it becomes possible to respond efficiently and considerately in terms of emotion to various fluctuating factors in urban life, contributing to an improvement in the quality of life of residents. 【0311】 The processing flow will be described below. 【0312】 Step 1: 【0313】 The server collects data in real time from the IoT sensor group installed in the city. This includes information such as traffic volume, energy consumption, and air quality, and the server stores this in the database. 【0314】 Step 2: 【0315】 The server analyzes the stored sensor data. Here, an AI predictive model is used to forecast traffic congestion, energy consumption peaks, and other factors. This enables the provision of information based on future conditions. 【0316】 Step 3: 【0317】 The server uses anomaly detection devices to detect data that deviates from normal conditions or abnormal situations. For example, large-scale traffic congestion or a sudden increase in energy consumption may be detected. Based on this, the server develops a rapid response plan and prepares to notify relevant organizations. 【0318】 Step 4: 【0319】 The device analyzes user input and behavioral patterns through an emotion engine to recognize the user's emotional state. This allows it to understand the user's stress levels and emotional trends. 【0320】 Step 5: 【0321】 The server considers aggregated emotional data to provide users with the most relevant information. For example, if a user is feeling stressed, the tone of notification messages will be gentler, and action suggestions will be more flexible. 【0322】 Step 6: 【0323】 Users can review information and recommended actions provided by their devices and adjust their daily schedules and routes as needed. This supports them in making emotionally conscious choices and improves their quality of life. 【0324】 (Example 2) 【0325】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0326】 In today's urban environment, there is a need to achieve sustainable development while accurately managing diverse environmental information in real time and providing information that is sensitive to users' feelings. However, achieving this with conventional systems is difficult, and they particularly lack the ability to respond quickly to abnormal situations and provide flexible information that responds to users' emotions. 【0327】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0328】 In this invention, the server includes means for aggregating data from an information gathering device for acquiring environmental information, means for estimating future conditions based on the data using a prediction function, optimization means for analyzing information based on the predicted data and providing information according to the user's situation, and means for identifying the user's emotions using an emotion analysis function and adjusting the information provision based on those emotions. This not only enables a rapid and appropriate response to changes in the urban environment, but also reduces the emotional burden on users and improves their quality of life. 【0329】 "Environmental information" refers to a variety of urban data related to infrastructure, such as traffic conditions, energy consumption, and air quality. 【0330】 "Information gathering devices" refer to various sensors and equipment used to collect environmental data within a city. 【0331】 A "predictive function" refers to a function that calculates future states and conditions based on past data. 【0332】 "Optimization methods" refer to the process of analyzing and adjusting data in order to provide users with appropriate and effective information. 【0333】 The "emotion analysis function" refers to a function that analyzes the user's emotional state through their input and behavioral patterns. 【0334】 "Anomaly response function" refers to the process by which a system identifies potential emergencies and quickly develops appropriate countermeasures. 【0335】 "Communication function" refers to the technology within a system used to transmit information and recommended actions to users. 【0336】 This invention is a system for supporting the sustainable development of cities and providing information optimized according to the user's emotions. This system consists primarily of a server, terminals, and users, which work together to achieve its functions. 【0337】 The server collects environmental information in real time from a wide variety of information gathering devices installed throughout the city. This includes traffic conditions, energy consumption, and air quality, and this data is stored in a database. The server has a predictive function that utilizes advanced machine learning algorithms to estimate future infrastructure demand and abnormal events based on past data. Based on these prediction results, information is provided to users through optimized means. 【0338】 The device analyzes the user's input and behavior patterns through its emotion analysis function to identify their emotional state. This analysis uses natural language processing technology, and the user's emotional data is sent to a server. 【0339】 A concrete example is when a server analyzes traffic data during the morning commute and determines that congestion is expected. In this case, if the sentiment analysis function recognizes that the user is busy and stressed, the server will provide the user with an encouraging message along with guidance on alternative routes. This kind of information provision allows users to travel efficiently while feeling reassured. 【0340】 An example of a prompt for a generated AI model is, "Please explain how to detect anomalies based on data collected from IoT sensors and provide prompt countermeasures." This invention enables efficient management of urban environments and the provision of services that take into account the feelings of users. 【0341】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0342】 Step 1: 【0343】 The server acquires environmental information in real time from information gathering devices within the city. This input data includes traffic conditions, energy consumption, and air quality. The server stores this acquired data in a database and uses it for future analysis and prediction. Specifically, it includes the function of aggregating data collected from sensors via the existing network. 【0344】 Step 2: 【0345】 The server analyzes the acquired environmental information using machine learning algorithms. The input is historical environmental data, from which trends and patterns are extracted. This allows for predictions of traffic volume increases and energy consumption peaks, and estimates future demand. The output includes, for example, forecasts of traffic congestion and peak energy usage times for the following week. 【0346】 Step 3: 【0347】 The device processes user input and behavioral data using sentiment analysis to identify the user's emotional state. Input includes text messages and activity logs provided by the user via smartphone or computer. This data is analyzed using natural language processing technology to evaluate the user's mental state. The output generates data indicating the user's current emotional state. 【0348】 Step 4: 【0349】 The server optimizes the information it provides to users based on predicted needs and emotional data. It takes emotional state and predicted data as input and determines what information to provide, how often, and in what format. For example, a scenario might involve providing users with high stress levels with encouraging messages along with recommended routes to avoid congestion. 【0350】 Step 5: 【0351】 Users receive information provided by the server via their devices and use it as a basis for their daily choices. The input consists of recommendations from the server, which the user uses to decide on their actions. The output generates new behavioral patterns and feedback from the user, which then serve as input for the next processing cycle. 【0352】 (Application Example 2) 【0353】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0354】 In modern urban life, residents often experience stress due to traffic congestion and environmental changes. Furthermore, real-time information provided often fails to consider the emotional state of users, leading to an emotional burden from excessive information. Therefore, there is a need for information provision that takes into account the emotional state of urban dwellers, as well as for swift and accurate responses to abnormal situations. 【0355】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0356】 In this invention, the server includes means for collecting information in real time from a data generation device, means for predicting future states using a prediction device based on the information, means for analyzing the predicted information and providing optimized information based on the user's emotional state, and means equipped with an emotion analysis device that analyzes the user's emotions and determines appropriate recommended actions based on the same. This enables flexible information provision that takes into account the user's emotional state and a rapid and efficient response to abnormal situations. 【0357】 A "data generation device" is a device that collects information in real time from various sensors and devices within a city. 【0358】 A "prediction device" is a device that predicts future states or events based on collected information. 【0359】 An "emotion analysis device" is a device that analyzes a user's input and behavioral patterns to identify their emotional state. 【0360】 "Optimized information" refers to information that has been adjusted to be necessary and appropriate, taking into account the user's current emotional state. 【0361】 "Recommended actions" are specific actions suggested based on the user's emotional state and anticipated future circumstances. 【0362】 The system that realizes this invention mainly consists of a server and terminals for each user. The server collects information in real time from multiple data generation devices located within the city. This includes urban infrastructure data such as traffic conditions, energy consumption, and air quality. More accurate information can be obtained by having various sensors and devices work together for data collection. The collected data is stored in a database (e.g., MySQL). 【0363】 The server then uses a machine learning platform (e.g., TensorFlow) to analyze the data collected by the predictor and forecast future states. These forecasts include predicting traffic congestion and assessing the likelihood of abnormal events occurring. Based on this information, the server optimizes resources for efficient use. 【0364】 Furthermore, the device is equipped with an emotion analysis device that analyzes the user's emotional state in real time. This analysis uses technologies that analyze facial expressions and voice (e.g., Microsoft Azure Face API). The data obtained from the emotion analysis is received by a server, which then provides optimized information based on a profile that includes the user's behavioral patterns. 【0365】 As a concrete example, consider a scenario where a server predicts morning traffic congestion based on information from a data generation device, and a user's device detects that the user is experiencing stress through emotion analysis. In this case, the server suggests a relatively less congested alternative route. It also provides a link to a service that streams relaxing music. 【0366】 For better results when inputting prompts to the generating AI model, consider the following example: "Suggest appropriate relaxation methods when the user is feeling stressed. Specifically, this includes alternative routes to avoid crowds and rest spots within the city." 【0367】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0368】 Step 1: 【0369】 The server collects information in real time from data generation devices located throughout the city. Input data includes traffic conditions, energy consumption, and air quality. This information is stored in a database, making it readily accessible when needed. 【0370】 Step 2: 【0371】 The server uses a prediction device to forecast future conditions based on the collected data. It leverages a machine learning platform to predict traffic congestion and assess the likelihood of abnormal events. The input is the data acquired in step 1, and the output is the future prediction data as a result of the analysis. 【0372】 Step 3: 【0373】 The device uses an emotion analysis device to analyze the user's emotional state in real time. Input consists of the user's facial expressions and voice data, which are processed to identify the emotional state. The output is the user's emotional state. 【0374】 Step 4: 【0375】 The server integrates the predictive data from step 2 and the sentiment data from step 3 to generate user-optimized information. This step takes the user's emotional state into account when creating appropriate route suggestions and encouraging messages. The output is a customized set of action suggestions for the user. 【0376】 Step 5: 【0377】 The terminal notifies the user of customized information provided by the server. The input is the output data from step 4. The information the user receives includes suggestions for alternative routes and relaxing activities. 【0378】 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. 【0379】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0380】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0381】 [Third Embodiment] 【0382】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0383】 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. 【0384】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0385】 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. 【0386】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0387】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0388】 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. 【0389】 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. 【0390】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0391】 The 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. 【0392】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0393】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0394】 This invention is an integrated management system for supporting the sustainable development of cities, which uses AI agents to monitor urban infrastructure in real time and optimize various resources. This system has a complex configuration that includes data generation devices, prediction devices, and anomaly detection devices. 【0395】 In this system, a server collects data in real time from various IoT sensors installed throughout the city. This data covers various aspects of urban infrastructure, such as traffic conditions, energy consumption, water resource usage, and air quality. The server stores the data in a database for continuous analysis. 【0396】 Next, the server applies an AI model to analyze the collected data and predict future demand and possible anomalies. In particular, by predicting traffic flow and energy consumption trends, it becomes possible to manage cities and reallocate resources efficiently. 【0397】 Anomaly detection devices are used to immediately detect events that deviate from normal conditions or emergencies by having the server perform real-time data analysis. After detection, a response plan is automatically generated quickly and notified to the relevant organizations to facilitate the early resolution of the problem. 【0398】 Furthermore, this system is equipped with communication devices and can provide important information to users. Users are expected to make better choices based on the information provided. For example, if traffic congestion is predicted, the server can send a notification to the user's smartphone app to encourage the use of public transportation. It may also recommend adjusting the usage time of home appliances to avoid peak energy consumption. 【0399】 As a concrete example, consider the system operation on a typical day. A server receives traffic data from sensors before the morning rush hour, and an advanced AI model predicts the day's traffic patterns. As a result, if higher-than-usual congestion is expected on certain roads or intersections, the server suggests alternative routes to the user. Based on this information, the user may be able to significantly reduce their commute time. 【0400】 Furthermore, if weather conditions worsen and a surge in electricity demand is expected, the server will use an anomaly detection device to issue a warning in advance and propose energy-saving measures to power companies and residents. In this way, the present invention will benefit a large number of users and enable the rationalization of urban management and improvement of sustainability. 【0401】 The following describes the processing flow. 【0402】 Step 1: 【0403】 The server establishes connections with each IoT sensor placed throughout the city. The server collects data such as traffic conditions, energy consumption, water resource usage, and air quality in real time and stores the collected data in a database. 【0404】 Step 2: 【0405】 The server retrieves the latest sensor data stored in the database. Based on this data, the server uses an AI model to predict future conditions such as traffic patterns and energy demand. The server evaluates the prediction results and identifies anomalies and necessary interventions. 【0406】 Step 3: 【0407】 The server uses an anomaly detection device to detect events that exceed the normal range in the collected data and predictive information. If an anomaly is detected, the server quickly and automatically generates a countermeasure plan and prepares to notify relevant organizations. 【0408】 Step 4: 【0409】 The server generates useful information for the user based on prediction and anomaly detection results. The terminal presents the generated information to the user, for example, recommending alternative routes in response to changes in traffic conditions or prompting optimization of appliance usage for energy saving. 【0410】 Step 5: 【0411】 Users can review information received through their devices and adjust their actions as needed. By following the notified recommendations, users can improve convenience and efficiency by changing their commute routes or adjusting their power usage. 【0412】 (Example 1) 【0413】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0414】 In urban areas, there is a need for optimized infrastructure operations and immediate response to emergencies. Currently, real-time data analysis and forecasting are not sufficiently performed, resulting in challenges in efficient resource management and rapid response. 【0415】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0416】 In this invention, the server includes means for collecting information in a time series from an information acquisition device, means for predicting future states using a prediction device based on the information, and means for storing the collected information in an information aggregate and performing preprocessing as necessary. This enables efficient resource management and rapid response to anomalies based on real-time data analysis and future prediction. 【0417】 An "information acquisition device" is a device used to collect data from sensors and devices installed within a city. 【0418】 "Methods for collecting information in a time series" refers to the process of continuously acquiring data along a time axis and processing it in real time. 【0419】 A "predictive device" is a device or system used to predict future states based on collected data. 【0420】 An "information collection" is a database or storage system that stores collected data and allows it to be retrieved and used when needed. 【0421】 "Preprocessing" refers to processes such as data normalization and cleaning that are performed to prepare data before data analysis or prediction. 【0422】 An "artificial intelligence model" is an algorithm or learning model used to analyze large amounts of data and identify specific patterns or trends. 【0423】 An "anomaly detection algorithm" is a computational method for detecting patterns or values ​​that are different from the norm within data and determining whether an anomaly exists. 【0424】 A "means for detecting anomalies in real time" refers to a device or algorithm for analyzing data in real time and identifying anomalies on the spot. 【0425】 An "emergency situation" refers to a situation or event that disrupts normal infrastructure operations or requires immediate attention. 【0426】 A "response plan" is a plan of specific actions and procedures to be taken in response to detected anomalies or emergencies. 【0427】 A "communication device" is a combination of hardware and software used to transmit information to users or other systems. 【0428】 "Improvement actions" refer to specific actions taken by users based on information and notifications from the system, aimed at improving the situation or increasing efficiency. 【0429】 This invention is an integrated management system aimed at the efficient and safe operation of infrastructure in urban areas. This system optimizes urban management by predicting future conditions based on data obtained from information acquisition devices and detecting anomalies in real time. The following specifically describes embodiments for carrying out this invention. 【0430】 The server first acquires information through numerous sensors. These sensors provide real-time data on various environmental indicators within the city, such as traffic, energy, water resources, and air quality. This information is then stored in a database in JSON format. The database utilizes a distributed database system to provide high-speed information access. 【0431】 In data analysis, the server utilizes AI frameworks such as TensorFlow and PyTorch. These frameworks allow the server to generate machine learning models based on collected data to predict traffic patterns and energy consumption trends. For example, the server can analyze past trends to predict traffic congestion before the morning rush hour and suggest alternative routes to the user on specific roads. 【0432】 In anomaly detection, the server uses an anomaly identification algorithm. This allows the server to detect data that deviates significantly from normal patterns and immediately notify the system administrator. For example, if the server detects a sudden increase in energy consumption, it can issue a warning to the power company and instruct them to take appropriate energy-saving measures. 【0433】 Furthermore, the server provides information to the user's terminal via communication equipment. The system utilizes a smartphone app to facilitate smooth communication. Users can receive traffic information on their devices and change their commute route based on that information. In particular, notifications encouraging the use of public transportation are sent to users, supporting efficient travel. 【0434】 Specific examples include servers using prompts such as "Please tell me the predicted traffic congestion patterns in your area for the next 24 hours" or "Please predict the energy consumption trends based on this week's weather and suggest measures to avoid peaks," to provide users with the necessary information. 【0435】 In this way, the present invention supports the sustainable development of cities by combining data-driven infrastructure management with real-time anomaly response. 【0436】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0437】 Step 1: 【0438】 The server collects data from various sensors. The input is real-time data from various urban infrastructure sensors. The server receives this data in JSON format and stores it in a database. Specifically, the server retrieves data using HTTP requests and automatically stores it in the database. 【0439】 Step 2: 【0440】 The server performs preprocessing on the collected data. The input is raw data collected from sensors. The server cleans this data, removes missing values, and performs unification processes such as normalization. The output is clean data prepared in an analyzable format. Specifically, the server unifies the data format and filters out unnecessary data. 【0441】 Step 3: 【0442】 The server uses an AI model to perform predictive analysis. The input is pre-processed data. Based on this, the server executes machine learning algorithms to predict trends in traffic volume and energy consumption. The output is predicted future data. Specifically, the server uses TensorFlow to build a neural network model and perform predictions. 【0443】 Step 4: 【0444】 The server applies anomaly detection algorithms to detect anomalies in real time. The input is the latest data, which is continuously updated. The server identifies data that deviates from normal patterns and generates alerts. The output is a notification that an anomaly has been detected. Specifically, the server uses statistical methods to extract outliers and sends alerts to relevant organizations. 【0445】 Step 5: 【0446】 The server performs communication processing to provide information to the user's device. The input consists of anomaly detection results and prediction data. Based on this, the server generates notifications for the user and sends them via smartphone apps or email. The output is the sent notification. Specifically, the server configures push notifications via an API to deliver important information to the user in real time. 【0447】 Step 6: 【0448】 The user selects an action based on the information received. The input is notification information sent from the server. Based on this information, the user selects a specific action, such as changing the route or saving energy. The output is the user's actual change in behavior. This action might involve displaying the suggested route on the device and starting navigation. 【0449】 (Application Example 1) 【0450】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0451】 Modern urban life presents numerous problems, including traffic congestion, energy waste, and environmental pollution, which hinder the sustainable development of cities. To address these issues, there is a need for a system that collects and efficiently analyzes information in real time, providing users with concrete and immediate actionable insights. Furthermore, the ability to quickly implement countermeasures in emergency situations is essential. 【0452】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0453】 In this invention, the server includes means for collecting information in real time from data generation means, means for predicting future states based on the collected information using prediction means, and means for analyzing the predicted information and proposing the optimal action to the user. This makes it possible to reduce various risks and waste in the urban environment and improve urban functions so that residents can live safely and comfortably. 【0454】 A "data generation means" is a device for generating and collecting various types of information within a city in real time. 【0455】 "Means of collecting information in real time" refers to methods of instantly gathering the latest data from across an entire city using various sensors and communication devices. 【0456】 "Predictive tools" refer to algorithms and computer devices that analyze collected data and use that information to scientifically predict future states. 【0457】 A "means for suggesting optimal actions" refers to a function that, based on predicted information, suggests the most rational and efficient course of action to the user. 【0458】 An "anomaly detection method" is a technology for analyzing and identifying situations that deviate from normal conditions, and a device for quickly detecting such anomalies. 【0459】 "Means for formulating countermeasures and distributing those countermeasures" refers to a system that quickly and automatically devises countermeasures when an anomaly is detected and transmits the necessary information to relevant organizations and users. 【0460】 "Communication means" refers to infrastructure for exchanging information between users and systems, which enables the rapid dissemination of recommended actions. 【0461】 In this embodiment of the invention, the server collects information in real time from a wide variety of sensors placed within the city. This includes traffic sensors, environmental sensors, energy consumption sensors, and the like. This information is first stored in a database system, which serves as the basis for continuous analysis. 【0462】 The server then uses machine learning frameworks such as TensorFlow and PyTorch to process the data and predict future states. These predictions include, for example, peak traffic congestion and increases or decreases in energy consumption. 【0463】 The analyzed information is transmitted to the user's device in real time. This is done using a smartphone app or a web interface. Based on the information provided on the app, users can decide on the optimal course of action. Specifically, this includes suggestions for alternative routes to avoid congestion and suggestions for optimizing energy use. 【0464】 If an abnormal condition is detected, the anomaly detection system will identify it and promptly formulate countermeasures. These countermeasures will then be notified to the relevant organizations and users via communication channels. 【0465】 As a concrete example, one morning, the server analyzes traffic data and predicts congestion on a specific road. The user's smartphone then displays alternative routes to avoid congestion, potentially shortening their commute time. Additionally, if a peak in electricity demand is predicted, the user receives advice on energy conservation. 【0466】 An example of a prompt message is, "How can I generate recommendations that predict and inform the user of the peak period for energy consumption next week?" This allows the user to take rational action based on the information provided. 【0467】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0468】 Step 1: 【0469】 The server collects data in real time from sensors placed throughout the city. This data includes traffic information, environmental parameters, and energy consumption. The input is raw data from the sensors, and the output is structured data for storage in the database. The structured data is processed based on a schema to ensure consistency. 【0470】 Step 2: 【0471】 The server stores the collected structured data in a database. For example, Amazon RDS is used as the database system. The input is the structured data obtained in step 1, and the output is the data properly stored in the database for long-term storage and analysis. 【0472】 Step 3: 【0473】 The server provides stored data to a machine learning model to predict future states. TensorFlow is used to analyze traffic flow and energy consumption trends. The input consists of historical and real-time data retrieved from a database, while the output is a prediction of future conditions generated by the predictive model. 【0474】 Step 4: 【0475】 The output of the predictive model is analyzed to create data for suggesting the optimal action for the user. A generative AI model is used in this process. The input is the predicted value obtained in step 3, and the output is specific action suggestions for the user. These suggestions are generated considering the user's behavioral patterns. 【0476】 Step 5: 【0477】 The system notifies the user's device, i.e., their smartphone or web platform, of suggestions from the server. This allows the user to receive information in real time and take appropriate action. The input is the action suggestion generated in step 4, and the output is the specific action suggestion displayed on the user's device. 【0478】 Step 6: 【0479】 When anomaly detection is required, the server analyzes real-time data and detects the anomaly. The anomaly detection algorithm operates to determine whether or not an emergency situation exists. The input is the latest data from the sensor, and the output is the presence or absence and nature of the anomaly. 【0480】 Step 7: 【0481】 If an anomaly is detected, the server quickly develops countermeasures and sends notifications to relevant organizations and users. Communication APIs such as Twilio may be used for these notifications. The input is the anomaly information detected in step 6, and the output is the countermeasures and related notification text. 【0482】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0483】 This invention is an integrated management system that supports the sustainable development of cities and has the function of recognizing user emotions and providing appropriate information. This system has a complex configuration that includes a data generation device, a prediction device, an anomaly detection device, and an emotion engine. 【0484】 First, the server collects environmental data in real time from various IoT sensors deployed throughout the city. This data encompasses a wide range of infrastructure information, including traffic conditions, energy consumption, and air quality. The server stores this data in a database and analyzes it as needed. 【0485】 Next, an AI predictive model is applied based on the data collected by the server to forecast future demand and unusual events. This includes providing information to optimize resources according to the predicted situation. Anomaly detection devices allow the server to immediately identify abnormal situations and quickly formulate response plans. 【0486】 Furthermore, a distinctive element of this invention, the emotion engine, analyzes the user's emotional state through the terminal. The emotion engine analyzes the user's input and behavioral patterns to recognize the current emotion. The server can then provide information optimized for the user, taking this emotional data into consideration. For example, if the user is feeling stressed, the server can adjust the format and frequency of the information it notifies to reduce the emotional burden. 【0487】 As a concrete example, suppose the server analyzes traffic data before the morning rush hour and determines that congestion is expected to be greater than usual. If the emotion engine recognizes that the user is busy and stressed, the server will provide an encouraging message to help the user cope with the situation, along with suggestions for alternative routes. In this way, the system functions to support a better life experience by providing flexible information that takes the user's emotions into consideration. 【0488】 This invention enables efficient and emotionally considerate responses to various fluctuating factors in urban life, thereby contributing to an improvement in the quality of life for residents. 【0489】 The following describes the processing flow. 【0490】 Step 1: 【0491】 The server collects data in real time from a group of IoT sensors installed throughout the city. This data includes information such as traffic volume, energy consumption, and air quality, and the server stores this data in a database. 【0492】 Step 2: 【0493】 The server analyzes the stored sensor data. Here, an AI predictive model is used to forecast traffic congestion, energy consumption peaks, and other factors. This enables the provision of information based on future conditions. 【0494】 Step 3: 【0495】 The server uses anomaly detection devices to detect data that deviates from normal conditions or abnormal situations. For example, large-scale traffic congestion or a sudden increase in energy consumption may be detected. Based on this, the server develops a rapid response plan and prepares to notify relevant organizations. 【0496】 Step 4: 【0497】 The device analyzes user input and behavioral patterns through an emotion engine to recognize the user's emotional state. This allows it to understand the user's stress levels and emotional trends. 【0498】 Step 5: 【0499】 The server considers aggregated emotional data to provide users with the most relevant information. For example, if a user is feeling stressed, the tone of notification messages will be gentler, and action suggestions will be more flexible. 【0500】 Step 6: 【0501】 Users can review information and recommended actions provided by their devices and adjust their daily schedules and routes as needed. This supports them in making emotionally conscious choices and improves their quality of life. 【0502】 (Example 2) 【0503】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0504】 In today's urban environment, there is a need to achieve sustainable development while accurately managing diverse environmental information in real time and providing information that is sensitive to users' feelings. However, achieving this with conventional systems is difficult, and they particularly lack the ability to respond quickly to abnormal situations and provide flexible information that responds to users' emotions. 【0505】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0506】 In this invention, the server includes means for aggregating data from an information gathering device for acquiring environmental information, means for estimating future conditions based on the data using a prediction function, optimization means for analyzing information based on the predicted data and providing information according to the user's situation, and means for identifying the user's emotions using an emotion analysis function and adjusting the information provision based on those emotions. This not only enables a rapid and appropriate response to changes in the urban environment, but also reduces the emotional burden on users and improves their quality of life. 【0507】 "Environmental information" refers to a variety of urban data related to infrastructure, such as traffic conditions, energy consumption, and air quality. 【0508】 "Information gathering devices" refer to various sensors and equipment used to collect environmental data within a city. 【0509】 A "predictive function" refers to a function that calculates future states and conditions based on past data. 【0510】 "Optimization methods" refer to the process of analyzing and adjusting data in order to provide users with appropriate and effective information. 【0511】 The "emotion analysis function" refers to a function that analyzes the user's emotional state through their input and behavioral patterns. 【0512】 "Anomaly response function" refers to the process by which a system identifies potential emergencies and quickly develops appropriate countermeasures. 【0513】 "Communication function" refers to the technology within a system used to transmit information and recommended actions to users. 【0514】 This invention is a system for supporting the sustainable development of cities and providing information optimized according to the user's emotions. This system consists primarily of a server, terminals, and users, which work together to achieve its functions. 【0515】 The server collects environmental information in real time from a wide variety of information gathering devices installed throughout the city. This includes traffic conditions, energy consumption, and air quality, and this data is stored in a database. The server has a predictive function that utilizes advanced machine learning algorithms to estimate future infrastructure demand and abnormal events based on past data. Based on these prediction results, information is provided to users through optimized means. 【0516】 The device analyzes the user's input and behavior patterns through its emotion analysis function to identify their emotional state. This analysis uses natural language processing technology, and the user's emotional data is sent to a server. 【0517】 A concrete example is when a server analyzes traffic data during the morning commute and determines that congestion is expected. In this case, if the sentiment analysis function recognizes that the user is busy and stressed, the server will provide the user with an encouraging message along with guidance on alternative routes. This kind of information provision allows users to travel efficiently while feeling reassured. 【0518】 An example of a prompt for a generated AI model is, "Please explain how to detect anomalies based on data collected from IoT sensors and provide prompt countermeasures." This invention enables efficient management of urban environments and the provision of services that take into account the feelings of users. 【0519】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0520】 Step 1: 【0521】 The server acquires environmental information in real time from information gathering devices within the city. This input data includes traffic conditions, energy consumption, and air quality. The server stores this acquired data in a database and uses it for future analysis and prediction. Specifically, it includes the function of aggregating data collected from sensors via the existing network. 【0522】 Step 2: 【0523】 The server analyzes the acquired environmental information using machine learning algorithms. The input is historical environmental data, from which trends and patterns are extracted. This allows for predictions of traffic volume increases and energy consumption peaks, and estimates future demand. The output includes, for example, forecasts of traffic congestion and peak energy usage times for the following week. 【0524】 Step 3: 【0525】 The device processes user input and behavioral data using sentiment analysis to identify the user's emotional state. Input includes text messages and activity logs provided by the user via smartphone or computer. This data is analyzed using natural language processing technology to evaluate the user's mental state. The output generates data indicating the user's current emotional state. 【0526】 Step 4: 【0527】 The server optimizes the information it provides to users based on predicted needs and emotional data. It takes emotional state and predicted data as input and determines what information to provide, how often, and in what format. For example, a scenario might involve providing users with high stress levels with encouraging messages along with recommended routes to avoid congestion. 【0528】 Step 5: 【0529】 Users receive information provided by the server via their devices and use it as a basis for their daily choices. The input consists of recommendations from the server, which the user uses to decide on their actions. The output generates new behavioral patterns and feedback from the user, which then serve as input for the next processing cycle. 【0530】 (Application Example 2) 【0531】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0532】 In modern urban life, residents often experience stress due to traffic congestion and environmental changes. Furthermore, real-time information provided often fails to consider the emotional state of users, leading to an emotional burden from excessive information. Therefore, there is a need for information provision that takes into account the emotional state of urban dwellers, as well as for swift and accurate responses to abnormal situations. 【0533】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0534】 In this invention, the server includes means for collecting information in real time from a data generation device, means for predicting future states using a prediction device based on the information, means for analyzing the predicted information and providing optimized information based on the user's emotional state, and means equipped with an emotion analysis device that analyzes the user's emotions and determines appropriate recommended actions based on the same. This enables flexible information provision that takes into account the user's emotional state and a rapid and efficient response to abnormal situations. 【0535】 A "data generation device" is a device that collects information in real time from various sensors and devices within a city. 【0536】 A "prediction device" is a device that predicts future states or events based on collected information. 【0537】 An "emotion analysis device" is a device that analyzes a user's input and behavioral patterns to identify their emotional state. 【0538】 "Optimized information" refers to information that has been adjusted to be necessary and appropriate, taking into account the user's current emotional state. 【0539】 "Recommended actions" are specific actions suggested based on the user's emotional state and anticipated future circumstances. 【0540】 The system that realizes this invention mainly consists of a server and terminals for each user. The server collects information in real time from multiple data generation devices located within the city. This includes urban infrastructure data such as traffic conditions, energy consumption, and air quality. More accurate information can be obtained by having various sensors and devices work together for data collection. The collected data is stored in a database (e.g., MySQL). 【0541】 The server then uses a machine learning platform (e.g., TensorFlow) to analyze the data collected by the predictor and forecast future states. These forecasts include predicting traffic congestion and assessing the likelihood of abnormal events occurring. Based on this information, the server optimizes resources for efficient use. 【0542】 Furthermore, the device is equipped with an emotion analysis device that analyzes the user's emotional state in real time. This analysis uses technologies that analyze facial expressions and voice (e.g., Microsoft Azure Face API). The data obtained from the emotion analysis is received by a server, which then provides optimized information based on a profile that includes the user's behavioral patterns. 【0543】 As a concrete example, consider a scenario where a server predicts morning traffic congestion based on information from a data generation device, and a user's device detects that the user is experiencing stress through emotion analysis. In this case, the server suggests a relatively less congested alternative route. It also provides a link to a service that streams relaxing music. 【0544】 For better results when inputting prompts to the generating AI model, consider the following example: "Suggest appropriate relaxation methods when the user is feeling stressed. Specifically, this includes alternative routes to avoid crowds and rest spots within the city." 【0545】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0546】 Step 1: 【0547】 The server collects information in real time from data generation devices located throughout the city. Input data includes traffic conditions, energy consumption, and air quality. This information is stored in a database, making it readily accessible when needed. 【0548】 Step 2: 【0549】 The server uses a prediction device to forecast future conditions based on the collected data. It leverages a machine learning platform to predict traffic congestion and assess the likelihood of abnormal events. The input is the data acquired in step 1, and the output is the future prediction data as a result of the analysis. 【0550】 Step 3: 【0551】 The device uses an emotion analysis device to analyze the user's emotional state in real time. Input consists of the user's facial expressions and voice data, which are processed to identify the emotional state. The output is the user's emotional state. 【0552】 Step 4: 【0553】 The server integrates the predictive data from step 2 and the sentiment data from step 3 to generate user-optimized information. This step takes the user's emotional state into account when creating appropriate route suggestions and encouraging messages. The output is a customized set of action suggestions for the user. 【0554】 Step 5: 【0555】 The terminal notifies the user of customized information provided by the server. The input is the output data from step 4. The information the user receives includes suggestions for alternative routes and relaxing activities. 【0556】 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. 【0557】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0558】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0559】 [Fourth Embodiment] 【0560】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0561】 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. 【0562】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0563】 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. 【0564】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0565】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0566】 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. 【0567】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0568】 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. 【0569】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0570】 The 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. 【0571】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0572】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0573】 This invention is an integrated management system for supporting the sustainable development of cities, which uses AI agents to monitor urban infrastructure in real time and optimize various resources. This system has a complex configuration that includes data generation devices, prediction devices, and anomaly detection devices. 【0574】 In this system, a server collects data in real time from various IoT sensors installed throughout the city. This data covers various aspects of urban infrastructure, such as traffic conditions, energy consumption, water resource usage, and air quality. The server stores the data in a database for continuous analysis. 【0575】 Next, the server applies an AI model to analyze the collected data and predict future demand and possible anomalies. In particular, by predicting traffic flow and energy consumption trends, it becomes possible to manage cities and reallocate resources efficiently. 【0576】 Anomaly detection devices are used to immediately detect events that deviate from normal conditions or emergencies by having the server perform real-time data analysis. After detection, a response plan is automatically generated quickly and notified to the relevant organizations to facilitate the early resolution of the problem. 【0577】 Furthermore, this system is equipped with communication devices and can provide important information to users. Users are expected to make better choices based on the information provided. For example, if traffic congestion is predicted, the server can send a notification to the user's smartphone app to encourage the use of public transportation. It may also recommend adjusting the usage time of home appliances to avoid peak energy consumption. 【0578】 As a concrete example, consider the system operation on a typical day. A server receives traffic data from sensors before the morning rush hour, and an advanced AI model predicts the day's traffic patterns. As a result, if higher-than-usual congestion is expected on certain roads or intersections, the server suggests alternative routes to the user. Based on this information, the user may be able to significantly reduce their commute time. 【0579】 Furthermore, if weather conditions worsen and a surge in electricity demand is expected, the server will use an anomaly detection device to issue a warning in advance and propose energy-saving measures to power companies and residents. In this way, the present invention will benefit a large number of users and enable the rationalization of urban management and improvement of sustainability. 【0580】 The following describes the processing flow. 【0581】 Step 1: 【0582】 The server establishes connections with each IoT sensor placed throughout the city. The server collects data such as traffic conditions, energy consumption, water resource usage, and air quality in real time and stores the collected data in a database. 【0583】 Step 2: 【0584】 The server retrieves the latest sensor data stored in the database. Based on this data, the server uses an AI model to predict future conditions such as traffic patterns and energy demand. The server evaluates the prediction results and identifies anomalies and necessary interventions. 【0585】 Step 3: 【0586】 The server uses an anomaly detection device to detect events that exceed the normal range in the collected data and predictive information. If an anomaly is detected, the server quickly and automatically generates a countermeasure plan and prepares to notify relevant organizations. 【0587】 Step 4: 【0588】 The server generates useful information for the user based on prediction and anomaly detection results. The terminal presents the generated information to the user, for example, recommending alternative routes in response to changes in traffic conditions or prompting optimization of appliance usage for energy saving. 【0589】 Step 5: 【0590】 Users can review information received through their devices and adjust their actions as needed. By following the notified recommendations, users can improve convenience and efficiency by changing their commute routes or adjusting their power usage. 【0591】 (Example 1) 【0592】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0593】 In urban areas, there is a need for optimized infrastructure operations and immediate response to emergencies. Currently, real-time data analysis and forecasting are not sufficiently performed, resulting in challenges in efficient resource management and rapid response. 【0594】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0595】 In this invention, the server includes means for collecting information in a time series from an information acquisition device, means for predicting future states using a prediction device based on the information, and means for storing the collected information in an information aggregate and performing preprocessing as necessary. This enables efficient resource management and rapid response to anomalies based on real-time data analysis and future prediction. 【0596】 An "information acquisition device" is a device used to collect data from sensors and devices installed within a city. 【0597】 "Methods for collecting information in a time series" refers to the process of continuously acquiring data along a time axis and processing it in real time. 【0598】 A "predictive device" is a device or system used to predict future states based on collected data. 【0599】 An "information collection" is a database or storage system that stores collected data and allows it to be retrieved and used when needed. 【0600】 "Preprocessing" refers to processes such as data normalization and cleaning that are performed to prepare data before data analysis or prediction. 【0601】 An "artificial intelligence model" is an algorithm or learning model used to analyze large amounts of data and identify specific patterns or trends. 【0602】 An "anomaly detection algorithm" is a computational method for detecting patterns or values ​​that are different from the norm within data and determining whether an anomaly exists. 【0603】 A "means for detecting anomalies in real time" refers to a device or algorithm for analyzing data in real time and identifying anomalies on the spot. 【0604】 An "emergency situation" refers to a situation or event that disrupts normal infrastructure operations or requires immediate attention. 【0605】 A "response plan" is a plan of specific actions and procedures to be taken in response to detected anomalies or emergencies. 【0606】 A "communication device" is a combination of hardware and software used to transmit information to users or other systems. 【0607】 "Improvement actions" refer to specific actions taken by users based on information and notifications from the system, aimed at improving the situation or increasing efficiency. 【0608】 This invention is an integrated management system aimed at the efficient and safe operation of infrastructure in urban areas. This system optimizes urban management by predicting future conditions based on data obtained from information acquisition devices and detecting anomalies in real time. The following specifically describes embodiments for carrying out this invention. 【0609】 The server first acquires information through numerous sensors. These sensors provide real-time data on various environmental indicators within the city, such as traffic, energy, water resources, and air quality. This information is then stored in a database in JSON format. The database utilizes a distributed database system to provide high-speed information access. 【0610】 In data analysis, the server utilizes AI frameworks such as TensorFlow and PyTorch. These frameworks allow the server to generate machine learning models based on collected data to predict traffic patterns and energy consumption trends. For example, the server can analyze past trends to predict traffic congestion before the morning rush hour and suggest alternative routes to the user on specific roads. 【0611】 In anomaly detection, the server uses an anomaly identification algorithm. This allows the server to detect data that deviates significantly from normal patterns and immediately notify the system administrator. For example, if the server detects a sudden increase in energy consumption, it can issue a warning to the power company and instruct them to take appropriate energy-saving measures. 【0612】 Furthermore, the server provides information to the user's terminal via communication equipment. The system utilizes a smartphone app to facilitate smooth communication. Users can receive traffic information on their devices and change their commute route based on that information. In particular, notifications encouraging the use of public transportation are sent to users, supporting efficient travel. 【0613】 Specific examples include servers using prompts such as "Please tell me the predicted traffic congestion patterns in your area for the next 24 hours" or "Please predict the energy consumption trends based on this week's weather and suggest measures to avoid peaks," to provide users with the necessary information. 【0614】 In this way, the present invention supports the sustainable development of cities by combining data-driven infrastructure management with real-time anomaly response. 【0615】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0616】 Step 1: 【0617】 The server collects data from various sensors. The input is real-time data from various urban infrastructure sensors. The server receives this data in JSON format and stores it in a database. Specifically, the server retrieves data using HTTP requests and automatically stores it in the database. 【0618】 Step 2: 【0619】 The server performs preprocessing on the collected data. The input is raw data collected from sensors. The server cleans this data, removes missing values, and performs unification processes such as normalization. The output is clean data prepared in an analyzable format. Specifically, the server unifies the data format and filters out unnecessary data. 【0620】 Step 3: 【0621】 The server uses an AI model to perform predictive analysis. The input is pre-processed data. Based on this, the server executes machine learning algorithms to predict trends in traffic volume and energy consumption. The output is predicted future data. Specifically, the server uses TensorFlow to build a neural network model and perform predictions. 【0622】 Step 4: 【0623】 The server applies anomaly detection algorithms to detect anomalies in real time. The input is the latest data, which is continuously updated. The server identifies data that deviates from normal patterns and generates alerts. The output is a notification that an anomaly has been detected. Specifically, the server uses statistical methods to extract outliers and sends alerts to relevant organizations. 【0624】 Step 5: 【0625】 The server performs communication processing to provide information to the user's device. The input consists of anomaly detection results and prediction data. Based on this, the server generates notifications for the user and sends them via smartphone apps or email. The output is the sent notification. Specifically, the server configures push notifications via an API to deliver important information to the user in real time. 【0626】 Step 6: 【0627】 The user selects an action based on the information received. The input is notification information sent from the server. Based on this information, the user selects a specific action, such as changing the route or saving energy. The output is the user's actual change in behavior. This action might involve displaying the suggested route on the device and starting navigation. 【0628】 (Application Example 1) 【0629】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0630】 Modern urban life presents numerous problems, including traffic congestion, energy waste, and environmental pollution, which hinder the sustainable development of cities. To address these issues, there is a need for a system that collects and efficiently analyzes information in real time, providing users with concrete and immediate actionable insights. Furthermore, the ability to quickly implement countermeasures in emergency situations is essential. 【0631】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0632】 In this invention, the server includes means for collecting information in real time from data generation means, means for predicting future states based on the collected information using prediction means, and means for analyzing the predicted information and proposing the optimal action to the user. This makes it possible to reduce various risks and waste in the urban environment and improve urban functions so that residents can live safely and comfortably. 【0633】 A "data generation means" is a device for generating and collecting various types of information within a city in real time. 【0634】 "Means of collecting information in real time" refers to methods of instantly gathering the latest data from across an entire city using various sensors and communication devices. 【0635】 "Predictive tools" refer to algorithms and computer devices that analyze collected data and use that information to scientifically predict future states. 【0636】 A "means for suggesting optimal actions" refers to a function that, based on predicted information, suggests the most rational and efficient course of action to the user. 【0637】 An "anomaly detection method" is a technology for analyzing and identifying situations that deviate from normal conditions, and a device for quickly detecting such anomalies. 【0638】 "Means for formulating countermeasures and distributing those countermeasures" refers to a system that quickly and automatically devises countermeasures when an anomaly is detected and transmits the necessary information to relevant organizations and users. 【0639】 "Communication means" refers to infrastructure for exchanging information between users and systems, which enables the rapid dissemination of recommended actions. 【0640】 In this embodiment of the invention, the server collects information in real time from a wide variety of sensors placed within the city. This includes traffic sensors, environmental sensors, energy consumption sensors, and the like. This information is first stored in a database system, which serves as the basis for continuous analysis. 【0641】 The server then uses machine learning frameworks such as TensorFlow and PyTorch to process the data and predict future states. These predictions include, for example, peak traffic congestion and increases or decreases in energy consumption. 【0642】 The analyzed information is transmitted to the user's device in real time. This is done using a smartphone app or a web interface. Based on the information provided on the app, users can decide on the optimal course of action. Specifically, this includes suggestions for alternative routes to avoid congestion and suggestions for optimizing energy use. 【0643】 If an abnormal condition is detected, the anomaly detection system will identify it and promptly formulate countermeasures. These countermeasures will then be notified to the relevant organizations and users via communication channels. 【0644】 As a concrete example, one morning, the server analyzes traffic data and predicts congestion on a specific road. The user's smartphone then displays alternative routes to avoid congestion, potentially shortening their commute time. Additionally, if a peak in electricity demand is predicted, the user receives advice on energy conservation. 【0645】 An example of a prompt message is, "How can I generate recommendations that predict and inform the user of the peak period for energy consumption next week?" This allows the user to take rational action based on the information provided. 【0646】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0647】 Step 1: 【0648】 The server collects data in real time from sensors placed throughout the city. This data includes traffic information, environmental parameters, and energy consumption. The input is raw data from the sensors, and the output is structured data for storage in the database. The structured data is processed based on a schema to ensure consistency. 【0649】 Step 2: 【0650】 The server stores the collected structured data in a database. For example, Amazon RDS is used as the database system. The input is the structured data obtained in step 1, and the output is the data properly stored in the database for long-term storage and analysis. 【0651】 Step 3: 【0652】 The server provides stored data to a machine learning model to predict future states. TensorFlow is used to analyze traffic flow and energy consumption trends. The input consists of historical and real-time data retrieved from a database, while the output is a prediction of future conditions generated by the predictive model. 【0653】 Step 4: 【0654】 The output of the predictive model is analyzed to create data for suggesting the optimal action for the user. A generative AI model is used in this process. The input is the predicted value obtained in step 3, and the output is specific action suggestions for the user. These suggestions are generated considering the user's behavioral patterns. 【0655】 Step 5: 【0656】 The system notifies the user's device, i.e., their smartphone or web platform, of suggestions from the server. This allows the user to receive information in real time and take appropriate action. The input is the action suggestion generated in step 4, and the output is the specific action suggestion displayed on the user's device. 【0657】 Step 6: 【0658】 When anomaly detection is required, the server analyzes real-time data and detects the anomaly. The anomaly detection algorithm operates to determine whether or not an emergency situation exists. The input is the latest data from the sensor, and the output is the presence or absence and nature of the anomaly. 【0659】 Step 7: 【0660】 If an anomaly is detected, the server quickly develops countermeasures and sends notifications to relevant organizations and users. Communication APIs such as Twilio may be used for these notifications. The input is the anomaly information detected in step 6, and the output is the countermeasures and related notification text. 【0661】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0662】 This invention is an integrated management system that supports the sustainable development of cities and has the function of recognizing user emotions and providing appropriate information. This system has a complex configuration that includes a data generation device, a prediction device, an anomaly detection device, and an emotion engine. 【0663】 First, the server collects environmental data in real time from various IoT sensors deployed throughout the city. This data encompasses a wide range of infrastructure information, including traffic conditions, energy consumption, and air quality. The server stores this data in a database and analyzes it as needed. 【0664】 Next, an AI predictive model is applied based on the data collected by the server to forecast future demand and unusual events. This includes providing information to optimize resources according to the predicted situation. Anomaly detection devices allow the server to immediately identify abnormal situations and quickly formulate response plans. 【0665】 Furthermore, a distinctive element of this invention, the emotion engine, analyzes the user's emotional state through the terminal. The emotion engine analyzes the user's input and behavioral patterns to recognize the current emotion. The server can then provide information optimized for the user, taking this emotional data into consideration. For example, if the user is feeling stressed, the server can adjust the format and frequency of the information it notifies to reduce the emotional burden. 【0666】 As a concrete example, suppose the server analyzes traffic data before the morning rush hour and determines that congestion is expected to be greater than usual. If the emotion engine recognizes that the user is busy and stressed, the server will provide an encouraging message to help the user cope with the situation, along with suggestions for alternative routes. In this way, the system functions to support a better life experience by providing flexible information that takes the user's emotions into consideration. 【0667】 This invention enables efficient and emotionally considerate responses to various fluctuating factors in urban life, thereby contributing to an improvement in the quality of life for residents. 【0668】 The following describes the processing flow. 【0669】 Step 1: 【0670】 The server collects data in real time from a group of IoT sensors installed throughout the city. This data includes information such as traffic volume, energy consumption, and air quality, and the server stores this data in a database. 【0671】 Step 2: 【0672】 The server analyzes the stored sensor data. Here, an AI predictive model is used to forecast traffic congestion, energy consumption peaks, and other factors. This enables the provision of information based on future conditions. 【0673】 Step 3: 【0674】 The server uses anomaly detection devices to detect data that deviates from normal conditions or abnormal situations. For example, large-scale traffic congestion or a sudden increase in energy consumption may be detected. Based on this, the server develops a rapid response plan and prepares to notify relevant organizations. 【0675】 Step 4: 【0676】 The device analyzes user input and behavioral patterns through an emotion engine to recognize the user's emotional state. This allows it to understand the user's stress levels and emotional trends. 【0677】 Step 5: 【0678】 The server considers aggregated emotional data to provide users with the most relevant information. For example, if a user is feeling stressed, the tone of notification messages will be gentler, and action suggestions will be more flexible. 【0679】 Step 6: 【0680】 Users can review information and recommended actions provided by their devices and adjust their daily schedules and routes as needed. This supports them in making emotionally conscious choices and improves their quality of life. 【0681】 (Example 2) 【0682】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0683】 In today's urban environment, there is a need to achieve sustainable development while accurately managing diverse environmental information in real time and providing information that is sensitive to users' feelings. However, achieving this with conventional systems is difficult, and they particularly lack the ability to respond quickly to abnormal situations and provide flexible information that responds to users' emotions. 【0684】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0685】 In this invention, the server includes means for aggregating data from an information gathering device for acquiring environmental information, means for estimating future conditions based on the data using a prediction function, optimization means for analyzing information based on the predicted data and providing information according to the user's situation, and means for identifying the user's emotions using an emotion analysis function and adjusting the information provision based on those emotions. This not only enables a rapid and appropriate response to changes in the urban environment, but also reduces the emotional burden on users and improves their quality of life. 【0686】 "Environmental information" refers to a variety of urban data related to infrastructure, such as traffic conditions, energy consumption, and air quality. 【0687】 "Information gathering devices" refer to various sensors and equipment used to collect environmental data within a city. 【0688】 A "predictive function" refers to a function that calculates future states and conditions based on past data. 【0689】 "Optimization methods" refer to the process of analyzing and adjusting data in order to provide users with appropriate and effective information. 【0690】 The "emotion analysis function" refers to a function that analyzes the user's emotional state through their input and behavioral patterns. 【0691】 "Anomaly response function" refers to the process by which a system identifies potential emergencies and quickly develops appropriate countermeasures. 【0692】 "Communication function" refers to the technology within a system used to transmit information and recommended actions to users. 【0693】 This invention is a system for supporting the sustainable development of cities and providing information optimized according to the user's emotions. This system consists primarily of a server, terminals, and users, which work together to achieve its functions. 【0694】 The server collects environmental information in real time from a wide variety of information gathering devices installed throughout the city. This includes traffic conditions, energy consumption, and air quality, and this data is stored in a database. The server has a predictive function that utilizes advanced machine learning algorithms to estimate future infrastructure demand and abnormal events based on past data. Based on these prediction results, information is provided to users through optimized means. 【0695】 The device analyzes the user's input and behavior patterns through its emotion analysis function to identify their emotional state. This analysis uses natural language processing technology, and the user's emotional data is sent to a server. 【0696】 A concrete example is when a server analyzes traffic data during the morning commute and determines that congestion is expected. In this case, if the sentiment analysis function recognizes that the user is busy and stressed, the server will provide the user with an encouraging message along with guidance on alternative routes. This kind of information provision allows users to travel efficiently while feeling reassured. 【0697】 An example of a prompt for a generated AI model is, "Please explain how to detect anomalies based on data collected from IoT sensors and provide prompt countermeasures." This invention enables efficient management of urban environments and the provision of services that take into account the feelings of users. 【0698】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0699】 Step 1: 【0700】 The server acquires environmental information in real time from information gathering devices within the city. This input data includes traffic conditions, energy consumption, and air quality. The server stores this acquired data in a database and uses it for future analysis and prediction. Specifically, it includes the function of aggregating data collected from sensors via the existing network. 【0701】 Step 2: 【0702】 The server analyzes the acquired environmental information using machine learning algorithms. The input is historical environmental data, from which trends and patterns are extracted. This allows for predictions of traffic volume increases and energy consumption peaks, and estimates future demand. The output includes, for example, forecasts of traffic congestion and peak energy usage times for the following week. 【0703】 Step 3: 【0704】 The device processes user input and behavioral data using sentiment analysis to identify the user's emotional state. Input includes text messages and activity logs provided by the user via smartphone or computer. This data is analyzed using natural language processing technology to evaluate the user's mental state. The output generates data indicating the user's current emotional state. 【0705】 Step 4: 【0706】 The server optimizes the information it provides to users based on predicted needs and emotional data. It takes emotional state and predicted data as input and determines what information to provide, how often, and in what format. For example, a scenario might involve providing users with high stress levels with encouraging messages along with recommended routes to avoid congestion. 【0707】 Step 5: 【0708】 Users receive information provided by the server via their devices and use it as a basis for their daily choices. The input consists of recommendations from the server, which the user uses to decide on their actions. The output generates new behavioral patterns and feedback from the user, which then serve as input for the next processing cycle. 【0709】 (Application Example 2) 【0710】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0711】 In modern urban life, residents often experience stress due to traffic congestion and environmental changes. Furthermore, real-time information provided often fails to consider the emotional state of users, leading to an emotional burden from excessive information. Therefore, there is a need for information provision that takes into account the emotional state of urban dwellers, as well as for swift and accurate responses to abnormal situations. 【0712】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0713】 In this invention, the server includes means for collecting information in real time from a data generation device, means for predicting future states using a prediction device based on the information, means for analyzing the predicted information and providing optimized information based on the user's emotional state, and means equipped with an emotion analysis device that analyzes the user's emotions and determines appropriate recommended actions based on the same. This enables flexible information provision that takes into account the user's emotional state and a rapid and efficient response to abnormal situations. 【0714】 A "data generation device" is a device that collects information in real time from various sensors and devices within a city. 【0715】 A "prediction device" is a device that predicts future states or events based on collected information. 【0716】 An "emotion analysis device" is a device that analyzes a user's input and behavioral patterns to identify their emotional state. 【0717】 "Optimized information" refers to information that has been adjusted to be necessary and appropriate, taking into account the user's current emotional state. 【0718】 "Recommended actions" are specific actions suggested based on the user's emotional state and anticipated future circumstances. 【0719】 The system that realizes this invention mainly consists of a server and terminals for each user. The server collects information in real time from multiple data generation devices located within the city. This includes urban infrastructure data such as traffic conditions, energy consumption, and air quality. More accurate information can be obtained by having various sensors and devices work together for data collection. The collected data is stored in a database (e.g., MySQL). 【0720】 The server then uses a machine learning platform (e.g., TensorFlow) to analyze the data collected by the predictor and forecast future states. These forecasts include predicting traffic congestion and assessing the likelihood of abnormal events occurring. Based on this information, the server optimizes resources for efficient use. 【0721】 Furthermore, the device is equipped with an emotion analysis device that analyzes the user's emotional state in real time. This analysis uses technologies that analyze facial expressions and voice (e.g., Microsoft Azure Face API). The data obtained from the emotion analysis is received by a server, which then provides optimized information based on a profile that includes the user's behavioral patterns. 【0722】 As a concrete example, consider a scenario where a server predicts morning traffic congestion based on information from a data generation device, and a user's device detects that the user is experiencing stress through emotion analysis. In this case, the server suggests a relatively less congested alternative route. It also provides a link to a service that streams relaxing music. 【0723】 For better results when inputting prompts to the generating AI model, consider the following example: "Suggest appropriate relaxation methods when the user is feeling stressed. Specifically, this includes alternative routes to avoid crowds and rest spots within the city." 【0724】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0725】 Step 1: 【0726】 The server collects information in real time from data generation devices located throughout the city. Input data includes traffic conditions, energy consumption, and air quality. This information is stored in a database, making it readily accessible when needed. 【0727】 Step 2: 【0728】 The server uses a prediction device to forecast future conditions based on the collected data. It leverages a machine learning platform to predict traffic congestion and assess the likelihood of abnormal events. The input is the data acquired in step 1, and the output is the future prediction data as a result of the analysis. 【0729】 Step 3: 【0730】 The device uses an emotion analysis device to analyze the user's emotional state in real time. Input consists of the user's facial expressions and voice data, which are processed to identify the emotional state. The output is the user's emotional state. 【0731】 Step 4: 【0732】 The server integrates the predictive data from step 2 and the sentiment data from step 3 to generate user-optimized information. This step takes the user's emotional state into account when creating appropriate route suggestions and encouraging messages. The output is a customized set of action suggestions for the user. 【0733】 Step 5: 【0734】 The terminal notifies the user of customized information provided by the server. The input is the output data from step 4. The information the user receives includes suggestions for alternative routes and relaxing activities. 【0735】 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. 【0736】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0737】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0738】 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. 【0739】 Figure 9 shows an 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. 【0740】 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. 【0741】 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. 【0742】 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, motorcycles, etc., 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, for example, based 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. 【0743】 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." 【0744】 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. 【0745】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0746】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0747】 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. 【0748】 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. 【0749】 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. 【0750】 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. 【0751】 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. 【0752】 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. 【0753】 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. 【0754】 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 the like 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. 【0755】 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 as being incorporated by reference. 【0756】 The following is further disclosed regarding the embodiments described above. 【0757】 (Claim 1) 【0758】 A means of collecting data in real time from a data generation device, 【0759】 A means for predicting future states using a prediction device based on the aforementioned data, 【0760】 A means for analyzing the aforementioned predicted data and providing optimal information to the user, 【0761】 A system that includes this. 【0762】 (Claim 2) 【0763】 The system according to claim 1, comprising means for detecting an emergency using an anomaly detection device and for quickly formulating a response plan. 【0764】 (Claim 3) 【0765】 The system according to claim 1, further comprising a communication device for notifying users of recommended actions. 【0766】 "Example 1" 【0767】 (Claim 1) 【0768】 A means of collecting information in chronological order from an information acquisition device, 【0769】 A means for predicting future states using a prediction device based on the aforementioned information, 【0770】 The means for storing the collected information in an information set and performing preprocessing as necessary, 【0771】 A means of analyzing information by applying artificial intelligence models and predicting the flow of public resources, 【0772】 A means for detecting anomalies in real time using an anomaly identification algorithm, 【0773】 A means for analyzing the aforementioned predicted information and providing appropriate information to the user, 【0774】 A system that includes this. 【0775】 (Claim 2) 【0776】 The system according to claim 1, comprising means for promptly formulating a response plan and notifying relevant organizations when an emergency is detected. 【0777】 (Claim 3) 【0778】 The system according to claim 1, further comprising a communication device for notifying users of corrective actions. 【0779】 "Application Example 1" 【0780】 (Claim 1) 【0781】 A means for collecting information in real time from a data generation means, 【0782】 A means for predicting future states using a prediction means based on the information collected above, 【0783】 A means for analyzing the aforementioned predicted information and proposing the optimal action to the user, 【0784】 A means for distributing the aforementioned proposal to computer applications accessible to users, 【0785】 A system that includes this. 【0786】 (Claim 2) 【0787】 The system according to claim 1, comprising means for detecting an emergency situation using an anomaly detection means, quickly formulating a countermeasure plan, and distributing the countermeasure to relevant organizations and users. 【0788】 (Claim 3) 【0789】 The system according to claim 1, further comprising communication means for communicating recommended actions to a user. 【0790】 "Example 2 of combining an emotion engine" 【0791】 (Claim 1) 【0792】 A means of aggregating data from an information collection device for acquiring environmental information, 【0793】 A means for estimating future conditions using a prediction function based on the aforementioned data, 【0794】 An optimization means that analyzes information based on the aforementioned predicted data and provides information tailored to the user's situation, 【0795】 A means for identifying the user's emotions using an emotion analysis function and adjusting the provision of information based on said emotions, 【0796】 ... 【0797】 A system that includes this. 【0798】 (Claim 2) 【0799】 The system according to claim 1, comprising means for identifying an emergency using an anomaly response function and for quickly generating a countermeasure plan. 【0800】 (Claim 3) 【0801】 The system according to claim 1, comprising a communication function for guiding the user's actions. 【0802】 "Application example 2 when combining with an emotional engine" 【0803】 (Claim 1) 【0804】 A means of collecting information in real time from a data generation device, 【0805】 A means for predicting a future state using a prediction device based on the aforementioned information, 【0806】 A means for analyzing the predicted information and providing optimized information based on the user's emotional state, 【0807】 A means equipped with an emotion analysis device that analyzes the user's emotions and determines appropriate recommended actions based on this analysis, 【0808】 A system that includes this. 【0809】 (Claim 2) 【0810】 The system according to claim 1, which includes means for detecting an emergency using an anomaly detection device, quickly formulating a response plan, and implementing it while taking into account the emotional state of the user. 【0811】 (Claim 3) 【0812】 The system according to claim 1, further comprising a communication device for notifying a user of recommended actions based on their emotional state. [Explanation of symbols] 【0813】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of collecting data in real time from a data generation device, A means for predicting future states using a prediction device based on the aforementioned data, A means for analyzing the aforementioned predicted data and providing optimal information to the user, A system that includes this. [Claim 2] The system according to claim 1, comprising means for detecting an emergency using an anomaly detection device and for quickly formulating a response plan. [Claim 3] The system according to claim 1, further comprising a communication device for notifying users of recommended actions.

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