system

Abstract

We provide the system. [Solution] A device for acquiring location information, A device for collecting and processing data, A device that dynamically calculates paths using machine learning algorithms, A device that provides a calculated route to the user, A device that updates safe routes based on real-time environmental information, A device that presents information visually and audibly, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the event of a large-scale disaster in an urban area, particularly during an earthquake, there is a problem that it is difficult to evacuate quickly and safely due to congestion on the evacuation route. Also, when there is a risk of a tsunami, more appropriate evacuation guidance is required, but there is a problem that conventional means cannot provide an optimally dynamic route for individual users.

Means for Solving the Problems

[0005] This invention provides a system that collects location information and real-time pedestrian flow data from user terminals and dynamically calculates the optimal evacuation route using a machine learning algorithm. The calculated evacuation route is presented to the user terminal visually and audibly. Furthermore, by providing evacuation routes to higher ground based on tsunami prediction information, the system enables rapid and safe evacuation during disasters.

[0006] A "user terminal" is a portable information processing device used to access the evacuation support system and receive necessary information.

[0007] "Location information" refers to data that indicates the geographical coordinates of a specific device or individual at its current location.

[0008] "Real-time human flow data" refers to data that shows the movement and concentration of people in a specific city or region at a specific time.

[0009] A "machine learning algorithm" is a computational technique that allows computers to learn from data and automatically adapt or make decisions without modification.

[0010] An "evacuation route" is a route designed to move from a dangerous area to a safe place.

[0011] "Tsunami prediction information" refers to predictive data regarding the likelihood of tsunamis occurring due to earthquakes or other geological events.

[0012] A "high ground" is a place or area at a high elevation that is considered safe from the effects of tsunamis and flooding.

[0013] "Means of calculation" refers to a device or method used to perform a predetermined operation based on specific input data and derive a result.

[0014] "Presenting visually and aurally" refers to methods of conveying information by displaying it on a screen or by using sound.

Brief Description of the Drawings

[0015] [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 apparatus 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 apparatus 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 apparatus 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 apparatus 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.

Modes for Carrying Out the Invention

[0016] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of this invention is designed to assist users in safe evacuation. A dedicated application is installed on the user's terminal, which provides evacuation guidance. The server is connected to a real-time data collection system that covers the entire city, and it acquires and processes information from traffic sensors, CCTV, and social media.

[0037] During system operation, the server first remotely detects situations requiring evacuation. When this situation occurs, the server collects human flow data in real time and runs machine learning algorithms to calculate evacuation routes. It receives location information from each user terminal and uses this information to dynamically calculate the optimal evacuation route for each individual.

[0038] The calculated evacuation route is sent to the user's terminal and presented as a visual map and voice navigation. This allows the user to evacuate safely along the route. Furthermore, if there is a possibility of a tsunami after the earthquake, the server can recalculate the evacuation route to safer high ground based on tsunami prediction information. This information is also immediately sent to the user's terminal, and the navigation is updated.

[0039] For example, let's say a user is inside a high-rise building. In this case, the server calculates the congestion status of nearby exits and evacuation stairwells and directs the user's terminal to the optimal exit. Then, if the server detects the possibility of a tsunami, it calculates a route to the nearest high ground. The terminal continuously presents this to the user and continues to provide support until the evacuation is complete. To eliminate this disruption, the user's terminal constantly complements others and receives more information, allowing it to operate more smoothly.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server connects to a real-time data collection network in urban areas and receives information from traffic sensors, CCTV, and social media. This information is aggregated to create initial data for understanding current pedestrian flow and congestion levels.

[0043] Step 2:

[0044] The user becomes aware of the disaster and launches a dedicated app on their device. The user allows location sharing, and the device sends that information to the server.

[0045] Step 3:

[0046] The server uses machine learning algorithms to calculate the optimal evacuation route based on location information received from users and real-time pedestrian flow data. The calculation takes into account current congestion levels, characteristics of the evacuation route, and the distance to a safe exit.

[0047] Step 4:

[0048] The server sends the calculated evacuation route to the user's device. The device then displays the received evacuation route visually to the user and begins voice navigation.

[0049] Step 5:

[0050] If a tsunami is predicted after an earthquake, the server collects tsunami prediction information and analyzes the affected area and arrival time. The server then recalculates safe evacuation routes to higher ground.

[0051] Step 6:

[0052] The server sends a new evacuation route to higher ground to the user's terminal, which then displays this information to the user. The user then continues their evacuation, following the visual and audio guidance, towards higher ground.

[0053] (Example 1)

[0054] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0055] In modern urban environments, rapid and safe evacuation is essential during disasters. However, when many people attempt to evacuate simultaneously, congestion and a lack of information make effective evacuation difficult. This invention aims to provide optimal evacuation routes to individual users through real-time information gathering and analysis, thereby enabling rapid and safe evacuation.

[0056] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0057] In this invention, the server includes means for receiving location data from user devices, means for collecting and analyzing crowd flow in real time, means for dynamically calculating escape routes using a machine learning model, means for storing information in a database, and means for analyzing the data and detecting anomalies. This enables the formulation and provision of evacuation plans in real time.

[0058] A "user device" is a communication terminal equipped with a user interface for receiving evacuation information during a disaster.

[0059] "Location data" refers to geographical coordinate information used by a user's device to determine its current location.

[0060] "Means for collecting and analyzing crowd flow in real time" refers to a system for instantly acquiring and analyzing people's movement patterns within a city.

[0061] A "machine learning model" is a collection of algorithms that learn from large amounts of data and use that data to recognize patterns and predict future situations.

[0062] "Methods for dynamically calculating escape routes" refer to technologies that instantly generate the optimal evacuation route based on changing environmental information.

[0063] "Methods for storing information in a database" refer to technologies that systematically accumulate collected information and make it available for retrieval when needed.

[0064] "Means of analyzing data and detecting anomalies" refers to a function that uses algorithms to detect events or situations that deviate from normal patterns.

[0065] This invention is a system that supports the safe evacuation of users during disasters. The system consists of a server and multiple user devices. The server has a real-time data collection function that covers the entire city, collecting and analyzing information from traffic sensors, surveillance cameras, and social media. Specifically, the software uses Python scripts for data collection and the Tensorflow® and PyTorch libraries for executing machine learning algorithms. The collected information is stored in a database and managed using database technologies such as MongoDB.

[0066] A dedicated evacuation application is installed on the user's device. The application uses GPS to collect the user's location data in real time and sends it to a server, receiving the optimal escape route for each individual. This route is provided to the user through voice guidance and a visual map. Text-to-speech libraries such as gTTS are used for voice guidance.

[0067] A concrete example would be a user inside a high-rise building. The server calculates the optimal exit based on congestion levels and guides the user to their device. Furthermore, in the event of a potential tsunami, the server automatically recalculates the evacuation route to safer, higher ground and updates the navigation in real time.

[0068] An example of a prompt message is, "If the user is in a high-rise building where evacuation is necessary, please explain a safe and rapid evacuation method and the reasons why." By inputting such a prompt message into an AI generation model, the AI ​​can suggest an evacuation method appropriate to the situation and generate support information to enhance safety in peaceful circumstances.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The server collects data in real time from city-wide traffic sensors, surveillance cameras, and social media. It uses data streams from each source as input. Specifically, the server sends requests to each data source using a Python script, retrieving data in JSON format. This data is then stored in a database for further analysis. The output is appropriately formatted data stored in the database.

[0072] Step 2:

[0073] The server analyzes the collected data to detect the occurrence of disasters and situations requiring evacuation. It uses crowd flow and other environmental data stored in a database as input. The server runs a machine learning model to detect anomalies from this data. Specifically, it implements an anomaly detection algorithm using scikit-learn and issues an alert when a pre-set threshold is exceeded. The output generates trigger information indicating that an evacuation situation has been detected.

[0074] Step 3:

[0075] The server dynamically calculates the optimal escape route for each user based on the detected situation. It uses crowd data and each user's location information as input. The server performs route optimization using machine learning libraries such as TensorFlow and PyTorch. Specifically, it aggregates information on congestion levels and dangerous locations, and then calculates the shortest and safest route based on this information. The output is individual evacuation route information delivered to each user.

[0076] Step 4:

[0077] The terminal receives evacuation routes sent from the server. It receives route information from the server as input. Specifically, the terminal uses the Google® Maps API to generate a visual map and the gTTS library to create audio guidance. As output, the user receives visual and audio navigation information.

[0078] Step 5:

[0079] The user begins a safe evacuation based on the information displayed on the device. Visual and audio guidance from the device is used as input. The specific action involves following a designated route and safely moving to the designated location. The output is the completion of the user's evacuation.

[0080] Step 6:

[0081] The server continues to monitor data in real time during evacuation and updates routes if new hazards are detected. It uses continuously collected environmental data as input. Specifically, if an anomaly or new hazard is detected, it executes step 3 again, quickly recalculates the route, and sends it to the terminal. The output provides the most up-to-date evacuation information at all times.

[0082] (Application Example 1)

[0083] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0084] In the event of natural disasters or emergencies, a system that provides optimal evacuation routes in real time is necessary for the efficient and safe evacuation of large numbers of users in urban environments. However, conventional systems have difficulty providing information optimized for individual users and have been inadequate in situations requiring flexible responses. Furthermore, to improve disaster prevention functions, especially in smart cities, more advanced data processing and information provision are necessary.

[0085] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0086] In this invention, the server includes a device for acquiring location information, a device for collecting and processing data, and a device for dynamically calculating routes using a machine learning algorithm. This makes it possible to provide each user with an optimized evacuation route in real time.

[0087] A "location information acquisition device" is a device designed to accurately determine a user's current location, making it possible to collect user location data in real time.

[0088] A "data collection and processing device" is a computing device that collects data from various sources and analyzes it appropriately. By rapidly processing the collected data, it is possible to grasp the current situation.

[0089] A "device that dynamically calculates routes using machine learning algorithms" is a computing device that has a program to calculate a route optimized for the user based on accumulated data and a learned model.

[0090] A "device that provides a calculated route to a user" is a device equipped with an interface for presenting the calculated travel route to the user through visual and auditory means.

[0091] A "device that updates safe routes based on real-time environmental information" is a device that takes in environmental information that changes over time and updates evacuation routes to the optimal ones each time.

[0092] A "device that presents information visually and audibly" is a display device that intuitively conveys necessary information to the user through images and sounds.

[0093] A description of embodiments for carrying out this invention will be given.

[0094] The system consists of a series of devices and programs for providing evacuation routes. The server uses location information acquisition devices to determine the user's current location and uses data collection and processing devices to analyze data from various sources. This allows for the preparation of appropriate evacuation information based on the user's environment.

[0095] Next, the server dynamically calculates the route using a machine learning algorithm and generates an optimized evacuation route. This algorithm reflects real-time changing environmental information, ensuring that the route is always based on the latest conditions. Furthermore, user terminals are equipped with devices that provide the calculated evacuation route, allowing users to receive information visually and audibly.

[0096] As a concrete example, consider a scenario where an earthquake occurs while a user is in a high-rise building in a city. This system quickly grasps the user's location and surrounding congestion, and calculates the optimal evacuation route. Based on real-time updated information, it guides the user visually and audibly, continuing to support them until they safely complete their evacuation. By using a prompt such as, "An earthquake occurred while I was returning from a fireworks display. Please tell me the evacuation route," the AI ​​model can generate specific evacuation instructions.

[0097] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0098] Step 1:

[0099] The server first receives location information from the user. It obtains location data from the smartphone's GPS module as input and uses this data to determine the current location. As output, it obtains the user's latitude and longitude numerical information and saves this data for use in the next step.

[0100] Step 2:

[0101] The server collects and processes real-time data from sensors and social media within the city. Its inputs include traffic sensor footage, CCTV images, and social media posts. The acquired data is analyzed and processed into information for evaluating current pedestrian and traffic conditions. As output, it generates summary data of the surrounding environment, which is then passed to a route calculation algorithm.

[0102] Step 3:

[0103] The server uses machine learning algorithms to dynamically calculate the optimal evacuation route. It uses the user's location and surrounding environment data as input to begin route calculation. Through data processing, it infers the best travel route in real time and generates a list of recommended evacuation routes as output.

[0104] Step 4:

[0105] The server provides the calculated evacuation route to the user's terminal. It takes the output of the route calculation algorithm as input and generates data to send to the terminal. By visually displaying the route on a map and adding voice navigation information, it creates detailed route guidance presented to the user as output.

[0106] Step 5:

[0107] The user initiates a safe evacuation based on the evacuation route displayed through the terminal. They receive information from the server as input and follow real-time navigation. The user is guided through video and audio, and the output is the actual evacuation action.

[0108] 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.

[0109] The system of this invention not only utilizes real-time data to support users in safe evacuation, but also provides evacuation guidance that takes into account the user's emotional state. A dedicated application is installed on the user's terminal, and location information is shared with the server. After receiving the location information, the server collects real-time pedestrian flow data within the city and works in conjunction with the emotion engine.

[0110] The emotion engine analyzes the user's emotional state from data such as voice, video, or touch data from the device, recognizing emotions such as stress and anxiety. Based on this emotional data, the server can calculate evacuation routes and prioritize presenting routes that are less stressful for the user.

[0111] The calculated evacuation route is transmitted to the device, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state, providing more relaxed guidance or additional explanations as needed.

[0112] For example, suppose a user inside a high-rise building detects an earthquake. If the user immediately launches the app, the server instantly retrieves their current location and emotional information. If the emotional engine detects a high stress level in the user, the server changes their destination, selecting a quieter route that avoids congestion, and sends this information to the device. The device then provides updated navigation information to help the user evacuate without stress.

[0113] Furthermore, if there is a high probability of a tsunami after an earthquake, the server collects tsunami prediction information and recalculates safe evacuation routes to higher ground. The emotion engine continuously monitors the user's state and, if necessary, further adjusts the evacuation route or guidance method.

[0114] In this way, the system of the present invention can flexibly respond to the emotional needs of users and support safe and efficient evacuation.

[0115] The following describes the processing flow.

[0116] Step 1:

[0117] When a user detects an earthquake, a dedicated app on their device is quickly launched. The device requests permission from the user to access their location, and once consent is obtained, it sends that information to a server.

[0118] Step 2:

[0119] The server begins analysis based on the received location information, along with real-time pedestrian flow data. During this process, it collects additional data from traffic sensors and other data sources to understand the surrounding congestion and evacuation route conditions.

[0120] Step 3:

[0121] The emotion engine evaluates the user's emotional state, particularly their stress level, based on data acquired from the user's device's sensors and camera. For example, it determines emotions through voice tone and facial expression analysis.

[0122] Step 4:

[0123] The server receives information from the emotion engine and calculates an evacuation route that takes the user's emotional state into account. For highly stressed users, it prioritizes routes that avoid congestion and allow for relaxation as much as possible.

[0124] Step 5:

[0125] The calculated evacuation route is sent to the device. The device displays the evacuation route on a map and begins voice guidance. The navigation is adjusted according to the user's emotional state, and if necessary, it plays calming messages to provide additional reassurance.

[0126] Step 6:

[0127] The server monitors the progress of the disaster and, if there is a possibility of a tsunami, collects and analyzes tsunami prediction information. Based on this new information, the server calculates evacuation routes to higher ground, adjusts the guidance to reflect the user's emotional state, and then sends it to the terminal.

[0128] Step 7:

[0129] While the user follows the evacuation route, the device continuously monitors the user's location and emotions. If the server deems it necessary, it readjusts the evacuation route and guidance messages, and the device notifies the user.

[0130] (Example 2)

[0131] 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".

[0132] In the event of a disaster or emergency, for users to evacuate safely and quickly, it is necessary not only to provide the shortest route, but also to design evacuation routes that take into account the user's emotional state. Conventional evacuation guidance systems have difficulty providing evacuation routes that take into account the user's emotional state, and have lacked evacuation guidance that reduces the user's stress and anxiety.

[0133] 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.

[0134] In this invention, the server includes means for receiving location information from a user terminal, means for collecting and processing real-time pedestrian flow data, means for analyzing the user's emotional state, means for prioritizing the calculation of low-stress evacuation routes based on the analyzed emotional state, and means for providing the calculated evacuation route to the user. This enables safe and low-stress evacuation guidance that is tailored to the user's emotional state.

[0135] A "user terminal" refers to an electronic device used by a user, such as a mobile information terminal or a smartphone.

[0136] "Location information" refers to geographical location data acquired by the user's device.

[0137] "Real-time pedestrian flow data" refers to data that shows the movement and concentration of people in a specific area at any given time.

[0138] A "machine learning algorithm" is a computational method that learns from data and automatically improves for a specific task.

[0139] An "evacuation route" is route information for moving from a designated location to a safe destination.

[0140] "Emotional state" refers to data that indicates the user's psychological state, such as stress, anxiety, or relaxation.

[0141] Navigation refers to visual and auditory instructions used to guide people to their destination.

[0142] The embodiments for carrying out the present invention are shown below.

[0143] This system consists primarily of a user terminal, a server, and an emotion engine. Users install a dedicated application on their user terminal and provide location information, audio, video, or touch data. This allows the terminal to obtain the user's current location and emotional state.

[0144] The server receives location information transmitted from the user's terminal. It also collects real-time pedestrian flow data within the city to secure the basic information necessary for calculating evacuation routes. The emotion engine uses a generative AI model to analyze the emotional state data received from the terminal. This model enables high-precision recognition of the user's stress and anxiety.

[0145] Furthermore, based on the analysis results, the server calculates evacuation routes to minimize user stress. Specifically, it avoids routes expected to be congested and suggests quieter routes. These calculation results are sent to the terminal and presented to the user as visual and audio navigation. The navigation information is flexibly adjusted according to the user's emotional state.

[0146] For example, when a user in a high-rise building detects an earthquake, the app immediately launches. The server quickly retrieves the user's current location and emotional information, and if the emotional engine detects a high stress level, it adjusts the destination. The server selects a quiet evacuation route that avoids congestion and sends it to the device. The device provides updated navigation information to help the user evacuate with as little stress as possible.

[0147] An example of a prompt is, "Calculate a real-time evacuation route considering the user's emotional state and suggest the optimal route." This prompt allows the generative AI model to assist in emotion analysis and evacuation route calculation.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The user launches a dedicated application on a device such as a smartphone. This allows the device to acquire location information and collect audio, video, and touch data from the user. As input, the device obtains the user's location information and emotional data. This data is then sent to the server as output.

[0151] Step 2:

[0152] The server receives location information from the terminal and simultaneously collects real-time pedestrian flow data within the city. This data is sent to the emotion engine as a prompt to evaluate the user's emotional state. In this step, the data is processed using the location information and pedestrian flow data acquired as input, and the results of the emotional state analysis are output.

[0153] Step 3:

[0154] The server calculates the optimal evacuation route for the user based on the analysis results of the emotion engine. This calculation uses a generative AI model to derive a route that reduces user stress. Using the emotion data obtained from the input, it outputs a low-stress route and provides it to the terminal.

[0155] Step 4:

[0156] The terminal receives evacuation route information transmitted from the server. This information is then presented to the user in the form of visual and audio navigation. The navigation is adjusted according to the user's emotional state, and more relaxed guidance methods or additional explanations are provided as needed. Using the route information obtained as input, audio and video navigation information is output.

[0157] Step 5:

[0158] If the user's location and emotional state change, the device sends the newly acquired data back to the server. The server recalculates the evacuation route according to the situation and, if necessary, sends the updated route information back to the device. It receives the updated user data as input and outputs the recalculated route information.

[0159] (Application Example 2)

[0160] 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".

[0161] Conventional evacuation guidance systems have the problem of potentially increasing user stress and anxiety because they present a uniform evacuation route without considering the user's emotional state. In particular, it has been difficult to ensure a safe and comfortable evacuation for users during natural disasters and emergencies.

[0162] 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.

[0163] In this invention, the server includes means for receiving the user's location information and emotional state, means for collecting and processing real-time pedestrian flow data and natural disaster information, and means for dynamically calculating a low-stress evacuation route based on the user's emotional state using a machine learning algorithm. This makes it possible to present a safe and low-stress evacuation route that corresponds to the individual emotional state of each user.

[0164] "User location information" refers to data that indicates the geographical location where the user is currently located.

[0165] "Emotional state" refers to the user's current psychological and emotional condition, and assesses a variety of emotions, including stress and anxiety.

[0166] "Real-time pedestrian flow data" refers to information that records and monitors the movement and gathering of people in a specific area in real time.

[0167] "Natural disaster information" refers to data that shows the current situation and predictions regarding natural disasters such as earthquakes, tsunamis, and typhoons.

[0168] A "machine learning algorithm" is a method for computers to automatically learn patterns and rules from data.

[0169] A "low-stress evacuation route" is a more comfortable and safe evacuation route chosen to reduce the psychological burden on users.

[0170] "Dynamic calculation" refers to a process that processes data in real time according to the situation, and continuously updates the optimal solution or result.

[0171] The system that realizes this invention consists of a user terminal, a server, and an emotion analysis engine. Users can access this system using smartphones or tablet devices.

[0172] The device uses its built-in GPS sensor to obtain the user's location information and simultaneously uses its camera and microphone to transmit audio and video data to a server in real time. This data is used to recognize the user's emotional state.

[0173] The server is connected to a high-performance data processing system operating in the cloud. Based on the received location information, the server collects real-time pedestrian flow data within the city and analyzes user sentiment data using sentiment analysis engines (specifically, Amazon Rekognition and Google Cloud Vision). The analyzed data is used to dynamically calculate the most suitable and least stressful evacuation route for the user, utilizing machine learning algorithms.

[0174] The calculated evacuation route is transmitted to the user's terminal, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state; for example, if the user is judged to be highly stressed, a gentler, more relaxing voice guidance is provided.

[0175] As a concrete example, imagine a user in a crowded fireworks display venue. The system can sense the user's stress level on the spot and suggest routes through quieter roads and parks, helping them to escape the crowds safely and quickly.

[0176] An example of a prompt might be, "After a fireworks display, please suggest a quiet and safe route to a user who is stressed by the crowds." This prompt is used to suggest the optimal navigation method for the user using a generative AI model.

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The user's device obtains its current location using its built-in GPS sensor and acquires audio and video data through its microphone and camera. This data is necessary to understand the user's emotional state and is sent to the server. The input is location information and audio / video data, and the output is the transmission of data to the server.

[0180] Step 2:

[0181] The server receives location information transmitted by the user and uses this to collect real-time pedestrian flow data. Furthermore, it supplies the received audio and video data to an emotion analysis engine to analyze the user's emotional data. In this process, an emotion model is used to assess stress and anxiety levels. The input is the user's audio and video data and location information, and the output is the analyzed emotional data.

[0182] Step 3:

[0183] The server uses analyzed sentiment data and real-time pedestrian flow data to apply machine learning algorithms and dynamically calculate the optimal evacuation route that minimizes stress for the user. Here, a travel route designed to reduce anxiety is calculated. The input is sentiment data and pedestrian flow data, and the output is information on the optimal evacuation route.

[0184] Step 4:

[0185] The calculated evacuation route information is transmitted to the user's device. Based on this information, the device provides voice and visual navigation to the user. The navigation is adjusted to take into account the user's emotional state, and in some cases, guidance is provided to help them relax. The input is the calculated evacuation route, and the output is the navigation instructions for the user.

[0186] Step 5:

[0187] The user moves according to the suggested evacuation route based on the navigation provided by the device. Furthermore, the user's emotional state is continuously monitored while they are moving, and the evacuation route and guidance methods are updated in real time as needed. The input is the user's new emotional state data, and the output is the updated navigation information.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Second Embodiment]

[0192] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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".

[0204] The system of this invention is designed to assist users in safe evacuation. A dedicated application is installed on the user's terminal, which provides evacuation guidance. The server is connected to a real-time data collection system that covers the entire city, and it acquires and processes information from traffic sensors, CCTV, and social media.

[0205] During system operation, the server first remotely detects situations requiring evacuation. When this situation occurs, the server collects human flow data in real time and runs machine learning algorithms to calculate evacuation routes. It receives location information from each user terminal and uses this information to dynamically calculate the optimal evacuation route for each individual.

[0206] The calculated evacuation route is sent to the user's terminal and presented as a visual map and voice navigation. This allows the user to evacuate safely along the route. Furthermore, if there is a possibility of a tsunami after the earthquake, the server can recalculate the evacuation route to safer high ground based on tsunami prediction information. This information is also immediately sent to the user's terminal, and the navigation is updated.

[0207] For example, let's say a user is inside a high-rise building. In this case, the server calculates the congestion status of nearby exits and evacuation stairwells and directs the user's terminal to the optimal exit. Then, if the server detects the possibility of a tsunami, it calculates a route to the nearest high ground. The terminal continuously presents this to the user and continues to provide support until the evacuation is complete. To eliminate this disruption, the user's terminal constantly complements others and receives more information, allowing it to operate more smoothly.

[0208] The following describes the processing flow.

[0209] Step 1:

[0210] The server connects to a real-time data collection network in urban areas and receives information from traffic sensors, CCTV, and social media. This information is aggregated to create initial data for understanding current pedestrian flow and congestion levels.

[0211] Step 2:

[0212] The user becomes aware of the disaster and launches a dedicated app on their device. The user allows location sharing, and the device sends that information to the server.

[0213] Step 3:

[0214] The server uses machine learning algorithms to calculate the optimal evacuation route based on location information received from users and real-time pedestrian flow data. The calculation takes into account current congestion levels, characteristics of the evacuation route, and the distance to a safe exit.

[0215] Step 4:

[0216] The server sends the calculated evacuation route to the user's device. The device then displays the received evacuation route visually to the user and begins voice navigation.

[0217] Step 5:

[0218] If a tsunami is predicted after an earthquake, the server collects tsunami prediction information and analyzes the affected area and arrival time. The server then recalculates safe evacuation routes to higher ground.

[0219] Step 6:

[0220] The server sends a new evacuation route to higher ground to the user's terminal, which then displays this information to the user. The user then continues their evacuation, following the visual and audio guidance, towards higher ground.

[0221] (Example 1)

[0222] 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".

[0223] In modern urban environments, rapid and safe evacuation is essential during disasters. However, when many people attempt to evacuate simultaneously, congestion and a lack of information make effective evacuation difficult. This invention aims to provide optimal evacuation routes to individual users through real-time information gathering and analysis, thereby enabling rapid and safe evacuation.

[0224] 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.

[0225] In this invention, the server includes means for receiving location data from user devices, means for collecting and analyzing crowd flow in real time, means for dynamically calculating escape routes using a machine learning model, means for storing information in a database, and means for analyzing the data and detecting anomalies. This enables the formulation and provision of evacuation plans in real time.

[0226] A "user device" is a communication terminal equipped with a user interface for receiving evacuation information during a disaster.

[0227] "Location data" refers to geographical coordinate information used by a user's device to determine its current location.

[0228] "Means for collecting and analyzing crowd flow in real time" refers to a system for instantly acquiring and analyzing people's movement patterns within a city.

[0229] A "machine learning model" is a collection of algorithms that learn from large amounts of data and use that data to recognize patterns and predict future situations.

[0230] "Methods for dynamically calculating escape routes" refer to technologies that instantly generate the optimal evacuation route based on changing environmental information.

[0231] "Methods for storing information in a database" refer to technologies that systematically accumulate collected information and make it available for retrieval when needed.

[0232] "Means of analyzing data and detecting anomalies" refers to a function that uses algorithms to detect events or situations that deviate from normal patterns.

[0233] This invention is a system that supports the safe evacuation of users during disasters. The system consists of a server and multiple user devices. The server has a real-time data collection function that covers the entire city, collecting and analyzing information from traffic sensors, surveillance cameras, and social media. Specifically, the software uses Python scripts for data collection and TensorFlow and PyTorch libraries for executing machine learning algorithms. The collected information is stored in a database and managed using database technologies such as MongoDB.

[0234] A dedicated evacuation application is installed on the user's device. The application uses GPS to collect the user's location data in real time and sends it to a server, receiving the optimal escape route for each individual. This route is provided to the user through voice guidance and a visual map. Text-to-speech libraries such as gTTS are used for voice guidance.

[0235] A concrete example would be a user inside a high-rise building. The server calculates the optimal exit based on congestion levels and guides the user to their device. Furthermore, in the event of a potential tsunami, the server automatically recalculates the evacuation route to safer, higher ground and updates the navigation in real time.

[0236] An example of a prompt message is, "If the user is in a high-rise building where evacuation is necessary, please explain a safe and rapid evacuation method and the reasons why." By inputting such a prompt message into an AI generation model, the AI ​​can suggest an evacuation method appropriate to the situation and generate support information to enhance safety in peaceful circumstances.

[0237] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0238] Step 1:

[0239] The server collects data in real time from city-wide traffic sensors, surveillance cameras, and social media. It uses data streams from each source as input. Specifically, the server sends requests to each data source using a Python script, retrieving data in JSON format. This data is then stored in a database for further analysis. The output is appropriately formatted data stored in the database.

[0240] Step 2:

[0241] The server analyzes the collected data to detect the occurrence of disasters and situations requiring evacuation. It uses crowd flow and other environmental data stored in a database as input. The server runs a machine learning model to detect anomalies from this data. Specifically, it implements an anomaly detection algorithm using scikit-learn and issues an alert when a pre-set threshold is exceeded. The output generates trigger information indicating that an evacuation situation has been detected.

[0242] Step 3:

[0243] The server dynamically calculates the optimal escape route for each user based on the detected situation. It uses crowd data and each user's location information as input. The server performs route optimization using machine learning libraries such as TensorFlow and PyTorch. Specifically, it aggregates information on congestion levels and dangerous locations, and then calculates the shortest and safest route based on this information. The output is individual evacuation route information delivered to each user.

[0244] Step 4:

[0245] The device receives evacuation routes sent from the server. It receives route information from the server as input. Specifically, the device uses the Google Maps API to generate a visual map and the gTTS library to create audio guidance. As output, the user receives visual and audio navigation information.

[0246] Step 5:

[0247] The user begins a safe evacuation based on the information displayed on the device. Visual and audio guidance from the device is used as input. The specific action involves following a designated route and safely moving to the designated location. The output is the completion of the user's evacuation.

[0248] Step 6:

[0249] The server continues to monitor data in real time during evacuation and updates routes if new hazards are detected. It uses continuously collected environmental data as input. Specifically, if an anomaly or new hazard is detected, it executes step 3 again, quickly recalculates the route, and sends it to the terminal. The output provides the most up-to-date evacuation information at all times.

[0250] (Application Example 1)

[0251] 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."

[0252] In the event of natural disasters or emergencies, a system that provides optimal evacuation routes in real time is necessary for the efficient and safe evacuation of large numbers of users in urban environments. However, conventional systems have difficulty providing information optimized for individual users and have been inadequate in situations requiring flexible responses. Furthermore, to improve disaster prevention functions, especially in smart cities, more advanced data processing and information provision are necessary.

[0253] 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.

[0254] In this invention, the server includes a device for acquiring location information, a device for collecting and processing data, and a device for dynamically calculating routes using a machine learning algorithm. This makes it possible to provide each user with an optimized evacuation route in real time.

[0255] A "location information acquisition device" is a device designed to accurately determine a user's current location, making it possible to collect user location data in real time.

[0256] A "data collection and processing device" is a computing device that collects data from various sources and analyzes it appropriately. By rapidly processing the collected data, it is possible to grasp the current situation.

[0257] A "device that dynamically calculates routes using machine learning algorithms" is a computing device that has a program to calculate a route optimized for the user based on accumulated data and a learned model.

[0258] A "device that provides a calculated route to a user" is a device equipped with an interface for presenting the calculated travel route to the user through visual and auditory means.

[0259] A "device that updates safe routes based on real-time environmental information" is a device that takes in environmental information that changes over time and updates evacuation routes to the optimal ones each time.

[0260] A "device that presents information visually and audibly" is a display device that intuitively conveys necessary information to the user through images and sounds.

[0261] A description of embodiments for carrying out this invention will be given.

[0262] The system consists of a series of devices and programs for providing evacuation routes. The server uses location information acquisition devices to determine the user's current location and uses data collection and processing devices to analyze data from various sources. This allows for the preparation of appropriate evacuation information based on the user's environment.

[0263] Next, the server dynamically calculates the route using a machine learning algorithm and generates an optimized evacuation route. This algorithm reflects real-time changing environmental information, ensuring that the route is always based on the latest conditions. Furthermore, user terminals are equipped with devices that provide the calculated evacuation route, allowing users to receive information visually and audibly.

[0264] As a concrete example, consider a scenario where an earthquake occurs while a user is in a high-rise building in a city. This system quickly grasps the user's location and surrounding congestion, and calculates the optimal evacuation route. Based on real-time updated information, it guides the user visually and audibly, continuing to support them until they safely complete their evacuation. By using a prompt such as, "An earthquake occurred while I was returning from a fireworks display. Please tell me the evacuation route," the AI ​​model can generate specific evacuation instructions.

[0265] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0266] Step 1:

[0267] The server first receives location information from the user. It obtains location data from the smartphone's GPS module as input and uses this data to determine the current location. As output, it obtains the user's latitude and longitude numerical information and saves this data for use in the next step.

[0268] Step 2:

[0269] The server collects and processes real-time data from sensors and social media within the city. Its inputs include traffic sensor footage, CCTV images, and social media posts. The acquired data is analyzed and processed into information for evaluating current pedestrian and traffic conditions. As output, it generates summary data of the surrounding environment, which is then passed to a route calculation algorithm.

[0270] Step 3:

[0271] The server uses machine learning algorithms to dynamically calculate the optimal evacuation route. It uses the user's location and surrounding environment data as input to begin route calculation. Through data processing, it infers the best travel route in real time and generates a list of recommended evacuation routes as output.

[0272] Step 4:

[0273] The server provides the calculated evacuation route to the user's terminal. It takes the output of the route calculation algorithm as input and generates data to send to the terminal. By visually displaying the route on a map and adding voice navigation information, it creates detailed route guidance presented to the user as output.

[0274] Step 5:

[0275] The user initiates a safe evacuation based on the evacuation route displayed through the terminal. They receive information from the server as input and follow real-time navigation. The user is guided through video and audio, and the output is the actual evacuation action.

[0276] 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.

[0277] The system of this invention not only utilizes real-time data to support users in safe evacuation, but also provides evacuation guidance that takes into account the user's emotional state. A dedicated application is installed on the user's terminal, and location information is shared with the server. After receiving the location information, the server collects real-time pedestrian flow data within the city and works in conjunction with the emotion engine.

[0278] The emotion engine analyzes the user's emotional state from data such as voice, video, or touch data from the device, recognizing emotions such as stress and anxiety. Based on this emotional data, the server can calculate evacuation routes and prioritize presenting routes that are less stressful for the user.

[0279] The calculated evacuation route is transmitted to the device, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state, providing more relaxed guidance or additional explanations as needed.

[0280] For example, suppose a user inside a high-rise building detects an earthquake. If the user immediately launches the app, the server instantly retrieves their current location and emotional information. If the emotional engine detects a high stress level in the user, the server changes their destination, selecting a quieter route that avoids congestion, and sends this information to the device. The device then provides updated navigation information to help the user evacuate without stress.

[0281] Furthermore, if there is a high probability of a tsunami after an earthquake, the server collects tsunami prediction information and recalculates safe evacuation routes to higher ground. The emotion engine continuously monitors the user's state and, if necessary, further adjusts the evacuation route or guidance method.

[0282] In this way, the system of the present invention can flexibly respond to the emotional needs of users and support safe and efficient evacuation.

[0283] The following describes the processing flow.

[0284] Step 1:

[0285] When the user senses an earthquake, they quickly launch the dedicated app on the terminal. The terminal requests permission for location information from the user and, upon obtaining consent, sends that information to the server.

[0286] Step 2:

[0287] Based on the received location information, the server begins analysis along with real-time pedestrian flow data. At this time, additional data is collected from traffic sensors and other data sources to understand the surrounding congestion situation and the status of evacuation routes.

[0288] Step 3:

[0289] Based on the data obtained from the sensors and cameras of the user terminal, the emotion engine evaluates the user's emotional state, particularly the stress level. For example, emotions are judged through voice tone and facial expression analysis.

[0290] Step 4:

[0291] The server receives the information from the emotion engine and calculates an evacuation route considering the user's emotional state. For highly stressed users, a route that avoids congestion as much as possible and allows for relaxation is preferentially selected.

[0292] Step 5:

[0293] The calculated evacuation route is sent to the terminal. The terminal displays the evacuation route on the map and starts voice guidance. The navigation is adjusted according to the user's emotional state and, if necessary, plays messages that give additional reassurance in a calm tone.

[0294] Step 6:

[0295] The server monitors the progress of the disaster and, if there is a possibility of a tsunami, collects and analyzes tsunami prediction information. Based on this new information, the server calculates evacuation routes to higher ground, adjusts the guidance to reflect the user's emotional state, and then sends it to the terminal.

[0296] Step 7:

[0297] While the user follows the evacuation route, the device continuously monitors the user's location and emotions. If the server deems it necessary, it readjusts the evacuation route and guidance messages, and the device notifies the user.

[0298] (Example 2)

[0299] 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".

[0300] In the event of a disaster or emergency, for users to evacuate safely and quickly, it is necessary not only to provide the shortest route, but also to design evacuation routes that take into account the user's emotional state. Conventional evacuation guidance systems have difficulty providing evacuation routes that take into account the user's emotional state, and have lacked evacuation guidance that reduces the user's stress and anxiety.

[0301] 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.

[0302] In this invention, the server includes means for receiving location information from a user terminal, means for collecting and processing real-time pedestrian flow data, means for analyzing the user's emotional state, means for prioritizing the calculation of low-stress evacuation routes based on the analyzed emotional state, and means for providing the calculated evacuation route to the user. This enables safe and low-stress evacuation guidance that is tailored to the user's emotional state.

[0303] The "user terminal" refers to an electronic device such as a mobile information terminal or a smartphone used by a user.

[0304] The "location information" refers to geographical location data obtained by the user terminal.

[0305] The "real-time pedestrian flow data" refers to data indicating the movement and concentration of people at each point in time in a specific area.

[0306] The "machine learning algorithm" is a computational method that learns based on data and automatically improves specific tasks.

[0307] The "evacuation route" refers to route information for moving from a designated location to a safe destination.

[0308] The "emotional state" refers to data indicating the psychological state of the user, such as stress, anxiety, relaxation, etc.

[0309] "Navigation" refers to visual and audio instructions for guiding the way to a destination.

[0310] The embodiments for implementing the present invention are shown below.

[0311] This system is mainly composed of a user terminal, a server, and an emotion engine. The user installs a dedicated application on the user terminal and provides location information, audio, video, or touch data. Thereby, the terminal can obtain the user's current location and emotional state.

[0312] The server receives the location information transmitted from the user terminal. Also, by collecting real-time pedestrian flow data in the city, it ensures the basic information necessary for calculating the evacuation route. The emotion engine uses a generated AI model to analyze the emotional state data received from the terminal. With this model, it is possible to accurately recognize the stress and anxiety of the user.

[0313] Furthermore, based on the analysis results, the server calculates evacuation routes to minimize user stress. Specifically, it avoids routes expected to be congested and suggests quieter routes. These calculation results are sent to the terminal and presented to the user as visual and audio navigation. The navigation information is flexibly adjusted according to the user's emotional state.

[0314] For example, when a user in a high-rise building detects an earthquake, the app immediately launches. The server quickly retrieves the user's current location and emotional information, and if the emotional engine detects a high stress level, it adjusts the destination. The server selects a quiet evacuation route that avoids congestion and sends it to the device. The device provides updated navigation information to help the user evacuate with as little stress as possible.

[0315] An example of a prompt is, "Calculate a real-time evacuation route considering the user's emotional state and suggest the optimal route." This prompt allows the generative AI model to assist in emotion analysis and evacuation route calculation.

[0316] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0317] Step 1:

[0318] The user launches a dedicated application on a device such as a smartphone. This allows the device to acquire location information and collect audio, video, and touch data from the user. As input, the device obtains the user's location information and emotional data. This data is then sent to the server as output.

[0319] Step 2:

[0320] The server receives location information from the terminal and simultaneously collects real-time pedestrian flow data within the city. This data is sent to the emotion engine as a prompt to evaluate the user's emotional state. In this step, the data is processed using the location information and pedestrian flow data acquired as input, and the results of the emotional state analysis are output.

[0321] Step 3:

[0322] The server calculates the optimal evacuation route for the user based on the analysis results of the emotion engine. This calculation uses a generative AI model to derive a route that reduces user stress. Using the emotion data obtained from the input, it outputs a low-stress route and provides it to the terminal.

[0323] Step 4:

[0324] The terminal receives evacuation route information transmitted from the server. This information is then presented to the user in the form of visual and audio navigation. The navigation is adjusted according to the user's emotional state, and more relaxed guidance methods or additional explanations are provided as needed. Using the route information obtained as input, audio and video navigation information is output.

[0325] Step 5:

[0326] If the user's location and emotional state change, the device sends the newly acquired data back to the server. The server recalculates the evacuation route according to the situation and, if necessary, sends the updated route information back to the device. It receives the updated user data as input and outputs the recalculated route information.

[0327] (Application Example 2)

[0328] 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."

[0329] Conventional evacuation guidance systems have the problem of potentially increasing user stress and anxiety because they present a uniform evacuation route without considering the user's emotional state. In particular, it has been difficult to ensure a safe and comfortable evacuation for users during natural disasters and emergencies.

[0330] 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.

[0331] In this invention, the server includes means for receiving the user's location information and emotional state, means for collecting and processing real-time pedestrian flow data and natural disaster information, and means for dynamically calculating a low-stress evacuation route based on the user's emotional state using a machine learning algorithm. This makes it possible to present a safe and low-stress evacuation route that corresponds to the individual emotional state of each user.

[0332] "User location information" refers to data that indicates the geographical location where the user is currently located.

[0333] "Emotional state" refers to the user's current psychological and emotional condition, and assesses a variety of emotions, including stress and anxiety.

[0334] "Real-time pedestrian flow data" refers to information that records and monitors the movement and gathering of people in a specific area in real time.

[0335] "Natural disaster information" refers to data that shows the current situation and predictions regarding natural disasters such as earthquakes, tsunamis, and typhoons.

[0336] A "machine learning algorithm" is a method for computers to automatically learn patterns and rules from data.

[0337] A "low-stress evacuation route" is a more comfortable and safe evacuation route chosen to reduce the psychological burden on users.

[0338] "Dynamic calculation" refers to a process that processes data in real time according to the situation, and continuously updates the optimal solution or result.

[0339] The system that realizes this invention consists of a user terminal, a server, and an emotion analysis engine. Users can access this system using smartphones or tablet devices.

[0340] The device uses its built-in GPS sensor to obtain the user's location information and simultaneously uses its camera and microphone to transmit audio and video data to a server in real time. This data is used to recognize the user's emotional state.

[0341] The server is connected to a high-performance data processing system operating in the cloud. Based on the received location information, the server collects real-time pedestrian flow data within the city and analyzes user sentiment data using sentiment analysis engines (specifically, Amazon Rekognition and Google Cloud Vision). The analyzed data is used to dynamically calculate the most suitable and least stressful evacuation route for the user, utilizing machine learning algorithms.

[0342] The calculated evacuation route is transmitted to the user's terminal, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state; for example, if the user is judged to be highly stressed, a gentler, more relaxing voice guidance is provided.

[0343] As a concrete example, imagine a user in a crowded fireworks display venue. The system can sense the user's stress level on the spot and suggest routes through quieter roads and parks, helping them to escape the crowds safely and quickly.

[0344] An example of a prompt might be, "After a fireworks display, please suggest a quiet and safe route to a user who is stressed by the crowds." This prompt is used to suggest the optimal navigation method for the user using a generative AI model.

[0345] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0346] Step 1:

[0347] The user's device obtains its current location using its built-in GPS sensor and acquires audio and video data through its microphone and camera. This data is necessary to understand the user's emotional state and is sent to the server. The input is location information and audio / video data, and the output is the transmission of data to the server.

[0348] Step 2:

[0349] The server receives location information transmitted by the user and uses this to collect real-time pedestrian flow data. Furthermore, it supplies the received audio and video data to an emotion analysis engine to analyze the user's emotional data. In this process, an emotion model is used to assess stress and anxiety levels. The input is the user's audio and video data and location information, and the output is the analyzed emotional data.

[0350] Step 3:

[0351] The server uses analyzed sentiment data and real-time pedestrian flow data to apply machine learning algorithms and dynamically calculate the optimal evacuation route that minimizes stress for the user. Here, a travel route designed to reduce anxiety is calculated. The input is sentiment data and pedestrian flow data, and the output is information on the optimal evacuation route.

[0352] Step 4:

[0353] The calculated evacuation route information is transmitted to the user's device. Based on this information, the device provides voice and visual navigation to the user. The navigation is adjusted to take into account the user's emotional state, and in some cases, guidance is provided to help them relax. The input is the calculated evacuation route, and the output is the navigation instructions for the user.

[0354] Step 5:

[0355] The user moves according to the suggested evacuation route based on the navigation provided by the device. Furthermore, the user's emotional state is continuously monitored while they are moving, and the evacuation route and guidance methods are updated in real time as needed. The input is the user's new emotional state data, and the output is the updated navigation information.

[0356] 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.

[0357] 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.

[0358] 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.

[0359] [Third Embodiment]

[0360] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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).

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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.

[0371] 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".

[0372] The system of this invention is designed to assist users in safe evacuation. A dedicated application is installed on the user's terminal, which provides evacuation guidance. The server is connected to a real-time data collection system that covers the entire city, and it acquires and processes information from traffic sensors, CCTV, and social media.

[0373] During system operation, the server first remotely detects situations requiring evacuation. When this situation occurs, the server collects human flow data in real time and runs machine learning algorithms to calculate evacuation routes. It receives location information from each user terminal and uses this information to dynamically calculate the optimal evacuation route for each individual.

[0374] The calculated evacuation route is sent to the user's terminal and presented as a visual map and voice navigation. This allows the user to evacuate safely along the route. Furthermore, if there is a possibility of a tsunami after the earthquake, the server can recalculate the evacuation route to safer high ground based on tsunami prediction information. This information is also immediately sent to the user's terminal, and the navigation is updated.

[0375] For example, let's say a user is inside a high-rise building. In this case, the server calculates the congestion status of nearby exits and evacuation stairwells and directs the user's terminal to the optimal exit. Then, if the server detects the possibility of a tsunami, it calculates a route to the nearest high ground. The terminal continuously presents this to the user and continues to provide support until the evacuation is complete. To eliminate this disruption, the user's terminal constantly complements others and receives more information, allowing it to operate more smoothly.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The server connects to a real-time data collection network in urban areas and receives information from traffic sensors, CCTV, and social media. This information is aggregated to create initial data for understanding current pedestrian flow and congestion levels.

[0379] Step 2:

[0380] The user becomes aware of the disaster and launches a dedicated app on their device. The user allows location sharing, and the device sends that information to the server.

[0381] Step 3:

[0382] The server uses machine learning algorithms to calculate the optimal evacuation route based on location information received from users and real-time pedestrian flow data. The calculation takes into account current congestion levels, characteristics of the evacuation route, and the distance to a safe exit.

[0383] Step 4:

[0384] The server sends the calculated evacuation route to the user's device. The device then displays the received evacuation route visually to the user and begins voice navigation.

[0385] Step 5:

[0386] If a tsunami is predicted after an earthquake, the server collects tsunami prediction information and analyzes the affected area and arrival time. The server then recalculates safe evacuation routes to higher ground.

[0387] Step 6:

[0388] The server sends a new evacuation route to higher ground to the user's terminal, which then displays this information to the user. The user then continues their evacuation, following the visual and audio guidance, towards higher ground.

[0389] (Example 1)

[0390] 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."

[0391] In modern urban environments, rapid and safe evacuation is essential during disasters. However, when many people attempt to evacuate simultaneously, congestion and a lack of information make effective evacuation difficult. This invention aims to provide optimal evacuation routes to individual users through real-time information gathering and analysis, thereby enabling rapid and safe evacuation.

[0392] 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.

[0393] In this invention, the server includes means for receiving location data from user devices, means for collecting and analyzing crowd flow in real time, means for dynamically calculating escape routes using a machine learning model, means for storing information in a database, and means for analyzing the data and detecting anomalies. This enables the formulation and provision of evacuation plans in real time.

[0394] A "user device" is a communication terminal equipped with a user interface for receiving evacuation information during a disaster.

[0395] "Location data" refers to geographical coordinate information used by a user's device to determine its current location.

[0396] "Means for collecting and analyzing crowd flow in real time" refers to a system for instantly acquiring and analyzing people's movement patterns within a city.

[0397] A "machine learning model" is a collection of algorithms that learn from large amounts of data and use that data to recognize patterns and predict future situations.

[0398] "Methods for dynamically calculating escape routes" refer to technologies that instantly generate the optimal evacuation route based on changing environmental information.

[0399] "Methods for storing information in a database" refer to technologies that systematically accumulate collected information and make it available for retrieval when needed.

[0400] "Means of analyzing data and detecting anomalies" refers to a function that uses algorithms to detect events or situations that deviate from normal patterns.

[0401] This invention is a system that supports the safe evacuation of users during disasters. The system consists of a server and multiple user devices. The server has a real-time data collection function that covers the entire city, collecting and analyzing information from traffic sensors, surveillance cameras, and social media. Specifically, the software uses Python scripts for data collection and TensorFlow and PyTorch libraries for executing machine learning algorithms. The collected information is stored in a database and managed using database technologies such as MongoDB.

[0402] A dedicated evacuation application is installed on the user's device. The application uses GPS to collect the user's location data in real time and sends it to a server, receiving the optimal escape route for each individual. This route is provided to the user through voice guidance and a visual map. Text-to-speech libraries such as gTTS are used for voice guidance.

[0403] A concrete example would be a user inside a high-rise building. The server calculates the optimal exit based on congestion levels and guides the user to their device. Furthermore, in the event of a potential tsunami, the server automatically recalculates the evacuation route to safer, higher ground and updates the navigation in real time.

[0404] An example of a prompt message is, "If the user is in a high-rise building where evacuation is necessary, please explain a safe and rapid evacuation method and the reasons why." By inputting such a prompt message into an AI generation model, the AI ​​can suggest an evacuation method appropriate to the situation and generate support information to enhance safety in peaceful circumstances.

[0405] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0406] Step 1:

[0407] The server collects data in real time from city-wide traffic sensors, surveillance cameras, and social media. It uses data streams from each source as input. Specifically, the server sends requests to each data source using a Python script, retrieving data in JSON format. This data is then stored in a database for further analysis. The output is appropriately formatted data stored in the database.

[0408] Step 2:

[0409] The server analyzes the collected data to detect the occurrence of disasters and situations requiring evacuation. It uses crowd flow and other environmental data stored in a database as input. The server runs a machine learning model to detect anomalies from this data. Specifically, it implements an anomaly detection algorithm using scikit-learn and issues an alert when a pre-set threshold is exceeded. The output generates trigger information indicating that an evacuation situation has been detected.

[0410] Step 3:

[0411] The server dynamically calculates the optimal escape route for each user based on the detected situation. It uses crowd data and each user's location information as input. The server performs route optimization using machine learning libraries such as TensorFlow and PyTorch. Specifically, it aggregates information on congestion levels and dangerous locations, and then calculates the shortest and safest route based on this information. The output is individual evacuation route information delivered to each user.

[0412] Step 4:

[0413] The device receives evacuation routes sent from the server. It receives route information from the server as input. Specifically, the device uses the Google Maps API to generate a visual map and the gTTS library to create audio guidance. As output, the user receives visual and audio navigation information.

[0414] Step 5:

[0415] The user begins a safe evacuation based on the information displayed on the device. Visual and audio guidance from the device is used as input. The specific action involves following a designated route and safely moving to the designated location. The output is the completion of the user's evacuation.

[0416] Step 6:

[0417] The server continues to monitor data in real time during evacuation and updates routes if new hazards are detected. It uses continuously collected environmental data as input. Specifically, if an anomaly or new hazard is detected, it executes step 3 again, quickly recalculates the route, and sends it to the terminal. The output provides the most up-to-date evacuation information at all times.

[0418] (Application Example 1)

[0419] 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."

[0420] In the event of natural disasters or emergencies, a system that provides optimal evacuation routes in real time is necessary for the efficient and safe evacuation of large numbers of users in urban environments. However, conventional systems have difficulty providing information optimized for individual users and have been inadequate in situations requiring flexible responses. Furthermore, to improve disaster prevention functions, especially in smart cities, more advanced data processing and information provision are necessary.

[0421] 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.

[0422] In this invention, the server includes a device for acquiring location information, a device for collecting and processing data, and a device for dynamically calculating routes using a machine learning algorithm. This makes it possible to provide each user with an optimized evacuation route in real time.

[0423] A "location information acquisition device" is a device designed to accurately determine a user's current location, making it possible to collect user location data in real time.

[0424] A "data collection and processing device" is a computing device that collects data from various sources and analyzes it appropriately. By rapidly processing the collected data, it is possible to grasp the current situation.

[0425] A "device that dynamically calculates routes using machine learning algorithms" is a computing device that has a program to calculate a route optimized for the user based on accumulated data and a learned model.

[0426] A "device that provides a calculated route to a user" is a device equipped with an interface for presenting the calculated travel route to the user through visual and auditory means.

[0427] A "device that updates safe routes based on real-time environmental information" is a device that takes in environmental information that changes over time and updates evacuation routes to the optimal ones each time.

[0428] A "device that presents information visually and audibly" is a display device that intuitively conveys necessary information to the user through images and sounds.

[0429] A description of embodiments for carrying out this invention will be given.

[0430] The system consists of a series of devices and programs for providing evacuation routes. The server uses location information acquisition devices to determine the user's current location and uses data collection and processing devices to analyze data from various sources. This allows for the preparation of appropriate evacuation information based on the user's environment.

[0431] Next, the server dynamically calculates the route using a machine learning algorithm and generates an optimized evacuation route. This algorithm reflects real-time changing environmental information, ensuring that the route is always based on the latest conditions. Furthermore, user terminals are equipped with devices that provide the calculated evacuation route, allowing users to receive information visually and audibly.

[0432] As a concrete example, consider a scenario where an earthquake occurs while a user is in a high-rise building in a city. This system quickly grasps the user's location and surrounding congestion, and calculates the optimal evacuation route. Based on real-time updated information, it guides the user visually and audibly, continuing to support them until they safely complete their evacuation. By using a prompt such as, "An earthquake occurred while I was returning from a fireworks display. Please tell me the evacuation route," the AI ​​model can generate specific evacuation instructions.

[0433] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0434] Step 1:

[0435] The server first receives location information from the user. It obtains location data from the smartphone's GPS module as input and uses this data to determine the current location. As output, it obtains the user's latitude and longitude numerical information and saves this data for use in the next step.

[0436] Step 2:

[0437] The server collects and processes real-time data from sensors and social media within the city. Its inputs include traffic sensor footage, CCTV images, and social media posts. The acquired data is analyzed and processed into information for evaluating current pedestrian and traffic conditions. As output, it generates summary data of the surrounding environment, which is then passed to a route calculation algorithm.

[0438] Step 3:

[0439] The server uses machine learning algorithms to dynamically calculate the optimal evacuation route. It uses the user's location and surrounding environment data as input to begin route calculation. Through data processing, it infers the best travel route in real time and generates a list of recommended evacuation routes as output.

[0440] Step 4:

[0441] The server provides the calculated evacuation route to the user's terminal. It takes the output of the route calculation algorithm as input and generates data to send to the terminal. By visually displaying the route on a map and adding voice navigation information, it creates detailed route guidance presented to the user as output.

[0442] Step 5:

[0443] The user initiates a safe evacuation based on the evacuation route displayed through the terminal. They receive information from the server as input and follow real-time navigation. The user is guided through video and audio, and the output is the actual evacuation action.

[0444] 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.

[0445] The system of this invention not only utilizes real-time data to support users in safe evacuation, but also provides evacuation guidance that takes into account the user's emotional state. A dedicated application is installed on the user's terminal, and location information is shared with the server. After receiving the location information, the server collects real-time pedestrian flow data within the city and works in conjunction with the emotion engine.

[0446] The emotion engine analyzes the user's emotional state from data such as voice, video, or touch data from the device, recognizing emotions such as stress and anxiety. Based on this emotional data, the server can calculate evacuation routes and prioritize presenting routes that are less stressful for the user.

[0447] The calculated evacuation route is transmitted to the device, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state, providing more relaxed guidance or additional explanations as needed.

[0448] For example, suppose a user inside a high-rise building detects an earthquake. If the user immediately launches the app, the server instantly retrieves their current location and emotional information. If the emotional engine detects a high stress level in the user, the server changes their destination, selecting a quieter route that avoids congestion, and sends this information to the device. The device then provides updated navigation information to help the user evacuate without stress.

[0449] Furthermore, if there is a high probability of a tsunami after an earthquake, the server collects tsunami prediction information and recalculates safe evacuation routes to higher ground. The emotion engine continuously monitors the user's state and, if necessary, further adjusts the evacuation route or guidance method.

[0450] In this way, the system of the present invention can flexibly respond to the emotional needs of users and support safe and efficient evacuation.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] When a user detects an earthquake, a dedicated app on their device is quickly launched. The device requests permission from the user to access their location, and once consent is obtained, it sends that information to a server.

[0454] Step 2:

[0455] The server begins analysis based on the received location information, along with real-time pedestrian flow data. During this process, it collects additional data from traffic sensors and other data sources to understand the surrounding congestion and evacuation route conditions.

[0456] Step 3:

[0457] The emotion engine evaluates the user's emotional state, particularly their stress level, based on data acquired from the user's device's sensors and camera. For example, it determines emotions through voice tone and facial expression analysis.

[0458] Step 4:

[0459] The server receives information from the emotion engine and calculates an evacuation route that takes the user's emotional state into account. For highly stressed users, it prioritizes routes that avoid congestion and allow for relaxation as much as possible.

[0460] Step 5:

[0461] The calculated evacuation route is sent to the device. The device displays the evacuation route on a map and begins voice guidance. The navigation is adjusted according to the user's emotional state, and if necessary, it plays calming messages to provide additional reassurance.

[0462] Step 6:

[0463] The server monitors the progress of the disaster and, if there is a possibility of a tsunami, collects and analyzes tsunami prediction information. Based on this new information, the server calculates evacuation routes to higher ground, adjusts the guidance to reflect the user's emotional state, and then sends it to the terminal.

[0464] Step 7:

[0465] While the user follows the evacuation route, the device continuously monitors the user's location and emotions. If the server deems it necessary, it readjusts the evacuation route and guidance messages, and the device notifies the user.

[0466] (Example 2)

[0467] 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."

[0468] In the event of a disaster or emergency, for users to evacuate safely and quickly, it is necessary not only to provide the shortest route, but also to design evacuation routes that take into account the user's emotional state. Conventional evacuation guidance systems have difficulty providing evacuation routes that take into account the user's emotional state, and have lacked evacuation guidance that reduces the user's stress and anxiety.

[0469] 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.

[0470] In this invention, the server includes means for receiving location information from a user terminal, means for collecting and processing real-time pedestrian flow data, means for analyzing the user's emotional state, means for prioritizing the calculation of low-stress evacuation routes based on the analyzed emotional state, and means for providing the calculated evacuation route to the user. This enables safe and low-stress evacuation guidance that is tailored to the user's emotional state.

[0471] A "user terminal" refers to an electronic device used by a user, such as a mobile information terminal or a smartphone.

[0472] "Location information" refers to geographical location data acquired by the user's device.

[0473] "Real-time pedestrian flow data" refers to data that shows the movement and concentration of people in a specific area at any given time.

[0474] A "machine learning algorithm" is a computational method that learns from data and automatically improves for a specific task.

[0475] An "evacuation route" is route information for moving from a designated location to a safe destination.

[0476] "Emotional state" refers to data that indicates the user's psychological state, such as stress, anxiety, or relaxation.

[0477] Navigation refers to visual and auditory instructions used to guide people to their destination.

[0478] The embodiments for carrying out the present invention are shown below.

[0479] This system consists primarily of a user terminal, a server, and an emotion engine. Users install a dedicated application on their user terminal and provide location information, audio, video, or touch data. This allows the terminal to obtain the user's current location and emotional state.

[0480] The server receives location information transmitted from the user's terminal. It also collects real-time pedestrian flow data within the city to secure the basic information necessary for calculating evacuation routes. The emotion engine uses a generative AI model to analyze the emotional state data received from the terminal. This model enables high-precision recognition of the user's stress and anxiety.

[0481] Furthermore, based on the analysis results, the server calculates evacuation routes to minimize user stress. Specifically, it avoids routes expected to be congested and suggests quieter routes. These calculation results are sent to the terminal and presented to the user as visual and audio navigation. The navigation information is flexibly adjusted according to the user's emotional state.

[0482] For example, when a user in a high-rise building detects an earthquake, the app immediately launches. The server quickly retrieves the user's current location and emotional information, and if the emotional engine detects a high stress level, it adjusts the destination. The server selects a quiet evacuation route that avoids congestion and sends it to the device. The device provides updated navigation information to help the user evacuate with as little stress as possible.

[0483] An example of a prompt is, "Calculate a real-time evacuation route considering the user's emotional state and suggest the optimal route." This prompt allows the generative AI model to assist in emotion analysis and evacuation route calculation.

[0484] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0485] Step 1:

[0486] The user launches a dedicated application on a device such as a smartphone. This allows the device to acquire location information and collect audio, video, and touch data from the user. As input, the device obtains the user's location information and emotional data. This data is then sent to the server as output.

[0487] Step 2:

[0488] The server receives location information from the terminal and simultaneously collects real-time pedestrian flow data within the city. This data is sent to the emotion engine as a prompt to evaluate the user's emotional state. In this step, the data is processed using the location information and pedestrian flow data acquired as input, and the results of the emotional state analysis are output.

[0489] Step 3:

[0490] The server calculates the optimal evacuation route for the user based on the analysis results of the emotion engine. This calculation uses a generative AI model to derive a route that reduces user stress. Using the emotion data obtained from the input, it outputs a low-stress route and provides it to the terminal.

[0491] Step 4:

[0492] The terminal receives evacuation route information transmitted from the server. This information is then presented to the user in the form of visual and audio navigation. The navigation is adjusted according to the user's emotional state, and more relaxed guidance methods or additional explanations are provided as needed. Using the route information obtained as input, audio and video navigation information is output.

[0493] Step 5:

[0494] If the user's location and emotional state change, the device sends the newly acquired data back to the server. The server recalculates the evacuation route according to the situation and, if necessary, sends the updated route information back to the device. It receives the updated user data as input and outputs the recalculated route information.

[0495] (Application Example 2)

[0496] 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."

[0497] Conventional evacuation guidance systems have the problem of potentially increasing user stress and anxiety because they present a uniform evacuation route without considering the user's emotional state. In particular, it has been difficult to ensure a safe and comfortable evacuation for users during natural disasters and emergencies.

[0498] 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.

[0499] In this invention, the server includes means for receiving the user's location information and emotional state, means for collecting and processing real-time pedestrian flow data and natural disaster information, and means for dynamically calculating a low-stress evacuation route based on the user's emotional state using a machine learning algorithm. This makes it possible to present a safe and low-stress evacuation route that corresponds to the individual emotional state of each user.

[0500] "User location information" refers to data that indicates the geographical location where the user is currently located.

[0501] "Emotional state" refers to the user's current psychological and emotional condition, and assesses a variety of emotions, including stress and anxiety.

[0502] "Real-time pedestrian flow data" refers to information that records and monitors the movement and gathering of people in a specific area in real time.

[0503] "Natural disaster information" refers to data that shows the current situation and predictions regarding natural disasters such as earthquakes, tsunamis, and typhoons.

[0504] A "machine learning algorithm" is a method for computers to automatically learn patterns and rules from data.

[0505] A "low-stress evacuation route" is a more comfortable and safe evacuation route chosen to reduce the psychological burden on users.

[0506] "Dynamic calculation" refers to a process that processes data in real time according to the situation, and continuously updates the optimal solution or result.

[0507] The system that realizes this invention consists of a user terminal, a server, and an emotion analysis engine. Users can access this system using smartphones or tablet devices.

[0508] The device uses its built-in GPS sensor to obtain the user's location information and simultaneously uses its camera and microphone to transmit audio and video data to a server in real time. This data is used to recognize the user's emotional state.

[0509] The server is connected to a high-performance data processing system operating in the cloud. Based on the received location information, the server collects real-time pedestrian flow data within the city and analyzes user sentiment data using sentiment analysis engines (specifically, Amazon Rekognition and Google Cloud Vision). The analyzed data is used to dynamically calculate the most suitable and least stressful evacuation route for the user, utilizing machine learning algorithms.

[0510] The calculated evacuation route is transmitted to the user's terminal, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state; for example, if the user is judged to be highly stressed, a gentler, more relaxing voice guidance is provided.

[0511] As a concrete example, imagine a user in a crowded fireworks display venue. The system can sense the user's stress level on the spot and suggest routes through quieter roads and parks, helping them to escape the crowds safely and quickly.

[0512] An example of a prompt might be, "After a fireworks display, please suggest a quiet and safe route to a user who is stressed by the crowds." This prompt is used to suggest the optimal navigation method for the user using a generative AI model.

[0513] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0514] Step 1:

[0515] The user's device obtains its current location using its built-in GPS sensor and acquires audio and video data through its microphone and camera. This data is necessary to understand the user's emotional state and is sent to the server. The input is location information and audio / video data, and the output is the transmission of data to the server.

[0516] Step 2:

[0517] The server receives location information transmitted by the user and uses this to collect real-time pedestrian flow data. Furthermore, it supplies the received audio and video data to an emotion analysis engine to analyze the user's emotional data. In this process, an emotion model is used to assess stress and anxiety levels. The input is the user's audio and video data and location information, and the output is the analyzed emotional data.

[0518] Step 3:

[0519] The server uses analyzed sentiment data and real-time pedestrian flow data to apply machine learning algorithms and dynamically calculate the optimal evacuation route that minimizes stress for the user. Here, a travel route designed to reduce anxiety is calculated. The input is sentiment data and pedestrian flow data, and the output is information on the optimal evacuation route.

[0520] Step 4:

[0521] The calculated evacuation route information is transmitted to the user's device. Based on this information, the device provides voice and visual navigation to the user. The navigation is adjusted to take into account the user's emotional state, and in some cases, guidance is provided to help them relax. The input is the calculated evacuation route, and the output is the navigation instructions for the user.

[0522] Step 5:

[0523] The user moves according to the suggested evacuation route based on the navigation provided by the device. Furthermore, the user's emotional state is continuously monitored while they are moving, and the evacuation route and guidance methods are updated in real time as needed. The input is the user's new emotional state data, and the output is the updated navigation information.

[0524] 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.

[0525] 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.

[0526] 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.

[0527] [Fourth Embodiment]

[0528] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0529] 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.

[0530] 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).

[0531] 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.

[0532] 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.

[0533] 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).

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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.

[0538] 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.

[0539] 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.

[0540] 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".

[0541] The system of this invention is designed to assist users in safe evacuation. A dedicated application is installed on the user's terminal, which provides evacuation guidance. The server is connected to a real-time data collection system that covers the entire city, and it acquires and processes information from traffic sensors, CCTV, and social media.

[0542] During system operation, the server first remotely detects situations requiring evacuation. When this situation occurs, the server collects human flow data in real time and runs machine learning algorithms to calculate evacuation routes. It receives location information from each user terminal and uses this information to dynamically calculate the optimal evacuation route for each individual.

[0543] The calculated evacuation route is sent to the user's terminal and presented as a visual map and voice navigation. This allows the user to evacuate safely along the route. Furthermore, if there is a possibility of a tsunami after the earthquake, the server can recalculate the evacuation route to safer high ground based on tsunami prediction information. This information is also immediately sent to the user's terminal, and the navigation is updated.

[0544] For example, let's say a user is inside a high-rise building. In this case, the server calculates the congestion status of nearby exits and evacuation stairwells and directs the user's terminal to the optimal exit. Then, if the server detects the possibility of a tsunami, it calculates a route to the nearest high ground. The terminal continuously presents this to the user and continues to provide support until the evacuation is complete. To eliminate this disruption, the user's terminal constantly complements others and receives more information, allowing it to operate more smoothly.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server connects to a real-time data collection network in urban areas and receives information from traffic sensors, CCTV, and social media. This information is aggregated to create initial data for understanding current pedestrian flow and congestion levels.

[0548] Step 2:

[0549] The user becomes aware of the disaster and launches a dedicated app on their device. The user allows location sharing, and the device sends that information to the server.

[0550] Step 3:

[0551] The server uses machine learning algorithms to calculate the optimal evacuation route based on location information received from users and real-time pedestrian flow data. The calculation takes into account current congestion levels, characteristics of the evacuation route, and the distance to a safe exit.

[0552] Step 4:

[0553] The server sends the calculated evacuation route to the user's device. The device then displays the received evacuation route visually to the user and begins voice navigation.

[0554] Step 5:

[0555] If a tsunami is predicted after an earthquake, the server collects tsunami prediction information and analyzes the affected area and arrival time. The server then recalculates safe evacuation routes to higher ground.

[0556] Step 6:

[0557] The server sends a new evacuation route to higher ground to the user's terminal, which then displays this information to the user. The user then continues their evacuation, following the visual and audio guidance, towards higher ground.

[0558] (Example 1)

[0559] 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".

[0560] In modern urban environments, rapid and safe evacuation is essential during disasters. However, when many people attempt to evacuate simultaneously, congestion and a lack of information make effective evacuation difficult. This invention aims to provide optimal evacuation routes to individual users through real-time information gathering and analysis, thereby enabling rapid and safe evacuation.

[0561] 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.

[0562] In this invention, the server includes means for receiving location data from user devices, means for collecting and analyzing crowd flow in real time, means for dynamically calculating escape routes using a machine learning model, means for storing information in a database, and means for analyzing the data and detecting anomalies. This enables the formulation and provision of evacuation plans in real time.

[0563] A "user device" is a communication terminal equipped with a user interface for receiving evacuation information during a disaster.

[0564] "Location data" refers to geographical coordinate information used by a user's device to determine its current location.

[0565] "Means for collecting and analyzing crowd flow in real time" refers to a system for instantly acquiring and analyzing people's movement patterns within a city.

[0566] A "machine learning model" is a collection of algorithms that learn from large amounts of data and use that data to recognize patterns and predict future situations.

[0567] "Methods for dynamically calculating escape routes" refer to technologies that instantly generate the optimal evacuation route based on changing environmental information.

[0568] "Methods for storing information in a database" refer to technologies that systematically accumulate collected information and make it available for retrieval when needed.

[0569] "Means of analyzing data and detecting anomalies" refers to a function that uses algorithms to detect events or situations that deviate from normal patterns.

[0570] This invention is a system that supports the safe evacuation of users during disasters. The system consists of a server and multiple user devices. The server has a real-time data collection function that covers the entire city, collecting and analyzing information from traffic sensors, surveillance cameras, and social media. Specifically, the software uses Python scripts for data collection and TensorFlow and PyTorch libraries for executing machine learning algorithms. The collected information is stored in a database and managed using database technologies such as MongoDB.

[0571] A dedicated evacuation application is installed on the user's device. The application uses GPS to collect the user's location data in real time and sends it to a server, receiving the optimal escape route for each individual. This route is provided to the user through voice guidance and a visual map. Text-to-speech libraries such as gTTS are used for voice guidance.

[0572] A concrete example would be a user inside a high-rise building. The server calculates the optimal exit based on congestion levels and guides the user to their device. Furthermore, in the event of a potential tsunami, the server automatically recalculates the evacuation route to safer, higher ground and updates the navigation in real time.

[0573] An example of a prompt message is, "If the user is in a high-rise building where evacuation is necessary, please explain a safe and rapid evacuation method and the reasons why." By inputting such a prompt message into an AI generation model, the AI ​​can suggest an evacuation method appropriate to the situation and generate support information to enhance safety in peaceful circumstances.

[0574] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0575] Step 1:

[0576] The server collects data in real time from city-wide traffic sensors, surveillance cameras, and social media. It uses data streams from each source as input. Specifically, the server sends requests to each data source using a Python script, retrieving data in JSON format. This data is then stored in a database for further analysis. The output is appropriately formatted data stored in the database.

[0577] Step 2:

[0578] The server analyzes the collected data to detect the occurrence of disasters and situations requiring evacuation. It uses crowd flow and other environmental data stored in a database as input. The server runs a machine learning model to detect anomalies from this data. Specifically, it implements an anomaly detection algorithm using scikit-learn and issues an alert when a pre-set threshold is exceeded. The output generates trigger information indicating that an evacuation situation has been detected.

[0579] Step 3:

[0580] The server dynamically calculates the optimal escape route for each user based on the detected situation. It uses crowd data and each user's location information as input. The server performs route optimization using machine learning libraries such as TensorFlow and PyTorch. Specifically, it aggregates information on congestion levels and dangerous locations, and then calculates the shortest and safest route based on this information. The output is individual evacuation route information delivered to each user.

[0581] Step 4:

[0582] The device receives evacuation routes sent from the server. It receives route information from the server as input. Specifically, the device uses the Google Maps API to generate a visual map and the gTTS library to create audio guidance. As output, the user receives visual and audio navigation information.

[0583] Step 5:

[0584] The user begins a safe evacuation based on the information displayed on the device. Visual and audio guidance from the device is used as input. The specific action involves following a designated route and safely moving to the designated location. The output is the completion of the user's evacuation.

[0585] Step 6:

[0586] The server continues to monitor data in real time during evacuation and updates routes if new hazards are detected. It uses continuously collected environmental data as input. Specifically, if an anomaly or new hazard is detected, it executes step 3 again, quickly recalculates the route, and sends it to the terminal. The output provides the most up-to-date evacuation information at all times.

[0587] (Application Example 1)

[0588] 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".

[0589] In the event of natural disasters or emergencies, a system that provides optimal evacuation routes in real time is necessary for the efficient and safe evacuation of large numbers of users in urban environments. However, conventional systems have difficulty providing information optimized for individual users and have been inadequate in situations requiring flexible responses. Furthermore, to improve disaster prevention functions, especially in smart cities, more advanced data processing and information provision are necessary.

[0590] 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.

[0591] In this invention, the server includes a device for acquiring location information, a device for collecting and processing data, and a device for dynamically calculating routes using a machine learning algorithm. This makes it possible to provide each user with an optimized evacuation route in real time.

[0592] A "location information acquisition device" is a device designed to accurately determine a user's current location, making it possible to collect user location data in real time.

[0593] A "data collection and processing device" is a computing device that collects data from various sources and analyzes it appropriately. By rapidly processing the collected data, it is possible to grasp the current situation.

[0594] A "device that dynamically calculates routes using machine learning algorithms" is a computing device that has a program to calculate a route optimized for the user based on accumulated data and a learned model.

[0595] A "device that provides a calculated route to a user" is a device equipped with an interface for presenting the calculated travel route to the user through visual and auditory means.

[0596] A "device that updates safe routes based on real-time environmental information" is a device that takes in environmental information that changes over time and updates evacuation routes to the optimal ones each time.

[0597] A "device that presents information visually and audibly" is a display device that intuitively conveys necessary information to the user through images and sounds.

[0598] A description of embodiments for carrying out this invention will be given.

[0599] The system consists of a series of devices and programs for providing evacuation routes. The server uses location information acquisition devices to determine the user's current location and uses data collection and processing devices to analyze data from various sources. This allows for the preparation of appropriate evacuation information based on the user's environment.

[0600] Next, the server dynamically calculates the route using a machine learning algorithm and generates an optimized evacuation route. This algorithm reflects real-time changing environmental information, ensuring that the route is always based on the latest conditions. Furthermore, user terminals are equipped with devices that provide the calculated evacuation route, allowing users to receive information visually and audibly.

[0601] As a concrete example, consider a scenario where an earthquake occurs while a user is in a high-rise building in a city. This system quickly grasps the user's location and surrounding congestion, and calculates the optimal evacuation route. Based on real-time updated information, it guides the user visually and audibly, continuing to support them until they safely complete their evacuation. By using a prompt such as, "An earthquake occurred while I was returning from a fireworks display. Please tell me the evacuation route," the AI ​​model can generate specific evacuation instructions.

[0602] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0603] Step 1:

[0604] The server first receives location information from the user. It obtains location data from the smartphone's GPS module as input and uses this data to determine the current location. As output, it obtains the user's latitude and longitude numerical information and saves this data for use in the next step.

[0605] Step 2:

[0606] The server collects and processes real-time data from sensors and social media within the city. Its inputs include traffic sensor footage, CCTV images, and social media posts. The acquired data is analyzed and processed into information for evaluating current pedestrian and traffic conditions. As output, it generates summary data of the surrounding environment, which is then passed to a route calculation algorithm.

[0607] Step 3:

[0608] The server uses machine learning algorithms to dynamically calculate the optimal evacuation route. It uses the user's location and surrounding environment data as input to begin route calculation. Through data processing, it infers the best travel route in real time and generates a list of recommended evacuation routes as output.

[0609] Step 4:

[0610] The server provides the calculated evacuation route to the user's terminal. It takes the output of the route calculation algorithm as input and generates data to send to the terminal. By visually displaying the route on a map and adding voice navigation information, it creates detailed route guidance presented to the user as output.

[0611] Step 5:

[0612] The user initiates a safe evacuation based on the evacuation route displayed through the terminal. They receive information from the server as input and follow real-time navigation. The user is guided through video and audio, and the output is the actual evacuation action.

[0613] 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.

[0614] The system of this invention not only utilizes real-time data to support users in safe evacuation, but also provides evacuation guidance that takes into account the user's emotional state. A dedicated application is installed on the user's terminal, and location information is shared with the server. After receiving the location information, the server collects real-time pedestrian flow data within the city and works in conjunction with the emotion engine.

[0615] The emotion engine analyzes the user's emotional state from data such as voice, video, or touch data from the device, recognizing emotions such as stress and anxiety. Based on this emotional data, the server can calculate evacuation routes and prioritize presenting routes that are less stressful for the user.

[0616] The calculated evacuation route is transmitted to the device, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state, providing more relaxed guidance or additional explanations as needed.

[0617] For example, suppose a user inside a high-rise building detects an earthquake. If the user immediately launches the app, the server instantly retrieves their current location and emotional information. If the emotional engine detects a high stress level in the user, the server changes their destination, selecting a quieter route that avoids congestion, and sends this information to the device. The device then provides updated navigation information to help the user evacuate without stress.

[0618] Furthermore, if there is a high probability of a tsunami after an earthquake, the server collects tsunami prediction information and recalculates safe evacuation routes to higher ground. The emotion engine continuously monitors the user's state and, if necessary, further adjusts the evacuation route or guidance method.

[0619] In this way, the system of the present invention can flexibly respond to the emotional needs of users and support safe and efficient evacuation.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] When a user detects an earthquake, a dedicated app on their device is quickly launched. The device requests permission from the user to access their location, and once consent is obtained, it sends that information to a server.

[0623] Step 2:

[0624] The server begins analysis based on the received location information, along with real-time pedestrian flow data. During this process, it collects additional data from traffic sensors and other data sources to understand the surrounding congestion and evacuation route conditions.

[0625] Step 3:

[0626] The emotion engine evaluates the user's emotional state, particularly their stress level, based on data acquired from the user's device's sensors and camera. For example, it determines emotions through voice tone and facial expression analysis.

[0627] Step 4:

[0628] The server receives information from the emotion engine and calculates an evacuation route that takes the user's emotional state into account. For highly stressed users, it prioritizes routes that avoid congestion and allow for relaxation as much as possible.

[0629] Step 5:

[0630] The calculated evacuation route is sent to the device. The device displays the evacuation route on a map and begins voice guidance. The navigation is adjusted according to the user's emotional state, and if necessary, it plays calming messages to provide additional reassurance.

[0631] Step 6:

[0632] The server monitors the progress of the disaster and, if there is a possibility of a tsunami, collects and analyzes tsunami prediction information. Based on this new information, the server calculates evacuation routes to higher ground, adjusts the guidance to reflect the user's emotional state, and then sends it to the terminal.

[0633] Step 7:

[0634] While the user follows the evacuation route, the device continuously monitors the user's location and emotions. If the server deems it necessary, it readjusts the evacuation route and guidance messages, and the device notifies the user.

[0635] (Example 2)

[0636] 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".

[0637] In the event of a disaster or emergency, for users to evacuate safely and quickly, it is necessary not only to provide the shortest route, but also to design evacuation routes that take into account the user's emotional state. Conventional evacuation guidance systems have difficulty providing evacuation routes that take into account the user's emotional state, and have lacked evacuation guidance that reduces the user's stress and anxiety.

[0638] 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.

[0639] In this invention, the server includes means for receiving location information from a user terminal, means for collecting and processing real-time pedestrian flow data, means for analyzing the user's emotional state, means for prioritizing the calculation of low-stress evacuation routes based on the analyzed emotional state, and means for providing the calculated evacuation route to the user. This enables safe and low-stress evacuation guidance that is tailored to the user's emotional state.

[0640] A "user terminal" refers to an electronic device used by a user, such as a mobile information terminal or a smartphone.

[0641] "Location information" refers to geographical location data acquired by the user's device.

[0642] "Real-time pedestrian flow data" refers to data that shows the movement and concentration of people in a specific area at any given time.

[0643] A "machine learning algorithm" is a computational method that learns from data and automatically improves for a specific task.

[0644] An "evacuation route" is route information for moving from a designated location to a safe destination.

[0645] "Emotional state" refers to data that indicates the user's psychological state, such as stress, anxiety, or relaxation.

[0646] Navigation refers to visual and auditory instructions used to guide people to their destination.

[0647] The embodiments for carrying out the present invention are shown below.

[0648] This system consists primarily of a user terminal, a server, and an emotion engine. Users install a dedicated application on their user terminal and provide location information, audio, video, or touch data. This allows the terminal to obtain the user's current location and emotional state.

[0649] The server receives location information transmitted from the user's terminal. It also collects real-time pedestrian flow data within the city to secure the basic information necessary for calculating evacuation routes. The emotion engine uses a generative AI model to analyze the emotional state data received from the terminal. This model enables high-precision recognition of the user's stress and anxiety.

[0650] Furthermore, based on the analysis results, the server calculates evacuation routes to minimize user stress. Specifically, it avoids routes expected to be congested and suggests quieter routes. These calculation results are sent to the terminal and presented to the user as visual and audio navigation. The navigation information is flexibly adjusted according to the user's emotional state.

[0651] For example, when a user in a high-rise building detects an earthquake, the app immediately launches. The server quickly retrieves the user's current location and emotional information, and if the emotional engine detects a high stress level, it adjusts the destination. The server selects a quiet evacuation route that avoids congestion and sends it to the device. The device provides updated navigation information to help the user evacuate with as little stress as possible.

[0652] An example of a prompt is, "Calculate a real-time evacuation route considering the user's emotional state and suggest the optimal route." This prompt allows the generative AI model to assist in emotion analysis and evacuation route calculation.

[0653] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0654] Step 1:

[0655] The user launches a dedicated application on a device such as a smartphone. This allows the device to acquire location information and collect audio, video, and touch data from the user. As input, the device obtains the user's location information and emotional data. This data is then sent to the server as output.

[0656] Step 2:

[0657] The server receives location information from the terminal and simultaneously collects real-time pedestrian flow data within the city. This data is sent to the emotion engine as a prompt to evaluate the user's emotional state. In this step, the data is processed using the location information and pedestrian flow data acquired as input, and the results of the emotional state analysis are output.

[0658] Step 3:

[0659] The server calculates the optimal evacuation route for the user based on the analysis results of the emotion engine. This calculation uses a generative AI model to derive a route that reduces user stress. Using the emotion data obtained from the input, it outputs a low-stress route and provides it to the terminal.

[0660] Step 4:

[0661] The terminal receives evacuation route information transmitted from the server. This information is then presented to the user in the form of visual and audio navigation. The navigation is adjusted according to the user's emotional state, and more relaxed guidance methods or additional explanations are provided as needed. Using the route information obtained as input, audio and video navigation information is output.

[0662] Step 5:

[0663] If the user's location and emotional state change, the device sends the newly acquired data back to the server. The server recalculates the evacuation route according to the situation and, if necessary, sends the updated route information back to the device. It receives the updated user data as input and outputs the recalculated route information.

[0664] (Application Example 2)

[0665] 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".

[0666] Conventional evacuation guidance systems have the problem of potentially increasing user stress and anxiety because they present a uniform evacuation route without considering the user's emotional state. In particular, it has been difficult to ensure a safe and comfortable evacuation for users during natural disasters and emergencies.

[0667] 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.

[0668] In this invention, the server includes means for receiving the user's location information and emotional state, means for collecting and processing real-time pedestrian flow data and natural disaster information, and means for dynamically calculating a low-stress evacuation route based on the user's emotional state using a machine learning algorithm. This makes it possible to present a safe and low-stress evacuation route that corresponds to the individual emotional state of each user.

[0669] "User location information" refers to data that indicates the geographical location where the user is currently located.

[0670] "Emotional state" refers to the user's current psychological and emotional condition, and assesses a variety of emotions, including stress and anxiety.

[0671] "Real-time pedestrian flow data" refers to information that records and monitors the movement and gathering of people in a specific area in real time.

[0672] "Natural disaster information" refers to data that shows the current situation and predictions regarding natural disasters such as earthquakes, tsunamis, and typhoons.

[0673] A "machine learning algorithm" is a method for computers to automatically learn patterns and rules from data.

[0674] A "low-stress evacuation route" is a more comfortable and safe evacuation route chosen to reduce the psychological burden on users.

[0675] "Dynamic calculation" refers to a process that processes data in real time according to the situation, and continuously updates the optimal solution or result.

[0676] The system that realizes this invention consists of a user terminal, a server, and an emotion analysis engine. Users can access this system using smartphones or tablet devices.

[0677] The device uses its built-in GPS sensor to obtain the user's location information and simultaneously uses its camera and microphone to transmit audio and video data to a server in real time. This data is used to recognize the user's emotional state.

[0678] The server is connected to a high-performance data processing system operating in the cloud. Based on the received location information, the server collects real-time pedestrian flow data within the city and analyzes user sentiment data using sentiment analysis engines (specifically, Amazon Rekognition and Google Cloud Vision). The analyzed data is used to dynamically calculate the most suitable and least stressful evacuation route for the user, utilizing machine learning algorithms.

[0679] The calculated evacuation route is transmitted to the user's terminal, and visual and audio navigation is provided. This navigation is adjusted according to the user's emotional state; for example, if the user is judged to be highly stressed, a gentler, more relaxing voice guidance is provided.

[0680] As a concrete example, imagine a user in a crowded fireworks display venue. The system can sense the user's stress level on the spot and suggest routes through quieter roads and parks, helping them to escape the crowds safely and quickly.

[0681] An example of a prompt might be, "After a fireworks display, please suggest a quiet and safe route to a user who is stressed by the crowds." This prompt is used to suggest the optimal navigation method for the user using a generative AI model.

[0682] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0683] Step 1:

[0684] The user's device obtains its current location using its built-in GPS sensor and acquires audio and video data through its microphone and camera. This data is necessary to understand the user's emotional state and is sent to the server. The input is location information and audio / video data, and the output is the transmission of data to the server.

[0685] Step 2:

[0686] The server receives location information transmitted by the user and uses this to collect real-time pedestrian flow data. Furthermore, it supplies the received audio and video data to an emotion analysis engine to analyze the user's emotional data. In this process, an emotion model is used to assess stress and anxiety levels. The input is the user's audio and video data and location information, and the output is the analyzed emotional data.

[0687] Step 3:

[0688] The server uses analyzed sentiment data and real-time pedestrian flow data to apply machine learning algorithms and dynamically calculate the optimal evacuation route that minimizes stress for the user. Here, a travel route designed to reduce anxiety is calculated. The input is sentiment data and pedestrian flow data, and the output is information on the optimal evacuation route.

[0689] Step 4:

[0690] The calculated evacuation route information is transmitted to the user's device. Based on this information, the device provides voice and visual navigation to the user. The navigation is adjusted to take into account the user's emotional state, and in some cases, guidance is provided to help them relax. The input is the calculated evacuation route, and the output is the navigation instructions for the user.

[0691] Step 5:

[0692] The user moves according to the suggested evacuation route based on the navigation provided by the device. Furthermore, the user's emotional state is continuously monitored while they are moving, and the evacuation route and guidance methods are updated in real time as needed. The input is the user's new emotional state data, and the output is the updated navigation information.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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."

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0714] The following is further disclosed regarding the embodiments described above.

[0715] (Claim 1)

[0716] A means of receiving location information from a user's terminal,

[0717] Means for collecting and processing real-time pedestrian flow data,

[0718] A method for dynamically calculating evacuation routes using machine learning algorithms,

[0719] A means of providing users with a calculated evacuation route,

[0720] A system that includes this.

[0721] (Claim 2)

[0722] The system according to claim 1, further comprising means for collecting tsunami prediction information and calculating evacuation routes to safe high ground.

[0723] (Claim 3)

[0724] The system according to claim 1, further comprising means for presenting evacuation guidance information visually and audibly.

[0725] "Example 1"

[0726] (Claim 1)

[0727] Means for receiving location data from a user device,

[0728] A means of collecting and analyzing crowd flow in real time,

[0729] A method for dynamically calculating escape routes using machine learning models,

[0730] A means of providing the user with a calculated escape route,

[0731] A means of storing information in a database,

[0732] A means of analyzing data and detecting anomalies,

[0733] ...

[0734] A system that includes this.

[0735] (Claim 2)

[0736] The system according to claim 1, further comprising means for aggregating tsunami prediction information and calculating evacuation routes to safe high ground.

[0737] (Claim 3)

[0738] The system according to claim 1, further comprising means for presenting escape guidance information visually and audibly.

[0739] "Application Example 1"

[0740] (Claim 1)

[0741] A device for acquiring location information,

[0742] A device for collecting and processing data,

[0743] A device that dynamically calculates paths using machine learning algorithms,

[0744] A device that provides a calculated route to the user,

[0745] A device that updates safe routes based on real-time environmental information,

[0746] A device that presents information visually and audibly,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, further comprising a device for collecting disaster prediction information and calculating a route to a safe area.

[0750] (Claim 3)

[0751] The system according to claim 1, further comprising a device for integrating visual information, generating voice instructions, and presenting them to a user.

[0752] "Example 2 of combining an emotion engine"

[0753] (Claim 1)

[0754] A means of receiving location information from a user's terminal,

[0755] Means for collecting and processing real-time pedestrian flow data,

[0756] A method for dynamically calculating evacuation routes using machine learning algorithms,

[0757] A means of analyzing the emotional state of users,

[0758] A method for prioritizing and calculating less stressful evacuation routes based on analyzed emotional states,

[0759] A means of providing users with a calculated evacuation route,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, further comprising means for collecting tsunami prediction information and calculating evacuation routes to safe high ground.

[0763] (Claim 3)

[0764] The system according to claim 1, further comprising means for presenting evacuation guidance information visually and audibly, and for adjusting navigation according to the user's emotional state.

[0765] "Application example 2 when combining with an emotional engine"

[0766] (Claim 1)

[0767] Means for receiving the user's location information and emotional state,

[0768] A means for collecting and processing real-time human flow data and natural disaster information,

[0769] A means of dynamically calculating a low-stress evacuation route based on the user's emotional state using a machine learning algorithm,

[0770] A means of providing users with evacuation routes that take into account calculated emotions,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, further comprising means for collecting tsunami prediction information and calculating an evacuation route that takes into account the feelings of those involved, leading to safe, higher ground.

[0774] (Claim 3)

[0775] The system according to claim 1, further comprising means for presenting visual and auditory guidance in accordance with the user's emotions. [Explanation of symbols]

[0776] 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 device for acquiring location information, A device for collecting and processing data, A device that dynamically calculates paths using machine learning algorithms, A device that provides a calculated route to the user, A device that updates safe routes based on real-time environmental information, A device that presents information visually and audibly, A system that includes this.

2. The system according to claim 1, further comprising a device for collecting disaster prediction information and calculating a route to a safe area.

3. The system according to claim 1, further comprising a device that integrates visual information, generates voice instructions, and presents them to the user.

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