System

Abstract

To provide according to an embodiment that achieves ease-to-evacuate urban planning when a disaster occurs.SOLUTION: A system according to an embodiment comprises: a review unit; an arrangement unit; an improvement unit; and a simulation unit. The review unit is configured to review urban planning. The arrangement unit is configured to optimally arrange an evacuation facility on the basis of a result reviewed by the review unit. The improvement unit is configured to improve a road network on the basis of the evacuation facility arranged by the arrangement unit. The simulation unit is configured to simulate a human flow on the basis of the road network improved by the improvement unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the placement of evacuation facilities and improvements to road networks in the event of a disaster were not adequately implemented, and there was a problem that the effectiveness and issues of evacuation plans were not clear.

[0005] The system according to the embodiment aims to realize urban planning that makes it easy to evacuate when a disaster occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a review unit, a placement unit, an improvement unit, and a simulation unit. The review unit reviews urban planning. The placement unit performs optimal placement of evacuation facilities based on the results of the review by the review unit. The improvement unit improves the road network based on the evacuation facilities placed by the placement unit. The simulation unit performs a people flow simulation based on the road network improved by the improvement unit. Effect of the Invention

[0007] The system according to the embodiment can realize urban planning that allows for easy evacuation in the event of a disaster. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded 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), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that proposes urban planning and evacuation sites that are easy to evacuate to when a disaster occurs. This system can grasp the congestion situation at the time of evacuation and the appropriate evacuation route by examining the urban planning and optimizing the placement of evacuation facilities and improving the road network. Furthermore, by using a people flow simulation, the congestion situation at the time of evacuation and the appropriate evacuation route can be grasped, and the placement of evacuation facilities considering disaster risk and people flow can be made in the urban planning. This makes the effects and problems of the evacuation plan clear, and a more effective evacuation plan can be formulated. For example, the urban planning is examined and the optimal placement of evacuation facilities and the improvement of the road network are performed. At this time, the placement of evacuation facilities is optimized taking into consideration disaster risk and people flow. For example, in areas with high disaster risk, many evacuation facilities are placed so that evacuees can evacuate quickly. In addition, by improving the road network, traffic congestion at the time of evacuation can be reduced and evacuees can evacuate smoothly. Next, the congestion situation at the time of evacuation and the appropriate evacuation route are grasped using a people flow simulation. The people flow simulation can simulate the movement of evacuees and predict the congestion situation at the time of evacuation and the appropriate evacuation route. For example, by simulating which route evacuees will take and at which point congestion will occur, improvements to the evacuation plan can be found. Furthermore, it enables the placement of evacuation facilities in urban planning, taking disaster risk and human flow into consideration. This optimizes the placement of evacuation facilities, allowing evacuees to evacuate quickly and safely. For example, the placement of evacuation facilities can be reviewed to allow evacuees to evacuate in a short time. In addition, the effectiveness of the evacuation plan can be improved by increasing the capacity of the evacuation facility and the number of evacuees that can be accommodated. Finally, the effects and issues of the evacuation plan are clarified by the results of the study. A more effective evacuation plan can be formulated by evaluating the effects and issues of the evacuation plan based on the results of the human flow simulation and the study of urban planning. For example, by finding improvements in the placement of evacuation facilities and road networks and revising the evacuation plan, evacuees can be evacuated quickly and safely. In this way, the present invention can improve the effectiveness of the evacuation plan and ensure the safety of evacuees by proposing urban planning and evacuation sites that are easy to evacuate to when a disaster occurs.This will enable the system to propose urban planning and evacuation sites that are easy to use, improving the effectiveness of evacuation plans and ensuring the safety of evacuees.

[0029] The evacuation planning system according to the embodiment includes a review unit, a placement unit, an improvement unit, and a simulation unit. The review unit reviews urban planning. The review of urban planning includes, for example, data analysis, simulation, expert opinions, and the like, but is not limited to such examples. For example, the review unit analyzes the geographical conditions and demographics of the city and evaluates disaster risks. The placement unit performs optimal placement of evacuation facilities based on the results reviewed by the review unit. For example, the optimal placement of evacuation facilities takes into account the distance of the evacuation facilities, ease of access, capacity, and the like, but is not limited to such examples. For example, the placement unit places many evacuation facilities in areas with high disaster risk, so that evacuees can evacuate quickly. The improvement unit improves the road network based on the evacuation facilities placed by the placement unit. The improvement of the road network includes, for example, road expansion, signal adjustment, traffic regulations, and the like, but is not limited to such examples. For example, the improvement unit expands roads on major evacuation routes to reduce traffic congestion during evacuation. The simulation unit performs a people flow simulation based on the road network improved by the improvement unit. The people flow simulation includes, for example, a simulation model, data to be used, evaluation criteria, etc., but is not limited to these examples. For example, the simulation unit simulates the movement of evacuees and predicts the congestion situation and appropriate evacuation routes at the time of evacuation. As a result, the evacuation planning system according to the embodiment can consistently perform everything from urban planning considerations to the placement of evacuation facilities, improvement of road networks, and people flow simulations.

[0030] The examination department examines the city plan. The examination of the city plan includes, for example, data analysis, simulation, expert opinions, etc., but is not limited to such examples. Specifically, the examination department analyzes the geographical conditions and demographics of the city in detail to evaluate the disaster risk. The analysis of the geographical conditions includes natural environmental elements such as topography, rivers, coastlines, and geology, and evaluates how these elements affect the disaster. The analysis of demographics includes social elements such as population density, age structure, and residential form, and evaluates how these elements affect the evacuation plan. Furthermore, the examination department collects past disaster data and meteorological data, and predicts future disaster risk based on these data. For example, data on past floods and earthquakes is analyzed to evaluate the degree of risk to which a specific area is exposed. In addition, based on meteorological data, it predicts how future climate change will affect the disaster risk. As a result, the examination department can provide basic data for comprehensively evaluating the disaster risk of the entire city and formulating an appropriate evacuation plan. Furthermore, the examination department incorporates the opinions of experts to formulate a more practical and effective city plan. For example, the department collects opinions from experts in urban planning and disaster prevention, and modifies and improves the plan based on these opinions. This enables the department to formulate highly effective urban plans that combine scientific data and expert knowledge.

[0031] The placement unit optimally places evacuation facilities based on the results of the consideration by the consideration unit. For example, the distance, ease of access, and capacity of the evacuation facilities are taken into consideration for the optimal placement of the evacuation facilities, but is not limited to such examples. Specifically, the placement unit places many evacuation facilities in areas with high disaster risk, so that evacuees can evacuate quickly. For example, in areas with high earthquake risk, highly earthquake-resistant evacuation facilities are placed, and in areas with high flood risk, evacuation facilities are placed on high ground. In addition, the distance of the evacuation facilities is planned so that the evacuation facilities are placed within walking distance of each household. This allows evacuees to evacuate quickly and safely. Furthermore, the placement unit considers the ease of access of the evacuation facilities and places the evacuation facilities in places with good access from major roads and public transportation. For example, the evacuation facilities are placed near major bus stops and train stations, so that evacuees can evacuate using public transportation. In addition, the placement unit considers the capacity of the evacuation facilities and places evacuation facilities with an appropriate capacity according to the population density of the area. This allows evacuees to evacuate safely without overcrowding. In addition, the deployment department will also consider the facilities and functions of the evacuation facilities and prepare the necessary equipment so that evacuees can stay there for a long period of time. For example, the evacuation facilities will be equipped with basic necessities such as food, water, medical supplies, and bedding, as well as electricity and communication facilities. This will allow evacuees to live in safety and security.

[0032] The improvement unit improves the road network based on the evacuation facilities arranged by the arrangement unit. The improvement of the road network includes, for example, road expansion, signal adjustment, traffic regulation, etc., but is not limited to such examples. Specifically, the improvement unit expands roads on major evacuation routes to reduce traffic congestion during evacuation. For example, the width of major roads that serve as evacuation routes is widened to allow vehicles and pedestrians to move smoothly. In addition, traffic lights on evacuation routes are adjusted to give priority to green signals during evacuation, thereby smoothing the flow of traffic. Furthermore, traffic regulations are implemented during evacuation, and measures such as making certain roads one-way are taken to prevent traffic congestion. This allows evacuees to evacuate quickly and safely. Furthermore, the improvement unit installs signs and guide boards on evacuation routes so that evacuees can evacuate without getting lost. For example, signs indicating the evacuation direction are installed on evacuation routes, and the distance and required time to evacuation facilities are displayed. In addition, guide boards indicating dangerous areas and detour routes on evacuation routes are installed so that evacuees can evacuate safely. Furthermore, the improvement unit not only develops evacuation routes, but also develops infrastructure around evacuation facilities. For example, the Improvement Department will improve the roads and sidewalks around evacuation facilities so that evacuees can reach the facilities safely. In addition, the Department will improve lighting facilities around evacuation facilities so that evacuees can evacuate safely even at night. In this way, the Improvement Department can comprehensively improve evacuation routes and infrastructure around evacuation facilities, and create an environment in which evacuees can evacuate quickly and safely.

[0033] The simulation unit performs a pedestrian flow simulation based on the road network improved by the improvement unit. The pedestrian flow simulation includes, for example, a simulation model, data to be used, and evaluation criteria, but is not limited to such examples. Specifically, the simulation unit simulates the movement of evacuees and predicts the congestion situation during evacuation and an appropriate evacuation route. The simulation model includes elements such as the movement speed of evacuees, evacuation route selection criteria, and evacuation facility capacity, and realistically reproduces the movement of evacuees based on these elements. The data to be used includes population distribution, road network information, location information of evacuation facilities, and the like, and performs a simulation based on these data. The evaluation criteria include evacuation time, congestion level, safety, and the like, and the simulation results are evaluated based on these criteria. For example, the simulation unit sets a specific disaster scenario and simulates the movement of evacuees based on the scenario. Based on the simulation results, it identifies congested areas and bottlenecks during evacuation and considers improvement measures. In addition, the simulation unit simulates multiple scenarios and formulates the most effective evacuation plan. For example, it simulates different disaster scenarios and different evacuation routes, and compares the evacuation time and congestion level in each scenario. This allows the Simulation Department to formulate optimal evacuation plans and minimize confusion during actual evacuations. Furthermore, the Simulation Department provides feedback on improvements to the evacuation plan based on the simulation results and continually reviews the evacuation plan. This allows the Simulation Department to always provide highly accurate evacuation plans based on the latest information and support rapid and appropriate responses.

[0034] The placement unit can place multiple evacuation facilities in areas with high disaster risk. Areas with high disaster risk are determined, for example, taking into consideration past disaster history, geographical conditions, population density, etc., but are not limited to these examples. For example, the placement unit places many evacuation facilities in areas where flooding has occurred frequently in the past. The placement unit can also place many evacuation facilities in areas with high earthquake risk. Furthermore, the placement unit can place many evacuation facilities in areas with high population density. In this way, the safety of evacuees can be ensured by placing many evacuation facilities in areas with high disaster risk.

[0035] The improvement unit can improve the road network to reduce traffic congestion during evacuation. Improvements to the road network to reduce traffic congestion include, but are not limited to, road expansion, traffic signal adjustment, and traffic regulation. For example, the improvement unit can expand roads on major evacuation routes to allow evacuees to evacuate smoothly. The improvement unit can also adjust traffic signals to reduce traffic congestion during evacuation. Furthermore, the improvement unit can also regulate traffic during evacuation to allow evacuees to evacuate smoothly. This reduces traffic congestion during evacuation, allowing evacuees to evacuate smoothly.

[0036] The simulation unit can simulate the movement of evacuees and predict the congestion situation during evacuation and an appropriate evacuation route. The simulation of the movement of evacuees includes, but is not limited to, evacuation routes, evacuation speeds, and behavior patterns of evacuees. For example, the simulation unit can simulate which route evacuees will take and predict points where congestion will occur. The simulation unit can also simulate the evacuation speeds of evacuees and predict evacuation times. Furthermore, the simulation unit can simulate the behavior patterns of evacuees and predict the congestion situation during evacuation. In this way, by simulating the movement of evacuees, it is possible to predict the congestion situation during evacuation and an appropriate evacuation route.

[0037] The placement unit can increase the capacity of the evacuation facility to increase the number of evacuees that can be accommodated. The capacity of the evacuation facility includes, for example, the number of people that can be accommodated, the area of ​​the facility, the completeness of the facilities, and the like, but is not limited to such examples. For example, the placement unit can increase the capacity of the evacuation facility to accommodate more evacuees. The placement unit can also expand the area of ​​the evacuation facility to increase the number of people that can be accommodated. Furthermore, the placement unit can also improve the facilities of the evacuation facility to increase the number of people that can be accommodated. In this way, by increasing the capacity of the evacuation facility, it becomes possible to accommodate more evacuees.

[0038] The examination unit can analyze past disaster data and select an appropriate urban planning examination method. Past disaster data includes, for example, disaster type, occurrence frequency, damage scale, etc., but is not limited to such examples. For example, the examination unit analyzes past earthquake data and selects an urban planning examination method for areas with high earthquake risk. The examination unit can also analyze past flood data and select an urban planning examination method for areas with high flood risk. Furthermore, the examination unit can analyze past typhoon data and select an urban planning examination method for areas with high typhoon risk. In this way, the optimal urban planning examination method can be selected by analyzing past disaster data.

[0039] When considering urban planning, the review unit can consider the demographic trends and economic conditions of the region. Demographic trends include, but are not limited to, for example, population increases and decreases, age structures, migration trends, and the like. For example, the review unit considers the population growth rate of the region and considers urban planning that anticipates future demand for evacuation facilities. The review unit can also consider the economic growth rate of the region and consider urban planning according to the budget. Furthermore, the review unit can consider the aging rate of the region and consider the placement of evacuation facilities that take the elderly into consideration. In this way, by considering the demographic trends and economic conditions of the region, it is possible to consider more realistic urban planning.

[0040] When considering an urban plan, the reviewing unit can formulate an appropriate plan by taking into account the geographical characteristics of the region. Geographical characteristics include, but are not limited to, for example, topography, climate, and risk of natural disasters. For example, the reviewing unit formulates an urban plan that takes into account landslide risk in mountainous regions. The reviewing unit can also formulate an urban plan that takes into account tsunami risk in coastal regions. Furthermore, the reviewing unit can also formulate an urban plan that takes into account flood risk in plain regions. In this way, an optimal urban plan can be formulated by taking into account the geographical characteristics of the region.

[0041] When considering an urban plan, the reviewing unit can formulate the plan by taking into consideration the historical background and culture of the region. Historical background includes, but is not limited to, past events, cultural heritage, and local traditions. For example, in an area with many historical buildings, the reviewing unit can formulate an urban plan that takes into consideration the preservation of buildings. In an area where many cultural events are held, the reviewing unit can also formulate an urban plan that takes into consideration evacuation plans for events. Furthermore, the reviewing unit can also formulate an urban plan that does not damage the scenery by taking into consideration the traditional architectural style of the area. In this way, an urban plan suitable for the area can be formulated by taking into consideration the historical background and culture of the area.

[0042] When arranging evacuation facilities, the arrangement unit can refer to past evacuation data and select an appropriate arrangement method. Past evacuation data includes, for example, evacuation routes, evacuation times, and the number of evacuees, but is not limited to such examples. For example, the arrangement unit analyzes past evacuation data and arranges more evacuation facilities in areas where there were many evacuees. The arrangement unit can also analyze past evacuation data and arrange fewer evacuation facilities in areas where there were few evacuees. Furthermore, the arrangement unit can analyze past evacuation data and arrange evacuation facilities taking into account the movement lines of evacuees. In this way, by referring to past evacuation data, an optimal arrangement method for evacuation facilities can be selected.

[0043] The placement unit can place evacuation facilities in consideration of the topography and infrastructure conditions of the area. Examples of topography include, but are not limited to, topography, altitude, and geology. Examples of infrastructure conditions include, but are not limited to, road networks, public transportation, and power supply. For example, in areas with flat topography, the placement unit places evacuation facilities over a wide area. In areas with undulating topography, the placement unit can also place evacuation facilities in easily accessible locations. Furthermore, in areas with well-developed infrastructure, the placement unit can also place evacuation facilities densely. In this way, the placement unit can optimally place evacuation facilities by considering the topography and infrastructure conditions of the area.

[0044] When arranging evacuation facilities, the arrangement unit can appropriately arrange them by taking into consideration the climatic conditions of the region. The climatic conditions include, but are not limited to, for example, temperature, precipitation, and wind speed. For example, the arrangement unit prioritizes arranging evacuation facilities with roofs in areas with a lot of rain. Furthermore, the arrangement unit can also prioritize arranging evacuation facilities with air conditioning equipment in hot areas. Furthermore, the arrangement unit can also prioritize arranging evacuation facilities with heating equipment in cold areas. In this way, it is possible to arrange optimal evacuation facilities by taking into consideration the climatic conditions of the region.

[0045] The placement unit can place evacuation facilities while taking into consideration the traffic conditions of the area. Traffic conditions include, but are not limited to, for example, traffic volume, congestion conditions, and the use of public transportation. For example, the placement unit places evacuation facilities in a dispersed manner in an area where traffic congestion is likely to occur. Furthermore, the placement unit can place evacuation facilities near stations in an area where public transportation is well developed. Furthermore, the placement unit can place evacuation facilities in a concentrated manner in an area with good transportation access. In this way, the optimal placement of evacuation facilities can be performed by taking into consideration the traffic conditions of the area.

[0046] When improving a road network, the improvement unit can select an appropriate improvement method by referring to past traffic data. The past traffic data includes, for example, traffic volume, congestion conditions, accident data, and the like, but is not limited to such examples. For example, the improvement unit analyzes past traffic congestion data and selects an improvement method for roads where congestion is likely to occur. The improvement unit can also analyze past traffic accident data and select an improvement method for roads where accidents are likely to occur. Furthermore, the improvement unit can analyze past traffic volume data and select an improvement method for roads with high traffic volume. In this way, by referring to past traffic data, an optimal improvement method for the road network can be selected.

[0047] When improving a road network, the improvement unit can make improvements while taking into consideration the traffic demand and supply of the region. Traffic demand and supply include, but are not limited to, traffic volume, utilization of public transportation, and road capacity. For example, the improvement unit widens or builds new roads in areas where traffic demand is high. The improvement unit can also develop public transportation facilities in areas where traffic supply is insufficient. Furthermore, the improvement unit can also make optimal improvements to the road network by taking into consideration the balance between traffic demand and supply. This makes it possible to make optimal improvements to the road network by taking into consideration the traffic demand and supply of the region.

[0048] When improving the road network, the improvement unit can appropriately improve the road network by taking into account the state of local public transportation. The state of public transportation includes, but is not limited to, for example, the frequency of operation, the number of users, and the service area. For example, in an area where public transportation is well developed, the improvement unit improves the road network by taking into account cooperation with the public transportation. In addition, in an area where public transportation is insufficient, the improvement unit can prioritize the development of public transportation. Furthermore, the improvement unit can also optimally improve the road network by taking into account the usage state of public transportation. As a result, the road network can be optimally improved by taking into account the state of public transportation in the area.

[0049] When improving the road network, the improvement department can make the improvement while taking into consideration the environmental impact of the region. The environmental impact includes, but is not limited to, for example, air pollution, noise, and reduction in green space. For example, in environmentally protected areas, the improvement department can improve the road network while minimizing the environmental impact. Also, in areas with high environmental load, the improvement department can also improve the road network to reduce the environmental load. Furthermore, the improvement department can perform an environmental impact assessment and make optimal improvements to the road network. In this way, the road network can be optimally improved by taking into consideration the environmental impact of the region.

[0050] The simulation unit can select an appropriate simulation method by referring to past evacuation simulation data during the simulation. The past evacuation simulation data includes, for example, evacuation routes, evacuation time, the number of evacuees, etc., but is not limited to such examples. For example, the simulation unit analyzes the past evacuation simulation data and selects a simulation method for an area where there were many evacuees. The simulation unit can also analyze the past evacuation simulation data and select a simulation method for an area where there were few evacuees. Furthermore, the simulation unit can analyze the past evacuation simulation data and select a simulation method that takes into account the movement lines of evacuees. In this way, the optimal simulation method can be selected by referring to the past evacuation simulation data.

[0051] The simulation unit can perform a simulation while taking into consideration the population density and building density of the region. The population density includes, for example, but is not limited to, the population distribution for each region, population increase and decrease, and fluctuations in population density. The building density includes, for example, but is not limited to, the number of buildings, the height of the buildings, and the use of the buildings. For example, in a region with a high population density, the simulation unit performs a simulation so that the flow lines of evacuees do not overlap. In addition, in a region with a high building density, the simulation unit can also perform a simulation so that an evacuation route is secured. Furthermore, the simulation unit can also simulate an optimal evacuation route by taking into consideration the balance between the population density and the building density. In this way, an optimal simulation can be performed by taking into consideration the population density and the building density of the region.

[0052] The simulation unit can improve the accuracy of the simulation by referring to local evacuation drill data during the simulation. The evacuation drill data includes, for example, the number of participants in the training, the location where the training is conducted, the results of the training, and the like, but is not limited to such examples. For example, the simulation unit analyzes the evacuation drill data and performs a simulation that reflects actual evacuation behavior. The simulation unit can also perform a simulation that optimizes the movement of evacuees based on the evacuation drill data. Furthermore, the simulation unit can also accurately simulate the congestion situation during evacuation by referring to the evacuation drill data. As a result, the accuracy of the simulation can be improved by referring to the evacuation drill data for the region.

[0053] During the simulation, the simulation unit can perform a simulation taking into account the diversity of evacuation routes in the area. The diversity of evacuation routes includes, but is not limited to, for example, the number of evacuation routes, the types of evacuation routes, and the usage status of evacuation routes. For example, the simulation unit simulates a plurality of evacuation routes and proposes an optimal evacuation route. The simulation unit can also perform a simulation taking into account the diversity of evacuation routes so as to disperse evacuees. Furthermore, the simulation unit can also perform a simulation that reflects the diversity of evacuation routes and reduces congestion during evacuation. In this way, an optimal simulation can be performed by taking into account the diversity of evacuation routes in the area.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] When reviewing urban planning, the review department can formulate plans taking into consideration the location of local medical facilities. For example, in areas with few medical facilities, the review department can add equipment for providing medical support to evacuation facilities. Also, in areas with many medical facilities, the review department can formulate plans to strengthen cooperation between evacuation facilities and medical facilities. Furthermore, the review department can formulate urban planning that takes into consideration access to medical facilities so that evacuees can receive medical support quickly. In this way, by taking into consideration the location of local medical facilities, evacuees can receive prompt and appropriate medical support.

[0056] When arranging evacuation facilities, the placement unit can place them taking into consideration the status of local educational facilities. For example, in an area with many educational facilities such as schools and universities, the placement unit links the evacuation facilities with the educational facilities. In an area with few educational facilities, the placement unit can also add equipment for providing educational support to the evacuation facilities. Furthermore, the placement unit can place evacuation facilities taking into consideration access to educational facilities so that evacuees can receive educational support. In this way, by taking into consideration the status of local educational facilities, evacuees can receive educational support.

[0057] The improvement department can install lanes exclusively for emergency vehicles to reduce traffic congestion during evacuations. For example, the improvement department can install lanes exclusively for emergency vehicles on major evacuation routes to allow ambulances and fire engines to move quickly. The improvement department can also reduce traffic congestion for general vehicles by installing lanes exclusively for emergency vehicles. Furthermore, the improvement department can also enable evacuees to evacuate quickly by installing lanes exclusively for emergency vehicles. As a result, by installing lanes exclusively for emergency vehicles, traffic congestion during evacuations can be reduced and evacuees can evacuate quickly.

[0058] The simulation unit can take into account the age and health condition of the evacuees when simulating the movements of the evacuees. For example, the simulation unit can simulate an evacuation route by taking into account the evacuation speed of the elderly and the disabled. The simulation unit can also distinguish between the movements of evacuees in good health and those in poor health and simulate the movements of these evacuees. Furthermore, the simulation unit can suggest evacuation routes according to the age and health condition of the evacuees. As a result, a more realistic evacuation simulation can be performed by taking into account the age and health condition of the evacuees.

[0059] When arranging evacuation facilities, the placement unit can consider the status of local animal evacuation facilities when arranging the evacuation facilities. For example, in an area where there are many evacuees who keep pets, the placement unit can also set up an animal evacuation facility. In an area where there are few animal evacuation facilities, the placement unit can also provide an animal evacuation space in the evacuation facility. Furthermore, the placement unit can also arrange the evacuation facility with consideration of access to the animal evacuation facility so that evacuees can evacuate with their pets. In this way, by considering the status of local animal evacuation facilities, evacuees who keep pets can evacuate with peace of mind.

[0060] The review unit can analyze past disaster data and select an appropriate evacuation drill method. For example, it can analyze past earthquake data and select an evacuation drill method for areas with high earthquake risk. It can also analyze past flood data and select an evacuation drill method for areas with high flood risk. It can also analyze past typhoon data and select an evacuation drill method for areas with high typhoon risk. In this way, it is possible to select the optimal evacuation drill method by analyzing past disaster data.

[0061] When considering urban planning, the review unit can formulate plans taking into account the tourism resources of the region. For example, in an area with many tourist destinations, urban planning can be formulated that takes into account the evacuation of tourists. Also, in an area with few tourist resources, urban planning can be formulated that takes into account the protection of tourist resources. Furthermore, in an area with many tourist events, urban planning can be formulated that takes into account evacuation plans for the events. In this way, by considering the tourism resources of the region, urban planning can be formulated that protects the tourism resources while ensuring the safety of tourists.

[0062] When considering urban planning, the review department can formulate plans taking into account the local energy supply situation. For example, in areas where the energy supply is unstable, emergency power sources can be installed in evacuation facilities. Also, in areas where the energy supply is stable, urban planning can be formulated that takes energy efficiency into consideration. Furthermore, in areas where the use of renewable energy is progressing, the placement of evacuation facilities that utilize renewable energy can be considered. In this way, by taking into account the local energy supply situation, urban planning can be formulated that allows evacuees to evacuate in peace of mind.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The review department reviews the city plan. The review of the city plan includes data analysis, simulations, expert opinions, etc. For example, the review department analyzes the city's geographical conditions and demographics and evaluates disaster risks. Step 2: The Allocation Department optimizes the placement of evacuation facilities based on the results of the review by the Review Department. The optimal placement of evacuation facilities takes into account factors such as the distance between evacuation facilities, ease of access, and capacity. For example, the Allocation Department places more evacuation facilities in areas with a high disaster risk, allowing evacuees to evacuate quickly. Step 3: The Improvement Department improves the road network based on the evacuation facilities arranged by the Arrangement Department. Road network improvement includes road widening, signal adjustment, traffic control, etc. For example, the Improvement Department widens roads on major evacuation routes to reduce traffic congestion during evacuation. Step 4: The simulation unit performs a people flow simulation based on the road network improved by the improvement unit. The people flow simulation includes a simulation model, the data to be used, and evaluation criteria. For example, the simulation unit simulates the movement of evacuees and predicts congestion conditions and appropriate evacuation routes during evacuation.

[0065] (Example 2) The system according to the embodiment of the present invention is a system that proposes urban planning and evacuation sites that are easy to evacuate to when a disaster occurs. This system can grasp the congestion situation at the time of evacuation and the appropriate evacuation route by examining the urban planning and optimizing the placement of evacuation facilities and improving the road network. Furthermore, by using a people flow simulation, the congestion situation at the time of evacuation and the appropriate evacuation route can be grasped, and the placement of evacuation facilities considering disaster risk and people flow can be made in the urban planning. This makes the effects and problems of the evacuation plan clear, and a more effective evacuation plan can be formulated. For example, the urban planning is examined and the optimal placement of evacuation facilities and the improvement of the road network are performed. At this time, the placement of evacuation facilities is optimized taking into consideration disaster risk and people flow. For example, in areas with high disaster risk, many evacuation facilities are placed so that evacuees can evacuate quickly. In addition, by improving the road network, traffic congestion at the time of evacuation can be reduced and evacuees can evacuate smoothly. Next, the congestion situation at the time of evacuation and the appropriate evacuation route are grasped using a people flow simulation. The people flow simulation can simulate the movement of evacuees and predict the congestion situation at the time of evacuation and the appropriate evacuation route. For example, by simulating which route evacuees will take and at which point congestion will occur, improvements to the evacuation plan can be found. Furthermore, it enables the placement of evacuation facilities in urban planning, taking disaster risk and human flow into consideration. This optimizes the placement of evacuation facilities, allowing evacuees to evacuate quickly and safely. For example, the placement of evacuation facilities can be reviewed to allow evacuees to evacuate in a short time. In addition, the effectiveness of the evacuation plan can be improved by increasing the capacity of the evacuation facility and the number of evacuees that can be accommodated. Finally, the effects and issues of the evacuation plan are clarified by the results of the study. A more effective evacuation plan can be formulated by evaluating the effects and issues of the evacuation plan based on the results of the human flow simulation and the study of urban planning. For example, by finding improvements in the placement of evacuation facilities and road networks and revising the evacuation plan, evacuees can be evacuated quickly and safely. In this way, the present invention can improve the effectiveness of the evacuation plan and ensure the safety of evacuees by proposing urban planning and evacuation sites that are easy to evacuate to when a disaster occurs.This will enable the system to propose urban planning and evacuation sites that are easy to use, improving the effectiveness of evacuation plans and ensuring the safety of evacuees.

[0066] The evacuation planning system according to the embodiment includes a review unit, a placement unit, an improvement unit, and a simulation unit. The review unit reviews urban planning. The review of urban planning includes, for example, data analysis, simulation, expert opinions, and the like, but is not limited to such examples. For example, the review unit analyzes the geographical conditions and demographics of the city and evaluates disaster risks. The placement unit performs optimal placement of evacuation facilities based on the results reviewed by the review unit. For example, the optimal placement of evacuation facilities takes into account the distance of the evacuation facilities, ease of access, capacity, and the like, but is not limited to such examples. For example, the placement unit places many evacuation facilities in areas with high disaster risk, so that evacuees can evacuate quickly. The improvement unit improves the road network based on the evacuation facilities placed by the placement unit. The improvement of the road network includes, for example, road expansion, signal adjustment, traffic regulations, and the like, but is not limited to such examples. For example, the improvement unit expands roads on major evacuation routes to reduce traffic congestion during evacuation. The simulation unit performs a people flow simulation based on the road network improved by the improvement unit. The people flow simulation includes, for example, a simulation model, data to be used, evaluation criteria, etc., but is not limited to these examples. For example, the simulation unit simulates the movement of evacuees and predicts the congestion situation and appropriate evacuation routes at the time of evacuation. As a result, the evacuation planning system according to the embodiment can consistently perform everything from urban planning considerations to the placement of evacuation facilities, improvement of road networks, and people flow simulations.

[0067] The examination department examines the city plan. The examination of the city plan includes, for example, data analysis, simulation, expert opinions, etc., but is not limited to such examples. Specifically, the examination department analyzes the geographical conditions and demographics of the city in detail to evaluate the disaster risk. The analysis of the geographical conditions includes natural environmental elements such as topography, rivers, coastlines, and geology, and evaluates how these elements affect the disaster. The analysis of demographics includes social elements such as population density, age structure, and residential form, and evaluates how these elements affect the evacuation plan. Furthermore, the examination department collects past disaster data and meteorological data, and predicts future disaster risk based on these data. For example, data on past floods and earthquakes is analyzed to evaluate the degree of risk to which a specific area is exposed. In addition, based on meteorological data, it predicts how future climate change will affect the disaster risk. As a result, the examination department can provide basic data for comprehensively evaluating the disaster risk of the entire city and formulating an appropriate evacuation plan. Furthermore, the examination department incorporates the opinions of experts to formulate a more practical and effective city plan. For example, the department collects opinions from experts in urban planning and disaster prevention, and modifies and improves the plan based on these opinions. This enables the department to formulate highly effective urban plans that combine scientific data and expert knowledge.

[0068] The placement unit optimally places evacuation facilities based on the results of the consideration by the consideration unit. For example, the distance, ease of access, and capacity of the evacuation facilities are taken into consideration for the optimal placement of the evacuation facilities, but is not limited to such examples. Specifically, the placement unit places many evacuation facilities in areas with high disaster risk, so that evacuees can evacuate quickly. For example, in areas with high earthquake risk, highly earthquake-resistant evacuation facilities are placed, and in areas with high flood risk, evacuation facilities are placed on high ground. In addition, the distance of the evacuation facilities is planned so that the evacuation facilities are placed within walking distance of each household. This allows evacuees to evacuate quickly and safely. Furthermore, the placement unit considers the ease of access of the evacuation facilities and places the evacuation facilities in places with good access from major roads and public transportation. For example, the evacuation facilities are placed near major bus stops and train stations, so that evacuees can evacuate using public transportation. In addition, the placement unit considers the capacity of the evacuation facilities and places evacuation facilities with an appropriate capacity according to the population density of the area. This allows evacuees to evacuate safely without overcrowding. In addition, the deployment department will also consider the facilities and functions of the evacuation facilities and prepare the necessary equipment so that evacuees can stay there for a long period of time. For example, the evacuation facilities will be equipped with basic necessities such as food, water, medical supplies, and bedding, as well as electricity and communication facilities. This will allow evacuees to live in safety and security.

[0069] The improvement unit improves the road network based on the evacuation facilities arranged by the arrangement unit. The improvement of the road network includes, for example, road expansion, signal adjustment, traffic regulation, etc., but is not limited to such examples. Specifically, the improvement unit expands roads on major evacuation routes to reduce traffic congestion during evacuation. For example, the width of major roads that serve as evacuation routes is widened to allow vehicles and pedestrians to move smoothly. In addition, traffic lights on evacuation routes are adjusted to give priority to green signals during evacuation, thereby smoothing the flow of traffic. Furthermore, traffic regulations are implemented during evacuation, and measures such as making certain roads one-way are taken to prevent traffic congestion. This allows evacuees to evacuate quickly and safely. Furthermore, the improvement unit installs signs and guide boards on evacuation routes so that evacuees can evacuate without getting lost. For example, signs indicating the evacuation direction are installed on evacuation routes, and the distance and required time to evacuation facilities are displayed. In addition, guide boards indicating dangerous areas and detour routes on evacuation routes are installed so that evacuees can evacuate safely. Furthermore, the improvement unit not only develops evacuation routes, but also develops infrastructure around evacuation facilities. For example, the Improvement Department will improve the roads and sidewalks around evacuation facilities so that evacuees can reach the facilities safely. In addition, the Department will improve lighting facilities around evacuation facilities so that evacuees can evacuate safely even at night. In this way, the Improvement Department can comprehensively improve evacuation routes and infrastructure around evacuation facilities, and create an environment in which evacuees can evacuate quickly and safely.

[0070] The simulation unit performs a pedestrian flow simulation based on the road network improved by the improvement unit. The pedestrian flow simulation includes, for example, a simulation model, data to be used, and evaluation criteria, but is not limited to such examples. Specifically, the simulation unit simulates the movement of evacuees and predicts the congestion situation during evacuation and an appropriate evacuation route. The simulation model includes elements such as the movement speed of evacuees, evacuation route selection criteria, and evacuation facility capacity, and realistically reproduces the movement of evacuees based on these elements. The data to be used includes population distribution, road network information, location information of evacuation facilities, and the like, and performs a simulation based on these data. The evaluation criteria include evacuation time, congestion level, safety, and the like, and the simulation results are evaluated based on these criteria. For example, the simulation unit sets a specific disaster scenario and simulates the movement of evacuees based on the scenario. Based on the simulation results, it identifies congested areas and bottlenecks during evacuation and considers improvement measures. In addition, the simulation unit simulates multiple scenarios and formulates the most effective evacuation plan. For example, it simulates different disaster scenarios and different evacuation routes, and compares the evacuation time and congestion level in each scenario. This allows the Simulation Department to formulate optimal evacuation plans and minimize confusion during actual evacuations. Furthermore, the Simulation Department provides feedback on improvements to the evacuation plan based on the simulation results and continually reviews the evacuation plan. This allows the Simulation Department to always provide highly accurate evacuation plans based on the latest information and support rapid and appropriate responses.

[0071] The placement unit can place multiple evacuation facilities in areas with high disaster risk. Areas with high disaster risk are determined, for example, taking into consideration past disaster history, geographical conditions, population density, etc., but are not limited to these examples. For example, the placement unit places many evacuation facilities in areas where flooding has occurred frequently in the past. The placement unit can also place many evacuation facilities in areas with high earthquake risk. Furthermore, the placement unit can place many evacuation facilities in areas with high population density. In this way, the safety of evacuees can be ensured by placing many evacuation facilities in areas with high disaster risk.

[0072] The improvement unit can improve the road network to reduce traffic congestion during evacuation. Improvements to the road network to reduce traffic congestion include, but are not limited to, road expansion, traffic signal adjustment, and traffic regulation. For example, the improvement unit can expand roads on major evacuation routes to allow evacuees to evacuate smoothly. The improvement unit can also adjust traffic signals to reduce traffic congestion during evacuation. Furthermore, the improvement unit can also regulate traffic during evacuation to allow evacuees to evacuate smoothly. This reduces traffic congestion during evacuation, allowing evacuees to evacuate smoothly.

[0073] The simulation unit can simulate the movement of evacuees and predict the congestion situation during evacuation and an appropriate evacuation route. The simulation of the movement of evacuees includes, but is not limited to, evacuation routes, evacuation speeds, and behavior patterns of evacuees. For example, the simulation unit can simulate which route evacuees will take and predict points where congestion will occur. The simulation unit can also simulate the evacuation speeds of evacuees and predict evacuation times. Furthermore, the simulation unit can simulate the behavior patterns of evacuees and predict the congestion situation during evacuation. In this way, by simulating the movement of evacuees, it is possible to predict the congestion situation during evacuation and an appropriate evacuation route.

[0074] The placement unit can increase the capacity of the evacuation facility to increase the number of evacuees that can be accommodated. The capacity of the evacuation facility includes, for example, the number of people that can be accommodated, the area of ​​the facility, the completeness of the facilities, and the like, but is not limited to such examples. For example, the placement unit can increase the capacity of the evacuation facility to accommodate more evacuees. The placement unit can also expand the area of ​​the evacuation facility to increase the number of people that can be accommodated. Furthermore, the placement unit can also improve the facilities of the evacuation facility to increase the number of people that can be accommodated. In this way, by increasing the capacity of the evacuation facility, it becomes possible to accommodate more evacuees.

[0075] The review unit can estimate the user's emotions and adjust the urban planning review method based on the estimated user's emotions. For example, when the user feels anxious, the review unit provides a detailed explanation of the urban planning review method. When the user is relaxed, the review unit can also provide a concise urban planning review method that focuses on the main points. Furthermore, when the user is in a hurry, the review unit can also provide a urban planning review method that quickly provides review results. This allows the urban planning review method to be adjusted according to the user's emotions, thereby providing a review method suitable for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] The examination unit can analyze past disaster data and select an appropriate urban planning examination method. Past disaster data includes, for example, disaster type, occurrence frequency, damage scale, etc., but is not limited to such examples. For example, the examination unit analyzes past earthquake data and selects an urban planning examination method for areas with high earthquake risk. The examination unit can also analyze past flood data and select an urban planning examination method for areas with high flood risk. Furthermore, the examination unit can analyze past typhoon data and select an urban planning examination method for areas with high typhoon risk. In this way, the optimal urban planning examination method can be selected by analyzing past disaster data.

[0077] When considering urban planning, the review unit can consider the demographic trends and economic conditions of the region. Demographic trends include, but are not limited to, for example, population increases and decreases, age structures, migration trends, and the like. For example, the review unit considers the population growth rate of the region and considers urban planning that anticipates future demand for evacuation facilities. The review unit can also consider the economic growth rate of the region and consider urban planning according to the budget. Furthermore, the review unit can consider the aging rate of the region and consider the placement of evacuation facilities that take the elderly into consideration. In this way, by considering the demographic trends and economic conditions of the region, it is possible to consider more realistic urban planning.

[0078] The examination unit can estimate the user's emotions and determine the priority of urban plans to be considered based on the estimated user's emotions. For example, when the user feels anxious, the examination unit preferentially considers urban plans in areas with a high disaster risk. In addition, when the user is relaxed, the examination unit can also consider overall urban plans in a balanced manner. Furthermore, when the user is in a hurry, the examination unit can also preferentially consider urban plans focused on important points. In this way, by determining the priority of urban plans according to the user's emotions, it is possible to provide a priority suitable for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] When considering an urban plan, the reviewing unit can formulate an appropriate plan by taking into account the geographical characteristics of the region. Geographical characteristics include, but are not limited to, for example, topography, climate, and risk of natural disasters. For example, the reviewing unit formulates an urban plan that takes into account landslide risk in mountainous regions. The reviewing unit can also formulate an urban plan that takes into account tsunami risk in coastal regions. Furthermore, the reviewing unit can also formulate an urban plan that takes into account flood risk in plain regions. In this way, an optimal urban plan can be formulated by taking into account the geographical characteristics of the region.

[0080] When considering an urban plan, the reviewing unit can formulate the plan by taking into consideration the historical background and culture of the region. Historical background includes, but is not limited to, past events, cultural heritage, and local traditions. For example, in an area with many historical buildings, the reviewing unit can formulate an urban plan that takes into consideration the preservation of buildings. In an area where many cultural events are held, the reviewing unit can also formulate an urban plan that takes into consideration evacuation plans for events. Furthermore, the reviewing unit can also formulate an urban plan that does not damage the scenery by taking into consideration the traditional architectural style of the area. In this way, an urban plan suitable for the area can be formulated by taking into consideration the historical background and culture of the area.

[0081] The placement unit can estimate the user's emotion and adjust the placement method of the evacuation facilities based on the estimated user's emotion. For example, when the user feels anxious, the placement unit describes the placement of the evacuation facilities in detail. Furthermore, when the user is relaxed, the placement unit can also briefly describe the placement of the evacuation facilities. Furthermore, when the user is in a hurry, the placement unit can also describe the placement of the evacuation facilities focusing on important points. In this way, the placement method of the evacuation facilities can be adjusted according to the user's emotion, thereby providing a placement method suitable for the user. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0082] When arranging evacuation facilities, the arrangement unit can refer to past evacuation data and select an appropriate arrangement method. Past evacuation data includes, for example, evacuation routes, evacuation times, and the number of evacuees, but is not limited to such examples. For example, the arrangement unit analyzes past evacuation data and arranges more evacuation facilities in areas where there were many evacuees. The arrangement unit can also analyze past evacuation data and arrange fewer evacuation facilities in areas where there were few evacuees. Furthermore, the arrangement unit can analyze past evacuation data and arrange evacuation facilities taking into account the movement lines of evacuees. In this way, by referring to past evacuation data, an optimal arrangement method for evacuation facilities can be selected.

[0083] The placement unit can place evacuation facilities in consideration of the topography and infrastructure conditions of the area. Examples of topography include, but are not limited to, topography, altitude, and geology. Examples of infrastructure conditions include, but are not limited to, road networks, public transportation, and power supply. For example, in areas with flat topography, the placement unit places evacuation facilities over a wide area. In areas with undulating topography, the placement unit can also place evacuation facilities in easily accessible locations. Furthermore, in areas with well-developed infrastructure, the placement unit can also place evacuation facilities densely. In this way, the placement unit can optimally place evacuation facilities by considering the topography and infrastructure conditions of the area.

[0084] The placement unit can estimate the user's emotions and determine the priority order of evacuation facilities to be placed based on the estimated user's emotions. For example, when the user feels anxious, the placement unit places evacuation facilities in areas with a high disaster risk with priority. In addition, when the user is relaxed, the placement unit can also place evacuation facilities taking into consideration the overall balance. Furthermore, when the user is in a hurry, the placement unit can also place evacuation facilities by focusing on important points. In this way, by determining the priority order of evacuation facilities according to the user's emotions, it is possible to provide a priority order suitable for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] When arranging evacuation facilities, the arrangement unit can appropriately arrange them by taking into consideration the climatic conditions of the region. The climatic conditions include, but are not limited to, for example, temperature, precipitation, and wind speed. For example, the arrangement unit prioritizes arranging evacuation facilities with roofs in areas with a lot of rain. Furthermore, the arrangement unit can also prioritize arranging evacuation facilities with air conditioning equipment in hot areas. Furthermore, the arrangement unit can also prioritize arranging evacuation facilities with heating equipment in cold areas. In this way, it is possible to arrange optimal evacuation facilities by taking into consideration the climatic conditions of the region.

[0086] The placement unit can place evacuation facilities while taking into consideration the traffic conditions of the area. Traffic conditions include, but are not limited to, for example, traffic volume, congestion conditions, and the use of public transportation. For example, the placement unit places evacuation facilities in a dispersed manner in an area where traffic congestion is likely to occur. Furthermore, the placement unit can place evacuation facilities near stations in an area where public transportation is well developed. Furthermore, the placement unit can place evacuation facilities in a concentrated manner in an area with good transportation access. In this way, the optimal placement of evacuation facilities can be performed by taking into consideration the traffic conditions of the area.

[0087] The improvement unit can estimate the user's emotion and adjust the road network improvement method based on the estimated user's emotion. For example, when the user feels anxious, the improvement unit provides a road network improvement method including a detailed explanation. In addition, when the user is relaxed, the improvement unit can also provide a road network improvement method that is concise and to the point. Furthermore, when the user is in a hurry, the improvement unit can also provide a road network improvement method that quickly provides improvement results. In this way, by adjusting the road network improvement method according to the user's emotion, an improvement method suitable for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When improving a road network, the improvement unit can select an appropriate improvement method by referring to past traffic data. The past traffic data includes, for example, traffic volume, congestion conditions, accident data, and the like, but is not limited to such examples. For example, the improvement unit analyzes past traffic congestion data and selects an improvement method for roads where congestion is likely to occur. The improvement unit can also analyze past traffic accident data and select an improvement method for roads where accidents are likely to occur. Furthermore, the improvement unit can analyze past traffic volume data and select an improvement method for roads with high traffic volume. In this way, by referring to past traffic data, an optimal improvement method for the road network can be selected.

[0089] When improving a road network, the improvement unit can make improvements while taking into consideration the traffic demand and supply of the region. Traffic demand and supply include, but are not limited to, traffic volume, utilization of public transportation, and road capacity. For example, the improvement unit widens or builds new roads in areas where traffic demand is high. The improvement unit can also develop public transportation facilities in areas where traffic supply is insufficient. Furthermore, the improvement unit can also make optimal improvements to the road network by taking into consideration the balance between traffic demand and supply. This makes it possible to make optimal improvements to the road network by taking into consideration the traffic demand and supply of the region.

[0090] The improvement unit can estimate the user's emotions and determine the priority of the road network to be improved based on the estimated user's emotions. For example, when the user feels anxious, the improvement unit prioritizes improving the road network in areas with a high disaster risk. In addition, when the user is relaxed, the improvement unit can also improve the road network taking into account the overall balance. Furthermore, when the user is in a hurry, the improvement unit can improve the road network by focusing on important points. In this way, by determining the priority of the road network according to the user's emotions, it is possible to provide a priority suitable for the user. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] When improving the road network, the improvement unit can appropriately improve the road network by taking into account the state of local public transportation. The state of public transportation includes, but is not limited to, for example, the frequency of operation, the number of users, and the service area. For example, in an area where public transportation is well developed, the improvement unit improves the road network by taking into account cooperation with the public transportation. In addition, in an area where public transportation is insufficient, the improvement unit can prioritize the development of public transportation. Furthermore, the improvement unit can also optimally improve the road network by taking into account the usage state of public transportation. As a result, the road network can be optimally improved by taking into account the state of public transportation in the area.

[0092] When improving the road network, the improvement department can make the improvement while taking into consideration the environmental impact of the region. The environmental impact includes, but is not limited to, for example, air pollution, noise, and reduction in green space. For example, in environmentally protected areas, the improvement department can improve the road network while minimizing the environmental impact. Also, in areas with high environmental load, the improvement department can also improve the road network to reduce the environmental load. Furthermore, the improvement department can perform an environmental impact assessment and make optimal improvements to the road network. In this way, the road network can be optimally improved by taking into consideration the environmental impact of the region.

[0093] The simulation unit can estimate the user's emotion and adjust the simulation method based on the estimated user's emotion. For example, when the user feels anxious, the simulation unit provides a detailed simulation result. Furthermore, when the user is relaxed, the simulation unit can also provide a concise and to-the-point simulation result. Furthermore, when the user is in a hurry, the simulation unit can also quickly provide a simulation result. In this way, by adjusting the simulation method according to the user's emotion, a simulation method suitable for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] The simulation unit can select an appropriate simulation method by referring to past evacuation simulation data during the simulation. The past evacuation simulation data includes, for example, evacuation routes, evacuation time, the number of evacuees, etc., but is not limited to such examples. For example, the simulation unit analyzes the past evacuation simulation data and selects a simulation method for an area where there were many evacuees. The simulation unit can also analyze the past evacuation simulation data and select a simulation method for an area where there were few evacuees. Furthermore, the simulation unit can analyze the past evacuation simulation data and select a simulation method that takes into account the movement lines of evacuees. In this way, the optimal simulation method can be selected by referring to the past evacuation simulation data.

[0095] The simulation unit can perform a simulation while taking into consideration the population density and building density of the region. The population density includes, for example, but is not limited to, the population distribution for each region, population increase and decrease, and fluctuations in population density. The building density includes, for example, but is not limited to, the number of buildings, the height of the buildings, and the use of the buildings. For example, in a region with a high population density, the simulation unit performs a simulation so that the flow lines of evacuees do not overlap. In addition, in a region with a high building density, the simulation unit can also perform a simulation so that an evacuation route is secured. Furthermore, the simulation unit can also simulate an optimal evacuation route by taking into consideration the balance between the population density and the building density. In this way, an optimal simulation can be performed by taking into consideration the population density and the building density of the region.

[0096] The simulation unit can estimate the user's emotion and adjust the display method of the simulation result based on the estimated user's emotion. For example, when the user feels anxious, the simulation unit displays a detailed simulation result. Also, when the user is relaxed, the simulation unit can display a concise simulation result that covers the main points. Furthermore, when the user is in a hurry, the simulation unit can quickly display the simulation result. In this way, by adjusting the display method of the simulation result according to the user's emotion, a display method suitable for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The simulation unit can improve the accuracy of the simulation by referring to local evacuation drill data during the simulation. The evacuation drill data includes, for example, the number of participants in the training, the location where the training is conducted, the results of the training, and the like, but is not limited to such examples. For example, the simulation unit analyzes the evacuation drill data and performs a simulation that reflects actual evacuation behavior. The simulation unit can also perform a simulation that optimizes the movement of evacuees based on the evacuation drill data. Furthermore, the simulation unit can also accurately simulate the congestion situation during evacuation by referring to the evacuation drill data. As a result, the accuracy of the simulation can be improved by referring to the evacuation drill data for the region.

[0098] During the simulation, the simulation unit can perform a simulation taking into account the diversity of evacuation routes in the area. The diversity of evacuation routes includes, but is not limited to, for example, the number of evacuation routes, the types of evacuation routes, and the usage status of evacuation routes. For example, the simulation unit simulates a plurality of evacuation routes and proposes an optimal evacuation route. The simulation unit can also perform a simulation taking into account the diversity of evacuation routes so as to disperse evacuees. Furthermore, the simulation unit can also perform a simulation that reflects the diversity of evacuation routes and reduces congestion during evacuation. In this way, an optimal simulation can be performed by taking into account the diversity of evacuation routes in the area.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] When reviewing urban planning, the review department can formulate plans taking into consideration the location of local medical facilities. For example, in areas with few medical facilities, the review department can add equipment for providing medical support to evacuation facilities. Also, in areas with many medical facilities, the review department can formulate plans to strengthen cooperation between evacuation facilities and medical facilities. Furthermore, the review department can formulate urban planning that takes into consideration access to medical facilities so that evacuees can receive medical support quickly. In this way, by taking into consideration the location of local medical facilities, evacuees can receive prompt and appropriate medical support.

[0101] When arranging evacuation facilities, the placement unit can place them taking into consideration the status of local educational facilities. For example, in an area with many educational facilities such as schools and universities, the placement unit links the evacuation facilities with the educational facilities. In an area with few educational facilities, the placement unit can also add equipment for providing educational support to the evacuation facilities. Furthermore, the placement unit can place evacuation facilities taking into consideration access to educational facilities so that evacuees can receive educational support. In this way, by taking into consideration the status of local educational facilities, evacuees can receive educational support.

[0102] The improvement department can install lanes exclusively for emergency vehicles to reduce traffic congestion during evacuations. For example, the improvement department can install lanes exclusively for emergency vehicles on major evacuation routes to allow ambulances and fire engines to move quickly. The improvement department can also reduce traffic congestion for general vehicles by installing lanes exclusively for emergency vehicles. Furthermore, the improvement department can also enable evacuees to evacuate quickly by installing lanes exclusively for emergency vehicles. As a result, by installing lanes exclusively for emergency vehicles, traffic congestion during evacuations can be reduced and evacuees can evacuate quickly.

[0103] The simulation unit can take into account the age and health condition of the evacuees when simulating the movements of the evacuees. For example, the simulation unit can simulate an evacuation route by taking into account the evacuation speed of the elderly and the disabled. The simulation unit can also distinguish between the movements of evacuees in good health and those in poor health and simulate the movements of these evacuees. Furthermore, the simulation unit can suggest evacuation routes according to the age and health condition of the evacuees. As a result, a more realistic evacuation simulation can be performed by taking into account the age and health condition of the evacuees.

[0104] When arranging evacuation facilities, the placement unit can consider the status of local animal evacuation facilities when arranging the evacuation facilities. For example, in an area where there are many evacuees who keep pets, the placement unit can also set up an animal evacuation facility. In an area where there are few animal evacuation facilities, the placement unit can also provide an animal evacuation space in the evacuation facility. Furthermore, the placement unit can also arrange the evacuation facility with consideration of access to the animal evacuation facility so that evacuees can evacuate with their pets. In this way, by considering the status of local animal evacuation facilities, evacuees who keep pets can evacuate with peace of mind.

[0105] The review unit can estimate the user's emotions and adjust the evacuation training plan based on the estimated user's emotions. For example, if the user feels anxious, a detailed evacuation training plan can be provided. Also, if the user is relaxed, a concise evacuation training plan that focuses on the main points can be provided. Furthermore, if the user is in a hurry, an evacuation training plan that quickly provides training results can be provided. In this way, by adjusting the evacuation training plan according to the user's emotions, a training plan suitable for the user can be provided.

[0106] The review unit can analyze past disaster data and select an appropriate evacuation drill method. For example, it can analyze past earthquake data and select an evacuation drill method for areas with high earthquake risk. It can also analyze past flood data and select an evacuation drill method for areas with high flood risk. It can also analyze past typhoon data and select an evacuation drill method for areas with high typhoon risk. In this way, it is possible to select the optimal evacuation drill method by analyzing past disaster data.

[0107] When considering urban planning, the review unit can formulate plans taking into account the tourism resources of the region. For example, in an area with many tourist destinations, urban planning can be formulated that takes into account the evacuation of tourists. Also, in an area with few tourist resources, urban planning can be formulated that takes into account the protection of tourist resources. Furthermore, in an area with many tourist events, urban planning can be formulated that takes into account evacuation plans for the events. In this way, by considering the tourism resources of the region, urban planning can be formulated that protects the tourism resources while ensuring the safety of tourists.

[0108] The examination unit can estimate the user's emotions and determine the priority of evacuation drills based on the estimated user's emotions. For example, if the user feels anxious, evacuation drills in areas with a high disaster risk are given priority. Also, if the user feels relaxed, evacuation drills can be carried out while taking into consideration the overall balance. Furthermore, if the user is in a hurry, evacuation drills that focus on important points can be carried out with priority. In this way, by determining the priority of evacuation drills according to the user's emotions, it is possible to provide a priority that is suitable for the user.

[0109] When considering urban planning, the review department can formulate plans taking into account the local energy supply situation. For example, in areas where the energy supply is unstable, emergency power sources can be installed in evacuation facilities. Also, in areas where the energy supply is stable, urban planning can be formulated that takes energy efficiency into consideration. Furthermore, in areas where the use of renewable energy is progressing, the placement of evacuation facilities that utilize renewable energy can be considered. In this way, by taking into account the local energy supply situation, urban planning can be formulated that allows evacuees to evacuate in peace of mind.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The review department reviews the city plan. The review of the city plan includes data analysis, simulations, expert opinions, etc. For example, the review department analyzes the city's geographical conditions and demographics and evaluates disaster risks. Step 2: The Allocation Department optimizes the placement of evacuation facilities based on the results of the review by the Review Department. The optimal placement of evacuation facilities takes into account factors such as the distance between evacuation facilities, ease of access, and capacity. For example, the Allocation Department places more evacuation facilities in areas with a high disaster risk, allowing evacuees to evacuate quickly. Step 3: The Improvement Department improves the road network based on the evacuation facilities arranged by the Arrangement Department. Road network improvement includes road widening, signal adjustment, traffic control, etc. For example, the Improvement Department widens roads on major evacuation routes to reduce traffic congestion during evacuation. Step 4: The simulation unit performs a people flow simulation based on the road network improved by the improvement unit. The people flow simulation includes a simulation model, the data to be used, and evaluation criteria. For example, the simulation unit simulates the movement of evacuees and predicts congestion conditions and appropriate evacuation routes during evacuation.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned examination unit, placement unit, improvement unit, and simulation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the examination unit is realized by the control unit 46A of the smart device 14, and analyzes the geographical conditions and demographics of the city to evaluate disaster risks. The placement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs optimal placement of evacuation facilities. The improvement unit is realized, for example, by the control unit 46A of the smart device 14, and improves the road network. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs people flow simulation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned examination unit, placement unit, improvement unit, and simulation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the examination unit is realized by the control unit 46A of the smart glasses 214, and analyzes the geographical conditions and demographics of the city to evaluate disaster risks. The placement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs optimal placement of evacuation facilities. The improvement unit is realized, for example, by the control unit 46A of the smart glasses 214, and improves the road network. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs people flow simulation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned examination unit, placement unit, improvement unit, and simulation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the examination unit is realized by the control unit 46A of the headset type terminal 314, and analyzes the geographical conditions and demographics of the city to evaluate disaster risks. The placement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs optimal placement of evacuation facilities. The improvement unit is realized, for example, by the control unit 46A of the headset type terminal 314, and performs improvement of the road network. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs people flow simulation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a 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.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the multiple elements including the above-mentioned examination unit, placement unit, improvement unit, and simulation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the examination unit is realized by the control unit 46A of the robot 414, and analyzes the geographical conditions and demographics of the city to evaluate disaster risks. The placement unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs optimal placement of evacuation facilities. The improvement unit is realized, for example, by the control unit 46A of the robot 414, and performs improvement of the road network. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs people flow simulation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, 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 upon request from the data processing device 12.

[0175] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) The review department that reviews urban planning; A placement unit that performs optimal placement of evacuation facilities based on the results of the consideration by the consideration unit; an improvement unit that improves a road network based on the evacuation facilities arranged by the arrangement unit; A simulation unit that performs a pedestrian flow simulation based on the road network improved by the improvement unit. A system characterized by: (Appendix 2) The placement unit is Place multiple evacuation facilities in areas with high disaster risk 2. The system of claim 1, (Appendix 3) The improvement unit includes: Improve road networks to reduce traffic congestion during evacuations. 2. The system of claim 1, (Appendix 4) The simulation unit is Simulating the movements of evacuees and predicting congestion and appropriate evacuation routes 2. The system of claim 1, (Appendix 5) The placement unit is Increase the capacity of evacuation facilities to accommodate more evacuees 2. The system of claim 1, (Appendix 6) The review unit, Estimating user emotions and adjusting urban planning consideration methods based on the estimated user emotions 2. The system of claim 1, (Appendix 7) The review unit, Analyzing past disaster data and selecting appropriate urban planning methods 2. The system of claim 1, (Appendix 8) The review unit, When considering urban planning, local demographics and economic conditions are taken into account. 2. The system of claim 1, (Appendix 9) The review unit, Estimating user emotions and determining priorities for urban planning to be considered based on the estimated user emotions 2. The system of claim 1, (Appendix 10) The review unit, When considering urban planning, formulate appropriate plans taking into account the geographical characteristics of the region. 2. The system of claim 1, (Appendix 11) The review unit, When considering urban planning, we formulate plans that take into account the historical background and culture of the area. 2. The system of claim 1, (Appendix 12) The placement unit is Estimating user emotions and adjusting the placement of evacuation facilities based on the estimated user emotions 3. The system of claim 2, (Appendix 13) The placement unit is When locating evacuation facilities, select the appropriate placement method by referring to past evacuation data. 3. The system of claim 2, (Appendix 14) The placement unit is When locating evacuation facilities, take into consideration the local topography and infrastructure conditions. 3. The system of claim 2, (Appendix 15) The placement unit is Estimate the user's emotions and prioritize the evacuation facilities to be placed based on the estimated user's emotions. 3. The system of claim 2, (Appendix 16) The placement unit is When locating evacuation facilities, appropriate locations should be selected taking into account local climatic conditions. 3. The system of claim 2, (Appendix 17) The placement unit is When locating evacuation facilities, local traffic conditions should be taken into consideration. 3. The system of claim 2, (Appendix 18) The improvement unit includes: To estimate a user's emotion and adjust a method of improving a road network based on the estimated user's emotion. 4. The system of claim 3. (Appendix 19) The improvement unit includes: When improving road networks, select appropriate improvement methods by referring to past traffic data 4. The system of claim 3. (Appendix 20) The improvement unit includes: When improving road networks, improvements are made taking into account local traffic demand and supply. 4. The system of claim 3. (Appendix 21) The improvement unit includes: Estimating user sentiment and prioritizing road network improvements based on the estimated user sentiment 4. The system of claim 3. (Appendix 22) The improvement unit includes: When improving the road network, make appropriate improvements to the road network taking into account the state of local public transportation. 4. The system of claim 3. (Appendix 23) The improvement unit includes: When improving the road network, the local environmental impact is taken into consideration. 4. The system of claim 3. (Appendix 24) The simulation unit is Estimating a user's emotions and adjusting a simulation method based on the estimated user's emotions 5. The system of claim 4. (Appendix 25) The simulation unit is When conducting a simulation, the appropriate simulation method is selected by referring to past evacuation simulation data. 5. The system of claim 4. (Appendix 26) The simulation unit is During the simulation, the population density and building density of the area are taken into account. 5. The system of claim 4. (Appendix 27) The simulation unit is Estimating a user's emotions and adjusting the way the simulation results are displayed based on the estimated user's emotions 5. The system of claim 4. (Appendix 28) The simulation unit is During the simulation, local evacuation drill data is referenced to improve the accuracy of the simulation. 5. The system of claim 4. (Appendix 29) The simulation unit is During the simulation, the diversity of evacuation routes in the area is taken into account. 5. The system of claim 4. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The review department that reviews urban planning; A placement unit that performs optimal placement of evacuation facilities based on the results of the consideration by the consideration unit; an improvement unit that improves a road network based on the evacuation facilities arranged by the arrangement unit; A simulation unit that performs a pedestrian flow simulation based on the road network improved by the improvement unit. A system characterized by:

2. The placement unit is Place multiple evacuation facilities in areas with high disaster risk 2. The system of claim 1 .

3. The improvement unit includes: Improve road networks to reduce traffic congestion during evacuations.

2. The system of claim 1 .

4. The simulation unit is Simulating the movements of evacuees and predicting congestion and appropriate evacuation routes 2. The system of claim 1 .

5. The placement unit is Increase the capacity of evacuation facilities to accommodate more evacuees 2. The system of claim 1 .

6. The review unit, Estimating user emotions and adjusting urban planning consideration methods based on the estimated user emotions 2. The system of claim 1 .

7. The review unit, Analyzing past disaster data and selecting appropriate urban planning methods 2. The system of claim 1 .

8. The review unit, When considering urban planning, local demographics and economic conditions are taken into account.

2. The system of claim 1 .

9. The review unit, Estimating user emotions and determining priorities for urban planning to be considered based on the estimated user emotions 2. The system of claim 1 .

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