system

Abstract

We provide the system. [Solution] A data collection method for acquiring electricity usage information in real time, A data preprocessing means that organizes the power usage information obtained by the data collection means and detects abnormal values, An analysis means for predicting trends in economic activity, which analyzes the power usage information processed by the aforementioned data preprocessing means, Based on the analysis results obtained by the aforementioned analysis means, an alert generation means detects abnormal economic activity and generates an alert, A report generation means that automatically generates a report summarizing the prediction results of the aforementioned analysis means, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In modern society, power consumption plays an important role as an indicator of economic activities. However, with the existing methods, it has been difficult to effectively utilize power consumption data in real time and accurately analyze economic activities. Also, there has been a lack of means to immediately detect abnormal power usage and prompt rapid responses from society and enterprises. As a result, efficient energy management and prediction of economic activities have not been realized, which has been a factor undermining corporate decision-making and the economic stability of society as a whole. 【Means for Solving the Problems】 【0005】 This invention provides a power usage management system that collects, organizes, and analyzes power consumption data in real time. Specifically, it employs a data collection means to acquire power usage information and introduces a data preprocessing means to organize the obtained information and detect anomalies. It also includes an analysis means that predicts trends in economic activity using time-series analysis models, and incorporates an alert generation means that detects and notifies of anomalies based on the analysis results. Furthermore, by adding a report generation means that automatically generates reports based on the obtained analysis results, a system is constructed that provides rapid and accurate decision-making support to companies and government agencies. This makes it possible to improve the accuracy of economic activity predictions and promote the efficient use of energy throughout society. 【0006】 "Electricity usage information" refers to various data related to electricity consumption, such as the amount of electricity used, the duration of use, and the usage patterns. 【0007】 "Data collection means" refers to a device or system for collecting and storing power usage information in real time from sensors or terminals. 【0008】 "Data preprocessing means" refers to a process or device for organizing collected power usage information and detecting and correcting abnormal or missing values. 【0009】 "Analysis methods" refer to algorithms and models used to predict trends in economic activity by analyzing electricity usage information. 【0010】 A "time series analysis model" refers to a mathematical model that uses past electricity usage data to extract trends and seasonality and predict future usage. 【0011】 An "alert generation method" refers to a mechanism or system that issues a warning when an anomaly is detected based on the analysis results. 【0012】 "Report generation means" refers to a device or program that automatically creates a report in report format based on analysis results and provides it to the relevant parties. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This 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 a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Carrying Out the Invention】 【0014】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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. 【0017】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 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). 【0020】 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." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 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. 【0024】 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). 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 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". 【0034】 This invention provides a system for evaluating the state of economic activity and detecting anomalies by utilizing power consumption data. The system consists of multiple means, enabling rapid and accurate economic forecasting and response. 【0035】 First, the server receives power usage information transmitted from each terminal. The terminals collect real-time data on power consumption from individual sensors and the company's energy management system and send it to the server. This data is aggregated by total power consumption and by time period. 【0036】 Next, the server preprocesses the power usage information. Specifically, it removes outliers and noise from the acquired data and makes corrections as needed. This preprocessing improves the accuracy of the analysis. For example, if there is a rapid fluctuation in a short period of time, it can be detected as an outlier and processed differently from other data. 【0037】 Subsequently, the server performs AI-powered analysis to predict trends in economic activity from electricity usage data. Using time-series analysis models, it evaluates trends and seasonality in consumption data and predicts future consumption patterns. This makes it possible to determine production activities and market trends in specific industries and regions. 【0038】 Furthermore, the server detects anomalies based on the analysis results and generates alerts as needed. These alerts are sent to users via email or mobile device notifications. For example, if there is a sudden surge in power usage in a certain area, it will be notified as an anomaly requiring immediate attention. 【0039】 Finally, the server generates a detailed report based on the analysis results. This report includes historical trends in power consumption, current usage, and forecast results, and is distributed to relevant parties on a regular basis. Users can then use this to make strategic decisions. In this way, the present invention realizes a system that enables efficient and accurate analysis of economic activities through the utilization of power consumption data. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 The terminal acquires power usage information in real time from various sensors and energy management devices, and transmits this data to a server using a secure communication protocol. This data includes timestamps and detailed usage information. 【0043】 Step 2: 【0044】 The server stores the received power usage information in a database and simultaneously verifies the data's integrity. Anomaly filters are used to detect noise and outliers, and inconsistent data is removed or corrected. 【0045】 Step 3: 【0046】 The server runs a time series analysis model based on optimized data. Here, it analyzes historical data to extract trends and seasonality and predict future electricity consumption patterns. Specifically, it aims to obtain more accurate results by using moving averages and the ARIMA model. 【0047】 Step 4: 【0048】 The server evaluates the analysis results and detects anomalies based on the set criteria. As a result, if a sudden fluctuation in consumption or a deviation from the predicted value is observed, an alert is generated. 【0049】 Step 5: 【0050】 The server sends the generated alerts to the user via email or mobile notification. This allows the user to quickly understand the situation and consider necessary actions. 【0051】 Step 6: 【0052】 The server periodically generates reports based on the analysis results. These reports include detailed data and forecasts that serve as indicators of economic activity and are provided to the user. The user uses these reports to make strategic decisions. 【0053】 (Example 1) 【0054】 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." 【0055】 A challenge in efficiently monitoring and forecasting economic activity is the use of real-time electricity consumption information. Conventional methods have struggled to quickly detect anomalies in electricity consumption data and generate appropriate alerts. Therefore, there is a need for a means to rapidly and accurately identify anomalies in economic activity and utilize this information for relevant decision-making. 【0056】 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. 【0057】 In this invention, the server includes a device for collecting power usage data in real time, a processing device for organizing the power usage data obtained by the device and detecting abnormal data, and a computing device for analyzing the power data cleaned by the processing device and predicting trends in economic activity. This enables rapid and accurate monitoring of economic activity based on power consumption and efficient response through anomaly detection. 【0058】 "Apparatus" refers to a machine element or system configured to perform a specific function. 【0059】 A "processing device" is a system of machines or programs used to organize received data and detect inappropriate or abnormal information. 【0060】 A "computational device" is a device or program that performs logic to analyze data and make predictions or inferences based on that data. 【0061】 A "warning device" is a system or mechanism that detects abnormal or inappropriate situations and notifies the user of them. 【0062】 A "report generation device" is a system that automatically creates reports in a visually appealing and easy-to-understand format based on analyzed data. 【0063】 Time series analysis is a statistical method used to analyze the temporal fluctuations of data and predict future trends based on past movements. 【0064】 "Electronic communication" is a method of sending and receiving information using digital signals. 【0065】 A "mobile communication terminal" is a portable device capable of sending and receiving information via wireless communication. 【0066】 The server receives information from devices that collect power usage data in real time, and processes it using a processing unit. Various hardware, such as sensors and energy management systems, are used for real-time data collection. This data is sent to the server from numerous terminals and then cleaned based on anomaly detection before proceeding to analysis. 【0067】 The server analyzes pre-processed power usage data using a computing device. This analysis utilizes generative AI models and time series analysis methods, employing software to extract data trends and seasonality. The computing device then uses this information to predict future power demand, thereby understanding trends in economic activity. 【0068】 Users can detect unusual economic trends based on information generated from the server. This is done through notifications from warning devices and detailed reports provided periodically by report generators. This allows users to take prompt action. 【0069】 For example, if a manufacturing company is using this system, a sudden increase in power consumption could be detected, and a warning could be issued indicating that adjustments to the production line are necessary. As a result, the company could immediately begin inspecting equipment and improving production efficiency. 【0070】 An example of a prompt to be input into the generating AI model is: "Based on electricity consumption data, predict the trends in economic activity in region A for the next month. In particular, focus your analysis on electricity demand from the manufacturing sector." 【0071】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0072】 Step 1: 【0073】 The terminals collect power usage data in real time from sensors and energy management systems. During this collection process, each terminal continuously acquires data from connected devices and periodically sends it to the server. The input is power usage information from each sensor, and the output is data packets sent to the server. 【0074】 Step 2: 【0075】 The server preprocesses the received power usage data using a processing unit. Data cleaning is performed here, where noise and outliers are detected and removed. The input is the raw data transmitted from the terminal, and the output is clean data with outliers removed. A noise filtering algorithm is applied in this step. 【0076】 Step 3: 【0077】 The server analyzes pre-processed data using a computing device. This analysis utilizes generative AI models to evaluate trends and seasonality in power usage. The input is clean data, and the output is predictive information about economic activity obtained through the analysis. Time series analysis methods used include ARIMA models and LSTMs. 【0078】 Step 4: 【0079】 The server processes the analysis results with a warning device and generates an alert if an anomaly is detected. This uses a variety of threshold judgments and anomaly detection algorithms. The input is analysis information, and the output is an alert notified to the user. The alert is sent via electronic communication or to a mobile device to quickly warn the user. 【0080】 Step 5: 【0081】 The server automatically generates reports using a report generation device based on the analysis results. This step aggregates information on detailed consumption trends, current conditions, and forecast results. The input is the analyzed data, and the output is a report in PDF or Excel format. The generated reports are distributed periodically to users and stakeholders. 【0082】 (Application Example 1) 【0083】 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." 【0084】 In modern cities, responding quickly to rapid fluctuations in electricity consumption and improving energy efficiency are crucial challenges. However, conventional systems often struggle to monitor electricity consumption in real time or detect anomalies, and they frequently fail to provide this information in a useful format for residents and management agencies. In this context, there is a need for sustainable urban management and the promotion of eco-friendly activities among residents. 【0085】 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. 【0086】 In this invention, the server includes an information acquisition means for acquiring power consumption information over time; an information preprocessing means for organizing the power consumption information obtained by the information acquisition means and detecting anomalies; an analysis means for analyzing the power consumption information processed by the information preprocessing means and predicting trends in economic activity; a visualization means for visualizing urban power consumption information and displaying energy consumption trends for each region; and a proposal means for making suggestions for energy efficiency improvements to residents and management organizations. This makes it possible to monitor the power consumption of the entire city in real time, quickly detect anomalies, and provide residents and local governments with timely and appropriate information. 【0087】 "Information acquisition means" refers to a system that has the function of collecting electricity consumption data over time. 【0088】 "Information preprocessing means" refers to a system that has the function of detecting anomalies from acquired power consumption information and organizing the data. 【0089】 "Analysis tools" are devices that analyze processed power consumption information and have the function of predicting trends in economic activity. 【0090】 A "warning generation means" is a device that detects abnormal economic activity based on analysis results and issues a warning. 【0091】 A "document generation means" is a device that has the function of automatically generating a document summarizing the prediction results obtained by the analysis means. 【0092】 A "visualization tool" is a device that visually represents electricity consumption information in urban areas and displays energy consumption trends for each region. 【0093】 A "proposal tool" is a tool that provides residents and management organizations with methods for improving energy efficiency based on the analyzed data. 【0094】 This invention provides a system that utilizes electricity consumption information to analyze and predict economic activity in urban areas. 【0095】 The server collects power consumption information over time from various sensors and energy management systems using information acquisition methods. This data is then processed and organized to detect anomalies using information preprocessing methods. This system manages data using AWS® servers and cloud services. The preprocessed data is then analyzed in detail using analysis methods, and time-series analysis is performed using AI technology such as TENSORFLOW®. This clarifies power consumption trends, periodicity, and abnormal consumption patterns. 【0096】 The information obtained through the analysis is visually represented as energy consumption trends in urban areas via visualization tools. This allows users to easily understand consumption patterns in specific regions. Furthermore, through the proposed tools, users and management organizations are presented with concrete methods for improving energy efficiency. These methods include, for example, optimizing air conditioning use during heat waves and seasonal energy-saving techniques. 【0097】 Users can receive rapid warnings when an anomaly is detected through the warning generation system. These warnings are sent via communication devices or mobile terminals, allowing them to obtain information to take appropriate action immediately. 【0098】 Furthermore, the server automatically generates reports periodically using document generation methods based on this analytical data. These reports serve as the basis for strategic decision-making for the user. 【0099】 By utilizing generative AI models, we can effectively analyze energy use across cities and provide residents and local governments with the insights necessary for sustainable urban management. To maximize the usefulness of this system, continuous data collection and analysis are necessary. 【0100】 An example of a prompt message would be: "Please describe an application that performs trend and anomaly detection of power consumption data in smart cities. Please also describe the specific functions and technologies used." 【0101】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0102】 Step 1: 【0103】 The server receives power consumption information from each terminal. Inputs are real-time data from sensors and energy management systems. Output is the storage of this data into an internal database for centralized management. Cloud services are used for data reception and initial storage. 【0104】 Step 2: 【0105】 The server uses data preprocessing to detect anomalies in the received data and cleanses the data. The input is the collected raw power consumption data, and the output is a clean dataset with anomalies removed. Statistical methods are used to identify anomalies and remove noise during data processing. 【0106】 Step 3: 【0107】 The server uses analytical tools to analyze a clean dataset and predict trends in economic activity. The input is pre-processed data, and the output is a prediction of future power consumption trends and periodicity. A time series analysis model powered by TensorFlow is used for the analysis. 【0108】 Step 4: 【0109】 The server visually represents the analysis results through visualization tools, making them easily understandable to the user. The input is the predicted results obtained from the analysis, and the output is visualized graphs and charts. This allows the user to intuitively grasp the energy consumption trends for each region. 【0110】 Step 5: 【0111】 The user receives specific suggestions for energy efficiency improvements based on analyzed data through the proposed method. The input is the analysis results and consumption trends, and the output is specific advice for energy saving. This advice is presented from the system to the user's terminal. 【0112】 Step 6: 【0113】 The server sends a warning to the user when an anomaly is detected through the warning generation mechanism. The input is the anomalous data pattern found during analysis, and the output is a warning notification to the user's terminal. The notification is made in real time to support the user's immediate response. 【0114】 Step 7: 【0115】 The server periodically generates and provides reports to users using document generation methods. The input consists of analyzed aggregate data and forecast results, and the output is a detailed energy report. This report serves as foundational information for users' strategic decision-making. 【0116】 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. 【0117】 This invention provides a system that analyzes economic activity based on power consumption data and responds in accordance with the user's emotions. The system is composed of multiple functions and can smoothly acquire, analyze, and notify real-time power usage information. 【0118】 First, the terminal collects power usage information in real time from each sensor and sends the acquired data to the server. This information includes power usage amount and usage time with timestamps, and serves as the basic data for analysis. 【0119】 The server preprocesses the data, detecting and removing outliers and noise. This clarified data is then analyzed using a time series analysis model. This model allows the server to extract trends and seasonality in power usage and predict trends in economic activity. 【0120】 In addition, the server is equipped with an emotion engine that processes and analyzes user emotion data. This emotion data is based on newly entered information and past usage history. For example, data on the user's past consumption patterns and emotional states is learned by the emotion engine. 【0121】 Next, if the server detects an anomaly based on the analysis results, it generates an alert. At this time, the emotion engine takes the user's emotional state into consideration and sends the alert in the most appropriate format. For example, if the user is stressed, the notification might be sent in a softer tone. 【0122】 Finally, the server automatically generates a report of the analysis results and provides it to the user. This report includes not only analysis results of electricity consumption trends and economic activity, but also advice tailored to the user's sentiments. Users can use this to optimize their electricity usage and develop economic strategies. 【0123】 In summary, this system integrates and utilizes electricity usage data and user sentiment data to provide more personalized support for economic activities. 【0124】 The following describes the processing flow. 【0125】 Step 1: 【0126】 The device collects power usage information in real time from various sensors and the internal network and transmits it to the server. The transmitted information includes power consumption, usage time, and device identification information. The data is securely transmitted in an encrypted state. 【0127】 Step 2: 【0128】 The server stores the received power usage information in a database and performs preprocessing. Here, it detects anomalies and filters out identified noise and missing values. The preprocessed data is then converted into a format suitable for analysis. 【0129】 Step 3: 【0130】 The server runs a time-series analysis model based on pre-processed data to extract trends and seasonality in power consumption. Using these analysis results, it predicts future trends in economic activity and conducts a detailed analysis of consumption patterns for relevant industries and regions. 【0131】 Step 4: 【0132】 Emotional data is input from the terminal or user. The server activates an emotion engine, learns the user's past emotional data and usage history, and analyzes their current emotional state. This allows the server to understand the user's emotional patterns. 【0133】 Step 5: 【0134】 The server combines analysis results and sentiment data to generate alerts regarding economic activity. These alerts are created based on detected abnormal power consumption, and the content and delivery method are adjusted using a sentiment engine. For example, notifications may be sent using stress-relieving language. 【0135】 Step 6: 【0136】 The server sends generated alerts to users via email or mobile notifications. Providing them in a format that considers the user's emotions allows for a more effective response. 【0137】 Step 7: 【0138】 The server automatically generates a report summarizing the analysis and prediction results to date. This report, which includes suggestions for improvement and sentiment-based insights, is sent to the user. The user can use the report to support strategic decision-making. 【0139】 (Example 2) 【0140】 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." 【0141】 In analyzing economic activity based on electricity usage data, conventional methods were insufficient in terms of real-time information and consideration of user sentiment. This resulted in a failure to optimize electricity usage or provide notifications tailored to individual needs, ultimately leading to decreased user satisfaction. 【0142】 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. 【0143】 In this invention, the server includes an information gathering means for acquiring power usage information in real time, an information preprocessing means for organizing the information obtained by the information gathering means and detecting abnormal values, and an information analysis means for analyzing the information processed by the information preprocessing means and predicting trends in economic activity. This enables a rapid and personalized response to trends in power usage. 【0144】 "Information gathering means" refers to a device or process that acquires electricity usage information in real time. 【0145】 "Information preprocessing means" refers to a method for organizing collected power usage information and detecting and removing abnormal values. 【0146】 "Information analysis means" refers to a system or method that analyzes pre-processed information to predict trends and periodicities in power usage. 【0147】 An "alert generation means" is a process or device that detects abnormal economic activity based on analysis results and generates a warning. 【0148】 An "emotional response system" is a system that takes into account the user's emotional information and sends notifications in the most optimal format. 【0149】 "Report generation means" refers to a device or method that automatically generates a report by summarizing the analyzed prediction results. 【0150】 This invention provides a system that integrates power usage information and user emotion data to support personalized economic activities. An embodiment of this system is described below. 【0151】 The device uses sensors installed in homes and businesses to obtain real-time power usage information. These sensors measure the amount of power consumed and can timestamp the data. The device sends encrypted data to a server over the network. The TLS protocol is used to ensure security and privacy. 【0152】 The server analyzes the received data. First, it uses statistical methods to detect and remove outliers. Next, it uses time series analysis models such as ARIMA and SARIMA to extract trends and periodicities in power usage. The results of this analysis are used to forecast economic activity. 【0153】 Furthermore, the server uses an emotion engine to process the user's emotional information. This information is collected from sources such as smartphone apps and wearable devices, and the system learns the relationship between the user's past consumption patterns and their current emotional state. 【0154】 Based on the prediction results, the server generates an alert when it detects unusual economic activity. This alert is optimized to the user's emotional state; for example, a gentler notification is sent to a user who is feeling stressed. 【0155】 Finally, the server automatically generates a report for the user based on these analysis results. This report includes an analysis of power consumption trends and sentiment-based advice, which the user can use to optimize their power usage and develop economic strategies. 【0156】 As a concrete example, we could analyze the tendency for electricity consumption to increase on holidays, and when users are relaxed, we could send notifications with scenic images and gentle language. 【0157】 An example of a prompt for a generative AI model is, "Please build a system that suggests the optimal energy-saving method based on power consumption patterns and user sentiment data." 【0158】 In this way, power data and emotional data are integrated and utilized to provide users with more personalized services. 【0159】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0160】 Step 1: 【0161】 The terminal acquires power usage information from sensors installed in homes and businesses. Its input is real-time power consumption data transmitted from power sensors. This data includes power consumption and a timestamp. The terminal encrypts this information and transmits it to a server over the network. The output is encrypted power usage data. 【0162】 Step 2: 【0163】 The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted power usage information, which is decrypted to obtain raw power data. The obtained data is preprocessed, and statistical methods are used to detect outliers and remove noise. Specifically, outliers are filtered, and a moving average method is applied to missing values. The output is clean power usage information. 【0164】 Step 3: 【0165】 The server analyzes the pre-processed data. The input is the clean power usage information obtained in step 2. Using this, a time series analysis model (e.g., ARIMA or SARIMA) is applied to extract trends and periodicities. Specifically, it analyzes the data trends and creates a predictive model. The output is predicted power consumption data. 【0166】 Step 4: 【0167】 The server processes user emotional information. The input is emotional data collected from smartphone apps and wearable devices. This data is fed into an emotional engine to analyze the user's past consumption patterns and current emotional state, learning the correlations. Specifically, it creates a user profile based on the emotional state. The output is the analyzed emotional profile. 【0168】 Step 5: 【0169】 The server detects anomalies based on the analysis results and generates alerts to notify the user. The inputs are the predicted economic activity data obtained in step 3 and the emotion profile from step 4. Using this information, the server selects an alert format that takes emotional states into account. The specific actions are generating the alert content and sending it. The output is the appropriate alert message sent to the user. 【0170】 Step 6: 【0171】 The server automatically generates a report, combining the results of previous analyses and considering user sentiment. The input consists of the data obtained in each previous step. The report includes an analysis of power consumption trends and advice for energy saving. The specific actions involve data integration and report formatting. The output is a detailed analytical report provided to the user. 【0172】 (Application Example 2) 【0173】 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 device 14 will be referred to as the "terminal." 【0174】 Analyzing economic activity based on electricity usage data plays a crucial role in various fields. However, while conventional systems can detect anomalies in electricity consumption, they fail to adequately provide information tailored to the emotional state of users. As a result, it is difficult to respond individually to electricity usage patterns, and there is a challenge in providing personalized services that meet the needs of users. 【0175】 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. 【0176】 In this invention, the server includes a data collection means for acquiring power usage information in real time, a data preprocessing means for organizing the power usage information obtained by the data collection means and detecting abnormal values, and an emotion data analysis means for adjusting the wording of notifications according to the user's emotional state. This makes it possible to acquire power usage information in real time and detect abnormalities, as well as provide information that takes into account the user's emotional state. 【0177】 "Electricity usage information" refers to data that shows the amount of electricity consumed and the duration of its use at a specific location or for a particular device. 【0178】 "Data collection means" refers to a device or process for acquiring power usage information in real time using sensors or monitoring equipment. 【0179】 "Data preprocessing means" refers to a method of organizing collected power usage information and processing it to detect and remove abnormal values ​​and noise. 【0180】 "Analysis means" refers to methods and devices for predicting trends and tendencies in economic activity based on processed electricity usage information. 【0181】 "Emotional data analysis means" is a technology that estimates a user's emotional state based on their past consumption patterns and current situation, and adjusts the wording of notifications accordingly. 【0182】 An "alert generation method" is a method or system for issuing warnings or notifications to users when an anomaly is detected based on analysis results. 【0183】 A "report generation method" is a method for automatically creating reports that standardize the results obtained through analysis and provide them to the user. 【0184】 The system implementing this invention uses a terminal and a server to acquire power usage information in real time and detect abnormal values. The terminal collects power usage information via sensors and transmits it to the server. The server receives this information and first performs data preprocessing, such as standardization and filtering, to remove abnormal values. It is desirable to use software such as the Pandas library for this processing. 【0185】 The server then uses the organized data to predict power usage trends and seasonality through time series analysis. This is done using the statsmodels library and applying models such as ARIMA. If an anomaly is detected in the predicted trend, an alert generation mechanism is activated, and a notification is sent with wording tailored to the user's emotional state. 【0186】 This emotion analysis system incorporates an emotion engine that estimates the user's current emotional state based on their past power consumption patterns and emotional data. The emotional state is analyzed based on pre-set indicators. 【0187】 When an alert is generated, it will be notified to the user via smartphone, smart glasses, or other means. The notification will be delivered in a gentle yet effective manner, using language that is sensitive to the user's emotions. 【0188】 As a concrete example, in one smart city, residents can receive notifications on their smartphones such as, "Electricity consumption is trending upwards this week, but why not set aside some time to relax this weekend?" This is expected to allow residents to manage their energy consumption while remaining mindful of their energy needs and without compromising their quality of life. 【0189】 An example of a prompt for a generating AI model is: "Generate advice for smart city residents based on recent electricity consumption trends and residents' emotional states." 【0190】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0191】 Step 1: 【0192】 The terminal collects power usage information in real time from each sensor. The collected information includes power consumption and usage time, and by adding a timestamp, it accurately records when and how much power was consumed. Sending this data to the server is the input. 【0193】 The output will be the raw data of the power usage information that the server receives. 【0194】 Step 2: 【0195】 The server filters the received power usage information using data preprocessing. Specifically, it uses the Pandas library to standardize and denoise the dataset, and detects and removes outliers. This process ensures that the data is in a clean format suitable for analysis. 【0196】 The input is the raw power usage data transmitted in step 1, and the output is a clean dataset with outliers removed. 【0197】 Step 3: 【0198】 The server uses the cleaned dataset to begin data calculations for time series analysis. It uses the ARIMA model from the statsmodels library to extract trends and seasonality in power consumption and predict future power consumption. 【0199】 The input is clean electricity usage data, and the output generates predictive data on future electricity consumption trends. 【0200】 Step 4: 【0201】 The server detects anomalies based on predicted trends. If an anomaly is detected, an alert generation mechanism is activated, and a notification is generated for the user. This notification not only informs the user of the anomaly, but also uses sentiment data analysis to create wording that takes the user's emotional state into consideration. 【0202】 The input consists of prediction data and sentiment data from step 3, and the output is a notification message for the user. 【0203】 Step 5: 【0204】 Users receive generated notifications to get advice on power consumption status and any anomalies. These notifications can be received via smartphones or smart glasses and are presented in a way that suits the user's emotional state, making them more receptive to receiving. 【0205】 The input is the notification message created in step 4, and the output is the impact on the user's behavior and emotions. 【0206】 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. 【0207】 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. 【0208】 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. 【0209】 [Second Embodiment] 【0210】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0211】 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. 【0212】 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). 【0213】 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. 【0214】 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. 【0215】 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). 【0216】 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. 【0217】 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. 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 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". 【0222】 This invention provides a system for evaluating the state of economic activity and detecting anomalies by utilizing power consumption data. The system consists of multiple means, enabling rapid and accurate economic forecasting and response. 【0223】 First, the server receives power usage information transmitted from each terminal. The terminals collect real-time data on power consumption from individual sensors and the company's energy management system and send it to the server. This data is aggregated by total power consumption and by time period. 【0224】 Next, the server preprocesses the power usage information. Specifically, it removes outliers and noise from the acquired data and makes corrections as needed. This preprocessing improves the accuracy of the analysis. For example, if there is a rapid fluctuation in a short period of time, it can be detected as an outlier and processed differently from other data. 【0225】 Subsequently, the server performs AI-powered analysis to predict trends in economic activity from electricity usage data. Using time-series analysis models, it evaluates trends and seasonality in consumption data and predicts future consumption patterns. This makes it possible to determine production activities and market trends in specific industries and regions. 【0226】 Furthermore, the server detects anomalies based on the analysis results and generates alerts as needed. These alerts are sent to users via email or mobile device notifications. For example, if there is a sudden surge in power usage in a certain area, it will be notified as an anomaly requiring immediate attention. 【0227】 Finally, the server generates a detailed report based on the analysis results. This report includes historical trends in power consumption, current usage, and forecast results, and is distributed to relevant parties on a regular basis. Users can then use this to make strategic decisions. In this way, the present invention realizes a system that enables efficient and accurate analysis of economic activities through the utilization of power consumption data. 【0228】 The following describes the processing flow. 【0229】 Step 1: 【0230】 The terminal acquires power usage information in real time from various sensors and energy management devices, and transmits this data to a server using a secure communication protocol. This data includes timestamps and detailed usage information. 【0231】 Step 2: 【0232】 The server stores the received power usage information in a database and simultaneously verifies the data's integrity. Anomaly filters are used to detect noise and outliers, and inconsistent data is removed or corrected. 【0233】 Step 3: 【0234】 The server runs a time series analysis model based on optimized data. Here, it analyzes historical data to extract trends and seasonality and predict future electricity consumption patterns. Specifically, it aims to obtain more accurate results by using moving averages and the ARIMA model. 【0235】 Step 4: 【0236】 The server evaluates the analysis results and detects anomalies based on the set criteria. As a result, if a sudden fluctuation in consumption or a deviation from the predicted value is observed, an alert is generated. 【0237】 Step 5: 【0238】 The server sends the generated alerts to the user via email or mobile notification. This allows the user to quickly understand the situation and consider necessary actions. 【0239】 Step 6: 【0240】 The server periodically generates reports based on the analysis results. These reports include detailed data and forecasts that serve as indicators of economic activity and are provided to the user. The user uses these reports to make strategic decisions. 【0241】 (Example 1) 【0242】 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." 【0243】 A challenge in efficiently monitoring and forecasting economic activity is the use of real-time electricity consumption information. Conventional methods have struggled to quickly detect anomalies in electricity consumption data and generate appropriate alerts. Therefore, there is a need for a means to rapidly and accurately identify anomalies in economic activity and utilize this information for relevant decision-making. 【0244】 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. 【0245】 In this invention, the server includes a device for collecting power usage data in real time, a processing device for organizing the power usage data obtained by the device and detecting abnormal data, and a computing device for analyzing the power data cleaned by the processing device and predicting trends in economic activity. This enables rapid and accurate monitoring of economic activity based on power consumption and efficient response through anomaly detection. 【0246】 "Apparatus" refers to a machine element or system configured to perform a specific function. 【0247】 A "processing device" is a system of machines or programs used to organize received data and detect inappropriate or abnormal information. 【0248】 A "computational device" is a device or program that performs logic to analyze data and make predictions or inferences based on that data. 【0249】 A "warning device" is a system or mechanism that detects abnormal or inappropriate situations and notifies the user of them. 【0250】 A "report generation device" is a system that automatically creates reports in a visually appealing and easy-to-understand format based on analyzed data. 【0251】 Time series analysis is a statistical method used to analyze the temporal fluctuations of data and predict future trends based on past movements. 【0252】 "Electronic communication" is a method of sending and receiving information using digital signals. 【0253】 A "mobile communication terminal" is a portable device capable of sending and receiving information via wireless communication. 【0254】 The server receives information from devices that collect power usage data in real time, and processes it using a processing unit. Various hardware, such as sensors and energy management systems, are used for real-time data collection. This data is sent to the server from numerous terminals and then cleaned based on anomaly detection before proceeding to analysis. 【0255】 The server analyzes pre-processed power usage data using a computing device. This analysis utilizes generative AI models and time series analysis methods, employing software to extract data trends and seasonality. The computing device then uses this information to predict future power demand, thereby understanding trends in economic activity. 【0256】 Users can detect unusual economic trends based on information generated from the server. This is done through notifications from warning devices and detailed reports provided periodically by report generators. This allows users to take prompt action. 【0257】 For example, if a manufacturing company is using this system, a sudden increase in power consumption could be detected, and a warning could be issued indicating that adjustments to the production line are necessary. As a result, the company could immediately begin inspecting equipment and improving production efficiency. 【0258】 An example of a prompt to be input into the generating AI model is: "Based on electricity consumption data, predict the trends in economic activity in region A for the next month. In particular, focus your analysis on electricity demand from the manufacturing sector." 【0259】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0260】 Step 1: 【0261】 The terminals collect power usage data in real time from sensors and energy management systems. During this collection process, each terminal continuously acquires data from connected devices and periodically sends it to the server. The input is power usage information from each sensor, and the output is data packets sent to the server. 【0262】 Step 2: 【0263】 The server preprocesses the received power usage data using a processing unit. Data cleaning is performed here, where noise and outliers are detected and removed. The input is the raw data transmitted from the terminal, and the output is clean data with outliers removed. A noise filtering algorithm is applied in this step. 【0264】 Step 3: 【0265】 The server analyzes pre-processed data using a computing device. This analysis utilizes generative AI models to evaluate trends and seasonality in power usage. The input is clean data, and the output is predictive information about economic activity obtained through the analysis. Time series analysis methods used include ARIMA models and LSTMs. 【0266】 Step 4: 【0267】 The server processes the analysis results with a warning device and generates an alert if an anomaly is detected. This uses a variety of threshold judgments and anomaly detection algorithms. The input is analysis information, and the output is an alert notified to the user. The alert is sent via electronic communication or to a mobile device to quickly warn the user. 【0268】 Step 5: 【0269】 The server automatically generates reports using a report generation device based on the analysis results. This step aggregates information on detailed consumption trends, current conditions, and forecast results. The input is the analyzed data, and the output is a report in PDF or Excel format. The generated reports are distributed periodically to users and stakeholders. 【0270】 (Application Example 1) 【0271】 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." 【0272】 In modern cities, responding quickly to rapid fluctuations in electricity consumption and improving energy efficiency are crucial challenges. However, conventional systems often struggle to monitor electricity consumption in real time or detect anomalies, and they frequently fail to provide this information in a useful format for residents and management agencies. In this context, there is a need for sustainable urban management and the promotion of eco-friendly activities among residents. 【0273】 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. 【0274】 In this invention, the server includes an information acquisition means for acquiring power consumption information over time; an information preprocessing means for organizing the power consumption information obtained by the information acquisition means and detecting anomalies; an analysis means for analyzing the power consumption information processed by the information preprocessing means and predicting trends in economic activity; a visualization means for visualizing urban power consumption information and displaying energy consumption trends for each region; and a proposal means for making suggestions for energy efficiency improvements to residents and management organizations. This makes it possible to monitor the power consumption of the entire city in real time, quickly detect anomalies, and provide residents and local governments with timely and appropriate information. 【0275】 "Information acquisition means" refers to a system that has the function of collecting electricity consumption data over time. 【0276】 "Information preprocessing means" refers to a system that has the function of detecting anomalies from acquired power consumption information and organizing the data. 【0277】 "Analysis tools" are devices that analyze processed power consumption information and have the function of predicting trends in economic activity. 【0278】 A "warning generation means" is a device that detects abnormal economic activity based on analysis results and issues a warning. 【0279】 A "document generation means" is a device that has the function of automatically generating a document summarizing the prediction results obtained by the analysis means. 【0280】 A "visualization tool" is a device that visually represents electricity consumption information in urban areas and displays energy consumption trends for each region. 【0281】 A "proposal tool" is a tool that provides residents and management organizations with methods for improving energy efficiency based on the analyzed data. 【0282】 This invention provides a system that utilizes electricity consumption information to analyze and predict economic activity in urban areas. 【0283】 By using the information acquisition means, the server collects power consumption information from each sensor and energy management system over time. This data is detected for abnormalities and sorted by the information preprocessing means. In this system, data is managed using an AWS server or cloud service. The preprocessed data is analyzed in detail by the analysis means, and time series analysis using TensorFlow as an AI technology is performed. As a result, the trends, periodicity, and abnormal consumption patterns of power consumption are clarified. 【0284】 The information obtained by the analysis means is visually represented as the energy consumption trend in the urban area through the visualization means. As a result, users can easily understand the consumption patterns in specific areas. Also, through the proposal means, specific methods for energy efficiency are presented to users and management organizations. This presentation includes, as specific examples, optimization of air conditioner use during intense heat and power saving methods according to seasons. 【0285】 Users can receive a prompt warning when an abnormality is detected by the warning generation means. This is notified through a communication device or mobile terminal. As a result, it is possible to obtain information for taking appropriate action immediately. 【0286】 Furthermore, based on these analysis data, the server automatically generates a report periodically using the document generation means. This report serves as a basis for strategic decision-making for users. 【0287】 By leveraging the generated AI model, the energy use of the entire city is effectively analyzed, providing insights necessary for sustainable city management to residents and local governments. To maximize the usefulness of this system, continuous data collection and analysis are required. 【0288】 An example of a prompt message would be: "Please describe an application that performs trend and anomaly detection of power consumption data in smart cities. Please also describe the specific functions and technologies used." 【0289】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0290】 Step 1: 【0291】 The server receives power consumption information from each terminal. Inputs are real-time data from sensors and energy management systems. Output is the storage of this data into an internal database for centralized management. Cloud services are used for data reception and initial storage. 【0292】 Step 2: 【0293】 The server uses data preprocessing to detect anomalies in the received data and cleanses the data. The input is the collected raw power consumption data, and the output is a clean dataset with anomalies removed. Statistical methods are used to identify anomalies and remove noise during data processing. 【0294】 Step 3: 【0295】 The server uses analytical tools to analyze a clean dataset and predict trends in economic activity. The input is pre-processed data, and the output is a prediction of future power consumption trends and periodicity. A time series analysis model powered by TensorFlow is used for the analysis. 【0296】 Step 4: 【0297】 The server visually represents the analysis results through visualization tools, making them easily understandable to the user. The input is the predicted results obtained from the analysis, and the output is visualized graphs and charts. This allows the user to intuitively grasp the energy consumption trends for each region. 【0298】 Step 5: 【0299】 The user receives specific suggestions for energy efficiency improvements based on analyzed data through the proposed method. The input is the analysis results and consumption trends, and the output is specific advice for energy saving. This advice is presented from the system to the user's terminal. 【0300】 Step 6: 【0301】 The server sends a warning to the user when an anomaly is detected through the warning generation mechanism. The input is the anomalous data pattern found during analysis, and the output is a warning notification to the user's terminal. The notification is made in real time to support the user's immediate response. 【0302】 Step 7: 【0303】 The server periodically generates and provides reports to users using document generation methods. The input consists of analyzed aggregate data and forecast results, and the output is a detailed energy report. This report serves as foundational information for users' strategic decision-making. 【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 provides a system that analyzes economic activities based on power consumption data and makes responses according to the user's emotions. The system is composed of a combination of multiple functions and can smoothly acquire, analyze, and notify real-time power usage information. 【0306】 First, the terminal collects power usage information from each sensor in real time and transmits the acquired data to the server. This information includes the power consumption amount and usage time with timestamps, which serves as the basic data for analysis. 【0307】 The server performs preprocessing of the data, detects and removes outliers and noise. This organized data is further analyzed using a time series analysis model. The server can extract trends and seasonality in power usage through this model and predict the trends of economic activities. 【0308】 In addition, the server is equipped with an emotion engine to process and analyze the user's emotion data. This emotion data is based on newly input information and past usage history. For example, data on what consumption patterns and what emotional states the user was in the past is learned by the emotion engine. 【0309】 Next, when the server detects an abnormality based on the analysis results, it generates an alert. At this time, the emotion engine takes into account the user's emotional state and sends the alert in an optimal format. As a specific example, when the user is in a stressed state, a response such as sending the notification in a gentle expression can be considered. 【0310】 Finally, the server automatically generates the analysis results as a report and provides it to the user. This report includes not only the trends of power consumption and the analysis results of economic activities but also advice according to the user's emotions. The user can use this to optimize power usage and construct economic strategies. 【0311】 In summary, this system integrates and utilizes electricity usage data and user sentiment data to provide more personalized support for economic activities. 【0312】 The following describes the processing flow. 【0313】 Step 1: 【0314】 The device collects power usage information in real time from various sensors and the internal network and transmits it to the server. The transmitted information includes power consumption, usage time, and device identification information. The data is securely transmitted in an encrypted state. 【0315】 Step 2: 【0316】 The server stores the received power usage information in a database and performs preprocessing. Here, it detects anomalies and filters out identified noise and missing values. The preprocessed data is then converted into a format suitable for analysis. 【0317】 Step 3: 【0318】 The server runs a time-series analysis model based on pre-processed data to extract trends and seasonality in power consumption. Using these analysis results, it predicts future trends in economic activity and conducts a detailed analysis of consumption patterns for relevant industries and regions. 【0319】 Step 4: 【0320】 Emotional data is input from the terminal or user. The server activates an emotion engine, learns the user's past emotional data and usage history, and analyzes their current emotional state. This allows the server to understand the user's emotional patterns. 【0321】 Step 5: 【0322】 The server combines analysis results and sentiment data to generate alerts regarding economic activity. These alerts are created based on detected abnormal power consumption, and the content and delivery method are adjusted using a sentiment engine. For example, notifications may be sent using stress-relieving language. 【0323】 Step 6: 【0324】 The server sends generated alerts to users via email or mobile notifications. Providing them in a format that considers the user's emotions allows for a more effective response. 【0325】 Step 7: 【0326】 The server automatically generates a report summarizing the analysis and prediction results to date. This report, which includes suggestions for improvement and sentiment-based insights, is sent to the user. The user can use the report to support strategic decision-making. 【0327】 (Example 2) 【0328】 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". 【0329】 In analyzing economic activity based on electricity usage data, conventional methods were insufficient in terms of real-time information and consideration of user sentiment. This resulted in a failure to optimize electricity usage or provide notifications tailored to individual needs, ultimately leading to decreased user satisfaction. 【0330】 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. 【0331】 In this invention, the server includes an information gathering means for acquiring power usage information in real time, an information preprocessing means for organizing the information obtained by the information gathering means and detecting abnormal values, and an information analysis means for analyzing the information processed by the information preprocessing means and predicting trends in economic activity. This enables a rapid and personalized response to trends in power usage. 【0332】 "Information gathering means" refers to a device or process that acquires electricity usage information in real time. 【0333】 "Information preprocessing means" refers to a method for organizing collected power usage information and detecting and removing abnormal values. 【0334】 "Information analysis means" refers to a system or method that analyzes pre-processed information to predict trends and periodicities in power usage. 【0335】 An "alert generation means" is a process or device that detects abnormal economic activity based on analysis results and generates a warning. 【0336】 An "emotional response system" is a system that takes into account the user's emotional information and sends notifications in the most optimal format. 【0337】 "Report generation means" refers to a device or method that automatically generates a report by summarizing the analyzed prediction results. 【0338】 This invention provides a system that integrates power usage information and user emotion data to support personalized economic activities. An embodiment of this system is described below. 【0339】 The device uses sensors installed in homes and businesses to obtain real-time power usage information. These sensors measure the amount of power consumed and can timestamp the data. The device sends encrypted data to a server over the network. The TLS protocol is used to ensure security and privacy. 【0340】 The server analyzes the received data. First, it uses statistical methods to detect and remove outliers. Next, it uses time series analysis models such as ARIMA and SARIMA to extract trends and periodicities in power usage. The results of this analysis are used to forecast economic activity. 【0341】 Furthermore, the server uses an emotion engine to process the user's emotional information. This information is collected from sources such as smartphone apps and wearable devices, and the system learns the relationship between the user's past consumption patterns and their current emotional state. 【0342】 Based on the prediction results, the server generates an alert when it detects unusual economic activity. This alert is optimized to the user's emotional state; for example, a gentler notification is sent to a user who is feeling stressed. 【0343】 Finally, the server automatically generates a report for the user based on these analysis results. This report includes an analysis of power consumption trends and sentiment-based advice, which the user can use to optimize their power usage and develop economic strategies. 【0344】 As a concrete example, we could analyze the tendency for electricity consumption to increase on holidays, and when users are relaxed, we could send notifications with scenic images and gentle language. 【0345】 An example of a prompt for a generative AI model is, "Please build a system that suggests the optimal energy-saving method based on power consumption patterns and user sentiment data." 【0346】 In this way, power data and emotional data are integrated and utilized to provide users with more personalized services. 【0347】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0348】 Step 1: 【0349】 The terminal acquires power usage information from sensors installed in homes and businesses. Its input is real-time power consumption data transmitted from power sensors. This data includes power consumption and a timestamp. The terminal encrypts this information and transmits it to a server over the network. The output is encrypted power usage data. 【0350】 Step 2: 【0351】 The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted power usage information, which is decrypted to obtain raw power data. The obtained data is preprocessed, and statistical methods are used to detect outliers and remove noise. Specifically, outliers are filtered, and a moving average method is applied to missing values. The output is clean power usage information. 【0352】 Step 3: 【0353】 The server analyzes the pre-processed data. The input is the clean power usage information obtained in step 2. Using this, a time series analysis model (e.g., ARIMA or SARIMA) is applied to extract trends and periodicities. Specifically, it analyzes the data trends and creates a predictive model. The output is predicted power consumption data. 【0354】 Step 4: 【0355】 The server processes user emotional information. The input is emotional data collected from smartphone apps and wearable devices. This data is fed into an emotional engine to analyze the user's past consumption patterns and current emotional state, learning the correlations. Specifically, it creates a user profile based on the emotional state. The output is the analyzed emotional profile. 【0356】 Step 5: 【0357】 The server detects anomalies based on the analysis results and generates alerts to notify the user. The inputs are the predicted economic activity data obtained in step 3 and the emotion profile from step 4. Using this information, the server selects an alert format that takes emotional states into account. The specific actions are generating the alert content and sending it. The output is the appropriate alert message sent to the user. 【0358】 Step 6: 【0359】 The server automatically generates a report, combining the results of previous analyses and considering user sentiment. The input consists of the data obtained in each previous step. The report includes an analysis of power consumption trends and advice for energy saving. The specific actions involve data integration and report formatting. The output is a detailed analytical report provided to the user. 【0360】 (Application Example 2) 【0361】 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." 【0362】 Analyzing economic activity based on electricity usage data plays a crucial role in various fields. However, while conventional systems can detect anomalies in electricity consumption, they fail to adequately provide information tailored to the emotional state of users. As a result, it is difficult to respond individually to electricity usage patterns, and there is a challenge in providing personalized services that meet the needs of users. 【0363】 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. 【0364】 In this invention, the server includes a data collection means for acquiring power usage information in real time, a data preprocessing means for organizing the power usage information obtained by the data collection means and detecting abnormal values, and an emotion data analysis means for adjusting the wording of notifications according to the user's emotional state. This makes it possible to acquire power usage information in real time and detect abnormalities, as well as provide information that takes into account the user's emotional state. 【0365】 "Electricity usage information" refers to data that shows the amount of electricity consumed and the duration of its use at a specific location or for a particular device. 【0366】 "Data collection means" refers to a device or process for acquiring power usage information in real time using sensors or monitoring equipment. 【0367】 "Data preprocessing means" refers to a method of organizing collected power usage information and processing it to detect and remove abnormal values ​​and noise. 【0368】 "Analysis means" refers to methods and devices for predicting trends and tendencies in economic activity based on processed electricity usage information. 【0369】 "Emotional data analysis means" is a technology that estimates a user's emotional state based on their past consumption patterns and current situation, and adjusts the wording of notifications accordingly. 【0370】 An "alert generation method" is a method or system for issuing warnings or notifications to users when an anomaly is detected based on analysis results. 【0371】 A "report generation method" is a method for automatically creating reports that standardize the results obtained through analysis and provide them to the user. 【0372】 The system implementing this invention uses a terminal and a server to acquire power usage information in real time and detect abnormal values. The terminal collects power usage information via sensors and transmits it to the server. The server receives this information and first performs data preprocessing, such as standardization and filtering, to remove abnormal values. It is desirable to use software such as the Pandas library for this processing. 【0373】 The server then uses the organized data to predict power usage trends and seasonality through time series analysis. This is done using the statsmodels library and applying models such as ARIMA. If an anomaly is detected in the predicted trend, an alert generation mechanism is activated, and a notification is sent with wording tailored to the user's emotional state. 【0374】 This emotion analysis system incorporates an emotion engine that estimates the user's current emotional state based on their past power consumption patterns and emotional data. The emotional state is analyzed based on pre-set indicators. 【0375】 When an alert is generated, it will be notified to the user via smartphone, smart glasses, or other means. The notification will be delivered in a gentle yet effective manner, using language that is sensitive to the user's emotions. 【0376】 As a concrete example, in one smart city, residents can receive notifications on their smartphones such as, "Electricity consumption is trending upwards this week, but why not set aside some time to relax this weekend?" This is expected to allow residents to manage their energy consumption while remaining mindful of their energy needs and without compromising their quality of life. 【0377】 An example of a prompt for a generating AI model is: "Generate advice for smart city residents based on recent electricity consumption trends and residents' emotional states." 【0378】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0379】 Step 1: 【0380】 The terminal collects power usage information in real time from each sensor. The collected information includes power consumption and usage time, and by adding a timestamp, it accurately records when and how much power was consumed. Sending this data to the server is the input. 【0381】 The output will be the raw data of the power usage information that the server receives. 【0382】 Step 2: 【0383】 The server filters the received power usage information using data preprocessing. Specifically, it uses the Pandas library to standardize and denoise the dataset, and detects and removes outliers. This process ensures that the data is in a clean format suitable for analysis. 【0384】 The input is the raw power usage data transmitted in step 1, and the output is a clean dataset with outliers removed. 【0385】 Step 3: 【0386】 The server uses the cleaned dataset to begin data calculations for time series analysis. It uses the ARIMA model from the statsmodels library to extract trends and seasonality in power consumption and predict future power consumption. 【0387】 The input is clean electricity usage data, and the output generates predictive data on future electricity consumption trends. 【0388】 Step 4: 【0389】 The server detects anomalies based on predicted trends. If an anomaly is detected, an alert generation mechanism is activated, and a notification is generated for the user. This notification not only informs the user of the anomaly, but also uses sentiment data analysis to create wording that takes the user's emotional state into consideration. 【0390】 The input consists of prediction data and sentiment data from step 3, and the output is a notification message for the user. 【0391】 Step 5: 【0392】 Users receive generated notifications to get advice on power consumption status and any anomalies. These notifications can be received via smartphones or smart glasses and are presented in a way that suits the user's emotional state, making them more receptive to receiving. 【0393】 The input is the notification message created in step 4, and the output is the impact on the user's behavior and emotions. 【0394】 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. 【0395】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0396】 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. 【0397】 [Third Embodiment] 【0398】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0399】 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. 【0400】 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). 【0401】 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. 【0402】 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. 【0403】 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). 【0404】 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. 【0405】 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. 【0406】 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. 【0407】 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. 【0408】 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. 【0409】 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". 【0410】 This invention provides a system for evaluating the state of economic activity and detecting anomalies by utilizing power consumption data. The system consists of multiple means, enabling rapid and accurate economic forecasting and response. 【0411】 First, the server receives power usage information transmitted from each terminal. The terminals collect real-time data on power consumption from individual sensors and the company's energy management system and send it to the server. This data is aggregated by total power consumption and by time period. 【0412】 Next, the server preprocesses the power usage information. Specifically, it removes outliers and noise from the acquired data and makes corrections as needed. This preprocessing improves the accuracy of the analysis. For example, if there is a rapid fluctuation in a short period of time, it can be detected as an outlier and processed differently from other data. 【0413】 Subsequently, the server performs AI-powered analysis to predict trends in economic activity from electricity usage data. Using time-series analysis models, it evaluates trends and seasonality in consumption data and predicts future consumption patterns. This makes it possible to determine production activities and market trends in specific industries and regions. 【0414】 Furthermore, the server detects anomalies based on the analysis results and generates alerts as needed. These alerts are sent to users via email or mobile device notifications. For example, if there is a sudden surge in power usage in a certain area, it will be notified as an anomaly requiring immediate attention. 【0415】 Finally, the server generates a detailed report based on the analysis results. This report includes historical trends in power consumption, current usage, and forecast results, and is distributed to relevant parties on a regular basis. Users can then use this to make strategic decisions. In this way, the present invention realizes a system that enables efficient and accurate analysis of economic activities through the utilization of power consumption data. 【0416】 The following describes the processing flow. 【0417】 Step 1: 【0418】 The terminal acquires power usage information in real time from various sensors and energy management devices, and transmits this data to a server using a secure communication protocol. This data includes timestamps and detailed usage information. 【0419】 Step 2: 【0420】 The server stores the received power usage information in a database and simultaneously verifies the data's integrity. Anomaly filters are used to detect noise and outliers, and inconsistent data is removed or corrected. 【0421】 Step 3: 【0422】 The server runs a time series analysis model based on optimized data. Here, it analyzes historical data to extract trends and seasonality and predict future electricity consumption patterns. Specifically, it aims to obtain more accurate results by using moving averages and the ARIMA model. 【0423】 Step 4: 【0424】 The server evaluates the analysis results and detects anomalies based on the set criteria. As a result, if a sudden fluctuation in consumption or a deviation from the predicted value is observed, an alert is generated. 【0425】 Step 5: 【0426】 The server sends the generated alerts to the user via email or mobile notification. This allows the user to quickly understand the situation and consider necessary actions. 【0427】 Step 6: 【0428】 The server periodically generates reports based on the analysis results. These reports include detailed data and forecasts that serve as indicators of economic activity and are provided to the user. The user uses these reports to make strategic decisions. 【0429】 (Example 1) 【0430】 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." 【0431】 A challenge in efficiently monitoring and forecasting economic activity is the use of real-time electricity consumption information. Conventional methods have struggled to quickly detect anomalies in electricity consumption data and generate appropriate alerts. Therefore, there is a need for a means to rapidly and accurately identify anomalies in economic activity and utilize this information for relevant decision-making. 【0432】 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. 【0433】 In this invention, the server includes a device for collecting power usage data in real time, a processing device for organizing the power usage data obtained by the device and detecting abnormal data, and a computing device for analyzing the power data cleaned by the processing device and predicting trends in economic activity. This enables rapid and accurate monitoring of economic activity based on power consumption and efficient response through anomaly detection. 【0434】 "Apparatus" refers to a machine element or system configured to perform a specific function. 【0435】 A "processing device" is a system of machines or programs used to organize received data and detect inappropriate or abnormal information. 【0436】 A "computational device" is a device or program that performs logic to analyze data and make predictions or inferences based on that data. 【0437】 A "warning device" is a system or mechanism that detects abnormal or inappropriate situations and notifies the user of them. 【0438】 A "report generation device" is a system that automatically creates reports in a visually appealing and easy-to-understand format based on analyzed data. 【0439】 Time series analysis is a statistical method used to analyze the temporal fluctuations of data and predict future trends based on past movements. 【0440】 "Electronic communication" is a method of sending and receiving information using digital signals. 【0441】 A "mobile communication terminal" is a portable device capable of sending and receiving information via wireless communication. 【0442】 The server receives information from devices that collect power usage data in real time, and processes it using a processing unit. Various hardware, such as sensors and energy management systems, are used for real-time data collection. This data is sent to the server from numerous terminals and then cleaned based on anomaly detection before proceeding to analysis. 【0443】 The server analyzes pre-processed power usage data using a computing device. This analysis utilizes generative AI models and time series analysis methods, employing software to extract data trends and seasonality. The computing device then uses this information to predict future power demand, thereby understanding trends in economic activity. 【0444】 Users can detect unusual economic trends based on information generated from the server. This is done through notifications from warning devices and detailed reports provided periodically by report generators. This allows users to take prompt action. 【0445】 For example, if a manufacturing company is using this system, a sudden increase in power consumption could be detected, and a warning could be issued indicating that adjustments to the production line are necessary. As a result, the company could immediately begin inspecting equipment and improving production efficiency. 【0446】 An example of a prompt to be input into the generating AI model is: "Based on electricity consumption data, predict the trends in economic activity in region A for the next month. In particular, focus your analysis on electricity demand from the manufacturing sector." 【0447】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0448】 Step 1: 【0449】 The terminals collect power usage data in real time from sensors and energy management systems. During this collection process, each terminal continuously acquires data from connected devices and periodically sends it to the server. The input is power usage information from each sensor, and the output is data packets sent to the server. 【0450】 Step 2: 【0451】 The server preprocesses the received power usage data using a processing unit. Data cleaning is performed here, where noise and outliers are detected and removed. The input is the raw data transmitted from the terminal, and the output is clean data with outliers removed. A noise filtering algorithm is applied in this step. 【0452】 Step 3: 【0453】 The server analyzes pre-processed data using a computing device. This analysis utilizes generative AI models to evaluate trends and seasonality in power usage. The input is clean data, and the output is predictive information about economic activity obtained through the analysis. Time series analysis methods used include ARIMA models and LSTMs. 【0454】 Step 4: 【0455】 The server processes the analysis results with a warning device and generates an alert if an anomaly is detected. This uses a variety of threshold judgments and anomaly detection algorithms. The input is analysis information, and the output is an alert notified to the user. The alert is sent via electronic communication or to a mobile device to quickly warn the user. 【0456】 Step 5: 【0457】 The server automatically generates reports using a report generation device based on the analysis results. This step aggregates information on detailed consumption trends, current conditions, and forecast results. The input is the analyzed data, and the output is a report in PDF or Excel format. The generated reports are distributed periodically to users and stakeholders. 【0458】 (Application Example 1) 【0459】 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." 【0460】 In modern cities, responding quickly to rapid fluctuations in electricity consumption and improving energy efficiency are crucial challenges. However, conventional systems often struggle to monitor electricity consumption in real time or detect anomalies, and they frequently fail to provide this information in a useful format for residents and management agencies. In this context, there is a need for sustainable urban management and the promotion of eco-friendly activities among residents. 【0461】 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. 【0462】 In this invention, the server includes an information acquisition means for acquiring power consumption information over time; an information preprocessing means for organizing the power consumption information obtained by the information acquisition means and detecting anomalies; an analysis means for analyzing the power consumption information processed by the information preprocessing means and predicting trends in economic activity; a visualization means for visualizing urban power consumption information and displaying energy consumption trends for each region; and a proposal means for making suggestions for energy efficiency improvements to residents and management organizations. This makes it possible to monitor the power consumption of the entire city in real time, quickly detect anomalies, and provide residents and local governments with timely and appropriate information. 【0463】 "Information acquisition means" refers to a system that has the function of collecting electricity consumption data over time. 【0464】 "Information preprocessing means" refers to a system that has the function of detecting anomalies from acquired power consumption information and organizing the data. 【0465】 "Analysis tools" are devices that analyze processed power consumption information and have the function of predicting trends in economic activity. 【0466】 A "warning generation means" is a device that detects abnormal economic activity based on analysis results and issues a warning. 【0467】 A "document generation means" is a device that has the function of automatically generating a document summarizing the prediction results obtained by the analysis means. 【0468】 A "visualization tool" is a device that visually represents electricity consumption information in urban areas and displays energy consumption trends for each region. 【0469】 A "proposal tool" is a tool that provides residents and management organizations with methods for improving energy efficiency based on the analyzed data. 【0470】 This invention provides a system that utilizes electricity consumption information to analyze and predict economic activity in urban areas. 【0471】 The server collects power consumption information over time from various sensors and energy management systems using information acquisition methods. This data is then processed and organized to detect anomalies using information preprocessing methods. This system manages data using AWS servers and cloud services. The preprocessed data is then analyzed in detail using analysis methods, and time-series analysis is performed using TensorFlow as an AI technology. This clarifies power consumption trends, periodicity, and abnormal consumption patterns. 【0472】 The information obtained through the analysis is visually represented as energy consumption trends in urban areas via visualization tools. This allows users to easily understand consumption patterns in specific regions. Furthermore, through the proposed tools, users and management organizations are presented with concrete methods for improving energy efficiency. These methods include, for example, optimizing air conditioning use during heat waves and seasonal energy-saving techniques. 【0473】 Users can receive rapid warnings when an anomaly is detected through the warning generation system. These warnings are sent via communication devices or mobile terminals, allowing them to obtain information to take appropriate action immediately. 【0474】 Furthermore, the server automatically generates reports periodically using document generation methods based on this analytical data. These reports serve as the basis for strategic decision-making for the user. 【0475】 By utilizing generative AI models, we can effectively analyze energy use across cities and provide residents and local governments with the insights necessary for sustainable urban management. To maximize the usefulness of this system, continuous data collection and analysis are necessary. 【0476】 An example of a prompt message would be: "Please describe an application that performs trend and anomaly detection of power consumption data in smart cities. Please also describe the specific functions and technologies used." 【0477】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0478】 Step 1: 【0479】 The server receives power consumption information from each terminal. Inputs are real-time data from sensors and energy management systems. Output is the storage of this data into an internal database for centralized management. Cloud services are used for data reception and initial storage. 【0480】 Step 2: 【0481】 The server uses data preprocessing to detect anomalies in the received data and cleanses the data. The input is the collected raw power consumption data, and the output is a clean dataset with anomalies removed. Statistical methods are used to identify anomalies and remove noise during data processing. 【0482】 Step 3: 【0483】 The server uses analytical tools to analyze a clean dataset and predict trends in economic activity. The input is pre-processed data, and the output is a prediction of future power consumption trends and periodicity. A time series analysis model powered by TensorFlow is used for the analysis. 【0484】 Step 4: 【0485】 The server visually represents the analysis results through visualization tools, making them easily understandable to the user. The input is the predicted results obtained from the analysis, and the output is visualized graphs and charts. This allows the user to intuitively grasp the energy consumption trends for each region. 【0486】 Step 5: 【0487】 The user receives specific suggestions for energy efficiency improvements based on analyzed data through the proposed method. The input is the analysis results and consumption trends, and the output is specific advice for energy saving. This advice is presented from the system to the user's terminal. 【0488】 Step 6: 【0489】 The server sends a warning to the user when an anomaly is detected through the warning generation mechanism. The input is the anomalous data pattern found during analysis, and the output is a warning notification to the user's terminal. The notification is made in real time to support the user's immediate response. 【0490】 Step 7: 【0491】 The server periodically generates and provides reports to users using document generation methods. The input consists of analyzed aggregate data and forecast results, and the output is a detailed energy report. This report serves as foundational information for users' strategic decision-making. 【0492】 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. 【0493】 This invention provides a system that analyzes economic activity based on power consumption data and responds in accordance with the user's emotions. The system is composed of multiple functions and can smoothly acquire, analyze, and notify real-time power usage information. 【0494】 First, the terminal collects power usage information in real time from each sensor and sends the acquired data to the server. This information includes power usage amount and usage time with timestamps, and serves as the basic data for analysis. 【0495】 The server preprocesses the data, detecting and removing outliers and noise. This clarified data is then analyzed using a time series analysis model. This model allows the server to extract trends and seasonality in power usage and predict trends in economic activity. 【0496】 In addition, the server is equipped with an emotion engine that processes and analyzes user emotion data. This emotion data is based on newly entered information and past usage history. For example, data on the user's past consumption patterns and emotional states is learned by the emotion engine. 【0497】 Next, if the server detects an anomaly based on the analysis results, it generates an alert. At this time, the emotion engine takes the user's emotional state into consideration and sends the alert in the most appropriate format. For example, if the user is stressed, the notification might be sent in a softer tone. 【0498】 Finally, the server automatically generates a report of the analysis results and provides it to the user. This report includes not only analysis results of electricity consumption trends and economic activity, but also advice tailored to the user's sentiments. Users can use this to optimize their electricity usage and develop economic strategies. 【0499】 In summary, this system integrates and utilizes electricity usage data and user sentiment data to provide more personalized support for economic activities. 【0500】 The following describes the processing flow. 【0501】 Step 1: 【0502】 The device collects power usage information in real time from various sensors and the internal network and transmits it to the server. The transmitted information includes power consumption, usage time, and device identification information. The data is securely transmitted in an encrypted state. 【0503】 Step 2: 【0504】 The server stores the received power usage information in a database and performs preprocessing. Here, it detects anomalies and filters out identified noise and missing values. The preprocessed data is then converted into a format suitable for analysis. 【0505】 Step 3: 【0506】 The server runs a time-series analysis model based on pre-processed data to extract trends and seasonality in power consumption. Using these analysis results, it predicts future trends in economic activity and conducts a detailed analysis of consumption patterns for relevant industries and regions. 【0507】 Step 4: 【0508】 Emotional data is input from the terminal or user. The server activates an emotion engine, learns the user's past emotional data and usage history, and analyzes their current emotional state. This allows the server to understand the user's emotional patterns. 【0509】 Step 5: 【0510】 The server combines analysis results and sentiment data to generate alerts regarding economic activity. These alerts are created based on detected abnormal power consumption, and the content and delivery method are adjusted using a sentiment engine. For example, notifications may be sent using stress-relieving language. 【0511】 Step 6: 【0512】 The server sends generated alerts to users via email or mobile notifications. Providing them in a format that considers the user's emotions allows for a more effective response. 【0513】 Step 7: 【0514】 The server automatically generates a report summarizing the analysis and prediction results to date. This report, which includes suggestions for improvement and sentiment-based insights, is sent to the user. The user can use the report to support strategic decision-making. 【0515】 (Example 2) 【0516】 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." 【0517】 In analyzing economic activity based on electricity usage data, conventional methods were insufficient in terms of real-time information and consideration of user sentiment. This resulted in a failure to optimize electricity usage or provide notifications tailored to individual needs, ultimately leading to decreased user satisfaction. 【0518】 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. 【0519】 In this invention, the server includes an information gathering means for acquiring power usage information in real time, an information preprocessing means for organizing the information obtained by the information gathering means and detecting abnormal values, and an information analysis means for analyzing the information processed by the information preprocessing means and predicting trends in economic activity. This enables a rapid and personalized response to trends in power usage. 【0520】 "Information gathering means" refers to a device or process that acquires electricity usage information in real time. 【0521】 "Information preprocessing means" refers to a method for organizing collected power usage information and detecting and removing abnormal values. 【0522】 "Information analysis means" refers to a system or method that analyzes pre-processed information to predict trends and periodicities in power usage. 【0523】 An "alert generation means" is a process or device that detects abnormal economic activity based on analysis results and generates a warning. 【0524】 An "emotional response system" is a system that takes into account the user's emotional information and sends notifications in the most optimal format. 【0525】 "Report generation means" refers to a device or method that automatically generates a report by summarizing the analyzed prediction results. 【0526】 This invention provides a system that integrates power usage information and user emotion data to support personalized economic activities. An embodiment of this system is described below. 【0527】 The device uses sensors installed in homes and businesses to obtain real-time power usage information. These sensors measure the amount of power consumed and can timestamp the data. The device sends encrypted data to a server over the network. The TLS protocol is used to ensure security and privacy. 【0528】 The server analyzes the received data. First, it uses statistical methods to detect and remove outliers. Next, it uses time series analysis models such as ARIMA and SARIMA to extract trends and periodicities in power usage. The results of this analysis are used to forecast economic activity. 【0529】 Furthermore, the server uses an emotion engine to process the user's emotional information. This information is collected from sources such as smartphone apps and wearable devices, and the system learns the relationship between the user's past consumption patterns and their current emotional state. 【0530】 Based on the prediction results, the server generates an alert when it detects unusual economic activity. This alert is optimized to the user's emotional state; for example, a gentler notification is sent to a user who is feeling stressed. 【0531】 Finally, the server automatically generates a report for the user based on these analysis results. This report includes an analysis of power consumption trends and sentiment-based advice, which the user can use to optimize their power usage and develop economic strategies. 【0532】 As a concrete example, we could analyze the tendency for electricity consumption to increase on holidays, and when users are relaxed, we could send notifications with scenic images and gentle language. 【0533】 An example of a prompt for a generative AI model is, "Please build a system that suggests the optimal energy-saving method based on power consumption patterns and user sentiment data." 【0534】 In this way, power data and emotional data are integrated and utilized to provide users with more personalized services. 【0535】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0536】 Step 1: 【0537】 The terminal acquires power usage information from sensors installed in homes and businesses. Its input is real-time power consumption data transmitted from power sensors. This data includes power consumption and a timestamp. The terminal encrypts this information and transmits it to a server over the network. The output is encrypted power usage data. 【0538】 Step 2: 【0539】 The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted power usage information, which is decrypted to obtain raw power data. The obtained data is preprocessed, and statistical methods are used to detect outliers and remove noise. Specifically, outliers are filtered, and a moving average method is applied to missing values. The output is clean power usage information. 【0540】 Step 3: 【0541】 The server analyzes the pre-processed data. The input is the clean power usage information obtained in step 2. Using this, a time series analysis model (e.g., ARIMA or SARIMA) is applied to extract trends and periodicities. Specifically, it analyzes the data trends and creates a predictive model. The output is predicted power consumption data. 【0542】 Step 4: 【0543】 The server processes user emotional information. The input is emotional data collected from smartphone apps and wearable devices. This data is fed into an emotional engine to analyze the user's past consumption patterns and current emotional state, learning the correlations. Specifically, it creates a user profile based on the emotional state. The output is the analyzed emotional profile. 【0544】 Step 5: 【0545】 The server detects anomalies based on the analysis results and generates alerts to notify the user. The inputs are the predicted economic activity data obtained in step 3 and the emotion profile from step 4. Using this information, the server selects an alert format that takes emotional states into account. The specific actions are generating the alert content and sending it. The output is the appropriate alert message sent to the user. 【0546】 Step 6: 【0547】 The server automatically generates a report, combining the results of previous analyses and considering user sentiment. The input consists of the data obtained in each previous step. The report includes an analysis of power consumption trends and advice for energy saving. The specific actions involve data integration and report formatting. The output is a detailed analytical report provided to the user. 【0548】 (Application Example 2) 【0549】 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." 【0550】 Analyzing economic activity based on electricity usage data plays a crucial role in various fields. However, while conventional systems can detect anomalies in electricity consumption, they fail to adequately provide information tailored to the emotional state of users. As a result, it is difficult to respond individually to electricity usage patterns, and there is a challenge in providing personalized services that meet the needs of users. 【0551】 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. 【0552】 In this invention, the server includes a data collection means for acquiring power usage information in real time, a data preprocessing means for organizing the power usage information obtained by the data collection means and detecting abnormal values, and an emotion data analysis means for adjusting the wording of notifications according to the user's emotional state. This makes it possible to acquire power usage information in real time and detect abnormalities, as well as provide information that takes into account the user's emotional state. 【0553】 "Electricity usage information" refers to data that shows the amount of electricity consumed and the duration of its use at a specific location or for a particular device. 【0554】 "Data collection means" refers to a device or process for acquiring power usage information in real time using sensors or monitoring equipment. 【0555】 "Data preprocessing means" refers to a method of organizing collected power usage information and processing it to detect and remove abnormal values ​​and noise. 【0556】 "Analysis means" refers to methods and devices for predicting trends and tendencies in economic activity based on processed electricity usage information. 【0557】 "Emotional data analysis means" is a technology that estimates a user's emotional state based on their past consumption patterns and current situation, and adjusts the wording of notifications accordingly. 【0558】 An "alert generation method" is a method or system for issuing warnings or notifications to users when an anomaly is detected based on analysis results. 【0559】 A "report generation method" is a method for automatically creating reports that standardize the results obtained through analysis and provide them to the user. 【0560】 The system implementing this invention uses a terminal and a server to acquire power usage information in real time and detect abnormal values. The terminal collects power usage information via sensors and transmits it to the server. The server receives this information and first performs data preprocessing, such as standardization and filtering, to remove abnormal values. It is desirable to use software such as the Pandas library for this processing. 【0561】 The server then uses the organized data to predict power usage trends and seasonality through time series analysis. This is done using the statsmodels library and applying models such as ARIMA. If an anomaly is detected in the predicted trend, an alert generation mechanism is activated, and a notification is sent with wording tailored to the user's emotional state. 【0562】 This emotion analysis system incorporates an emotion engine that estimates the user's current emotional state based on their past power consumption patterns and emotional data. The emotional state is analyzed based on pre-set indicators. 【0563】 When an alert is generated, it will be notified to the user via smartphone, smart glasses, or other means. The notification will be delivered in a gentle yet effective manner, using language that is sensitive to the user's emotions. 【0564】 As a concrete example, in one smart city, residents can receive notifications on their smartphones such as, "Electricity consumption is trending upwards this week, but why not set aside some time to relax this weekend?" This is expected to allow residents to manage their energy consumption while remaining mindful of their energy needs and without compromising their quality of life. 【0565】 An example of a prompt for a generating AI model is: "Generate advice for smart city residents based on recent electricity consumption trends and residents' emotional states." 【0566】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0567】 Step 1: 【0568】 The terminal collects power usage information in real time from each sensor. The collected information includes power consumption and usage time, and by adding a timestamp, it accurately records when and how much power was consumed. Sending this data to the server is the input. 【0569】 The output will be the raw data of the power usage information that the server receives. 【0570】 Step 2: 【0571】 The server filters the received power usage information using data preprocessing. Specifically, it uses the Pandas library to standardize and denoise the dataset, and detects and removes outliers. This process ensures that the data is in a clean format suitable for analysis. 【0572】 The input is the raw power usage data transmitted in step 1, and the output is a clean dataset with outliers removed. 【0573】 Step 3: 【0574】 The server uses the cleaned dataset to begin data calculations for time series analysis. It uses the ARIMA model from the statsmodels library to extract trends and seasonality in power consumption and predict future power consumption. 【0575】 The input is clean electricity usage data, and the output generates predictive data on future electricity consumption trends. 【0576】 Step 4: 【0577】 The server detects anomalies based on predicted trends. If an anomaly is detected, an alert generation mechanism is activated, and a notification is generated for the user. This notification not only informs the user of the anomaly, but also uses sentiment data analysis to create wording that takes the user's emotional state into consideration. 【0578】 The input consists of prediction data and sentiment data from step 3, and the output is a notification message for the user. 【0579】 Step 5: 【0580】 Users receive generated notifications to get advice on power consumption status and any anomalies. These notifications can be received via smartphones or smart glasses and are presented in a way that suits the user's emotional state, making them more receptive to receiving. 【0581】 The input is the notification message created in step 4, and the output is the impact on the user's behavior and emotions. 【0582】 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. 【0583】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0584】 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. 【0585】 [Fourth Embodiment] 【0586】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0587】 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. 【0588】 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). 【0589】 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. 【0590】 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. 【0591】 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). 【0592】 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. 【0593】 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. 【0594】 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. 【0595】 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. 【0596】 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. 【0597】 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. 【0598】 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". 【0599】 This invention provides a system for evaluating the state of economic activity and detecting anomalies by utilizing power consumption data. The system consists of multiple means, enabling rapid and accurate economic forecasting and response. 【0600】 First, the server receives power usage information transmitted from each terminal. The terminals collect real-time data on power consumption from individual sensors and the company's energy management system and send it to the server. This data is aggregated by total power consumption and by time period. 【0601】 Next, the server preprocesses the power usage information. Specifically, it removes outliers and noise from the acquired data and makes corrections as needed. This preprocessing improves the accuracy of the analysis. For example, if there is a rapid fluctuation in a short period of time, it can be detected as an outlier and processed differently from other data. 【0602】 Subsequently, the server performs AI-powered analysis to predict trends in economic activity from electricity usage data. Using time-series analysis models, it evaluates trends and seasonality in consumption data and predicts future consumption patterns. This makes it possible to determine production activities and market trends in specific industries and regions. 【0603】 Furthermore, the server detects anomalies based on the analysis results and generates alerts as needed. These alerts are sent to users via email or mobile device notifications. For example, if there is a sudden surge in power usage in a certain area, it will be notified as an anomaly requiring immediate attention. 【0604】 Finally, the server generates a detailed report based on the analysis results. This report includes historical trends in power consumption, current usage, and forecast results, and is distributed to relevant parties on a regular basis. Users can then use this to make strategic decisions. In this way, the present invention realizes a system that enables efficient and accurate analysis of economic activities through the utilization of power consumption data. 【0605】 The following describes the processing flow. 【0606】 Step 1: 【0607】 The terminal acquires power usage information in real time from various sensors and energy management devices, and transmits this data to a server using a secure communication protocol. This data includes timestamps and detailed usage information. 【0608】 Step 2: 【0609】 The server stores the received power usage information in a database and simultaneously verifies the data's integrity. Anomaly filters are used to detect noise and outliers, and inconsistent data is removed or corrected. 【0610】 Step 3: 【0611】 The server runs a time series analysis model based on optimized data. Here, it analyzes historical data to extract trends and seasonality and predict future electricity consumption patterns. Specifically, it aims to obtain more accurate results by using moving averages and the ARIMA model. 【0612】 Step 4: 【0613】 The server evaluates the analysis results and detects anomalies based on the set criteria. As a result, if a sudden fluctuation in consumption or a deviation from the predicted value is observed, an alert is generated. 【0614】 Step 5: 【0615】 The server sends the generated alerts to the user via email or mobile notification. This allows the user to quickly understand the situation and consider necessary actions. 【0616】 Step 6: 【0617】 The server periodically generates reports based on the analysis results. These reports include detailed data and forecasts that serve as indicators of economic activity and are provided to the user. The user uses these reports to make strategic decisions. 【0618】 (Example 1) 【0619】 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". 【0620】 A challenge in efficiently monitoring and forecasting economic activity is the use of real-time electricity consumption information. Conventional methods have struggled to quickly detect anomalies in electricity consumption data and generate appropriate alerts. Therefore, there is a need for a means to rapidly and accurately identify anomalies in economic activity and utilize this information for relevant decision-making. 【0621】 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. 【0622】 In this invention, the server includes a device for collecting power usage data in real time, a processing device for organizing the power usage data obtained by the device and detecting abnormal data, and a computing device for analyzing the power data cleaned by the processing device and predicting trends in economic activity. This enables rapid and accurate monitoring of economic activity based on power consumption and efficient response through anomaly detection. 【0623】 "Apparatus" refers to a machine element or system configured to perform a specific function. 【0624】 A "processing device" is a system of machines or programs used to organize received data and detect inappropriate or abnormal information. 【0625】 A "computational device" is a device or program that performs logic to analyze data and make predictions or inferences based on that data. 【0626】 A "warning device" is a system or mechanism that detects abnormal or inappropriate situations and notifies the user of them. 【0627】 A "report generation device" is a system that automatically creates reports in a visually appealing and easy-to-understand format based on analyzed data. 【0628】 Time series analysis is a statistical method used to analyze the temporal fluctuations of data and predict future trends based on past movements. 【0629】 "Electronic communication" is a method of sending and receiving information using digital signals. 【0630】 A "mobile communication terminal" is a portable device capable of sending and receiving information via wireless communication. 【0631】 The server receives information from devices that collect power usage data in real time, and processes it using a processing unit. Various hardware, such as sensors and energy management systems, are used for real-time data collection. This data is sent to the server from numerous terminals and then cleaned based on anomaly detection before proceeding to analysis. 【0632】 The server analyzes pre-processed power usage data using a computing device. This analysis utilizes generative AI models and time series analysis methods, employing software to extract data trends and seasonality. The computing device then uses this information to predict future power demand, thereby understanding trends in economic activity. 【0633】 Users can detect unusual economic trends based on information generated from the server. This is done through notifications from warning devices and detailed reports provided periodically by report generators. This allows users to take prompt action. 【0634】 For example, if a manufacturing company is using this system, a sudden increase in power consumption could be detected, and a warning could be issued indicating that adjustments to the production line are necessary. As a result, the company could immediately begin inspecting equipment and improving production efficiency. 【0635】 An example of a prompt to be input into the generating AI model is: "Based on electricity consumption data, predict the trends in economic activity in region A for the next month. In particular, focus your analysis on electricity demand from the manufacturing sector." 【0636】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0637】 Step 1: 【0638】 The terminals collect power usage data in real time from sensors and energy management systems. During this collection process, each terminal continuously acquires data from connected devices and periodically sends it to the server. The input is power usage information from each sensor, and the output is data packets sent to the server. 【0639】 Step 2: 【0640】 The server preprocesses the received power usage data using a processing unit. Data cleaning is performed here, where noise and outliers are detected and removed. The input is the raw data transmitted from the terminal, and the output is clean data with outliers removed. A noise filtering algorithm is applied in this step. 【0641】 Step 3: 【0642】 The server analyzes pre-processed data using a computing device. This analysis utilizes generative AI models to evaluate trends and seasonality in power usage. The input is clean data, and the output is predictive information about economic activity obtained through the analysis. Time series analysis methods used include ARIMA models and LSTMs. 【0643】 Step 4: 【0644】 The server processes the analysis results with a warning device and generates an alert if an anomaly is detected. This uses a variety of threshold judgments and anomaly detection algorithms. The input is analysis information, and the output is an alert notified to the user. The alert is sent via electronic communication or to a mobile device to quickly warn the user. 【0645】 Step 5: 【0646】 The server automatically generates reports using a report generation device based on the analysis results. This step aggregates information on detailed consumption trends, current conditions, and forecast results. The input is the analyzed data, and the output is a report in PDF or Excel format. The generated reports are distributed periodically to users and stakeholders. 【0647】 (Application Example 1) 【0648】 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". 【0649】 In modern cities, responding quickly to rapid fluctuations in electricity consumption and improving energy efficiency are crucial challenges. However, conventional systems often struggle to monitor electricity consumption in real time or detect anomalies, and they frequently fail to provide this information in a useful format for residents and management agencies. In this context, there is a need for sustainable urban management and the promotion of eco-friendly activities among residents. 【0650】 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. 【0651】 In this invention, the server includes an information acquisition means for acquiring power consumption information over time; an information preprocessing means for organizing the power consumption information obtained by the information acquisition means and detecting anomalies; an analysis means for analyzing the power consumption information processed by the information preprocessing means and predicting trends in economic activity; a visualization means for visualizing urban power consumption information and displaying energy consumption trends for each region; and a proposal means for making suggestions for energy efficiency improvements to residents and management organizations. This makes it possible to monitor the power consumption of the entire city in real time, quickly detect anomalies, and provide residents and local governments with timely and appropriate information. 【0652】 "Information acquisition means" refers to a system that has the function of collecting electricity consumption data over time. 【0653】 "Information preprocessing means" refers to a system that has the function of detecting anomalies from acquired power consumption information and organizing the data. 【0654】 "Analysis tools" are devices that analyze processed power consumption information and have the function of predicting trends in economic activity. 【0655】 A "warning generation means" is a device that detects abnormal economic activity based on analysis results and issues a warning. 【0656】 A "document generation means" is a device that has the function of automatically generating a document summarizing the prediction results obtained by the analysis means. 【0657】 A "visualization tool" is a device that visually represents electricity consumption information in urban areas and displays energy consumption trends for each region. 【0658】 A "proposal tool" is a tool that provides residents and management organizations with methods for improving energy efficiency based on the analyzed data. 【0659】 This invention provides a system that utilizes electricity consumption information to analyze and predict economic activity in urban areas. 【0660】 The server collects power consumption information over time from various sensors and energy management systems using information acquisition methods. This data is then processed and organized to detect anomalies using information preprocessing methods. This system manages data using AWS servers and cloud services. The preprocessed data is then analyzed in detail using analysis methods, and time-series analysis is performed using TensorFlow as an AI technology. This clarifies power consumption trends, periodicity, and abnormal consumption patterns. 【0661】 The information obtained through the analysis is visually represented as energy consumption trends in urban areas via visualization tools. This allows users to easily understand consumption patterns in specific regions. Furthermore, through the proposed tools, users and management organizations are presented with concrete methods for improving energy efficiency. These methods include, for example, optimizing air conditioning use during heat waves and seasonal energy-saving techniques. 【0662】 Users can receive rapid warnings when an anomaly is detected through the warning generation system. These warnings are sent via communication devices or mobile terminals, allowing them to obtain information to take appropriate action immediately. 【0663】 Furthermore, the server automatically generates reports periodically using document generation methods based on this analytical data. These reports serve as the basis for strategic decision-making for the user. 【0664】 By utilizing generative AI models, we can effectively analyze energy use across cities and provide residents and local governments with the insights necessary for sustainable urban management. To maximize the usefulness of this system, continuous data collection and analysis are necessary. 【0665】 An example of a prompt message would be: "Please describe an application that performs trend and anomaly detection of power consumption data in smart cities. Please also describe the specific functions and technologies used." 【0666】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0667】 Step 1: 【0668】 The server receives power consumption information from each terminal. Inputs are real-time data from sensors and energy management systems. Output is the storage of this data into an internal database for centralized management. Cloud services are used for data reception and initial storage. 【0669】 Step 2: 【0670】 The server uses data preprocessing to detect anomalies in the received data and cleanses the data. The input is the collected raw power consumption data, and the output is a clean dataset with anomalies removed. Statistical methods are used to identify anomalies and remove noise during data processing. 【0671】 Step 3: 【0672】 The server uses analytical tools to analyze a clean dataset and predict trends in economic activity. The input is pre-processed data, and the output is a prediction of future power consumption trends and periodicity. A time series analysis model powered by TensorFlow is used for the analysis. 【0673】 Step 4: 【0674】 The server visually represents the analysis results through visualization tools, making them easily understandable to the user. The input is the predicted results obtained from the analysis, and the output is visualized graphs and charts. This allows the user to intuitively grasp the energy consumption trends for each region. 【0675】 Step 5: 【0676】 The user receives specific suggestions for energy efficiency improvements based on analyzed data through the proposed method. The input is the analysis results and consumption trends, and the output is specific advice for energy saving. This advice is presented from the system to the user's terminal. 【0677】 Step 6: 【0678】 The server sends a warning to the user when an anomaly is detected through the warning generation mechanism. The input is the anomalous data pattern found during analysis, and the output is a warning notification to the user's terminal. The notification is made in real time to support the user's immediate response. 【0679】 Step 7: 【0680】 The server periodically generates and provides reports to users using document generation methods. The input consists of analyzed aggregate data and forecast results, and the output is a detailed energy report. This report serves as foundational information for users' strategic decision-making. 【0681】 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. 【0682】 This invention provides a system that analyzes economic activity based on power consumption data and responds in accordance with the user's emotions. The system is composed of multiple functions and can smoothly acquire, analyze, and notify real-time power usage information. 【0683】 First, the terminal collects power usage information in real time from each sensor and sends the acquired data to the server. This information includes power usage amount and usage time with timestamps, and serves as the basic data for analysis. 【0684】 The server preprocesses the data, detecting and removing outliers and noise. This clarified data is then analyzed using a time series analysis model. This model allows the server to extract trends and seasonality in power usage and predict trends in economic activity. 【0685】 In addition, the server is equipped with an emotion engine that processes and analyzes user emotion data. This emotion data is based on newly entered information and past usage history. For example, data on the user's past consumption patterns and emotional states is learned by the emotion engine. 【0686】 Next, if the server detects an anomaly based on the analysis results, it generates an alert. At this time, the emotion engine takes the user's emotional state into consideration and sends the alert in the most appropriate format. For example, if the user is stressed, the notification might be sent in a softer tone. 【0687】 Finally, the server automatically generates a report of the analysis results and provides it to the user. This report includes not only analysis results of electricity consumption trends and economic activity, but also advice tailored to the user's sentiments. Users can use this to optimize their electricity usage and develop economic strategies. 【0688】 In summary, this system integrates and utilizes electricity usage data and user sentiment data to provide more personalized support for economic activities. 【0689】 The following describes the processing flow. 【0690】 Step 1: 【0691】 The device collects power usage information in real time from various sensors and the internal network and transmits it to the server. The transmitted information includes power consumption, usage time, and device identification information. The data is securely transmitted in an encrypted state. 【0692】 Step 2: 【0693】 The server stores the received power usage information in a database and performs preprocessing. Here, it detects anomalies and filters out identified noise and missing values. The preprocessed data is then converted into a format suitable for analysis. 【0694】 Step 3: 【0695】 The server runs a time-series analysis model based on pre-processed data to extract trends and seasonality in power consumption. Using these analysis results, it predicts future trends in economic activity and conducts a detailed analysis of consumption patterns for relevant industries and regions. 【0696】 Step 4: 【0697】 Emotional data is input from the terminal or user. The server activates an emotion engine, learns the user's past emotional data and usage history, and analyzes their current emotional state. This allows the server to understand the user's emotional patterns. 【0698】 Step 5: 【0699】 The server combines analysis results and sentiment data to generate alerts regarding economic activity. These alerts are created based on detected abnormal power consumption, and the content and delivery method are adjusted using a sentiment engine. For example, notifications may be sent using stress-relieving language. 【0700】 Step 6: 【0701】 The server sends generated alerts to users via email or mobile notifications. Providing them in a format that considers the user's emotions allows for a more effective response. 【0702】 Step 7: 【0703】 The server automatically generates a report summarizing the analysis and prediction results to date. This report, which includes suggestions for improvement and sentiment-based insights, is sent to the user. The user can use the report to support strategic decision-making. 【0704】 (Example 2) 【0705】 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". 【0706】 In analyzing economic activity based on electricity usage data, conventional methods were insufficient in terms of real-time information and consideration of user sentiment. This resulted in a failure to optimize electricity usage or provide notifications tailored to individual needs, ultimately leading to decreased user satisfaction. 【0707】 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. 【0708】 In this invention, the server includes an information gathering means for acquiring power usage information in real time, an information preprocessing means for organizing the information obtained by the information gathering means and detecting abnormal values, and an information analysis means for analyzing the information processed by the information preprocessing means and predicting trends in economic activity. This enables a rapid and personalized response to trends in power usage. 【0709】 "Information gathering means" refers to a device or process that acquires electricity usage information in real time. 【0710】 "Information preprocessing means" refers to a method for organizing collected power usage information and detecting and removing abnormal values. 【0711】 "Information analysis means" refers to a system or method that analyzes pre-processed information to predict trends and periodicities in power usage. 【0712】 An "alert generation means" is a process or device that detects abnormal economic activity based on analysis results and generates a warning. 【0713】 An "emotional response system" is a system that takes into account the user's emotional information and sends notifications in the most optimal format. 【0714】 "Report generation means" refers to a device or method that automatically generates a report by summarizing the analyzed prediction results. 【0715】 This invention provides a system that integrates power usage information and user emotion data to support personalized economic activities. An embodiment of this system is described below. 【0716】 The device uses sensors installed in homes and businesses to obtain real-time power usage information. These sensors measure the amount of power consumed and can timestamp the data. The device sends encrypted data to a server over the network. The TLS protocol is used to ensure security and privacy. 【0717】 The server analyzes the received data. First, it uses statistical methods to detect and remove outliers. Next, it uses time series analysis models such as ARIMA and SARIMA to extract trends and periodicities in power usage. The results of this analysis are used to forecast economic activity. 【0718】 Furthermore, the server uses an emotion engine to process the user's emotional information. This information is collected from sources such as smartphone apps and wearable devices, and the system learns the relationship between the user's past consumption patterns and their current emotional state. 【0719】 Based on the prediction results, the server generates an alert when it detects unusual economic activity. This alert is optimized to the user's emotional state; for example, a gentler notification is sent to a user who is feeling stressed. 【0720】 Finally, the server automatically generates a report for the user based on these analysis results. This report includes an analysis of power consumption trends and sentiment-based advice, which the user can use to optimize their power usage and develop economic strategies. 【0721】 As a concrete example, we could analyze the tendency for electricity consumption to increase on holidays, and when users are relaxed, we could send notifications with scenic images and gentle language. 【0722】 An example of a prompt for a generative AI model is, "Please build a system that suggests the optimal energy-saving method based on power consumption patterns and user sentiment data." 【0723】 In this way, power data and emotional data are integrated and utilized to provide users with more personalized services. 【0724】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0725】 Step 1: 【0726】 The terminal acquires power usage information from sensors installed in homes and businesses. Its input is real-time power consumption data transmitted from power sensors. This data includes power consumption and a timestamp. The terminal encrypts this information and transmits it to a server over the network. The output is encrypted power usage data. 【0727】 Step 2: 【0728】 The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted power usage information, which is decrypted to obtain raw power data. The obtained data is preprocessed, and statistical methods are used to detect outliers and remove noise. Specifically, outliers are filtered, and a moving average method is applied to missing values. The output is clean power usage information. 【0729】 Step 3: 【0730】 The server analyzes the pre-processed data. The input is the clean power usage information obtained in step 2. Using this, a time series analysis model (e.g., ARIMA or SARIMA) is applied to extract trends and periodicities. Specifically, it analyzes the data trends and creates a predictive model. The output is predicted power consumption data. 【0731】 Step 4: 【0732】 The server processes user emotional information. The input is emotional data collected from smartphone apps and wearable devices. This data is fed into an emotional engine to analyze the user's past consumption patterns and current emotional state, learning the correlations. Specifically, it creates a user profile based on the emotional state. The output is the analyzed emotional profile. 【0733】 Step 5: 【0734】 The server detects anomalies based on the analysis results and generates alerts to notify the user. The inputs are the predicted economic activity data obtained in step 3 and the emotion profile from step 4. Using this information, the server selects an alert format that takes emotional states into account. The specific actions are generating the alert content and sending it. The output is the appropriate alert message sent to the user. 【0735】 Step 6: 【0736】 The server automatically generates a report, combining the results of previous analyses and considering user sentiment. The input consists of the data obtained in each previous step. The report includes an analysis of power consumption trends and advice for energy saving. The specific actions involve data integration and report formatting. The output is a detailed analytical report provided to the user. 【0737】 (Application Example 2) 【0738】 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". 【0739】 Analyzing economic activity based on electricity usage data plays a crucial role in various fields. However, while conventional systems can detect anomalies in electricity consumption, they fail to adequately provide information tailored to the emotional state of users. As a result, it is difficult to respond individually to electricity usage patterns, and there is a challenge in providing personalized services that meet the needs of users. 【0740】 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. 【0741】 In this invention, the server includes a data collection means for acquiring power usage information in real time, a data preprocessing means for organizing the power usage information obtained by the data collection means and detecting abnormal values, and an emotion data analysis means for adjusting the wording of notifications according to the user's emotional state. This makes it possible to acquire power usage information in real time and detect abnormalities, as well as provide information that takes into account the user's emotional state. 【0742】 "Electricity usage information" refers to data that shows the amount of electricity consumed and the duration of its use at a specific location or for a particular device. 【0743】 "Data collection means" refers to a device or process for acquiring power usage information in real time using sensors or monitoring equipment. 【0744】 "Data preprocessing means" refers to a method of organizing collected power usage information and processing it to detect and remove abnormal values ​​and noise. 【0745】 "Analysis means" refers to methods and devices for predicting trends and tendencies in economic activity based on processed electricity usage information. 【0746】 "Emotional data analysis means" is a technology that estimates a user's emotional state based on their past consumption patterns and current situation, and adjusts the wording of notifications accordingly. 【0747】 An "alert generation method" is a method or system for issuing warnings or notifications to users when an anomaly is detected based on analysis results. 【0748】 A "report generation method" is a method for automatically creating reports that standardize the results obtained through analysis and provide them to the user. 【0749】 The system implementing this invention uses a terminal and a server to acquire power usage information in real time and detect abnormal values. The terminal collects power usage information via sensors and transmits it to the server. The server receives this information and first performs data preprocessing, such as standardization and filtering, to remove abnormal values. It is desirable to use software such as the Pandas library for this processing. 【0750】 The server then uses the organized data to predict power usage trends and seasonality through time series analysis. This is done using the statsmodels library and applying models such as ARIMA. If an anomaly is detected in the predicted trend, an alert generation mechanism is activated, and a notification is sent with wording tailored to the user's emotional state. 【0751】 This emotion analysis system incorporates an emotion engine that estimates the user's current emotional state based on their past power consumption patterns and emotional data. The emotional state is analyzed based on pre-set indicators. 【0752】 When an alert is generated, it will be notified to the user via smartphone, smart glasses, or other means. The notification will be delivered in a gentle yet effective manner, using language that is sensitive to the user's emotions. 【0753】 As a concrete example, in one smart city, residents can receive notifications on their smartphones such as, "Electricity consumption is trending upwards this week, but why not set aside some time to relax this weekend?" This is expected to allow residents to manage their energy consumption while remaining mindful of their energy needs and without compromising their quality of life. 【0754】 An example of a prompt for a generating AI model is: "Generate advice for smart city residents based on recent electricity consumption trends and residents' emotional states." 【0755】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0756】 Step 1: 【0757】 The terminal collects power usage information in real time from each sensor. The collected information includes power consumption and usage time, and by adding a timestamp, it accurately records when and how much power was consumed. Sending this data to the server is the input. 【0758】 The output will be the raw data of the power usage information that the server receives. 【0759】 Step 2: 【0760】 The server filters the received power usage information using data preprocessing. Specifically, it uses the Pandas library to standardize and denoise the dataset, and detects and removes outliers. This process ensures that the data is in a clean format suitable for analysis. 【0761】 The input is the raw power usage data transmitted in step 1, and the output is a clean dataset with outliers removed. 【0762】 Step 3: 【0763】 The server uses the cleaned dataset to begin data calculations for time series analysis. It uses the ARIMA model from the statsmodels library to extract trends and seasonality in power consumption and predict future power consumption. 【0764】 The input is clean electricity usage data, and the output generates predictive data on future electricity consumption trends. 【0765】 Step 4: 【0766】 The server detects anomalies based on predicted trends. If an anomaly is detected, an alert generation mechanism is activated, and a notification is generated for the user. This notification not only informs the user of the anomaly, but also uses sentiment data analysis to create wording that takes the user's emotional state into consideration. 【0767】 The input consists of prediction data and sentiment data from step 3, and the output is a notification message for the user. 【0768】 Step 5: 【0769】 Users receive generated notifications to get advice on power consumption status and any anomalies. These notifications can be received via smartphones or smart glasses and are presented in a way that suits the user's emotional state, making them more receptive to receiving. 【0770】 The input is the notification message created in step 4, and the output is the impact on the user's behavior and emotions. 【0771】 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. 【0772】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0773】 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. 【0774】 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. 【0775】 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. 【0776】 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. 【0777】 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. 【0778】 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. 【0779】 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." 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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. 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference. 【0792】 The following is further disclosed regarding the embodiments described above. 【0793】 (Claim 1) 【0794】 A data collection method for acquiring electricity usage information in real time, 【0795】 A data preprocessing means that organizes the power usage information obtained by the data collection means and detects abnormal values, 【0796】 An analysis means for predicting trends in economic activity, which analyzes the power usage information processed by the aforementioned data preprocessing means, 【0797】 Based on the analysis results obtained by the aforementioned analysis means, an alert generation means detects abnormal economic activity and generates an alert, 【0798】 A report generation means that automatically generates a report summarizing the prediction results of the aforementioned analysis means, 【0799】 A system that includes this. 【0800】 (Claim 2) 【0801】 The system according to claim 1, characterized in that the analysis means extracts trends and seasonality of power usage information using a time series analysis model. 【0802】 (Claim 3) 【0803】 The system according to claim 1, characterized in that the alert generation means sends a notification via email or to a mobile device when an anomaly is detected. 【0804】 "Example 1" 【0805】 (Claim 1) 【0806】 A device that collects electricity usage data in real time, 【0807】 A processing device that organizes the power usage data obtained by the aforementioned device and detects abnormal data, 【0808】 A computing device that analyzes the power data cleaned by the aforementioned processing device and predicts trends in economic activity, 【0809】 A warning device that detects abnormal economic trends and generates warnings based on the analysis results obtained by the aforementioned computing device, 【0810】 A report generation device that automatically forms a report by aggregating the prediction results of the aforementioned computing device, 【0811】 A system that includes this. 【0812】 (Claim 2) 【0813】 The system according to claim 1, characterized in that the computing device extracts trends and seasonal variations in power data using a time series analysis method. 【0814】 (Claim 3) 【0815】 The system according to claim 1, characterized in that the warning device notifies an electronic communication or mobile communication terminal when an abnormality is detected. 【0816】 "Application Example 1" 【0817】 (Claim 1) 【0818】 A means of acquiring information to obtain power consumption information over time, 【0819】 Information preprocessing means for organizing power consumption information obtained by the information acquisition means and detecting abnormalities, 【0820】 An analysis means for predicting trends in economic activity, which analyzes the power consumption information processed by the aforementioned information preprocessing means, 【0821】 Based on the analysis results obtained by the aforementioned analysis means, a warning generation means detects abnormal economic activity and generates a warning, 【0822】 A document generation means that automatically generates a document summarizing the prediction results of the analysis means, 【0823】 A visualization method that visualizes electricity consumption information in urban areas and displays energy consumption trends by region, 【0824】 A means of proposing energy efficiency improvements to residents and management organizations, 【0825】 A system that includes this. 【0826】 (Claim 2) 【0827】 The system according to claim 1, characterized in that the analysis means evaluates the trends and periodicity of power consumption information using a time series analysis method. 【0828】 (Claim 3) 【0829】 The system according to claim 1, characterized in that the warning generation means notifies a communication device or mobile terminal when an abnormality is detected. 【0830】 "Example 2 of combining an emotion engine" 【0831】 (Claim 1) 【0832】 A means of collecting information to acquire electricity usage information in real time, 【0833】 Information preprocessing means for organizing power usage information obtained by the information gathering means and detecting abnormal values, 【0834】 An information analysis means for predicting trends in economic activity, which analyzes the power usage information processed by the aforementioned information preprocessing means, 【0835】 Based on the analysis results obtained by the aforementioned information analysis means, an alert generation means detects abnormal economic activity and generates an alert, 【0836】 An emotion response means that integrates the prediction results of the information analysis means with the user's emotion information and sends an alert in the optimal format, 【0837】 A report generation means that automatically generates a report summarizing the prediction results of the aforementioned information analysis means, 【0838】 A system that includes this. 【0839】 (Claim 2) 【0840】 The system according to claim 1, characterized in that the information analysis means extracts trends and periodicities of power usage information using a time series analysis model. 【0841】 (Claim 3) 【0842】 The system according to claim 1, characterized in that the alert generation means provides notification using electronic communication means when an anomaly is detected. 【0843】 "Application example 2 when combining with an emotional engine" 【0844】 (Claim 1) 【0845】 A data collection method for acquiring electricity usage information in real time, 【0846】 A data preprocessing means that organizes the power usage information obtained by the data collection means and detects abnormal values, 【0847】 An analysis means for predicting trends in economic activity, which analyzes the power usage information processed by the aforementioned data preprocessing means, 【0848】 Based on the analysis results obtained by the aforementioned analysis means, an alert generation means detects abnormal economic activity and generates an alert, 【0849】 A report generation means that automatically generates a report summarizing the prediction results of the aforementioned analysis means, 【0850】 A means of analyzing emotion data to adjust the wording of notifications according to the user's emotional state, 【0851】 A system that includes this. 【0852】 (Claim 2) 【0853】 The system according to claim 1, characterized in that the analysis means extracts trends and seasonality of power usage information using a time series analysis model. 【0854】 (Claim 3) 【0855】 The system according to claim 1, wherein the alert generation means is characterized by notifying an electronic communication device when an anomaly is detected, and further, the emotion data analysis means provides a notification that includes considerations tailored to the user's emotions. [Explanation of symbols] 【0856】 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 data collection method for acquiring electricity usage information in real time, A data preprocessing means that organizes the power usage information obtained by the data collection means and detects abnormal values, An analysis means for predicting trends in economic activity, which analyzes the power usage information processed by the aforementioned data preprocessing means, Based on the analysis results obtained by the aforementioned analysis means, an alert generation means detects abnormal economic activity and generates an alert, A report generation means that automatically generates a report summarizing the prediction results of the aforementioned analysis means, A system that includes this. [Claim 2] The system according to claim 1, characterized in that the analysis means extracts trends and seasonality of power usage information using a time series analysis model. [Claim 3] The system according to claim 1, characterized in that the alert generation means sends a notification via email or to a mobile device when an anomaly is detected.

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