system

Abstract

We provide the system. [Solution] A data storage means that receives multiple environmental data acquired from a data acquisition device and records them, A data processing means for pre-processing stored data, A prediction model generation means that generates a disaster prediction model using preprocessed data, An energy analysis means that identifies energy consumption patterns using preprocessed data, Based on the aforementioned prediction model and energy consumption patterns, a planning support means is provided to assist in formulating regional development plans. A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the context of the need for sustainable development of regions and rapid response to disasters, it is important to establish effective data collection and analysis methods. However, in conventional technologies, there are many fragmentary pieces of information in the processes of data collection, analysis, prediction, and utilization, and there is a problem that it is difficult to manage comprehensively and efficiently. In particular, in disaster-resistant city construction and optimal energy utilization, these problems have become urgent issues to be solved.

Means for Solving the Problems

[0005] This invention provides a data storage means for receiving and recording environmental data from a data collection device. It also incorporates a prediction model generation means for preprocessing the stored data and generating a disaster prediction model based on it. Furthermore, it includes an energy analysis means for identifying energy consumption patterns and a planning support means for supporting regional development plans based on these analysis results, thereby efficiently solving the aforementioned problems. This realizes a system that enables the creation of disaster-resilient cities and the optimization of energy use.

[0006] A "data collection device" is a device that includes multiple sensing devices installed to acquire environmental information.

[0007] "Environmental data" refers to data that indicates the physical and natural conditions of a region, such as weather, earthquakes, and wind speed.

[0008] "Data storage means" refers to equipment or configuration for recording collected data and storing it in a searchable format.

[0009] "Data processing means" refers to a method or apparatus for correcting incomplete data and shaping it into an analyzable state.

[0010] A "predictive model generation method" is a means for constructing a model that predicts future situations based on past data.

[0011] "Energy analysis methods" are means for identifying trends and patterns in energy consumption and deriving efficient utilization methods.

[0012] "Planning support means" refers to functions or devices that support the formulation of policies and plans based on collected and analyzed data.

[0013] A "display device" is a device used to present information visually. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a tagged 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.

[0018] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a tagged 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, and the like.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system that combines AI technology and big data to promote regional safety and sustainable development. Its main components consist of data collection devices, servers, terminals, and users, and comprehensive urban planning is achieved through communication between these devices.

[0036] First, the data acquisition device is equipped with multiple sensing devices, which are used to acquire environmental data such as rain gauges, seismometers, and anemometers. The collected data is transmitted to a server via the network.

[0037] The server stores the received environmental data and uses data processing tools to correct for outliers and handle missing values. Based on the processed data, an AI algorithm is applied to generate a disaster prediction model. This allows for the identification of areas with a high probability of disaster and the issuance of warnings to users.

[0038] Furthermore, the server uses energy analysis tools to analyze energy consumption data within the region and propose an optimal energy use plan. This promotes improved energy efficiency and ensures availability in the region.

[0039] Users can view analysis results provided by the server through a dashboard, and regional development managers can use this information to formulate disaster prevention plans and infrastructure development plans. Furthermore, users can provide feedback, and this information is also stored on the server, contributing to the overall improvement of the system.

[0040] The terminals function as an interface with local residents and businesses, not only providing necessary information but also playing a role in transmitting information from them to the server. For example, residents can receive evacuation information in real time during disasters through the terminals and report their safety status to the server.

[0041] One specific example is a system that quickly issues evacuation advisories for high-risk areas. When heavy rain is predicted, the server analyzes the collected data and immediately notifies users living in the affected area, enabling a rapid response. This helps mitigate damage and ensure the safety of local residents.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives environmental data transmitted from each data collection device and records it in a database. This includes timestamped data from each device.

[0045] Step 2:

[0046] The server preprocesses the recorded data using data processing tools. This process includes removing outliers, imputing missing data, and normalizing the data.

[0047] Step 3:

[0048] The server inputs pre-processed data into an AI algorithm to generate a disaster prediction model. This model is then compared with historical disaster data to improve its accuracy and perform a risk assessment for a specific area.

[0049] Step 4:

[0050] The server analyzes energy consumption data to identify peak and surplus energy patterns. This analysis utilizes machine learning techniques to propose efficient energy use plans.

[0051] Step 5:

[0052] Users view the analysis results provided by the server on a dashboard. This allows users to understand the local situation and make decisions regarding disaster prevention plans and energy use plans.

[0053] Step 6:

[0054] The terminal provides local residents with information on disaster predictions and energy planning. Furthermore, it receives feedback and emergency reports from residents and transmits them to a server.

[0055] Step 7:

[0056] Based on the information provided, users take specific actions and provide feedback via their devices as needed. This feedback is used for the continuous improvement of the system.

[0057] (Example 1)

[0058] 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."

[0059] In recent years, there has been a growing need for systems that can balance regional safety and sustainable development. However, methods for comprehensively analyzing environmental information and resource consumption to simultaneously achieve disaster prediction and efficient resource utilization are not yet well established. This challenge highlights the increasing need for systems that provide rapid information and support decision-making.

[0060] 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.

[0061] In this invention, the server includes an information holding means for recording information acquired from a data collection means, a data processing means for pre-processing the stored information, and a predictive model generation means for generating a predictive model based on the pre-processed information. This enables integrated disaster prediction and resource consumption analysis, facilitating rapid regional safety assurance and efficient resource utilization.

[0062] A "data collection means" is a device configured to acquire environmental information using multiple sensors and transmit it to a server.

[0063] "Information retention means" refers to means for recording environmental information obtained from data collection means so that it can be used for subsequent processing and analysis.

[0064] "Data processing means" refers to means of performing pre-processing on information stored in an information storage means, such as correcting outliers or supplementing missing information.

[0065] A "predictive model generation method" is a means for generating a disaster prediction model based on pre-processed environmental information, and utilizes technologies such as generation AI models.

[0066] "Resource analysis tools" are means of analyzing data to identify patterns in resource consumption within a region and to promote optimal resource utilization.

[0067] "Planning support measures" refer to means for formulating and supporting regional development plans based on disaster prediction models and resource consumption patterns.

[0068] "Communication means" refers to means for notifying users of information and receiving information from users, and includes communication via terminals.

[0069] This invention provides a system that integrates AI technology and big data to promote regional safety and sustainable development. A server plays a central role, receiving environmental information from multiple sensors through data collection means. The sensors include devices that measure rainfall, wind speed, and seismic motion. This information is stored in the server by information storage means.

[0070] The server preprocesses the information using data processing tools. Specifically, it corrects outliers and fills in missing information. Next, based on the preprocessed information, it generates a disaster prediction model using a generative AI model. The AI ​​algorithm predicts, for example, heavy rain and strong winds, and notifies nearby residents of the results.

[0071] Furthermore, the server utilizes resource analysis tools to identify resource consumption patterns within a region. This makes it possible to formulate efficient energy utilization plans. For example, it can reduce the overall power load by proposing reallocation during peak power consumption in a particular region.

[0072] The terminal acts as an interface with the user, providing information transmitted from the server to local residents and businesses in real time. In emergencies, it displays evacuation routes and evacuation orders, and users can report on their safety status. Furthermore, feedback information obtained through the terminal is used to further improve the system.

[0073] An example of a prompt is, "Generate predictions of natural disasters that may occur in the next 48 hours and perform a risk assessment." By using such prompts to operate a generation AI model, it is possible to simultaneously promote regional safety and optimal resource utilization.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The server receives a wide range of environmental information from data collection devices. Inputs include raw data provided by sensors such as rain gauges, seismometers, and anemometers. The server records this data in a database using information storage devices. This record forms the basis for subsequent data processing and analysis.

[0077] Step 2:

[0078] The server retrieves the raw data stored in the information storage means and performs preprocessing using the data processing means. The input is the data recorded in step 1. Specific operations include detecting and correcting outliers and imputing missing data. This prepares the data for analysis, generating a clean dataset as output.

[0079] Step 3:

[0080] The server launches a generative AI model using preprocessed data to generate a disaster prediction model. The input is the clean dataset from step 2. This model calculates the probability of a disaster occurring based on historical data. The output provides a risk assessment for a specific area and warning information based on that assessment.

[0081] Step 4:

[0082] The server analyzes regional resource consumption patterns using resource analysis tools. Input includes historical data on electricity consumption and water usage. The analysis generates specific suggestions for improving energy efficiency. The output is a proposed plan for leveling out peak electricity usage.

[0083] Step 5:

[0084] The terminal notifies local residents and businesses of information from the server. The input is the output data from steps 3 and 4. Specifically, it displays evacuation advisories based on disaster predictions and suggestions for optimizing resource use in real time. This allows users to make quick decisions.

[0085] Step 6:

[0086] Users provide feedback to the server through their terminals. The input consists of reports and opinions from users. The server collects this feedback to improve the system and enhance its accuracy. The output provides reference data for system adjustments.

[0087] (Application Example 1)

[0088] 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."

[0089] In recent years, rapid environmental changes and the increasing frequency of natural disasters have made ensuring safe communities a critical issue. Current disaster prevention systems rely on individual data collection, making rapid and accurate disaster prediction and energy management difficult. Furthermore, insufficient information provision to residents and users hinders rapid evacuation and safety assurance. A comprehensive system is needed to address these challenges and improve community safety.

[0090] 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.

[0091] In this invention, the server includes data storage means, data processing means, predictive model generation means, energy analysis means, planning support means, and notification means. This enables highly accurate disaster prediction based on local environmental data and efficient energy management. Furthermore, real-time information provision based on the user's location information can support rapid evacuation and safety measures.

[0092] "Data storage means" refers to a device or method for receiving environmental data acquired from a data collection device and recording it.

[0093] "Data processing means" refers to a device or method that performs preprocessing on stored data, including the correction of outliers and the handling of missing values.

[0094] "Predictive model generation means" refers to an apparatus or method for generating a disaster prediction model using pre-processed data.

[0095] "Energy analysis means" refers to an apparatus or method for identifying energy consumption patterns based on pre-processed data.

[0096] "Planning support means" refers to a device or method for supporting the formulation of regional development plans based on generated predictive models and energy consumption patterns.

[0097] "Notification means" refers to a device or method for providing disaster-related information in real time based on the user's location information.

[0098] This system consists of a data acquisition device, a server, terminals, and users. The data acquisition device includes multiple sensing devices that acquire environmental data. Specifically, measuring instruments such as rain gauges, seismometers, and anemometers are installed, and the data obtained from these devices is transmitted to the server via the network.

[0099] The server records the received environmental data using data storage means. Next, this data is preprocessed using data processing means. This includes correcting for outliers and imputing missing values ​​in the collected data. Then, based on the preprocessed data, a prediction model generation means is used to generate a disaster prediction model. The model uses advanced AI algorithms and predicts future disaster risks based on pattern analysis of the data.

[0100] Furthermore, energy analysis tools are used to identify energy consumption patterns. These tools analyze energy consumption data within the region and provide an optimal energy use plan. The planning support tools provide information to support regional development and infrastructure planning based on the aforementioned predictive models and energy consumption patterns.

[0101] The terminal functions as an interface with the user, visualizing and providing the user with the results of acquired disaster predictions and energy analysis. The notification system uses the user's location information to issue real-time disaster warnings and provide appropriate evacuation information.

[0102] As a concrete example, when a user is using their smartphone, the system detects a heavy rain forecast and immediately sends a notification saying, "Heavy rain is forecast. Please evacuate to a safe place." Such notifications are automatically generated based on the results of information analysis from the server.

[0103] Regarding the generated AI model and prompt statements, this system operates as follows:

[0104] When a user is using the emergency disaster notification app, if their current location is Tokyo, the app can provide timely information based on heavy rain warnings from the server.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The data collection device acquires various environmental data from devices such as rain gauges, seismometers, and anemometers. This data is transmitted to a server via a network. The input is the latest measurement value from each sensing device, and the output is the transmitted, unprocessed environmental data.

[0108] Step 2:

[0109] The server records the received environmental data using a data storage device. The input is the unprocessed data sent in the previous step, and the output is the stored data. Specifically, the data is stored in a recording medium such as a database.

[0110] Step 3:

[0111] The server preprocesses the stored data using data processing techniques. This includes correcting outliers and imputing missing values. The input is the stored data, and the output is the preprocessed, clean data. Specifically, it replaces outliers with certain baseline values ​​and imputs missing data using time-series forecasting.

[0112] Step 4:

[0113] The server generates a disaster prediction model using preprocessed data and a predictive model generation mechanism. The input is clean data, and the output is a predictive model showing disaster risk. An AI algorithm is used to analyze data patterns and quantify the likelihood of a disaster.

[0114] Step 5:

[0115] The server analyzes pre-processed data using energy analysis tools to identify energy consumption patterns. The input is clean data, and the output is the analysis result showing energy consumption patterns within a region. Here, time series analysis and statistical methods are used to identify trends and anomalies in energy use.

[0116] Step 6:

[0117] The server provides information for regional development and infrastructure planning using planning support tools based on the generated predictive models and energy consumption patterns. The inputs are predictive models and energy consumption patterns, and the output is a display of information for planning. This supports sustainable development in the region.

[0118] Step 7:

[0119] The terminal provides users with real-time disaster information using notification methods. Input is disaster prediction data from a server, and output is disaster warnings and evacuation information in a format visible to the user. Specifically, it sends notifications to smartphones and other devices, and displays the information on the interface.

[0120] 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.

[0121] This invention is a system for ensuring local safety and sustainability, and in particular, it supports the development of development plans that take user emotions into consideration. The system consists of a data collection device, a server, an emotion engine, a terminal, and a user.

[0122] The data collection device is equipped with multiple sensing devices to acquire environmental data. The sensing devices collect various types of information, including weather data and energy consumption data, and this data is transmitted to a server via the network.

[0123] The server records the received environmental data and formats it using data processing tools to enable proper analysis. This data is then used to generate a disaster prediction model and identify regional risk areas. Furthermore, energy consumption patterns are analyzed using energy analysis tools to create an efficient energy use plan.

[0124] The emotion engine collects emotional data from user feedback and survey results to recognize the user's emotional state. The server takes in the emotional data obtained from the emotion engine and analyzes the user's needs and frustrations. This information is used to improve the proposed regional development plan.

[0125] Users can view analysis results provided by the server through a dashboard, and regional development managers can formulate disaster prevention plans and infrastructure development plans based on sentiment data and local situation data. Furthermore, suggestions for improving communication and awareness-raising activities are provided based on sentiment data.

[0126] The terminals function as an interface with local residents, providing information on disaster predictions and energy planning. Residents can use the terminals to provide real-time feedback to the system, which allows the emotion engine to function effectively.

[0127] As a concrete example, in areas with a high risk of disaster, emotional data regarding users' fears and anxieties is collected. Based on this data, the server can enhance suggestions for rapid evacuation measures and customize the provision of information by type to promote a sense of security. This improves residents' sense of security and leads to the implementation of more effective local planning.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server receives environmental data transmitted from each data collection device and stores it in a database. This data includes measurements such as temperature, humidity, and wind speed.

[0131] Step 2:

[0132] The server preprocesses the stored environmental data using data processing tools. Here, data in different formats is unified, and missing data is corrected, preparing the data for analysis.

[0133] Step 3:

[0134] The server uses pre-processed data to run an AI algorithm and generate a disaster prediction model. This model can quantify and visualize the disaster risk in a specific area.

[0135] Step 4:

[0136] The server uses energy analysis tools to analyze energy consumption data for the entire region and generates reports showing peak demand and optimized energy allocation.

[0137] Step 5:

[0138] The emotion engine analyzes user feedback and survey data obtained from the device to recognize the user's emotional state (e.g., anxiety or reassurance). This data is updated in real time.

[0139] Step 6:

[0140] The server takes emotional data from the emotion engine, integrates it with environmental data and disaster prediction models, and adjusts regional development plans. In particular, it creates plan proposals that take into account the emotional state of users.

[0141] Step 7:

[0142] Users can view analysis results and sentiment data provided by the server through a dashboard. This allows users to gain a deeper understanding of local safety and efficient energy use.

[0143] Step 8:

[0144] The terminal notifies local residents of analysis results, disaster predictions, energy plans, and suggestions based on user sentiment. Furthermore, it contributes to continuous system improvement by providing an interface for residents to provide feedback.

[0145] (Example 2)

[0146] 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 as the "terminal".

[0147] When formulating regional safety and sustainable development plans, there is a lack of means to reflect not only environmental data but also the feelings and needs of residents. Therefore, traditional planning methods may not adequately consider local conditions and the wishes of residents.

[0148] 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.

[0149] In this invention, the server includes information storage means, information processing means, prediction generation means, energy analysis means, sentiment analysis means, and plan improvement support means. This makes it possible to support the formulation of development plans suitable for the region based on both environmental data and residents' sentiment data.

[0150] "Information storage means" refers to a function for recording environmental data acquired from a data collection device and efficiently storing and managing it as needed.

[0151] "Information processing means" refers to functions that preprocess stored data and prepare it in a format suitable for analysis and model generation.

[0152] The "prediction generation means" is a function that generates a disaster prediction model based on pre-processed data and identifies regional risks.

[0153] An "energy analysis tool" is a function that analyzes energy consumption patterns based on pre-processed data and provides information for efficient energy use.

[0154] "Emotional analysis tools" are functions that analyze emotional data based on feedback collected from users to understand the emotional state and needs of local residents.

[0155] "Plan improvement support tools" are support functions for improving and proposing regional development plans based on predictive models, energy consumption patterns, and sentiment data.

[0156] This invention is a system that supports the development of development plans that improve local safety and sustainability. The embodiments of the system are described in detail below.

[0157] The server receives environmental data from multiple data acquisition devices. These devices include weather sensors and energy meters, which acquire data such as temperature, humidity, wind direction, and energy consumption in real time. The server records this data using information storage means. The recorded data is preprocessed by information processing means and converted into a format suitable for disaster prediction and energy consumption pattern analysis.

[0158] The server generates a disaster prediction model using pre-processed data via a prediction generation method and analyzes energy consumption data using an energy analysis method. This enables the identification of regional risk areas and the proposal of efficient energy use plans.

[0159] The sentiment analysis tool analyzes sentiment data based on feedback and survey results provided by users through their terminals. The analysis results are sent to a server, where the plan improvement support tool refines the proposed development plan to better reflect the emotional state and needs of the residents.

[0160] The terminals play a crucial role in providing local residents with visualized disaster prediction data and energy consumption data. Users use this information to provide feedback on local development plans and disaster prevention activities, which in turn facilitates effective sentiment analysis. This feedback loop enhances residents' sense of security and improves the effectiveness of local development plans.

[0161] As a concrete example, consider the application of the system in areas with a high risk of disaster. Based on user feedback regarding fear and anxiety, the server will enhance its suggestions for rapid evacuation information. Furthermore, it aims to improve residents' sense of security by providing customized information to promote a sense of reassurance. An example of a prompt message to the generating AI model in this system would be, "Generate appropriate evacuation plans and energy efficiency suggestions through local disaster prediction data and resident sentiment analysis."

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server receives environmental data transmitted from data collection devices installed in the region. This data includes information such as temperature, humidity, and wind speed from weather sensors, and power consumption obtained from energy meters. The received data is stored in a time-series database by an information storage system. The input is environmental data, and the output is an organized dataset.

[0165] Step 2:

[0166] The server preprocesses the accumulated environmental data using information processing tools. Specific operations include imputing missing values, removing outliers, and converting data formats. The preprocessed data is output as cleaned data suitable for analysis.

[0167] Step 3:

[0168] The server uses pre-processed data to create a disaster prediction model using a prediction generation mechanism. Specifically, it applies statistical analysis and machine learning algorithms to predict the likelihood of a disaster. The output of this step is a predictive model showing the risk level for each region.

[0169] Step 4:

[0170] The server inputs preprocessed data into an energy analysis system to analyze energy consumption patterns. This involves statistical analysis to identify peak energy demand times and specific usage patterns. The output is the analysis results for formulating an optimal energy consumption strategy.

[0171] Step 5:

[0172] Users input their emotions through feedback and surveys via their devices. This data is sent to a server and processed by sentiment analysis tools. Specifically, natural language processing technology is used to classify emotions into categories such as positive and negative. The output is sentiment data that reflects the user's emotional state.

[0173] Step 6:

[0174] The server proposes a regional development plan using plan improvement support tools, based on the generated predictive models, energy consumption analysis results, and sentiment data. At this stage, different data sources are integrated to develop a customized plan tailored to the needs of the local residents. The output of this step is the proposed improved regional development plan.

[0175] Step 7:

[0176] The terminal displays disaster predictions, energy consumption information, and improved planning suggestions to the user. This information is presented in a dashboard format for intuitive understanding. Users can provide real-time feedback based on this information, contributing further sentiment data to the system. The output is updated information tailored to the user's feedback.

[0177] (Application Example 2)

[0178] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0179] To ensure the safety and sustainability of a region, regional development plans that consider the emotional state of residents, along with environmental data, are necessary. Conventional systems have only formulated development plans based on environmental data and have not adequately incorporated residents' emotions, resulting in difficulties in providing safety information and evacuation plans that enhance residents' sense of security.

[0180] 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.

[0181] In this invention, the server includes data storage means, data processing means, sentiment analysis means, and information provision means. This makes it possible to provide local risk information and personalize evacuation alerts using both environmental data and residents' sentiment data.

[0182] "Data storage means" refers to a device or function that has the function of recording multiple environmental data received from a data collection device.

[0183] "Data processing means" refers to a device or function that has the function of formatting stored data so that it can be properly analyzed.

[0184] "Predictive model generation means" refers to a device or function that has the function of generating a disaster prediction model based on pre-processed data.

[0185] "Energy analysis means" refers to a device or function that has the function of identifying energy consumption patterns based on pre-processed data.

[0186] "Planning support means" refers to a device or function that has the function of supporting the formulation of regional development plans based on predictive models and energy consumption patterns.

[0187] "Emotional analysis means" refers to a device or function that collects and analyzes emotional data to recognize the emotional state of residents and use that information to provide safety information.

[0188] "Information provision means" refers to a device or function that has the function of providing personalized safety information to residents.

[0189] The system for carrying out this invention includes a data collection device, a server, an emotion engine, and a user terminal. The data collection device includes multiple sensors that acquire weather data and energy consumption data, which are transmitted to the server via a network. The server utilizes data analysis using Python, emotion recognition using TENSORFLOW®, a backend using Flask, and a mobile frontend using React Native.

[0190] The server records the received environmental data in a data storage device and formats it into an analyzable form using a data processing device. Then, using the formatted data, a disaster prediction model is generated by a prediction model generation device, and energy consumption patterns are analyzed by an energy analysis device. Furthermore, the emotion engine collects emotion data based on feedback and surveys from residents, and this is interpreted by an emotion analysis device. The analyzed data is used by a planning support device to propose regional development plans and identify areas for improvement.

[0191] User terminals provide residents with personalized safety information in real time. For example, if the risk of disaster increases in a certain area and residents are feeling anxious, the server will immediately send evacuation alerts and safety information appropriate to the situation to the terminals, thereby improving residents' sense of security.

[0192] An example of a prompt might be, "Please suggest how AI technology can improve the way we provide evacuation information to residents based on local disaster prediction models." Thus, providing and integrating information based on emotional and environmental data is crucial.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server receives environmental data transmitted from the data acquisition device and stores it in the data storage means. At this point, the inputs are weather data and energy consumption data, and the output is a record of the organized environmental data.

[0196] Step 2:

[0197] The server uses data processing tools to format the received environmental data for analysis. The input is raw data stored on the server, and the output is data converted into an analyzable format. Specifically, this involves data format conversion and the imputation of missing values.

[0198] Step 3:

[0199] The server constructs a disaster prediction model using a predictive model generation mechanism with formatted environmental data. The input is formatted environmental data, and the output is the disaster prediction model. In this process, data patterns are learned using machine learning algorithms.

[0200] Step 4:

[0201] The server identifies energy consumption patterns using energy analysis tools. The input is formatted energy data, and the output is the analysis result of the consumption pattern. Specifically, it performs time-series analysis and anomaly detection.

[0202] Step 5:

[0203] The server collects emotional data from users using an emotion engine and analyzes it using emotion analysis tools. Inputs are user feedback and survey results, and outputs are analysis results indicating the emotional state. Emotions are evaluated using natural language processing techniques and AI models.

[0204] Step 6:

[0205] The server, through planning support mechanisms, develops regional development plans based on emotional and environmental data. Inputs include disaster prediction models, energy consumption patterns, and emotional analysis results, while output is the improved and proposed development plan. This enables appropriate, region-specific support.

[0206] Step 7:

[0207] The terminal displays safety information provided by the server to residents. The input is personalized safety information from the server, and the output is information presented in an easy-to-understand format for the user. This allows users to easily obtain and check relevant safety information in real time.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] This invention is a system that combines AI technology and big data to promote regional safety and sustainable development. Its main components consist of data collection devices, servers, terminals, and users, and comprehensive urban planning is achieved through communication between these devices.

[0225] First, the data acquisition device is equipped with multiple sensing devices, which are used to acquire environmental data such as rain gauges, seismometers, and anemometers. The collected data is transmitted to a server via the network.

[0226] The server stores the received environmental data and uses data processing tools to correct for outliers and handle missing values. Based on the processed data, an AI algorithm is applied to generate a disaster prediction model. This allows for the identification of areas with a high probability of disaster and the issuance of warnings to users.

[0227] Furthermore, the server uses energy analysis tools to analyze energy consumption data within the region and propose an optimal energy use plan. This promotes improved energy efficiency and ensures availability in the region.

[0228] Users can view analysis results provided by the server through a dashboard, and regional development managers can use this information to formulate disaster prevention plans and infrastructure development plans. Furthermore, users can provide feedback, and this information is also stored on the server, contributing to the overall improvement of the system.

[0229] The terminals function as an interface with local residents and businesses, not only providing necessary information but also playing a role in transmitting information from them to the server. For example, residents can receive evacuation information in real time during disasters through the terminals and report their safety status to the server.

[0230] One specific example is a system that quickly issues evacuation advisories for high-risk areas. When heavy rain is predicted, the server analyzes the collected data and immediately notifies users living in the affected area, enabling a rapid response. This helps mitigate damage and ensure the safety of local residents.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The server receives environmental data transmitted from each data collection device and records it in a database. This includes timestamped data from each device.

[0234] Step 2:

[0235] The server preprocesses the recorded data using data processing tools. This process includes removing outliers, imputing missing data, and normalizing the data.

[0236] Step 3:

[0237] The server inputs pre-processed data into an AI algorithm to generate a disaster prediction model. This model is then compared with historical disaster data to improve its accuracy and perform a risk assessment for a specific area.

[0238] Step 4:

[0239] The server analyzes energy consumption data to identify peak and surplus energy patterns. This analysis utilizes machine learning techniques to propose efficient energy use plans.

[0240] Step 5:

[0241] Users view the analysis results provided by the server on a dashboard. This allows users to understand the local situation and make decisions regarding disaster prevention plans and energy use plans.

[0242] Step 6:

[0243] The terminal provides local residents with information on disaster predictions and energy planning. Furthermore, it receives feedback and emergency reports from residents and transmits them to a server.

[0244] Step 7:

[0245] Based on the information provided, users take specific actions and provide feedback via their devices as needed. This feedback is used for the continuous improvement of the system.

[0246] (Example 1)

[0247] 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".

[0248] In recent years, there has been a growing need for systems that can balance regional safety and sustainable development. However, methods for comprehensively analyzing environmental information and resource consumption to simultaneously achieve disaster prediction and efficient resource utilization are not yet well established. This challenge highlights the increasing need for systems that provide rapid information and support decision-making.

[0249] 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.

[0250] In this invention, the server includes an information holding means for recording information acquired from a data collection means, a data processing means for pre-processing the stored information, and a predictive model generation means for generating a predictive model based on the pre-processed information. This enables integrated disaster prediction and resource consumption analysis, facilitating rapid regional safety assurance and efficient resource utilization.

[0251] A "data collection means" is a device configured to acquire environmental information using multiple sensors and transmit it to a server.

[0252] "Information retention means" refers to means for recording environmental information obtained from data collection means so that it can be used for subsequent processing and analysis.

[0253] "Data processing means" refers to means of performing pre-processing on information stored in an information storage means, such as correcting outliers or supplementing missing information.

[0254] A "predictive model generation method" is a means for generating a disaster prediction model based on pre-processed environmental information, and utilizes technologies such as generation AI models.

[0255] "Resource analysis tools" are means of analyzing data to identify patterns in resource consumption within a region and to promote optimal resource utilization.

[0256] "Planning support measures" refer to means for formulating and supporting regional development plans based on disaster prediction models and resource consumption patterns.

[0257] "Communication means" refers to means for notifying users of information and receiving information from users, and includes communication via terminals.

[0258] This invention provides a system that integrates AI technology and big data to promote regional safety and sustainable development. A server plays a central role, receiving environmental information from multiple sensors through data collection means. The sensors include devices that measure rainfall, wind speed, and seismic motion. This information is stored in the server by information storage means.

[0259] The server preprocesses the information using data processing tools. Specifically, it corrects outliers and fills in missing information. Next, based on the preprocessed information, it generates a disaster prediction model using a generative AI model. The AI ​​algorithm predicts, for example, heavy rain and strong winds, and notifies nearby residents of the results.

[0260] Furthermore, the server utilizes resource analysis tools to identify resource consumption patterns within a region. This makes it possible to formulate efficient energy utilization plans. For example, it can reduce the overall power load by proposing reallocation during peak power consumption in a particular region.

[0261] The terminal acts as an interface with the user, providing information transmitted from the server to local residents and businesses in real time. In emergencies, it displays evacuation routes and evacuation orders, and users can report on their safety status. Furthermore, feedback information obtained through the terminal is used to further improve the system.

[0262] An example of a prompt is, "Generate predictions of natural disasters that may occur in the next 48 hours and perform a risk assessment." By using such prompts to operate a generation AI model, it is possible to simultaneously promote regional safety and optimal resource utilization.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server receives a wide range of environmental information from data collection devices. Inputs include raw data provided by sensors such as rain gauges, seismometers, and anemometers. The server records this data in a database using information storage devices. This record forms the basis for subsequent data processing and analysis.

[0266] Step 2:

[0267] The server retrieves the raw data stored in the information storage means and performs preprocessing using the data processing means. The input is the data recorded in step 1. Specific operations include detecting and correcting outliers and imputing missing data. This prepares the data for analysis, generating a clean dataset as output.

[0268] Step 3:

[0269] The server launches a generative AI model using preprocessed data to generate a disaster prediction model. The input is the clean dataset from step 2. This model calculates the probability of a disaster occurring based on historical data. The output provides a risk assessment for a specific area and warning information based on that assessment.

[0270] Step 4:

[0271] The server analyzes regional resource consumption patterns using resource analysis tools. Input includes historical data on electricity consumption and water usage. The analysis generates specific suggestions for improving energy efficiency. The output is a proposed plan for leveling out peak electricity usage.

[0272] Step 5:

[0273] The terminal notifies local residents and businesses of information from the server. The input is the output data from steps 3 and 4. Specifically, it displays evacuation advisories based on disaster predictions and suggestions for optimizing resource use in real time. This allows users to make quick decisions.

[0274] Step 6:

[0275] Users provide feedback to the server through their terminals. The input consists of reports and opinions from users. The server collects this feedback to improve the system and enhance its accuracy. The output provides reference data for system adjustments.

[0276] (Application Example 1)

[0277] 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."

[0278] In recent years, rapid environmental changes and the increasing frequency of natural disasters have made ensuring safe communities a critical issue. Current disaster prevention systems rely on individual data collection, making rapid and accurate disaster prediction and energy management difficult. Furthermore, insufficient information provision to residents and users hinders rapid evacuation and safety assurance. A comprehensive system is needed to address these challenges and improve community safety.

[0279] 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.

[0280] In this invention, the server includes a data storage means, a data processing means, a prediction model generation means, an energy analysis means, a planning support means, and a notification means. Thereby, highly accurate disaster prediction based on regional environmental data and efficient energy management become possible. Furthermore, by providing real-time information based on the user's location information, it is possible to support prompt evacuation actions and ensure safety.

[0281] The "data storage means" is a device or method for receiving environmental data obtained from a data collection device and recording it.

[0282] The "data processing means" is a device or method for performing preprocessing on the stored data, including correction of abnormal values and processing of missing values.

[0283] The "prediction model generation means" is a device or method for generating a disaster prediction model using the preprocessed data.

[0284] The "energy analysis means" is a device or method for identifying the pattern of energy consumption based on the preprocessed data.

[0285] The "planning support means" is a device or method for supporting the formulation of a regional development plan based on the generated prediction model and energy consumption pattern.

[0286] The "notification means" is a device or method for providing information related to disasters in real time based on the user's location information.

[0287] This system is composed of a data collection device, a server, a terminal, and a user. The data collection device includes a plurality of sensing devices, and environmental data is obtained thereby. Specifically, measuring instruments such as rain gauges, seismographs, and wind speed meters are arranged, and the data obtained from these devices is transmitted to the server via a network.

[0288] The server records the received environmental data using data storage means. Next, this data is preprocessed using data processing means. This includes correcting for outliers and imputing missing values ​​in the collected data. Then, based on the preprocessed data, a prediction model generation means is used to generate a disaster prediction model. The model uses advanced AI algorithms and predicts future disaster risks based on pattern analysis of the data.

[0289] Furthermore, energy analysis tools are used to identify energy consumption patterns. These tools analyze energy consumption data within the region and provide an optimal energy use plan. The planning support tools provide information to support regional development and infrastructure planning based on the aforementioned predictive models and energy consumption patterns.

[0290] The terminal functions as an interface with the user, visualizing and providing the user with the results of acquired disaster predictions and energy analysis. The notification system uses the user's location information to issue real-time disaster warnings and provide appropriate evacuation information.

[0291] As a concrete example, when a user is using their smartphone, the system detects a heavy rain forecast and immediately sends a notification saying, "Heavy rain is forecast. Please evacuate to a safe place." Such notifications are automatically generated based on the results of information analysis from the server.

[0292] Regarding the generated AI model and prompt statements, this system operates as follows:

[0293] When a user is using the emergency disaster notification app, if their current location is Tokyo, the app can provide timely information based on heavy rain warnings from the server.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The data collection device acquires various environmental data from devices such as rain gauges, seismometers, and anemometers. This data is transmitted to a server via a network. The input is the latest measurement value from each sensing device, and the output is the transmitted, unprocessed environmental data.

[0297] Step 2:

[0298] The server records the received environmental data using a data storage device. The input is the unprocessed data sent in the previous step, and the output is the stored data. Specifically, the data is stored in a recording medium such as a database.

[0299] Step 3:

[0300] The server preprocesses the stored data using data processing techniques. This includes correcting outliers and imputing missing values. The input is the stored data, and the output is the preprocessed, clean data. Specifically, it replaces outliers with certain baseline values ​​and imputs missing data using time-series forecasting.

[0301] Step 4:

[0302] The server generates a disaster prediction model using preprocessed data and a predictive model generation mechanism. The input is clean data, and the output is a predictive model showing disaster risk. An AI algorithm is used to analyze data patterns and quantify the likelihood of a disaster.

[0303] Step 5:

[0304] The server analyzes pre-processed data using energy analysis tools to identify energy consumption patterns. The input is clean data, and the output is the analysis result showing energy consumption patterns within a region. Here, time series analysis and statistical methods are used to identify trends and anomalies in energy use.

[0305] Step 6:

[0306] The server provides information for formulating regional development and infrastructure plans using the generated prediction model and energy consumption patterns through planning support means. The input is the prediction model and energy consumption patterns, and the output is the display of information for planning. This supports the sustainable development of the region.

[0307] Step 7:

[0308] The terminal provides disaster information to the user in real time using notification means. The input is disaster prediction data from the server, and the output is disaster warnings and evacuation information in a form visible to the user. As a specific operation, notifications are sent to smartphones and other devices so that information is displayed on the interface.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0310] This invention is a system for ensuring the safety and sustainability of a region, and particularly supports the formulation of a development plan considering the user's emotion. This system is composed of a data collection device, a server, an emotion engine, a terminal, and a user.

[0311] A plurality of sensing devices are installed in the data collection device to acquire environmental data. The sensing devices collect various information including weather data and energy consumption data, and the data is transmitted to the server through the network.

[0312] The server records the received environmental data and formats it so that normal analysis can be performed by data processing means. Using this data, a disaster prediction model is generated to identify the risk areas of the region. Also, the energy consumption pattern is analyzed by energy analysis means to create an efficient energy utilization plan.

[0313] The emotion engine collects emotional data from user feedback and survey results to recognize the user's emotional state. The server takes in the emotional data obtained from the emotion engine and analyzes the user's needs and frustrations. This information is used to improve the proposed regional development plan.

[0314] Users can view analysis results provided by the server through a dashboard, and regional development managers can formulate disaster prevention plans and infrastructure development plans based on sentiment data and local situation data. Furthermore, suggestions for improving communication and awareness-raising activities are provided based on sentiment data.

[0315] The terminals function as an interface with local residents, providing information on disaster predictions and energy planning. Residents can use the terminals to provide real-time feedback to the system, which allows the emotion engine to function effectively.

[0316] As a concrete example, in areas with a high risk of disaster, emotional data regarding users' fears and anxieties is collected. Based on this data, the server can enhance suggestions for rapid evacuation measures and customize the provision of information by type to promote a sense of security. This improves residents' sense of security and leads to the implementation of more effective local planning.

[0317] The following describes the processing flow.

[0318] Step 1:

[0319] The server receives environmental data transmitted from each data collection device and stores it in a database. This data includes measurements such as temperature, humidity, and wind speed.

[0320] Step 2:

[0321] The server preprocesses the stored environmental data using data processing tools. Here, data in different formats is unified, and missing data is corrected, preparing the data for analysis.

[0322] Step 3:

[0323] The server uses pre-processed data to run an AI algorithm and generate a disaster prediction model. This model can quantify and visualize the disaster risk in a specific area.

[0324] Step 4:

[0325] The server uses energy analysis tools to analyze energy consumption data for the entire region and generates reports showing peak demand and optimized energy allocation.

[0326] Step 5:

[0327] The emotion engine analyzes user feedback and survey data obtained from the device to recognize the user's emotional state (e.g., anxiety or reassurance). This data is updated in real time.

[0328] Step 6:

[0329] The server takes emotional data from the emotion engine, integrates it with environmental data and disaster prediction models, and adjusts regional development plans. In particular, it creates plan proposals that take into account the emotional state of users.

[0330] Step 7:

[0331] Users can view analysis results and sentiment data provided by the server through a dashboard. This allows users to gain a deeper understanding of local safety and efficient energy use.

[0332] Step 8:

[0333] The terminal notifies local residents of analysis results, disaster predictions, energy plans, and suggestions based on user sentiment. Furthermore, it contributes to continuous system improvement by providing an interface for residents to provide feedback.

[0334] (Example 2)

[0335] 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".

[0336] When formulating regional safety and sustainable development plans, there is a lack of means to reflect not only environmental data but also the feelings and needs of residents. Therefore, traditional planning methods may not adequately consider local conditions and the wishes of residents.

[0337] 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.

[0338] In this invention, the server includes information storage means, information processing means, prediction generation means, energy analysis means, sentiment analysis means, and plan improvement support means. This makes it possible to support the formulation of development plans suitable for the region based on both environmental data and residents' sentiment data.

[0339] "Information storage means" refers to a function for recording environmental data acquired from a data collection device and efficiently storing and managing it as needed.

[0340] "Information processing means" refers to functions that preprocess stored data and prepare it in a format suitable for analysis and model generation.

[0341] The "prediction generation means" is a function that generates a disaster prediction model based on pre-processed data and identifies regional risks.

[0342] An "energy analysis tool" is a function that analyzes energy consumption patterns based on pre-processed data and provides information for efficient energy use.

[0343] "Emotional analysis tools" are functions that analyze emotional data based on feedback collected from users to understand the emotional state and needs of local residents.

[0344] "Plan improvement support tools" are support functions for improving and proposing regional development plans based on predictive models, energy consumption patterns, and sentiment data.

[0345] This invention is a system that supports the development of development plans that improve local safety and sustainability. The embodiments of the system are described in detail below.

[0346] The server receives environmental data from multiple data acquisition devices. These devices include weather sensors and energy meters, which acquire data such as temperature, humidity, wind direction, and energy consumption in real time. The server records this data using information storage means. The recorded data is preprocessed by information processing means and converted into a format suitable for disaster prediction and energy consumption pattern analysis.

[0347] The server generates a disaster prediction model using pre-processed data via a prediction generation method and analyzes energy consumption data using an energy analysis method. This enables the identification of regional risk areas and the proposal of efficient energy use plans.

[0348] The sentiment analysis tool analyzes sentiment data based on feedback and survey results provided by users through their terminals. The analysis results are sent to a server, where the plan improvement support tool refines the proposed development plan to better reflect the emotional state and needs of the residents.

[0349] The terminals play a crucial role in providing local residents with visualized disaster prediction data and energy consumption data. Users use this information to provide feedback on local development plans and disaster prevention activities, which in turn facilitates effective sentiment analysis. This feedback loop enhances residents' sense of security and improves the effectiveness of local development plans.

[0350] As a concrete example, consider the application of the system in areas with a high risk of disaster. Based on user feedback regarding fear and anxiety, the server will enhance its suggestions for rapid evacuation information. Furthermore, it aims to improve residents' sense of security by providing customized information to promote a sense of reassurance. An example of a prompt message to the generating AI model in this system would be, "Generate appropriate evacuation plans and energy efficiency suggestions through local disaster prediction data and resident sentiment analysis."

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The server receives environmental data transmitted from data collection devices installed in the region. This data includes information such as temperature, humidity, and wind speed from weather sensors, and power consumption obtained from energy meters. The received data is stored in a time-series database by an information storage system. The input is environmental data, and the output is an organized dataset.

[0354] Step 2:

[0355] The server preprocesses the accumulated environmental data using information processing tools. Specific operations include imputing missing values, removing outliers, and converting data formats. The preprocessed data is output as cleaned data suitable for analysis.

[0356] Step 3:

[0357] The server uses pre-processed data to create a disaster prediction model using a prediction generation mechanism. Specifically, it applies statistical analysis and machine learning algorithms to predict the likelihood of a disaster. The output of this step is a predictive model showing the risk level for each region.

[0358] Step 4:

[0359] The server inputs preprocessed data into an energy analysis system to analyze energy consumption patterns. This involves statistical analysis to identify peak energy demand times and specific usage patterns. The output is the analysis results for formulating an optimal energy consumption strategy.

[0360] Step 5:

[0361] Users input their emotions through feedback and surveys via their devices. This data is sent to a server and processed by sentiment analysis tools. Specifically, natural language processing technology is used to classify emotions into categories such as positive and negative. The output is sentiment data that reflects the user's emotional state.

[0362] Step 6:

[0363] The server proposes a regional development plan using plan improvement support tools, based on the generated predictive models, energy consumption analysis results, and sentiment data. At this stage, different data sources are integrated to develop a customized plan tailored to the needs of the local residents. The output of this step is the proposed improved regional development plan.

[0364] Step 7:

[0365] The terminal displays disaster predictions, energy consumption information, and improved planning suggestions to the user. This information is presented in a dashboard format for intuitive understanding. Users can provide real-time feedback based on this information, contributing further sentiment data to the system. The output is updated information tailored to the user's feedback.

[0366] (Application Example 2)

[0367] 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."

[0368] To ensure the safety and sustainability of a region, regional development plans that consider the emotional state of residents, along with environmental data, are necessary. Conventional systems have only formulated development plans based on environmental data and have not adequately incorporated residents' emotions, resulting in difficulties in providing safety information and evacuation plans that enhance residents' sense of security.

[0369] 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.

[0370] In this invention, the server includes data storage means, data processing means, sentiment analysis means, and information provision means. This makes it possible to provide local risk information and personalize evacuation alerts using both environmental data and residents' sentiment data.

[0371] "Data storage means" refers to a device or function that has the function of recording multiple environmental data received from a data collection device.

[0372] "Data processing means" refers to a device or function that has the function of formatting stored data so that it can be properly analyzed.

[0373] "Predictive model generation means" refers to a device or function that has the function of generating a disaster prediction model based on pre-processed data.

[0374] "Energy analysis means" refers to a device or function that has the function of identifying energy consumption patterns based on pre-processed data.

[0375] "Planning support means" refers to a device or function that has the function of supporting the formulation of regional development plans based on predictive models and energy consumption patterns.

[0376] "Emotional analysis means" refers to a device or function that collects and analyzes emotional data to recognize the emotional state of residents and use that information to provide safety information.

[0377] "Information provision means" refers to a device or function that has the function of providing personalized safety information to residents.

[0378] The system for carrying out this invention includes a data collection device, a server, an emotion engine, and a user terminal. The data collection device includes multiple sensors that acquire weather data and energy consumption data, which are transmitted to the server via a network. The server utilizes data analysis using Python, emotion recognition using TensorFlow, a backend using Flask, and a mobile frontend using React Native.

[0379] The server records the received environmental data in a data storage device and formats it into an analyzable form using a data processing device. Then, using the formatted data, a disaster prediction model is generated by a prediction model generation device, and energy consumption patterns are analyzed by an energy analysis device. Furthermore, the emotion engine collects emotion data based on feedback and surveys from residents, and this is interpreted by an emotion analysis device. The analyzed data is used by a planning support device to propose regional development plans and identify areas for improvement.

[0380] User terminals provide residents with personalized safety information in real time. For example, if the risk of disaster increases in a certain area and residents are feeling anxious, the server will immediately send evacuation alerts and safety information appropriate to the situation to the terminals, thereby improving residents' sense of security.

[0381] An example of a prompt might be, "Please suggest how AI technology can improve the way we provide evacuation information to residents based on local disaster prediction models." Thus, providing and integrating information based on emotional and environmental data is crucial.

[0382] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0383] Step 1:

[0384] The server receives environmental data transmitted from the data acquisition device and stores it in the data storage means. At this point, the inputs are weather data and energy consumption data, and the output is a record of the organized environmental data.

[0385] Step 2:

[0386] The server uses data processing tools to format the received environmental data for analysis. The input is raw data stored on the server, and the output is data converted into an analyzable format. Specifically, this involves data format conversion and the imputation of missing values.

[0387] Step 3:

[0388] The server constructs a disaster prediction model using a predictive model generation mechanism with formatted environmental data. The input is formatted environmental data, and the output is the disaster prediction model. In this process, data patterns are learned using machine learning algorithms.

[0389] Step 4:

[0390] The server identifies energy consumption patterns using energy analysis tools. The input is formatted energy data, and the output is the analysis result of the consumption pattern. Specifically, it performs time-series analysis and anomaly detection.

[0391] Step 5:

[0392] The server collects emotional data from users using an emotion engine and analyzes it using emotion analysis tools. Inputs are user feedback and survey results, and outputs are analysis results indicating the emotional state. Emotions are evaluated using natural language processing techniques and AI models.

[0393] Step 6:

[0394] The server, through planning support mechanisms, develops regional development plans based on emotional and environmental data. Inputs include disaster prediction models, energy consumption patterns, and emotional analysis results, while output is the improved and proposed development plan. This enables appropriate, region-specific support.

[0395] Step 7:

[0396] The terminal displays safety information provided by the server to residents. The input is personalized safety information from the server, and the output is information presented in an easy-to-understand format for the user. This allows users to easily obtain and check relevant safety information in real time.

[0397] 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.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] This invention is a system that combines AI technology and big data to promote regional safety and sustainable development. Its main components consist of data collection devices, servers, terminals, and users, and comprehensive urban planning is achieved through communication between these devices.

[0414] First, the data acquisition device is equipped with multiple sensing devices, which are used to acquire environmental data such as rain gauges, seismometers, and anemometers. The collected data is transmitted to a server via the network.

[0415] The server stores the received environmental data and uses data processing tools to correct for outliers and handle missing values. Based on the processed data, an AI algorithm is applied to generate a disaster prediction model. This allows for the identification of areas with a high probability of disaster and the issuance of warnings to users.

[0416] Furthermore, the server uses energy analysis tools to analyze energy consumption data within the region and propose an optimal energy use plan. This promotes improved energy efficiency and ensures availability in the region.

[0417] Users can view analysis results provided by the server through a dashboard, and regional development managers can use this information to formulate disaster prevention plans and infrastructure development plans. Furthermore, users can provide feedback, and this information is also stored on the server, contributing to the overall improvement of the system.

[0418] The terminals function as an interface with local residents and businesses, not only providing necessary information but also playing a role in transmitting information from them to the server. For example, residents can receive evacuation information in real time during disasters through the terminals and report their safety status to the server.

[0419] One specific example is a system that quickly issues evacuation advisories for high-risk areas. When heavy rain is predicted, the server analyzes the collected data and immediately notifies users living in the affected area, enabling a rapid response. This helps mitigate damage and ensure the safety of local residents.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The server receives environmental data transmitted from each data collection device and records it in a database. This includes timestamped data from each device.

[0423] Step 2:

[0424] The server preprocesses the recorded data using data processing tools. This process includes removing outliers, imputing missing data, and normalizing the data.

[0425] Step 3:

[0426] The server inputs pre-processed data into an AI algorithm to generate a disaster prediction model. This model is then compared with historical disaster data to improve its accuracy and perform a risk assessment for a specific area.

[0427] Step 4:

[0428] The server analyzes energy consumption data to identify peak and surplus energy patterns. This analysis utilizes machine learning techniques to propose efficient energy use plans.

[0429] Step 5:

[0430] Users view the analysis results provided by the server on a dashboard. This allows users to understand the local situation and make decisions regarding disaster prevention plans and energy use plans.

[0431] Step 6:

[0432] The terminal provides local residents with information on disaster predictions and energy planning. Furthermore, it receives feedback and emergency reports from residents and transmits them to a server.

[0433] Step 7:

[0434] Based on the information provided, users take specific actions and provide feedback via their devices as needed. This feedback is used for the continuous improvement of the system.

[0435] (Example 1)

[0436] 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."

[0437] In recent years, there has been a growing need for systems that can balance regional safety and sustainable development. However, methods for comprehensively analyzing environmental information and resource consumption to simultaneously achieve disaster prediction and efficient resource utilization are not yet well established. This challenge highlights the increasing need for systems that provide rapid information and support decision-making.

[0438] 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.

[0439] In this invention, the server includes an information holding means for recording information acquired from a data collection means, a data processing means for pre-processing the stored information, and a predictive model generation means for generating a predictive model based on the pre-processed information. This enables integrated disaster prediction and resource consumption analysis, facilitating rapid regional safety assurance and efficient resource utilization.

[0440] A "data collection means" is a device configured to acquire environmental information using multiple sensors and transmit it to a server.

[0441] "Information retention means" refers to means for recording environmental information obtained from data collection means so that it can be used for subsequent processing and analysis.

[0442] "Data processing means" refers to means of performing pre-processing on information stored in an information storage means, such as correcting outliers or supplementing missing information.

[0443] A "predictive model generation method" is a means for generating a disaster prediction model based on pre-processed environmental information, and utilizes technologies such as generation AI models.

[0444] "Resource analysis tools" are means of analyzing data to identify patterns in resource consumption within a region and to promote optimal resource utilization.

[0445] "Planning support measures" refer to means for formulating and supporting regional development plans based on disaster prediction models and resource consumption patterns.

[0446] "Communication means" refers to means for notifying users of information and receiving information from users, and includes communication via terminals.

[0447] This invention provides a system that integrates AI technology and big data to promote regional safety and sustainable development. A server plays a central role, receiving environmental information from multiple sensors through data collection means. The sensors include devices that measure rainfall, wind speed, and seismic motion. This information is stored in the server by information storage means.

[0448] The server preprocesses the information using data processing tools. Specifically, it corrects outliers and fills in missing information. Next, based on the preprocessed information, it generates a disaster prediction model using a generative AI model. The AI ​​algorithm predicts, for example, heavy rain and strong winds, and notifies nearby residents of the results.

[0449] Furthermore, the server utilizes resource analysis tools to identify resource consumption patterns within a region. This makes it possible to formulate efficient energy utilization plans. For example, it can reduce the overall power load by proposing reallocation during peak power consumption in a particular region.

[0450] The terminal acts as an interface with the user, providing information transmitted from the server to local residents and businesses in real time. In emergencies, it displays evacuation routes and evacuation orders, and users can report on their safety status. Furthermore, feedback information obtained through the terminal is used to further improve the system.

[0451] An example of a prompt is, "Generate predictions of natural disasters that may occur in the next 48 hours and perform a risk assessment." By using such prompts to operate a generation AI model, it is possible to simultaneously promote regional safety and optimal resource utilization.

[0452] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0453] Step 1:

[0454] The server receives a wide range of environmental information from data collection devices. Inputs include raw data provided by sensors such as rain gauges, seismometers, and anemometers. The server records this data in a database using information storage devices. This record forms the basis for subsequent data processing and analysis.

[0455] Step 2:

[0456] The server retrieves the raw data stored in the information storage means and performs preprocessing using the data processing means. The input is the data recorded in step 1. Specific operations include detecting and correcting outliers and imputing missing data. This prepares the data for analysis, generating a clean dataset as output.

[0457] Step 3:

[0458] The server launches a generative AI model using preprocessed data to generate a disaster prediction model. The input is the clean dataset from step 2. This model calculates the probability of a disaster occurring based on historical data. The output provides a risk assessment for a specific area and warning information based on that assessment.

[0459] Step 4:

[0460] The server analyzes regional resource consumption patterns using resource analysis tools. Input includes historical data on electricity consumption and water usage. The analysis generates specific suggestions for improving energy efficiency. The output is a proposed plan for leveling out peak electricity usage.

[0461] Step 5:

[0462] The terminal notifies local residents and businesses of information from the server. The input is the output data from steps 3 and 4. Specifically, it displays evacuation advisories based on disaster predictions and suggestions for optimizing resource use in real time. This allows users to make quick decisions.

[0463] Step 6:

[0464] Users provide feedback to the server through their terminals. The input consists of reports and opinions from users. The server collects this feedback to improve the system and enhance its accuracy. The output provides reference data for system adjustments.

[0465] (Application Example 1)

[0466] 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."

[0467] In recent years, rapid environmental changes and the increasing frequency of natural disasters have made ensuring safe communities a critical issue. Current disaster prevention systems rely on individual data collection, making rapid and accurate disaster prediction and energy management difficult. Furthermore, insufficient information provision to residents and users hinders rapid evacuation and safety assurance. A comprehensive system is needed to address these challenges and improve community safety.

[0468] 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.

[0469] In this invention, the server includes data storage means, data processing means, predictive model generation means, energy analysis means, planning support means, and notification means. This enables highly accurate disaster prediction based on local environmental data and efficient energy management. Furthermore, real-time information provision based on the user's location information can support rapid evacuation and safety measures.

[0470] "Data storage means" refers to a device or method for receiving environmental data acquired from a data collection device and recording it.

[0471] "Data processing means" refers to a device or method that performs preprocessing on stored data, including the correction of outliers and the handling of missing values.

[0472] "Predictive model generation means" refers to an apparatus or method for generating a disaster prediction model using pre-processed data.

[0473] "Energy analysis means" refers to an apparatus or method for identifying energy consumption patterns based on pre-processed data.

[0474] "Planning support means" refers to a device or method for supporting the formulation of regional development plans based on generated predictive models and energy consumption patterns.

[0475] "Notification means" refers to a device or method for providing disaster-related information in real time based on the user's location information.

[0476] This system consists of a data acquisition device, a server, terminals, and users. The data acquisition device includes multiple sensing devices that acquire environmental data. Specifically, measuring instruments such as rain gauges, seismometers, and anemometers are installed, and the data obtained from these devices is transmitted to the server via the network.

[0477] The server records the received environmental data using data storage means. Next, this data is preprocessed using data processing means. This includes correcting for outliers and imputing missing values ​​in the collected data. Then, based on the preprocessed data, a prediction model generation means is used to generate a disaster prediction model. The model uses advanced AI algorithms and predicts future disaster risks based on pattern analysis of the data.

[0478] Furthermore, energy analysis tools are used to identify energy consumption patterns. These tools analyze energy consumption data within the region and provide an optimal energy use plan. The planning support tools provide information to support regional development and infrastructure planning based on the aforementioned predictive models and energy consumption patterns.

[0479] The terminal functions as an interface with the user, visualizing and providing the user with the results of acquired disaster predictions and energy analysis. The notification system uses the user's location information to issue real-time disaster warnings and provide appropriate evacuation information.

[0480] As a concrete example, when a user is using their smartphone, the system detects a heavy rain forecast and immediately sends a notification saying, "Heavy rain is forecast. Please evacuate to a safe place." Such notifications are automatically generated based on the results of information analysis from the server.

[0481] Regarding the generated AI model and prompt statements, this system operates as follows:

[0482] When a user is using the emergency disaster notification app, if their current location is Tokyo, the app can provide timely information based on heavy rain warnings from the server.

[0483] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0484] Step 1:

[0485] The data collection device acquires various environmental data from devices such as rain gauges, seismometers, and anemometers. This data is transmitted to a server via a network. The input is the latest measurement value from each sensing device, and the output is the transmitted, unprocessed environmental data.

[0486] Step 2:

[0487] The server records the received environmental data using a data storage device. The input is the unprocessed data sent in the previous step, and the output is the stored data. Specifically, the data is stored in a recording medium such as a database.

[0488] Step 3:

[0489] The server preprocesses the stored data using data processing techniques. This includes correcting outliers and imputing missing values. The input is the stored data, and the output is the preprocessed, clean data. Specifically, it replaces outliers with certain baseline values ​​and imputs missing data using time-series forecasting.

[0490] Step 4:

[0491] The server generates a disaster prediction model using preprocessed data and a predictive model generation mechanism. The input is clean data, and the output is a predictive model showing disaster risk. An AI algorithm is used to analyze data patterns and quantify the likelihood of a disaster.

[0492] Step 5:

[0493] The server analyzes pre-processed data using energy analysis tools to identify energy consumption patterns. The input is clean data, and the output is the analysis result showing energy consumption patterns within a region. Here, time series analysis and statistical methods are used to identify trends and anomalies in energy use.

[0494] Step 6:

[0495] The server provides information for regional development and infrastructure planning using planning support tools based on the generated predictive models and energy consumption patterns. The inputs are predictive models and energy consumption patterns, and the output is a display of information for planning. This supports sustainable development in the region.

[0496] Step 7:

[0497] The terminal provides users with real-time disaster information using notification methods. Input is disaster prediction data from a server, and output is disaster warnings and evacuation information in a format visible to the user. Specifically, it sends notifications to smartphones and other devices, and displays the information on the interface.

[0498] 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.

[0499] This invention is a system for ensuring local safety and sustainability, and in particular, it supports the development of development plans that take user emotions into consideration. The system consists of a data collection device, a server, an emotion engine, a terminal, and a user.

[0500] The data collection device is equipped with multiple sensing devices to acquire environmental data. The sensing devices collect various types of information, including weather data and energy consumption data, and this data is transmitted to a server via the network.

[0501] The server records the received environmental data and formats it using data processing tools to enable proper analysis. This data is then used to generate a disaster prediction model and identify regional risk areas. Furthermore, energy consumption patterns are analyzed using energy analysis tools to create an efficient energy use plan.

[0502] The emotion engine collects emotional data from user feedback and survey results to recognize the user's emotional state. The server takes in the emotional data obtained from the emotion engine and analyzes the user's needs and frustrations. This information is used to improve the proposed regional development plan.

[0503] Users can view analysis results provided by the server through a dashboard, and regional development managers can formulate disaster prevention plans and infrastructure development plans based on sentiment data and local situation data. Furthermore, suggestions for improving communication and awareness-raising activities are provided based on sentiment data.

[0504] The terminals function as an interface with local residents, providing information on disaster predictions and energy planning. Residents can use the terminals to provide real-time feedback to the system, which allows the emotion engine to function effectively.

[0505] As a concrete example, in areas with a high risk of disaster, emotional data regarding users' fears and anxieties is collected. Based on this data, the server can enhance suggestions for rapid evacuation measures and customize the provision of information by type to promote a sense of security. This improves residents' sense of security and leads to the implementation of more effective local planning.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server receives environmental data transmitted from each data collection device and stores it in a database. This data includes measurements such as temperature, humidity, and wind speed.

[0509] Step 2:

[0510] The server preprocesses the stored environmental data using data processing tools. Here, data in different formats is unified, and missing data is corrected, preparing the data for analysis.

[0511] Step 3:

[0512] The server uses pre-processed data to run an AI algorithm and generate a disaster prediction model. This model can quantify and visualize the disaster risk in a specific area.

[0513] Step 4:

[0514] The server uses energy analysis tools to analyze energy consumption data for the entire region and generates reports showing peak demand and optimized energy allocation.

[0515] Step 5:

[0516] The emotion engine analyzes user feedback and survey data obtained from the device to recognize the user's emotional state (e.g., anxiety or reassurance). This data is updated in real time.

[0517] Step 6:

[0518] The server takes emotional data from the emotion engine, integrates it with environmental data and disaster prediction models, and adjusts regional development plans. In particular, it creates plan proposals that take into account the emotional state of users.

[0519] Step 7:

[0520] Users can view analysis results and sentiment data provided by the server through a dashboard. This allows users to gain a deeper understanding of local safety and efficient energy use.

[0521] Step 8:

[0522] The terminal notifies local residents of analysis results, disaster predictions, energy plans, and suggestions based on user sentiment. Furthermore, it contributes to continuous system improvement by providing an interface for residents to provide feedback.

[0523] (Example 2)

[0524] 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."

[0525] When formulating regional safety and sustainable development plans, there is a lack of means to reflect not only environmental data but also the feelings and needs of residents. Therefore, traditional planning methods may not adequately consider local conditions and the wishes of residents.

[0526] 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.

[0527] In this invention, the server includes information storage means, information processing means, prediction generation means, energy analysis means, sentiment analysis means, and plan improvement support means. This makes it possible to support the formulation of development plans suitable for the region based on both environmental data and residents' sentiment data.

[0528] "Information storage means" refers to a function for recording environmental data acquired from a data collection device and efficiently storing and managing it as needed.

[0529] "Information processing means" refers to functions that preprocess stored data and prepare it in a format suitable for analysis and model generation.

[0530] The "prediction generation means" is a function that generates a disaster prediction model based on pre-processed data and identifies regional risks.

[0531] An "energy analysis tool" is a function that analyzes energy consumption patterns based on pre-processed data and provides information for efficient energy use.

[0532] "Emotional analysis tools" are functions that analyze emotional data based on feedback collected from users to understand the emotional state and needs of local residents.

[0533] "Plan improvement support tools" are support functions for improving and proposing regional development plans based on predictive models, energy consumption patterns, and sentiment data.

[0534] This invention is a system that supports the development of development plans that improve local safety and sustainability. The embodiments of the system are described in detail below.

[0535] The server receives environmental data from multiple data acquisition devices. These devices include weather sensors and energy meters, which acquire data such as temperature, humidity, wind direction, and energy consumption in real time. The server records this data using information storage means. The recorded data is preprocessed by information processing means and converted into a format suitable for disaster prediction and energy consumption pattern analysis.

[0536] The server generates a disaster prediction model using pre-processed data via a prediction generation method and analyzes energy consumption data using an energy analysis method. This enables the identification of regional risk areas and the proposal of efficient energy use plans.

[0537] The sentiment analysis tool analyzes sentiment data based on feedback and survey results provided by users through their terminals. The analysis results are sent to a server, where the plan improvement support tool refines the proposed development plan to better reflect the emotional state and needs of the residents.

[0538] The terminals play a crucial role in providing local residents with visualized disaster prediction data and energy consumption data. Users use this information to provide feedback on local development plans and disaster prevention activities, which in turn facilitates effective sentiment analysis. This feedback loop enhances residents' sense of security and improves the effectiveness of local development plans.

[0539] As a concrete example, consider the application of the system in areas with a high risk of disaster. Based on user feedback regarding fear and anxiety, the server will enhance its suggestions for rapid evacuation information. Furthermore, it aims to improve residents' sense of security by providing customized information to promote a sense of reassurance. An example of a prompt message to the generating AI model in this system would be, "Generate appropriate evacuation plans and energy efficiency suggestions through local disaster prediction data and resident sentiment analysis."

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The server receives environmental data transmitted from data collection devices installed in the region. This data includes information such as temperature, humidity, and wind speed from weather sensors, and power consumption obtained from energy meters. The received data is stored in a time-series database by an information storage system. The input is environmental data, and the output is an organized dataset.

[0543] Step 2:

[0544] The server preprocesses the accumulated environmental data using information processing tools. Specific operations include imputing missing values, removing outliers, and converting data formats. The preprocessed data is output as cleaned data suitable for analysis.

[0545] Step 3:

[0546] The server uses pre-processed data to create a disaster prediction model using a prediction generation mechanism. Specifically, it applies statistical analysis and machine learning algorithms to predict the likelihood of a disaster. The output of this step is a predictive model showing the risk level for each region.

[0547] Step 4:

[0548] The server inputs preprocessed data into an energy analysis system to analyze energy consumption patterns. This involves statistical analysis to identify peak energy demand times and specific usage patterns. The output is the analysis results for formulating an optimal energy consumption strategy.

[0549] Step 5:

[0550] Users input their emotions through feedback and surveys via their devices. This data is sent to a server and processed by sentiment analysis tools. Specifically, natural language processing technology is used to classify emotions into categories such as positive and negative. The output is sentiment data that reflects the user's emotional state.

[0551] Step 6:

[0552] The server proposes a regional development plan using plan improvement support tools, based on the generated predictive models, energy consumption analysis results, and sentiment data. At this stage, different data sources are integrated to develop a customized plan tailored to the needs of the local residents. The output of this step is the proposed improved regional development plan.

[0553] Step 7:

[0554] The terminal displays disaster predictions, energy consumption information, and improved planning suggestions to the user. This information is presented in a dashboard format for intuitive understanding. Users can provide real-time feedback based on this information, contributing further sentiment data to the system. The output is updated information tailored to the user's feedback.

[0555] (Application Example 2)

[0556] 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."

[0557] To ensure the safety and sustainability of a region, regional development plans that consider the emotional state of residents, along with environmental data, are necessary. Conventional systems have only formulated development plans based on environmental data and have not adequately incorporated residents' emotions, resulting in difficulties in providing safety information and evacuation plans that enhance residents' sense of security.

[0558] 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.

[0559] In this invention, the server includes data storage means, data processing means, sentiment analysis means, and information provision means. This makes it possible to provide local risk information and personalize evacuation alerts using both environmental data and residents' sentiment data.

[0560] "Data storage means" refers to a device or function that has the function of recording multiple environmental data received from a data collection device.

[0561] "Data processing means" refers to a device or function that has the function of formatting stored data so that it can be properly analyzed.

[0562] "Predictive model generation means" refers to a device or function that has the function of generating a disaster prediction model based on pre-processed data.

[0563] "Energy analysis means" refers to a device or function that has the function of identifying energy consumption patterns based on pre-processed data.

[0564] "Planning support means" refers to a device or function that has the function of supporting the formulation of regional development plans based on predictive models and energy consumption patterns.

[0565] "Emotional analysis means" refers to a device or function that collects and analyzes emotional data to recognize the emotional state of residents and use that information to provide safety information.

[0566] "Information provision means" refers to a device or function that has the function of providing personalized safety information to residents.

[0567] The system for carrying out this invention includes a data collection device, a server, an emotion engine, and a user terminal. The data collection device includes multiple sensors that acquire weather data and energy consumption data, which are transmitted to the server via a network. The server utilizes data analysis using Python, emotion recognition using TensorFlow, a backend using Flask, and a mobile frontend using React Native.

[0568] The server records the received environmental data in a data storage device and formats it into an analyzable form using a data processing device. Then, using the formatted data, a disaster prediction model is generated by a prediction model generation device, and energy consumption patterns are analyzed by an energy analysis device. Furthermore, the emotion engine collects emotion data based on feedback and surveys from residents, and this is interpreted by an emotion analysis device. The analyzed data is used by a planning support device to propose regional development plans and identify areas for improvement.

[0569] User terminals provide residents with personalized safety information in real time. For example, if the risk of disaster increases in a certain area and residents are feeling anxious, the server will immediately send evacuation alerts and safety information appropriate to the situation to the terminals, thereby improving residents' sense of security.

[0570] An example of a prompt might be, "Please suggest how AI technology can improve the way we provide evacuation information to residents based on local disaster prediction models." Thus, providing and integrating information based on emotional and environmental data is crucial.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] The server receives environmental data transmitted from the data acquisition device and stores it in the data storage means. At this point, the inputs are weather data and energy consumption data, and the output is a record of the organized environmental data.

[0574] Step 2:

[0575] The server uses data processing tools to format the received environmental data for analysis. The input is raw data stored on the server, and the output is data converted into an analyzable format. Specifically, this involves data format conversion and the imputation of missing values.

[0576] Step 3:

[0577] The server constructs a disaster prediction model using a predictive model generation mechanism with formatted environmental data. The input is formatted environmental data, and the output is the disaster prediction model. In this process, data patterns are learned using machine learning algorithms.

[0578] Step 4:

[0579] The server identifies energy consumption patterns using energy analysis tools. The input is formatted energy data, and the output is the analysis result of the consumption pattern. Specifically, it performs time-series analysis and anomaly detection.

[0580] Step 5:

[0581] The server collects emotional data from users using an emotion engine and analyzes it using emotion analysis tools. Inputs are user feedback and survey results, and outputs are analysis results indicating the emotional state. Emotions are evaluated using natural language processing techniques and AI models.

[0582] Step 6:

[0583] The server, through planning support mechanisms, develops regional development plans based on emotional and environmental data. Inputs include disaster prediction models, energy consumption patterns, and emotional analysis results, while output is the improved and proposed development plan. This enables appropriate, region-specific support.

[0584] Step 7:

[0585] The terminal displays safety information provided by the server to residents. The input is personalized safety information from the server, and the output is information presented in an easy-to-understand format for the user. This allows users to easily obtain and check relevant safety information in real time.

[0586] 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.

[0587] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] 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.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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).

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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".

[0603] This invention is a system that combines AI technology and big data to promote regional safety and sustainable development. Its main components consist of data collection devices, servers, terminals, and users, and comprehensive urban planning is achieved through communication between these devices.

[0604] First, the data acquisition device is equipped with multiple sensing devices, which are used to acquire environmental data such as rain gauges, seismometers, and anemometers. The collected data is transmitted to a server via the network.

[0605] The server stores the received environmental data and uses data processing tools to correct for outliers and handle missing values. Based on the processed data, an AI algorithm is applied to generate a disaster prediction model. This allows for the identification of areas with a high probability of disaster and the issuance of warnings to users.

[0606] Furthermore, the server uses energy analysis tools to analyze energy consumption data within the region and propose an optimal energy use plan. This promotes improved energy efficiency and ensures availability in the region.

[0607] Users can view analysis results provided by the server through a dashboard, and regional development managers can use this information to formulate disaster prevention plans and infrastructure development plans. Furthermore, users can provide feedback, and this information is also stored on the server, contributing to the overall improvement of the system.

[0608] The terminals function as an interface with local residents and businesses, not only providing necessary information but also playing a role in transmitting information from them to the server. For example, residents can receive evacuation information in real time during disasters through the terminals and report their safety status to the server.

[0609] One specific example is a system that quickly issues evacuation advisories for high-risk areas. When heavy rain is predicted, the server analyzes the collected data and immediately notifies users living in the affected area, enabling a rapid response. This helps mitigate damage and ensure the safety of local residents.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The server receives environmental data transmitted from each data collection device and records it in a database. This includes timestamped data from each device.

[0613] Step 2:

[0614] The server preprocesses the recorded data using data processing tools. This process includes removing outliers, imputing missing data, and normalizing the data.

[0615] Step 3:

[0616] The server inputs pre-processed data into an AI algorithm to generate a disaster prediction model. This model is then compared with historical disaster data to improve its accuracy and perform a risk assessment for a specific area.

[0617] Step 4:

[0618] The server analyzes energy consumption data to identify peak and surplus energy patterns. This analysis utilizes machine learning techniques to propose efficient energy use plans.

[0619] Step 5:

[0620] Users view the analysis results provided by the server on a dashboard. This allows users to understand the local situation and make decisions regarding disaster prevention plans and energy use plans.

[0621] Step 6:

[0622] The terminal provides local residents with information on disaster predictions and energy planning. Furthermore, it receives feedback and emergency reports from residents and transmits them to a server.

[0623] Step 7:

[0624] Based on the information provided, users take specific actions and provide feedback via their devices as needed. This feedback is used for the continuous improvement of the system.

[0625] (Example 1)

[0626] 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".

[0627] In recent years, there has been a growing need for systems that can balance regional safety and sustainable development. However, methods for comprehensively analyzing environmental information and resource consumption to simultaneously achieve disaster prediction and efficient resource utilization are not yet well established. This challenge highlights the increasing need for systems that provide rapid information and support decision-making.

[0628] 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.

[0629] In this invention, the server includes an information holding means for recording information acquired from a data collection means, a data processing means for pre-processing the stored information, and a predictive model generation means for generating a predictive model based on the pre-processed information. This enables integrated disaster prediction and resource consumption analysis, facilitating rapid regional safety assurance and efficient resource utilization.

[0630] A "data collection means" is a device configured to acquire environmental information using multiple sensors and transmit it to a server.

[0631] "Information retention means" refers to means for recording environmental information obtained from data collection means so that it can be used for subsequent processing and analysis.

[0632] "Data processing means" refers to means of performing pre-processing on information stored in an information storage means, such as correcting outliers or supplementing missing information.

[0633] A "predictive model generation method" is a means for generating a disaster prediction model based on pre-processed environmental information, and utilizes technologies such as generation AI models.

[0634] "Resource analysis tools" are means of analyzing data to identify patterns in resource consumption within a region and to promote optimal resource utilization.

[0635] "Planning support measures" refer to means for formulating and supporting regional development plans based on disaster prediction models and resource consumption patterns.

[0636] "Communication means" refers to means for notifying users of information and receiving information from users, and includes communication via terminals.

[0637] This invention provides a system that integrates AI technology and big data to promote regional safety and sustainable development. A server plays a central role, receiving environmental information from multiple sensors through data collection means. The sensors include devices that measure rainfall, wind speed, and seismic motion. This information is stored in the server by information storage means.

[0638] The server preprocesses the information using data processing tools. Specifically, it corrects outliers and fills in missing information. Next, based on the preprocessed information, it generates a disaster prediction model using a generative AI model. The AI ​​algorithm predicts, for example, heavy rain and strong winds, and notifies nearby residents of the results.

[0639] Furthermore, the server utilizes resource analysis tools to identify resource consumption patterns within a region. This makes it possible to formulate efficient energy utilization plans. For example, it can reduce the overall power load by proposing reallocation during peak power consumption in a particular region.

[0640] The terminal acts as an interface with the user, providing information transmitted from the server to local residents and businesses in real time. In emergencies, it displays evacuation routes and evacuation orders, and users can report on their safety status. Furthermore, feedback information obtained through the terminal is used to further improve the system.

[0641] An example of a prompt is, "Generate predictions of natural disasters that may occur in the next 48 hours and perform a risk assessment." By using such prompts to operate a generation AI model, it is possible to simultaneously promote regional safety and optimal resource utilization.

[0642] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0643] Step 1:

[0644] The server receives a wide range of environmental information from data collection devices. Inputs include raw data provided by sensors such as rain gauges, seismometers, and anemometers. The server records this data in a database using information storage devices. This record forms the basis for subsequent data processing and analysis.

[0645] Step 2:

[0646] The server retrieves the raw data stored in the information storage means and performs preprocessing using the data processing means. The input is the data recorded in step 1. Specific operations include detecting and correcting outliers and imputing missing data. This prepares the data for analysis, generating a clean dataset as output.

[0647] Step 3:

[0648] The server launches a generative AI model using preprocessed data to generate a disaster prediction model. The input is the clean dataset from step 2. This model calculates the probability of a disaster occurring based on historical data. The output provides a risk assessment for a specific area and warning information based on that assessment.

[0649] Step 4:

[0650] The server analyzes regional resource consumption patterns using resource analysis tools. Input includes historical data on electricity consumption and water usage. The analysis generates specific suggestions for improving energy efficiency. The output is a proposed plan for leveling out peak electricity usage.

[0651] Step 5:

[0652] The terminal notifies local residents and businesses of information from the server. The input is the output data from steps 3 and 4. Specifically, it displays evacuation advisories based on disaster predictions and suggestions for optimizing resource use in real time. This allows users to make quick decisions.

[0653] Step 6:

[0654] Users provide feedback to the server through their terminals. The input consists of reports and opinions from users. The server collects this feedback to improve the system and enhance its accuracy. The output provides reference data for system adjustments.

[0655] (Application Example 1)

[0656] 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".

[0657] In recent years, rapid environmental changes and the increasing frequency of natural disasters have made ensuring safe communities a critical issue. Current disaster prevention systems rely on individual data collection, making rapid and accurate disaster prediction and energy management difficult. Furthermore, insufficient information provision to residents and users hinders rapid evacuation and safety assurance. A comprehensive system is needed to address these challenges and improve community safety.

[0658] 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.

[0659] In this invention, the server includes data storage means, data processing means, predictive model generation means, energy analysis means, planning support means, and notification means. This enables highly accurate disaster prediction based on local environmental data and efficient energy management. Furthermore, real-time information provision based on the user's location information can support rapid evacuation and safety measures.

[0660] "Data storage means" refers to a device or method for receiving environmental data acquired from a data collection device and recording it.

[0661] "Data processing means" refers to a device or method that performs preprocessing on stored data, including the correction of outliers and the handling of missing values.

[0662] "Predictive model generation means" refers to an apparatus or method for generating a disaster prediction model using pre-processed data.

[0663] "Energy analysis means" refers to an apparatus or method for identifying energy consumption patterns based on pre-processed data.

[0664] "Planning support means" refers to a device or method for supporting the formulation of regional development plans based on generated predictive models and energy consumption patterns.

[0665] "Notification means" refers to a device or method for providing disaster-related information in real time based on the user's location information.

[0666] This system consists of a data acquisition device, a server, terminals, and users. The data acquisition device includes multiple sensing devices that acquire environmental data. Specifically, measuring instruments such as rain gauges, seismometers, and anemometers are installed, and the data obtained from these devices is transmitted to the server via the network.

[0667] The server records the received environmental data using data storage means. Next, this data is preprocessed using data processing means. This includes correcting for outliers and imputing missing values ​​in the collected data. Then, based on the preprocessed data, a prediction model generation means is used to generate a disaster prediction model. The model uses advanced AI algorithms and predicts future disaster risks based on pattern analysis of the data.

[0668] Furthermore, energy analysis tools are used to identify energy consumption patterns. These tools analyze energy consumption data within the region and provide an optimal energy use plan. The planning support tools provide information to support regional development and infrastructure planning based on the aforementioned predictive models and energy consumption patterns.

[0669] The terminal functions as an interface with the user, visualizing and providing the user with the results of acquired disaster predictions and energy analysis. The notification system uses the user's location information to issue real-time disaster warnings and provide appropriate evacuation information.

[0670] As a concrete example, when a user is using their smartphone, the system detects a heavy rain forecast and immediately sends a notification saying, "Heavy rain is forecast. Please evacuate to a safe place." Such notifications are automatically generated based on the results of information analysis from the server.

[0671] Regarding the generated AI model and prompt statements, this system operates as follows:

[0672] When a user is using the emergency disaster notification app, if their current location is Tokyo, the app can provide timely information based on heavy rain warnings from the server.

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The data collection device acquires various environmental data from devices such as rain gauges, seismometers, and anemometers. This data is transmitted to a server via a network. The input is the latest measurement value from each sensing device, and the output is the transmitted, unprocessed environmental data.

[0676] Step 2:

[0677] The server records the received environmental data using a data storage device. The input is the unprocessed data sent in the previous step, and the output is the stored data. Specifically, the data is stored in a recording medium such as a database.

[0678] Step 3:

[0679] The server preprocesses the stored data using data processing techniques. This includes correcting outliers and imputing missing values. The input is the stored data, and the output is the preprocessed, clean data. Specifically, it replaces outliers with certain baseline values ​​and imputs missing data using time-series forecasting.

[0680] Step 4:

[0681] The server generates a disaster prediction model using preprocessed data and a predictive model generation mechanism. The input is clean data, and the output is a predictive model showing disaster risk. An AI algorithm is used to analyze data patterns and quantify the likelihood of a disaster.

[0682] Step 5:

[0683] The server analyzes pre-processed data using energy analysis tools to identify energy consumption patterns. The input is clean data, and the output is the analysis result showing energy consumption patterns within a region. Here, time series analysis and statistical methods are used to identify trends and anomalies in energy use.

[0684] Step 6:

[0685] The server provides information for regional development and infrastructure planning using planning support tools based on the generated predictive models and energy consumption patterns. The inputs are predictive models and energy consumption patterns, and the output is a display of information for planning. This supports sustainable development in the region.

[0686] Step 7:

[0687] The terminal provides users with real-time disaster information using notification methods. Input is disaster prediction data from a server, and output is disaster warnings and evacuation information in a format visible to the user. Specifically, it sends notifications to smartphones and other devices, and displays the information on the interface.

[0688] 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.

[0689] This invention is a system for ensuring local safety and sustainability, and in particular, it supports the development of development plans that take user emotions into consideration. The system consists of a data collection device, a server, an emotion engine, a terminal, and a user.

[0690] The data collection device is equipped with multiple sensing devices to acquire environmental data. The sensing devices collect various types of information, including weather data and energy consumption data, and this data is transmitted to a server via the network.

[0691] The server records the received environmental data and formats it using data processing tools to enable proper analysis. This data is then used to generate a disaster prediction model and identify regional risk areas. Furthermore, energy consumption patterns are analyzed using energy analysis tools to create an efficient energy use plan.

[0692] The emotion engine collects emotional data from user feedback and survey results to recognize the user's emotional state. The server takes in the emotional data obtained from the emotion engine and analyzes the user's needs and frustrations. This information is used to improve the proposed regional development plan.

[0693] Users can view analysis results provided by the server through a dashboard, and regional development managers can formulate disaster prevention plans and infrastructure development plans based on sentiment data and local situation data. Furthermore, suggestions for improving communication and awareness-raising activities are provided based on sentiment data.

[0694] The terminals function as an interface with local residents, providing information on disaster predictions and energy planning. Residents can use the terminals to provide real-time feedback to the system, which allows the emotion engine to function effectively.

[0695] As a concrete example, in areas with a high risk of disaster, emotional data regarding users' fears and anxieties is collected. Based on this data, the server can enhance suggestions for rapid evacuation measures and customize the provision of information by type to promote a sense of security. This improves residents' sense of security and leads to the implementation of more effective local planning.

[0696] The following describes the processing flow.

[0697] Step 1:

[0698] The server receives environmental data transmitted from each data collection device and stores it in a database. This data includes measurements such as temperature, humidity, and wind speed.

[0699] Step 2:

[0700] The server preprocesses the stored environmental data using data processing tools. Here, data in different formats is unified, and missing data is corrected, preparing the data for analysis.

[0701] Step 3:

[0702] The server uses pre-processed data to run an AI algorithm and generate a disaster prediction model. This model can quantify and visualize the disaster risk in a specific area.

[0703] Step 4:

[0704] The server uses energy analysis tools to analyze energy consumption data for the entire region and generates reports showing peak demand and optimized energy allocation.

[0705] Step 5:

[0706] The emotion engine analyzes user feedback and survey data obtained from the device to recognize the user's emotional state (e.g., anxiety or reassurance). This data is updated in real time.

[0707] Step 6:

[0708] The server takes emotional data from the emotion engine, integrates it with environmental data and disaster prediction models, and adjusts regional development plans. In particular, it creates plan proposals that take into account the emotional state of users.

[0709] Step 7:

[0710] Users can view analysis results and sentiment data provided by the server through a dashboard. This allows users to gain a deeper understanding of local safety and efficient energy use.

[0711] Step 8:

[0712] The terminal notifies local residents of analysis results, disaster predictions, energy plans, and suggestions based on user sentiment. Furthermore, it contributes to continuous system improvement by providing an interface for residents to provide feedback.

[0713] (Example 2)

[0714] 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".

[0715] When formulating regional safety and sustainable development plans, there is a lack of means to reflect not only environmental data but also the feelings and needs of residents. Therefore, traditional planning methods may not adequately consider local conditions and the wishes of residents.

[0716] 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.

[0717] In this invention, the server includes information storage means, information processing means, prediction generation means, energy analysis means, sentiment analysis means, and plan improvement support means. This makes it possible to support the formulation of development plans suitable for the region based on both environmental data and residents' sentiment data.

[0718] "Information storage means" refers to a function for recording environmental data acquired from a data collection device and efficiently storing and managing it as needed.

[0719] "Information processing means" refers to functions that preprocess stored data and prepare it in a format suitable for analysis and model generation.

[0720] The "prediction generation means" is a function that generates a disaster prediction model based on pre-processed data and identifies regional risks.

[0721] An "energy analysis tool" is a function that analyzes energy consumption patterns based on pre-processed data and provides information for efficient energy use.

[0722] "Emotional analysis tools" are functions that analyze emotional data based on feedback collected from users to understand the emotional state and needs of local residents.

[0723] "Plan improvement support tools" are support functions for improving and proposing regional development plans based on predictive models, energy consumption patterns, and sentiment data.

[0724] This invention is a system that supports the development of development plans that improve local safety and sustainability. The embodiments of the system are described in detail below.

[0725] The server receives environmental data from multiple data acquisition devices. These devices include weather sensors and energy meters, which acquire data such as temperature, humidity, wind direction, and energy consumption in real time. The server records this data using information storage means. The recorded data is preprocessed by information processing means and converted into a format suitable for disaster prediction and energy consumption pattern analysis.

[0726] The server generates a disaster prediction model using pre-processed data via a prediction generation method and analyzes energy consumption data using an energy analysis method. This enables the identification of regional risk areas and the proposal of efficient energy use plans.

[0727] The sentiment analysis tool analyzes sentiment data based on feedback and survey results provided by users through their terminals. The analysis results are sent to a server, where the plan improvement support tool refines the proposed development plan to better reflect the emotional state and needs of the residents.

[0728] The terminals play a crucial role in providing local residents with visualized disaster prediction data and energy consumption data. Users use this information to provide feedback on local development plans and disaster prevention activities, which in turn facilitates effective sentiment analysis. This feedback loop enhances residents' sense of security and improves the effectiveness of local development plans.

[0729] As a concrete example, consider the application of the system in areas with a high risk of disaster. Based on user feedback regarding fear and anxiety, the server will enhance its suggestions for rapid evacuation information. Furthermore, it aims to improve residents' sense of security by providing customized information to promote a sense of reassurance. An example of a prompt message to the generating AI model in this system would be, "Generate appropriate evacuation plans and energy efficiency suggestions through local disaster prediction data and resident sentiment analysis."

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The server receives environmental data transmitted from data collection devices installed in the region. This data includes information such as temperature, humidity, and wind speed from weather sensors, and power consumption obtained from energy meters. The received data is stored in a time-series database by an information storage system. The input is environmental data, and the output is an organized dataset.

[0733] Step 2:

[0734] The server preprocesses the accumulated environmental data using information processing tools. Specific operations include imputing missing values, removing outliers, and converting data formats. The preprocessed data is output as cleaned data suitable for analysis.

[0735] Step 3:

[0736] The server uses pre-processed data to create a disaster prediction model using a prediction generation mechanism. Specifically, it applies statistical analysis and machine learning algorithms to predict the likelihood of a disaster. The output of this step is a predictive model showing the risk level for each region.

[0737] Step 4:

[0738] The server inputs preprocessed data into an energy analysis system to analyze energy consumption patterns. This involves statistical analysis to identify peak energy demand times and specific usage patterns. The output is the analysis results for formulating an optimal energy consumption strategy.

[0739] Step 5:

[0740] Users input their emotions through feedback and surveys via their devices. This data is sent to a server and processed by sentiment analysis tools. Specifically, natural language processing technology is used to classify emotions into categories such as positive and negative. The output is sentiment data that reflects the user's emotional state.

[0741] Step 6:

[0742] The server proposes a regional development plan using plan improvement support tools, based on the generated predictive models, energy consumption analysis results, and sentiment data. At this stage, different data sources are integrated to develop a customized plan tailored to the needs of the local residents. The output of this step is the proposed improved regional development plan.

[0743] Step 7:

[0744] The terminal displays disaster predictions, energy consumption information, and improved planning suggestions to the user. This information is presented in a dashboard format for intuitive understanding. Users can provide real-time feedback based on this information, contributing further sentiment data to the system. The output is updated information tailored to the user's feedback.

[0745] (Application Example 2)

[0746] 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".

[0747] To ensure the safety and sustainability of a region, regional development plans that consider the emotional state of residents, along with environmental data, are necessary. Conventional systems have only formulated development plans based on environmental data and have not adequately incorporated residents' emotions, resulting in difficulties in providing safety information and evacuation plans that enhance residents' sense of security.

[0748] 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.

[0749] In this invention, the server includes data storage means, data processing means, sentiment analysis means, and information provision means. This makes it possible to provide local risk information and personalize evacuation alerts using both environmental data and residents' sentiment data.

[0750] "Data storage means" refers to a device or function that has the function of recording multiple environmental data received from a data collection device.

[0751] "Data processing means" refers to a device or function that has the function of formatting stored data so that it can be properly analyzed.

[0752] "Predictive model generation means" refers to a device or function that has the function of generating a disaster prediction model based on pre-processed data.

[0753] "Energy analysis means" refers to a device or function that has the function of identifying energy consumption patterns based on pre-processed data.

[0754] "Planning support means" refers to a device or function that has the function of supporting the formulation of regional development plans based on predictive models and energy consumption patterns.

[0755] "Emotional analysis means" refers to a device or function that collects and analyzes emotional data to recognize the emotional state of residents and use that information to provide safety information.

[0756] "Information provision means" refers to a device or function that has the function of providing personalized safety information to residents.

[0757] The system for carrying out this invention includes a data collection device, a server, an emotion engine, and a user terminal. The data collection device includes multiple sensors that acquire weather data and energy consumption data, which are transmitted to the server via a network. The server utilizes data analysis using Python, emotion recognition using TensorFlow, a backend using Flask, and a mobile frontend using React Native.

[0758] The server records the received environmental data in a data storage device and formats it into an analyzable form using a data processing device. Then, using the formatted data, a disaster prediction model is generated by a prediction model generation device, and energy consumption patterns are analyzed by an energy analysis device. Furthermore, the emotion engine collects emotion data based on feedback and surveys from residents, and this is interpreted by an emotion analysis device. The analyzed data is used by a planning support device to propose regional development plans and identify areas for improvement.

[0759] User terminals provide residents with personalized safety information in real time. For example, if the risk of disaster increases in a certain area and residents are feeling anxious, the server will immediately send evacuation alerts and safety information appropriate to the situation to the terminals, thereby improving residents' sense of security.

[0760] An example of a prompt might be, "Please suggest how AI technology can improve the way we provide evacuation information to residents based on local disaster prediction models." Thus, providing and integrating information based on emotional and environmental data is crucial.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] The server receives environmental data transmitted from the data acquisition device and stores it in the data storage means. At this point, the inputs are weather data and energy consumption data, and the output is a record of the organized environmental data.

[0764] Step 2:

[0765] The server uses data processing tools to format the received environmental data for analysis. The input is raw data stored on the server, and the output is data converted into an analyzable format. Specifically, this involves data format conversion and the imputation of missing values.

[0766] Step 3:

[0767] The server constructs a disaster prediction model using a predictive model generation mechanism with formatted environmental data. The input is formatted environmental data, and the output is the disaster prediction model. In this process, data patterns are learned using machine learning algorithms.

[0768] Step 4:

[0769] The server identifies energy consumption patterns using energy analysis tools. The input is formatted energy data, and the output is the analysis result of the consumption pattern. Specifically, it performs time-series analysis and anomaly detection.

[0770] Step 5:

[0771] The server collects emotional data from users using an emotion engine and analyzes it using emotion analysis tools. Inputs are user feedback and survey results, and outputs are analysis results indicating the emotional state. Emotions are evaluated using natural language processing techniques and AI models.

[0772] Step 6:

[0773] The server, through planning support mechanisms, develops regional development plans based on emotional and environmental data. Inputs include disaster prediction models, energy consumption patterns, and emotional analysis results, while output is the improved and proposed development plan. This enables appropriate, region-specific support.

[0774] Step 7:

[0775] The terminal displays safety information provided by the server to residents. The input is personalized safety information from the server, and the output is information presented in an easy-to-understand format for the user. This allows users to easily obtain and check relevant safety information in real time.

[0776] 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.

[0777] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] A data storage means that receives multiple environmental data acquired from a data acquisition device and records them,

[0800] A data processing means for pre-processing stored data,

[0801] A prediction model generation means that generates a disaster prediction model using preprocessed data,

[0802] An energy analysis means that identifies energy consumption patterns using preprocessed data,

[0803] Based on the aforementioned prediction model and energy consumption patterns, a planning support means is provided to assist in formulating regional development plans.

[0804] A system that includes this.

[0805] (Claim 2)

[0806] The system according to claim 1, characterized in that the data acquisition device includes a plurality of sensing devices and has a configuration for acquiring data from the sensing devices.

[0807] (Claim 3)

[0808] The system according to claim 1, wherein the planning support means includes a display device for visualizing information and presents the visualized results to the regional development manager.

[0809] "Example 1"

[0810] (Claim 1)

[0811] An information storage means that receives multiple pieces of environmental information acquired from a data collection means and records them,

[0812] A data processing means for pre-processing stored information,

[0813] A prediction model generation means that generates a disaster prediction model using preprocessed information,

[0814] A resource analysis means that identifies resource consumption patterns using pre-processed information,

[0815] Based on the aforementioned prediction model and resource consumption patterns, a planning support means is provided to assist in formulating regional development plans.

[0816] A means of communication that displays information and notifies the user,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, characterized in that the data collection means includes a plurality of sensors and has a configuration for acquiring information from the sensors.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the planning support means includes a display device for visualizing information, and presents the visualized results to a local administrator.

[0822] "Application Example 1"

[0823] (Claim 1)

[0824] A data storage means that receives multiple environmental data acquired from a data acquisition device and records them,

[0825] A data processing means for pre-processing stored data,

[0826] A prediction model generation means that generates a disaster prediction model using preprocessed data,

[0827] An energy analysis means that identifies energy consumption patterns using preprocessed data,

[0828] Based on the aforementioned prediction model and energy consumption patterns, a planning support means is provided to assist in formulating regional development plans.

[0829] A notification method that sends disaster notifications to users based on location information,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, characterized in that the data acquisition device includes a plurality of sensing devices and has a configuration for acquiring data from the sensing devices.

[0833] (Claim 3)

[0834] The system according to claim 1, wherein the planning support means includes a display device for visualizing information, presents the visualized results to the regional development manager, and notifies the user in real time.

[0835] "Example 2 of combining an emotion engine"

[0836] (Claim 1)

[0837] An information storage means that receives multiple environmental data acquired from a data acquisition device and records them,

[0838] Information processing means for pre-processing stored information,

[0839] A prediction generation means that generates a disaster prediction model using preprocessed information,

[0840] An energy analysis means that identifies energy consumption patterns using pre-processed information,

[0841] A sentiment analysis tool that collects user feedback and analyzes emotional data,

[0842] A plan improvement support means that proposes improvements to regional development plans based on the aforementioned predictive model, energy consumption patterns, and sentiment data,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, characterized in that the data acquisition device includes a plurality of detection devices and has a configuration for acquiring information from the detection devices.

[0846] (Claim 3)

[0847] The system according to claim 1, wherein the plan improvement support means includes a display device for visualizing information, and presents the visualized results to a local administrator.

[0848] "Application example 2 when combining with an emotional engine"

[0849] (Claim 1)

[0850] A data storage means that receives multiple environmental data acquired from a data acquisition device and records them,

[0851] A data processing means for pre-processing stored data,

[0852] A prediction model generation means that generates a disaster prediction model using preprocessed data,

[0853] An energy analysis means that identifies energy consumption patterns using preprocessed data,

[0854] Based on the aforementioned prediction model and energy consumption patterns, a planning support means is provided to assist in formulating regional development plans.

[0855] A sentiment analysis means that collects and analyzes sentiment data and provides safety information based on it,

[0856] A means of providing information to residents that offers personalized safety information,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, characterized in that the data acquisition device includes a plurality of sensing devices and has a configuration for acquiring data from the sensing devices.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the planning support means includes a display device for visualizing information, and presents the visualized results to the local development manager and residents. [Explanation of Symbols]

[0862] 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

1. A data storage means that receives multiple environmental data acquired from a data acquisition device and records them, A data processing means for pre-processing stored data, A prediction model generation means that generates a disaster prediction model using preprocessed data, An energy analysis means that identifies energy consumption patterns using preprocessed data, Based on the aforementioned prediction model and energy consumption patterns, a planning support means is provided to assist in formulating regional development plans. A system that includes this.

2. The system according to claim 1, characterized in that the data acquisition device includes a plurality of sensing devices and has a configuration for acquiring data from the sensing devices.

3. The system according to claim 1, characterized in that the planning support means includes a display device for visualizing information and presents the visualized results to the regional development manager.

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