system

Abstract

We provide the system. [Solution] A means of acquiring data using sensors to collect information about the city, A means for creating a virtual space model based on acquired data, A means of reflecting policy changes in a virtual space model and performing simulations to predict their impact, A means of analyzing simulation results and providing an optimized urban plan for a specific objective, A means of incorporating and analyzing residents' opinions and reflecting them in urban planning, A means of displaying results on a user device in real time using 3D visualization technology, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the complexity of urban infrastructure, population dynamics, and traffic conditions, there is a problem that it is difficult to effectively alleviate traffic congestion, reduce environmental impact, and strengthen disaster prevention measures with conventional urban planning methods. Also, it is difficult to fully understand the diverse needs of residents and reflect them in urban planning. In such a situation, there is a need for means to efficiently and flexibly grasp the current situation and formulate future plans.

Means for Solving the Problems

[0005] This invention provides a means for collecting urban data and generating a digital twin model, thereby recreating real-world urban conditions in a virtual space. Based on this model, simulations are performed to predict the impact of policy changes, generating concrete proposals for infrastructure development and environmental measures. Furthermore, by analyzing feedback from residents using natural language processing technology and incorporating it into urban planning, optimal measures that address diverse needs are presented. This invention makes it possible to provide comprehensive and adaptive solutions to complex urban challenges.

[0006] "City data" refers to various types of information related to cities, such as infrastructure conditions, population dynamics, traffic volume, and environmental data.

[0007] "Sensor information" refers to information including real-time data collected from the urban environment, and includes physical and environmental data acquired through sensors.

[0008] A "digital twin model" refers to a virtual model that reproduces the structure and condition of a real-world city in a digital space.

[0009] "Policy change" refers to changes or revisions in urban planning and administrative policies, including the introduction of new policies and modifications to rules.

[0010] "Simulation" refers to the computational process of virtually reproducing the impact of policy changes using a digital twin model and predicting the results.

[0011] An "optimized urban planning proposal" refers to a proposal of the most effective measures to achieve the set goals, based on simulation results.

[0012] "Feedback" refers to opinions and requests provided by residents, which are information that influences urban planning and policy.

[0013] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes structuring and semantic analysis of text data.

[0014] A "dashboard" is an interface that visually displays analysis results and important information, and refers to a screen that allows users to easily understand and manipulate information. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that utilizes urban data to recreate real cities in digital space and perform simulations and optimizations of urban planning. The implementation of this invention consists of several main modules, each of which works in conjunction with the others.

[0037] First, the server collects data in real time from various sensors. This includes data from traffic volume sensors and air pollution sensors, for example. This forms the basis for a digital twin model in which the latest urban conditions are constantly updated.

[0038] Next, the server integrates the collected data to build a digital twin model of the city. This visually reproduces elements such as the city's infrastructure and demographics in a virtual space. This model plays a crucial role in subsequent simulations.

[0039] Local government officials can use terminals to input new policies and infrastructure plans. For example, various plans such as the construction of new roads or changes to public transport routes can be entered. This information is used as a condition for simulations.

[0040] Subsequently, the server performs simulations based on the digital twin model. It predicts the impact of the input plan on the urban environment and performs calculations for each scenario. This makes it possible to visually and quantitatively evaluate the strengths and weaknesses of the plan.

[0041] Furthermore, using optimization algorithms, the system proposes optimal urban planning in light of goals such as mitigating traffic congestion and reducing environmental impact. The server automatically generates this proposal in report format, making it viewable on a dashboard on the user's device.

[0042] Furthermore, residents, as users, can directly submit feedback on urban planning and policies through a resident participation platform. This information is analyzed on the server using natural language processing technology and used to create more suitable proposals that reflect residents' opinions.

[0043] For example, suppose a local government has devised a new road plan to alleviate traffic congestion. The server conducts simulations based on the plan and provides the local government with an optimized proposal. At the same time, it can also incorporate the opinions of residents (the users) and present alternative plans, including the placement of public spaces preferred by residents.

[0044] Thus, the present invention supports the efficiency of urban planning and provides an innovative tool for evaluating feasible ideas from multiple perspectives.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server collects data in real time from various sensors placed throughout the city. This includes traffic volume sensors, air pollution sensors, and demographic data, which are retrieved via APIs. The collected data is stored in a database, which serves as the foundation for building a digital twin model.

[0048] Step 2:

[0049] The server uses the collected data to generate a digital twin model of the city. The data is cleansed, synchronized, and missing values ​​are imputed. Next, 3D modeling software is used to recreate the city's infrastructure, buildings, and roads in the virtual space.

[0050] Step 3:

[0051] Local government officials input new policies and infrastructure plans using terminals. Specifically, they use a dedicated GUI (Graphical User Interface) to register details such as new road construction and changes to traffic regulations in a policy input form. This data is stored in the system and used as conditions for simulations.

[0052] Step 4:

[0053] The server runs simulations predicting the impact of policy changes based on a digital twin model. Using a dedicated simulation engine, it runs various scenarios in parallel, collecting and analyzing the results. Parameters such as traffic congestion, environmental impact, and disaster risk are evaluated.

[0054] Step 5:

[0055] The server applies an optimization algorithm based on the simulation results to generate the most effective urban planning proposal for achieving the goal. This process utilizes machine learning models to find the optimal solution based on historical data. The results are generated as a visual report and provided to municipal officials.

[0056] Step 6:

[0057] Residents, as users, input their opinions and feedback on the plan through a resident participation platform. The platform provides an intuitive interface and allows users to input information in text or survey formats.

[0058] Step 7:

[0059] The server analyzes feedback from residents using natural language processing technology and integrates it into urban planning proposals. The opinion data is subjected to text analysis, classifying positive and negative opinions and extracting keywords. This allows for the generation of more effective proposals that take residents' feedback into account.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In today's urban environment, there is a need for urban planning that quickly incorporates traffic congestion, increasing environmental burden, and diverse opinions from residents. However, conventional methods have the challenge of delays in optimal planning due to the time required for data collection, analysis, simulation, and feedback.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes an information gathering device for acquiring data, a device for generating a virtual environment based on the acquired information, and a device for evaluating the analysis results and generating a plan proposal optimized for a specific purpose. This makes it possible to analyze urban data in real time, quickly generate an optimized urban planning proposal, and effectively reflect the opinions of participants.

[0065] An "information gathering device" is a collection of hardware and software, such as sensors, databases, and network devices, used to acquire various data from the real world.

[0066] A "virtual environment" is a digital space that can change in real time, such as a digital twin model built based on collected data.

[0067] An "analysis system" is a software and hardware system used to perform simulations using collected data and evaluate the results.

[0068] A "plan proposal" refers to specific measures and concepts for traffic management and environmental improvement that are generated based on the analysis results.

[0069] "Natural language processing technology" is a technology used to understand and analyze feedback expressed in human language.

[0070] A "visualization device" is a device such as a display or projector that displays digital information in a way that is easy for humans to understand.

[0071] The system of the present invention reproduces urban environments in digital space and supports more efficient urban planning. Specific embodiments are shown below.

[0072] First, a server takes the lead in collecting data in real time from various sensors installed throughout the city. This data includes traffic volume, air pollution, and water quality. This data is then collected by the server via information gathering devices. The server stores this data in a database and prepares it for analysis.

[0073] Next, the server uses the collected data to generate a virtual environment, or digital twin model. GIS (Geographic Information System) and 3D modeling software are used to visually reproduce the city's infrastructure and other elements. This allows users to view the overall picture of the city in a virtual space.

[0074] Following this, the server utilizes the generated AI model and functions as an analysis device. This analysis device reflects new policy and infrastructure information in the virtual environment and performs complex simulations to predict their impact. For example, it runs a simulation to predict how a plan to establish a new bus route will alleviate traffic congestion. An example of a prompt statement might be, "What infrastructure improvements can be considered to alleviate traffic congestion?"

[0075] Subsequently, a plan proposal is generated based on the analysis results and displayed on the terminal's dashboard via a visualization device. Through this interface, local government officials can review the proposal and make necessary decisions.

[0076] Furthermore, feedback provided by residents through the resident participation platform is analyzed on the server using natural language processing technology. This analysis is then used to further refine the plan. For example, if a resident requests more parks, this is reflected in the simulation, leading to the development of urban planning proposals that better align with residents' wishes.

[0077] This system will expedite the urban planning process and enable the creation of plans that meet diverse needs.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects data in real time from various sensors installed throughout the city. The input consists of numerical data transmitted from traffic volume sensors and air pollution sensors. The server stores this data in a database, preparing it for future analysis. Specific operations include normalizing the data and saving it in a consistent format.

[0081] Step 2:

[0082] The server generates a virtual environment based on the collected data. The data input at this stage is the sensor data normalized in Step 1. The server uses GIS and 3D modeling tools to construct a digital twin model and visually recreate the city. The output is a 3D model of the city, representing infrastructure and land use. Specifically, this process involves rendering a combination of geographic information and 3D building data.

[0083] Step 3:

[0084] Through a terminal, local government officials input new policies and infrastructure plans. This input data includes information on the placement of planned roads and public facilities. The terminal sends this information to a server, which is then used as a simulation condition. Specifically, officials use a GUI to place the planned infrastructure on a map and save the information to the database.

[0085] Step 4:

[0086] The server performs simulations based on the digital twin model and input from staff. The input consists of the 3D model from Step 2 and the policy data from Step 3. The server uses a generative AI model to predict the impact on the city and generates analysis results as output. Specific data processing includes traffic flow simulations and environmental impact assessments.

[0087] Step 5:

[0088] The server analyzes the simulation results and generates optimized plan proposals. The input data is the analysis results obtained in the previous step. The generated proposals are displayed on the terminal's dashboard via a visualization device. The specific output is a report that includes optimized transportation route plans and environmental improvement measures.

[0089] Step 6:

[0090] Users provide feedback using a community participation platform. The input consists of opinions and requests posted by residents. The server analyzes this feedback using natural language processing technology and incorporates it into urban planning. Specifically, the opinions are categorized by topic and statistically analyzed, and these results are used as conditions for re-simulation.

[0091] Step 7:

[0092] Ultimately, the server generates an updated draft plan and provides it to local government officials. The input consists of analyzed feedback and updated simulation results. The output is a final urban planning proposal that reflects residents' opinions and can be viewed on a terminal. Specific actions include creating a plan document that reflects residents' requests and preparing materials for a reporting meeting based on that plan.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] Modern cities face a wide variety of problems, including traffic congestion, environmental pollution, and urban planning that fails to adequately reflect residents' opinions. Furthermore, addressing these issues requires accurately predicting the impact of new policies and infrastructure and presenting it in a visually clear and understandable way. However, previous methods have struggled to respond to real-time changes and effectively incorporate resident feedback.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for acquiring data by sensors for collecting urban information, means for creating a virtual space model based on the acquired data, and means for performing simulations to reflect policy changes in the virtual space model and predict their impact. This makes it possible to visualize complex urban problems in real time and provide optimal urban planning that reflects the opinions of residents.

[0098] "Urban information" refers to various types of data necessary for urban management and planning, such as traffic volume, air pollution, and population dynamics.

[0099] A "sensor" is a device that measures physical phenomena and records them as data, and it forms the foundation of data collection.

[0100] "Means of acquiring data" refers to the process of collecting information about the city through sensors and incorporating it into the system.

[0101] A "virtual space model" is a digital 3D representation of a real city, used for urban planning simulations.

[0102] "Means of running simulations" refer to methods for reflecting policy and infrastructure changes in a virtual space model and calculating and predicting their impact.

[0103] "Optimized urban planning" refers to urban design proposed to implement the most effective plan for achieving specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0104] "Residents' opinions" refer to the thoughts and feedback of local residents regarding urban planning and policies.

[0105] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and converts it into information, and is used in the analysis of resident feedback.

[0106] "3D visualization technology" is a technology that displays virtual space models and simulation results in three dimensions, enabling real-time display on user devices.

[0107] A "policymaker" is an administrator or local government official who is responsible for formulating and implementing urban planning and public policies.

[0108] To implement this invention, a system is needed that acquires information from cities in real time, creates a digital twin model based on that information, and performs simulations.

[0109] First, the server acquires data in real time from various sensors (such as traffic sensors and air pollution sensors) to collect information about the city. This data is used to comprehensively understand the detailed current state of the entire city. The data collected by the sensors is stored in a database and forms the basis for all subsequent processing.

[0110] Next, the server uses the acquired data to create a virtual space model, or digital twin. The digital twin model is visually constructed using 3D rendering technologies such as Three.js. This makes it possible to faithfully reproduce the current state of the city in a digital environment.

[0111] The system also allows users to input new policies and infrastructure plans. The server receives this input and simulates the impact of these changes on a virtual space model. Using Python and Pandas, the system analyzes the simulation results. Based on this analysis, it proposes optimized urban plans for specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0112] Residents, as users, can provide opinions and feedback through a smartphone app. The server analyzes this feedback using natural language processing technology (e.g., NLTK) and incorporates it into urban planning. This process leads to improvements in the plan that meet the needs of the residents.

[0113] Ultimately, the device visualizes the optimized urban planning and simulation results on an application using React Native, for example, and provides them to policymakers and residents. This enables visual and intuitive urban planning proposals, supporting efficient decision-making.

[0114] For example, when proposing a plan for a new bypass road to alleviate traffic congestion in a small city, feedback from residents regarding the addition of commercial facilities is incorporated and presented as an optimized plan.

[0115] The following prompt could be used as input to the generative AI model: "Given an infrastructure plan to alleviate urban traffic congestion, what optimization proposals can you suggest? How will you incorporate resident feedback?"

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server acquires real-time urban information from various sensors. Input data comes from traffic and air pollution sensors. The server converts this data into a specific format and stores it in a database. This makes it possible to provide the fundamental data necessary for future urban planning.

[0119] Step 2:

[0120] The server generates a virtual space model using data stored in a database. The input is city data acquired from sensors. Based on the acquired data, the server creates a virtual space model using 3D rendering technology with Three.js. The output is a digital twin model of a real city.

[0121] Step 3:

[0122] Users input new policies and infrastructure plans through their terminals. This input is data about policies and plans that will be added to the virtual space model. The server reflects this in the virtual space model and prepares it for the next simulation step.

[0123] Step 4:

[0124] The server runs the simulation. The inputs are a digital twin model and policy data entered by the user. The server uses Python and Pandas to calculate the impact of policy changes on the model. The output is the simulation's predicted impact data.

[0125] Step 5:

[0126] Residents, acting as users, provide feedback on the simulation results via a smartphone app. The input is resident feedback data. The server analyzes this feedback using natural language processing technology with NLTK and utilizes it to improve urban planning as needed. The output is the improved feedback analysis results.

[0127] Step 6:

[0128] The terminal displays the simulation results and optimized urban plan on a visualization device. The input is the final urban planning data sent from the server. Using React Native and other tools, it is presented directly to policymakers and residents as 3D models and graphs. The output is a visualized urban planning proposal.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention combines a system that utilizes a digital twin model of a city to predict the impact of policy changes on the urban environment and propose optimal urban planning solutions with an emotion engine that recognizes user emotions. This makes it possible to analyze feedback from residents more precisely and reflect it in urban planning.

[0131] First, the server collects sensor information from within the city and builds a digital twin model of the city. This model is updated in real time, recreating the real-world urban conditions in a virtual space.

[0132] Next, local government officials use terminals to input new policies and infrastructure plans into the system. Based on this information, the server uses a digital twin model to perform simulations and predict the impact of policy changes on the urban environment.

[0133] Furthermore, an optimization algorithm is used to generate urban planning proposals based on the simulation results. The server creates this in report format and displays it on a dashboard on the terminal, providing visual support to local government officials.

[0134] This is where the emotion engine, a key feature of the present invention, comes into play. Residents, acting as users, provide feedback on urban planning through a dedicated participatory platform. The server analyzes the collected feedback using a combination of natural language processing technology and the emotion engine. This extracts emotional states from residents' text data and utilizes them as important elements for influencing urban planning.

[0135] For example, if a new infrastructure plan is supported by residents but also raises many concerns, the emotion engine will extract both positive and negative sentiments. Based on these results, the server can adjust the proposal and derive revised versions to address residents' concerns. The revised urban planning proposal will then be reflected on a dashboard, allowing municipal officials to make decisions based on it immediately.

[0136] Thus, this invention, which incorporates an emotion engine, enables the realization of effective and highly acceptable urban planning by more accurately reflecting the diverse voices of residents.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The server receives real-time data acquired from city sensors via a remote API and stores it in a database. This data includes traffic volume, weather information, and demographic data, and is used for subsequent processing.

[0140] Step 2:

[0141] The server constructs a digital twin model based on the collected data. It organizes and cleanses the data, and then uses 3D modeling tools to recreate the urban environment in a virtual space. This generates a model that accurately represents the current state of the city.

[0142] Step 3:

[0143] Using a terminal, local government officials input details of new infrastructure plans and policy changes into a user interface. This input includes information such as the location of new roads and plans for expanding public facilities. This information is then registered in the system as simulation parameters.

[0144] Step 4:

[0145] The server applies the input policy changes to a digital twin model and runs the scenario using a simulation engine. It comprehensively analyzes the impact of policy changes on the urban environment and predicts traffic flow, energy consumption, and environmental impact.

[0146] Step 5:

[0147] The server analyzes the simulation results and generates optimal urban planning proposals. These proposals aim to alleviate traffic congestion and reduce environmental impact, and are compiled using algorithms to create balanced measures addressing diverse objectives.

[0148] Step 6:

[0149] Residents, as users, provide feedback on the proposed plan through a resident participation platform. Here, they can freely write their opinions on their impressions and suggestions, and submit them through a user-friendly interface.

[0150] Step 7:

[0151] The server analyzes residents' feedback using natural language processing technology and an emotion engine. It quantitatively evaluates emotional states from text data, distinguishing between positive and negative emotions. This makes it possible to consider residents' emotional responses in urban planning.

[0152] Step 8:

[0153] Based on the sentiment analysis results, the server revises the urban planning proposals and generates new proposals. Proposals that reflect residents' sentiments are automatically displayed on a dashboard, making them easily accessible to local government officials for use in decision-making.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] The challenge lies in accurately predicting the impact of policy changes on the urban environment and more appropriately reflecting the diverse opinions of residents in urban planning. Traditional urban planning has struggled to accurately reflect residents' feelings and opinions, often resulting in dissatisfaction and misunderstandings due to policy changes. Furthermore, a lack of rapid data processing and optimized proposal development has hindered the realization of effective urban planning.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for acquiring data from a measuring device for collecting urban information, means for constructing a virtual model based on the acquired information, means for applying policy changes to the virtual model and conducting a simulated experiment to estimate the impact, means for processing residents' opinions using sentiment analysis technology and deriving improved urban planning proposals, and means for presenting the improved proposals on a visual information display device. This makes it possible to realize urban planning that reflects the feelings and opinions of residents, resulting in more effective and acceptable policies.

[0159] "Urban information" refers to data on environmental conditions and human activity within a city, including traffic volume, temperature, and noise levels.

[0160] "Measuring devices" refer to sensors and various measuring instruments placed to collect urban information.

[0161] "Means of acquiring data" refers to the process and technology of collecting urban information from measuring devices and transferring it to servers or other locations.

[0162] A "virtual model" refers to a digital twin or computer model used to recreate the physical and social conditions of a city in a digital space.

[0163] "Methods for conducting simulated experiments" refer to techniques and processes that involve modifying a virtual model and evaluating the results through simulation.

[0164] "Opinions" refer to feedback that expresses the thoughts and feelings that residents have regarding urban policies and plans.

[0165] "Emotional analysis technology" refers to a method that uses natural language processing to extract and analyze emotional and opinion tendencies from text data.

[0166] A "visual information display device" refers to an electronic device or software used to visually display analysis results or proposed plans.

[0167] "Methods for deriving improvement proposals" refers to the process of analyzing residents' opinions using sentiment analysis technology and proposing necessary modifications and improvements to urban planning.

[0168] The system of the present invention makes it possible to predict the impact of policy changes in urban planning on the urban environment and to accurately reflect the diverse opinions of residents. Specific embodiments of the present invention are described below.

[0169] First, the server acquires data from measuring devices that collect urban information. This includes traffic sensors, weather sensors, noise meters, and so on. The collected data is then incorporated into a virtual model built on the server. To intuitively reproduce various elements of the city, the virtual model typically utilizes "Unity" or "Azure® Digital Twins."

[0170] Next, municipal employees using the terminals input new policy information and infrastructure proposals through a dedicated interface. Based on this information, the server applies the policy changes to a virtual model and estimates their impact through simulated experiments. The simulation uses Python and its analysis libraries, Pandas and NumPy.

[0171] Furthermore, to gather residents' opinions, users submit feedback through a participatory platform. This platform is built using "Microsoft Forms" and "Google Forms." The server utilizes sentiment analysis technology and analyzes the feedback using natural language processing tools such as "NLTK" and "Hugging Face Transformers." This extracts the tendencies of residents' thoughts and emotions, which are then used to create improvement proposals for urban planning.

[0172] Finally, improvement proposals are displayed on a visual information display device and provided to staff in a dashboard format. This uses tools such as Tableau and Power BI. This makes it easier for staff to make quick and informed decisions.

[0173] For example, when considering the establishment of a new park, if residents frequently express concerns about public safety, it becomes possible to derive concrete improvement measures such as "installing surveillance cameras" or "restricting usage hours."

[0174] Furthermore, one possible prompt for the generating AI model could be: "Based on resident feedback data regarding the construction of a new park, analyze the positive and negative elements and generate improvement suggestions." This would allow for a more precise reflection of residents' voices in urban planning.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server acquires necessary urban information from measuring devices placed throughout the city. This input data includes traffic volume, ambient noise, and weather conditions. The server receives this data and performs data preprocessing, such as imputing missing values ​​and detecting and correcting anomalies. The processed data is then stored in a database. The final output is a basic dataset for a digital twin.

[0178] Step 2:

[0179] The server builds a virtual model based on the data collected in Step 1. The server uses Unity or Azure Digital Twins to create a 3D model that replicates the city's conditions. Because this virtual model is updated in real time, it always reflects the latest city situation. The output is a digital twin model of the specific city.

[0180] Step 3:

[0181] Local government officials using terminals input information about new policies into the system. This input includes policy details, objectives, and expected changes. The server receives this information and applies it to a virtual model to simulate the policy change. Specifically, this process uses Python and its libraries, Pandas and NumPy, to perform data analysis. The output is predicted data on the impact of the policy change.

[0182] Step 4:

[0183] The server evaluates the simulation results obtained in step 3 and generates an optimized urban planning proposal. The server utilizes optimization techniques such as "Scikit-learn" and "TENSORFLOW®" to evaluate numerous scenarios. The output is the urban planning proposal best suited to the specific objective.

[0184] Step 5:

[0185] Users provide feedback on urban planning through a participatory platform. This feedback is entered as opinions in text format. The server analyzes these opinions using natural language processing and sentiment analysis techniques, such as "NLTK" and "Hugging Face Transformers." The output after analysis extracts the emotional state and opinion trends of the residents.

[0186] Step 6:

[0187] Based on the analysis results obtained in Step 5, the server creates proposed improvements to the urban plan. The resulting improvement proposals can be viewed on a dashboard displayed on the terminal via visual information display devices such as "Tableau" and "Power BI." This allows local government officials to make decisions quickly.

[0188] Through these steps, this system aims to more effectively incorporate residents' opinions into urban planning.

[0189] (Application Example 2)

[0190] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0191] In modern urban planning and development processes, it is difficult to effectively incorporate the diverse opinions and feelings of residents. Conventional methods fail to accurately analyze resident feedback and reflect it in urban design, resulting in the failure to realize optimal urban planning that meets residents' needs. This invention aims to solve these problems and realize efficient and highly acceptable urban planning that reflects the feelings of residents.

[0192] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0193] In this invention, the server includes means for acquiring detector information for collecting urban information, means for generating a virtual space model based on the acquired detector information, means for performing simulated calculations to reflect policy changes in the virtual space model and predict their impact, means for analyzing opinions using an emotion analysis system that identifies the emotional state of residents' opinions, and means for generating optimized urban design proposals and displaying the proposals on a visual display device. This makes it possible to accurately analyze the diverse opinions of residents along with their emotions and reflect them in urban planning.

[0194] "Urban information" refers to various types of data related to cities, including data on the environment, infrastructure, transportation, and resident activities.

[0195] "Detector information" refers to data generated from various sensors and devices placed within a city, and is used to understand the state of the urban environment in real time.

[0196] A "virtual space model" is a model used to digitally reproduce real-world urban environments and serves as a foundation for conducting simulations of policy changes and other changes.

[0197] "Simulated calculation" refers to a simulation process in which assumptions such as policy changes are set in a virtual space model, and the effects of those changes are predicted.

[0198] "Resident opinions" refer to feedback and comments from residents regarding urban planning and policies, and are collected in order to reflect them in urban design from various perspectives.

[0199] A "sentiment analysis system" is a system that analyzes the emotional states contained in residents' opinions using natural language processing and machine learning, and processes the data based on the results.

[0200] A "visual display device" is a device that visually displays digital information and analysis results, and serves as a means of providing urban design proposals and other information to government officials.

[0201] This invention provides a system for urban planning that utilizes a digital twin model of a city. A server collects urban information in real time and constructs it as a virtual space model. This model is created using various detector information and can accurately reproduce the urban environment. The server uses this virtual space model to perform simulation calculations of policy changes and infrastructure plans, and predicts their impact.

[0202] Furthermore, the server collects opinions from residents and analyzes them using a combination of natural language processing technology and sentiment analysis systems. During this process, it identifies the emotional states contained in the residents' opinions and uses this information as crucial data for urban planning. Based on this analysis, it generates optimized urban design proposals and provides them to administrative staff using visual display devices.

[0203] As a concrete example, consider a case where residents send feedback on urban planning via their smartphones. If the feedback is, for example, "I'm looking forward to the new park plan, but I'm worried about transportation," the server receives this and uses an emotion analysis system to extract the positive emotion of "excitement" and the negative emotion of "worry." Based on these analysis results, the urban design proposal is adjusted to address the residents' concerns and is visually displayed on the terminals of administrative staff.

[0204] The program is implemented using Python, and libraries such as TensorFlow and BERT are used for natural language processing. Unity and Blender are used for simulating the virtual space model, and a dashboard-style web application is used to visualize the simulation results.

[0205] An example of a prompt would be: "We would like to conduct a sentiment analysis of resident feedback regarding a new urban planning project. Based on the feedback, 'I'm really happy about the new park plan, but I'm a little worried about traffic congestion,' analyze the residents' sentiments and generate suggestions for revising the plan based on that analysis."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] Users input feedback via their smartphones. This feedback consists of text data containing opinions and feelings about urban planning. This text is then sent to the server.

[0209] Step 2:

[0210] The server passes the string data received from the user to the sentiment analysis system. The sentiment analysis system uses natural language processing techniques to analyze the string and identify emotional states such as positive or negative. In this process, emotional attributes are extracted from the input data and output as structured data.

[0211] Step 3:

[0212] The server receives the results of the sentiment analysis and merges them into a virtual space model. Here, simulations of policy changes and infrastructure plans are performed. In this process, the simulation in the virtual space is re-run, taking into account the sentiment feedback of the residents. The output is predictive data regarding the impact on the urban environment.

[0213] Step 4:

[0214] The server generates optimized urban design proposals based on the predicted data from the simulation. In this generation process, various parameters are adjusted based on the predicted data, and urban planning proposals that match the needs of residents are output.

[0215] Step 5:

[0216] The generated urban design proposals are transmitted to terminals via visual display devices and displayed. Government officials, who are users of these terminals, can then make policy decisions based on this information. In this final stage, the proposals are presented in a visual dashboard format.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system that utilizes urban data to recreate real cities in digital space and perform simulations and optimizations of urban planning. The implementation of this invention consists of several main modules, each of which works in conjunction with the others.

[0234] First, the server collects data in real time from various sensors. This includes data from traffic volume sensors and air pollution sensors, for example. This forms the basis for a digital twin model in which the latest urban conditions are constantly updated.

[0235] Next, the server integrates the collected data to build a digital twin model of the city. This visually reproduces elements such as the city's infrastructure and demographics in a virtual space. This model plays a crucial role in subsequent simulations.

[0236] Local government officials can use terminals to input new policies and infrastructure plans. For example, various plans such as the construction of new roads or changes to public transport routes can be entered. This information is used as a condition for simulations.

[0237] Subsequently, the server performs simulations based on the digital twin model. It predicts the impact of the input plan on the urban environment and performs calculations for each scenario. This makes it possible to visually and quantitatively evaluate the strengths and weaknesses of the plan.

[0238] Furthermore, using optimization algorithms, the system proposes optimal urban planning in light of goals such as mitigating traffic congestion and reducing environmental impact. The server automatically generates this proposal in report format, making it viewable on a dashboard on the user's device.

[0239] Furthermore, residents, as users, can directly submit feedback on urban planning and policies through a resident participation platform. This information is analyzed on the server using natural language processing technology and used to create more suitable proposals that reflect residents' opinions.

[0240] For example, suppose a local government has devised a new road plan to alleviate traffic congestion. The server conducts simulations based on the plan and provides the local government with an optimized proposal. At the same time, it can also incorporate the opinions of residents (the users) and present alternative plans, including the placement of public spaces preferred by residents.

[0241] Thus, the present invention supports the efficiency of urban planning and provides an innovative tool for evaluating feasible ideas from multiple perspectives.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The server collects data in real time from various sensors placed throughout the city. This includes traffic volume sensors, air pollution sensors, and demographic data, which are retrieved via APIs. The collected data is stored in a database, which serves as the foundation for building a digital twin model.

[0245] Step 2:

[0246] The server uses the collected data to generate a digital twin model of the city. The data is cleansed, synchronized, and missing values ​​are imputed. Next, 3D modeling software is used to recreate the city's infrastructure, buildings, and roads in the virtual space.

[0247] Step 3:

[0248] Local government officials input new policies and infrastructure plans using terminals. Specifically, they use a dedicated GUI (Graphical User Interface) to register details such as new road construction and changes to traffic regulations in a policy input form. This data is stored in the system and used as conditions for simulations.

[0249] Step 4:

[0250] The server runs simulations predicting the impact of policy changes based on a digital twin model. Using a dedicated simulation engine, it runs various scenarios in parallel, collecting and analyzing the results. Parameters such as traffic congestion, environmental impact, and disaster risk are evaluated.

[0251] Step 5:

[0252] The server applies an optimization algorithm based on the simulation results to generate the most effective urban planning proposal for achieving the goal. This process utilizes machine learning models to find the optimal solution based on historical data. The results are generated as a visual report and provided to municipal officials.

[0253] Step 6:

[0254] Residents, as users, input their opinions and feedback on the plan through a resident participation platform. The platform provides an intuitive interface and allows users to input information in text or survey formats.

[0255] Step 7:

[0256] The server analyzes feedback from residents using natural language processing technology and integrates it into urban planning proposals. The opinion data is subjected to text analysis, classifying positive and negative opinions and extracting keywords. This allows for the generation of more effective proposals that take residents' feedback into account.

[0257] (Example 1)

[0258] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0259] In today's urban environment, there is a need for urban planning that quickly incorporates traffic congestion, increasing environmental burden, and diverse opinions from residents. However, conventional methods have the challenge of delays in optimal planning due to the time required for data collection, analysis, simulation, and feedback.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes an information gathering device for acquiring data, a device for generating a virtual environment based on the acquired information, and a device for evaluating the analysis results and generating a plan proposal optimized for a specific purpose. This makes it possible to analyze urban data in real time, quickly generate an optimized urban planning proposal, and effectively reflect the opinions of participants.

[0262] An "information gathering device" is a collection of hardware and software, such as sensors, databases, and network devices, used to acquire various data from the real world.

[0263] A "virtual environment" is a digital space that can change in real time, such as a digital twin model built based on collected data.

[0264] An "analysis system" is a software and hardware system used to perform simulations using collected data and evaluate the results.

[0265] A "plan proposal" refers to specific measures and concepts for traffic management and environmental improvement that are generated based on the analysis results.

[0266] "Natural language processing technology" is a technology used to understand and analyze feedback expressed in human language.

[0267] A "visualization device" is a device such as a display or projector that displays digital information in a way that is easy for humans to understand.

[0268] The system of the present invention reproduces urban environments in digital space and supports more efficient urban planning. Specific embodiments are shown below.

[0269] First, a server takes the lead in collecting data in real time from various sensors installed throughout the city. This data includes traffic volume, air pollution, and water quality. This data is then collected by the server via information gathering devices. The server stores this data in a database and prepares it for analysis.

[0270] Next, the server uses the collected data to generate a virtual environment, or digital twin model. GIS (Geographic Information System) and 3D modeling software are used to visually reproduce the city's infrastructure and other elements. This allows users to view the overall picture of the city in a virtual space.

[0271] Following this, the server utilizes the generated AI model and functions as an analysis device. This analysis device reflects new policy and infrastructure information in the virtual environment and performs complex simulations to predict their impact. For example, it runs a simulation to predict how a plan to establish a new bus route will alleviate traffic congestion. An example of a prompt statement might be, "What infrastructure improvements can be considered to alleviate traffic congestion?"

[0272] Subsequently, a plan proposal is generated based on the analysis results and displayed on the terminal's dashboard via a visualization device. Through this interface, local government officials can review the proposal and make necessary decisions.

[0273] Furthermore, feedback provided by residents through the resident participation platform is analyzed on the server using natural language processing technology. This analysis is then used to further refine the plan. For example, if a resident requests more parks, this is reflected in the simulation, leading to the development of urban planning proposals that better align with residents' wishes.

[0274] This system will expedite the urban planning process and enable the creation of plans that meet diverse needs.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The server collects data in real time from various sensors installed throughout the city. The input consists of numerical data transmitted from traffic volume sensors and air pollution sensors. The server stores this data in a database, preparing it for future analysis. Specific operations include normalizing the data and saving it in a consistent format.

[0278] Step 2:

[0279] The server generates a virtual environment based on the collected data. The data input at this stage is the sensor data normalized in Step 1. The server uses GIS and 3D modeling tools to construct a digital twin model and visually recreate the city. The output is a 3D model of the city, representing infrastructure and land use. Specifically, this process involves rendering a combination of geographic information and 3D building data.

[0280] Step 3:

[0281] Through a terminal, local government officials input new policies and infrastructure plans. This input data includes information on the placement of planned roads and public facilities. The terminal sends this information to a server, which is then used as a simulation condition. Specifically, officials use a GUI to place the planned infrastructure on a map and save the information to the database.

[0282] Step 4:

[0283] The server performs a simulation based on the digital twin model and the input from the staff. The input is the three-dimensional model in Step 2 and the policy data in Step 3. The server uses a generative AI model to predict the impact on the city and generates analysis results as output. Specific data processing includes traffic flow simulation and environmental impact assessment.

[0284] Step 5:

[0285] The server analyzes the simulation results and generates an optimized plan proposal. The input data is the analysis results obtained in the previous step. The generated proposal is displayed on the dashboard of the terminal through a visualization device. The specific output is a report including an optimized traffic route plan and environmental improvement measures.

[0286] Step 6:

[0287] The user provides feedback using the resident participation platform. The input is the opinions and demands posted by the residents. The server analyzes these feedbacks using natural language analysis technology and reflects them in the urban planning. Specific operations include topic classification and statistical analysis of opinions, which are adopted as the conditions for re-simulation.

[0288] Step 7:

[0289] Finally, the server generates an updated plan proposal and provides it to the local government staff. The input is the analyzed feedback and the updated simulation results. The output is the final urban planning proposal that reflects the opinions of the residents and can be listed on the terminal. Specific operations include creating a plan document that reflects the demands of the residents and preparing materials for a briefing session based on it.

[0290] (Application Example 1) [[ID=​​​​

[0292] Modern cities face a wide variety of problems, including traffic congestion, environmental pollution, and urban planning that fails to adequately reflect residents' opinions. Furthermore, addressing these issues requires accurately predicting the impact of new policies and infrastructure and presenting it in a visually clear and understandable way. However, previous methods have struggled to respond to real-time changes and effectively incorporate resident feedback.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for acquiring data by sensors for collecting urban information, means for creating a virtual space model based on the acquired data, and means for performing simulations to reflect policy changes in the virtual space model and predict their impact. This makes it possible to visualize complex urban problems in real time and provide optimal urban planning that reflects the opinions of residents.

[0295] "Urban information" refers to various types of data necessary for urban management and planning, such as traffic volume, air pollution, and population dynamics.

[0296] A "sensor" is a device that measures physical phenomena and records them as data, and it forms the foundation of data collection.

[0297] "Means of acquiring data" refers to the process of collecting information about the city through sensors and incorporating it into the system.

[0298] A "virtual space model" is a digital 3D representation of a real city, used for urban planning simulations.

[0299] "Means of running simulations" refer to methods for reflecting policy and infrastructure changes in a virtual space model and calculating and predicting their impact.

[0300] The "optimized urban plan" is an urban design proposed to implement the most effective plan for specific goals, such as alleviating traffic congestion and reducing environmental impact.

[0301] The "opinions of residents" refer to the thoughts and feedback of local residents on urban plans and policies.

[0302] The "natural language processing technology" is a technology for analyzing human language, understanding its meaning, and converting it into information, which is used for analyzing resident feedback.

[0303] The "3D visualization technology" is a technology for stereoscopically displaying virtual space models and simulation results, enabling real-time display on user devices.

[0304] The "policy makers" refer to government officials and local government staff who are responsible for formulating and implementing urban plans and public policies.

[0305] To implement this invention, a system is required to acquire information from the city in real time, create a digital twin model based on it, and simulate it.

[0306] First, the server acquires data in real time from various sensors (e.g., traffic volume sensors and air pollution sensors) for collecting information about the city. This data is used to comprehensively grasp the detailed current situation of the entire city. The data collected by the sensors is stored in a database and serves as the basis for all subsequent processing.

[0307] Next, the server uses the acquired data to create a virtual space model, i.e., a digital twin. The digital twin model is visually constructed using 3D rendering technologies such as Three.js. This makes it possible to faithfully reproduce the current situation of the city in a digital environment.

[0308] The system also allows users to input new policies and infrastructure plans. The server receives this input and simulates the impact of these changes on a virtual space model. Using Python and Pandas, the system analyzes the simulation results. Based on this analysis, it proposes optimized urban plans for specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0309] Residents, as users, can provide opinions and feedback through a smartphone app. The server analyzes this feedback using natural language processing technology (e.g., NLTK) and incorporates it into urban planning. This process leads to improvements in the plan that meet the needs of the residents.

[0310] Ultimately, the device visualizes the optimized urban planning and simulation results on an application using React Native, for example, and provides them to policymakers and residents. This enables visual and intuitive urban planning proposals, supporting efficient decision-making.

[0311] For example, when proposing a plan for a new bypass road to alleviate traffic congestion in a small city, feedback from residents regarding the addition of commercial facilities is incorporated and presented as an optimized plan.

[0312] The following prompt could be used as input to the generative AI model: "Given an infrastructure plan to alleviate urban traffic congestion, what optimization proposals can you suggest? How will you incorporate resident feedback?"

[0313] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0314] Step 1:

[0315] The server acquires real-time urban information from various sensors. Input data comes from traffic and air pollution sensors. The server converts this data into a specific format and stores it in a database. This makes it possible to provide the fundamental data necessary for future urban planning.

[0316] Step 2:

[0317] The server generates a virtual space model using data stored in a database. The input is city data acquired from sensors. Based on the acquired data, the server creates a virtual space model using 3D rendering technology with Three.js. The output is a digital twin model of a real city.

[0318] Step 3:

[0319] Users input new policies and infrastructure plans through their terminals. This input is data about policies and plans that will be added to the virtual space model. The server reflects this in the virtual space model and prepares it for the next simulation step.

[0320] Step 4:

[0321] The server runs the simulation. The inputs are a digital twin model and policy data entered by the user. The server uses Python and Pandas to calculate the impact of policy changes on the model. The output is the simulation's predicted impact data.

[0322] Step 5:

[0323] Residents, acting as users, provide feedback on the simulation results via a smartphone app. The input is resident feedback data. The server analyzes this feedback using natural language processing technology with NLTK and utilizes it to improve urban planning as needed. The output is the improved feedback analysis results.

[0324] Step 6:

[0325] The terminal displays the simulation results and optimized urban plan on a visualization device. The input is the final urban planning data sent from the server. Using React Native and other tools, it is presented directly to policymakers and residents as 3D models and graphs. The output is a visualized urban planning proposal.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention combines a system that utilizes a digital twin model of a city to predict the impact of policy changes on the urban environment and propose optimal urban planning solutions with an emotion engine that recognizes user emotions. This makes it possible to analyze feedback from residents more precisely and reflect it in urban planning.

[0328] First, the server collects sensor information from within the city and builds a digital twin model of the city. This model is updated in real time, recreating the real-world urban conditions in a virtual space.

[0329] Next, local government officials use terminals to input new policies and infrastructure plans into the system. Based on this information, the server uses a digital twin model to perform simulations and predict the impact of policy changes on the urban environment.

[0330] Furthermore, an optimization algorithm is used to generate urban planning proposals based on the simulation results. The server creates this in report format and displays it on a dashboard on the terminal, providing visual support to local government officials.

[0331] This is where the emotion engine, a key feature of the present invention, comes into play. Residents, acting as users, provide feedback on urban planning through a dedicated participatory platform. The server analyzes the collected feedback using a combination of natural language processing technology and the emotion engine. This extracts emotional states from residents' text data and utilizes them as important elements for influencing urban planning.

[0332] For example, if a new infrastructure plan is supported by residents but also raises many concerns, the emotion engine will extract both positive and negative sentiments. Based on these results, the server can adjust the proposal and derive revised versions to address residents' concerns. The revised urban planning proposal will then be reflected on a dashboard, allowing municipal officials to make decisions based on it immediately.

[0333] Thus, this invention, which incorporates an emotion engine, enables the realization of effective and highly acceptable urban planning by more accurately reflecting the diverse voices of residents.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The server receives real-time data acquired from city sensors via a remote API and stores it in a database. This data includes traffic volume, weather information, and demographic data, and is used for subsequent processing.

[0337] Step 2:

[0338] The server constructs a digital twin model based on the collected data. It organizes and cleanses the data, and then uses 3D modeling tools to recreate the urban environment in a virtual space. This generates a model that accurately represents the current state of the city.

[0339] Step 3:

[0340] Using a terminal, local government officials input details of new infrastructure plans and policy changes into a user interface. This input includes information such as the location of new roads and plans for expanding public facilities. This information is then registered in the system as simulation parameters.

[0341] Step 4:

[0342] The server applies the input policy changes to a digital twin model and runs the scenario using a simulation engine. It comprehensively analyzes the impact of policy changes on the urban environment and predicts traffic flow, energy consumption, and environmental impact.

[0343] Step 5:

[0344] The server analyzes the simulation results and generates optimal urban planning proposals. These proposals aim to alleviate traffic congestion and reduce environmental impact, and are compiled using algorithms to create balanced measures addressing diverse objectives.

[0345] Step 6:

[0346] Residents, as users, provide feedback on the proposed plan through a resident participation platform. Here, they can freely write their opinions on their impressions and suggestions, and submit them through a user-friendly interface.

[0347] Step 7:

[0348] The server analyzes residents' feedback using natural language processing technology and an emotion engine. It quantitatively evaluates emotional states from text data, distinguishing between positive and negative emotions. This makes it possible to consider residents' emotional responses in urban planning.

[0349] Step 8:

[0350] Based on the sentiment analysis results, the server revises the urban planning proposals and generates new proposals. Proposals that reflect residents' sentiments are automatically displayed on a dashboard, making them easily accessible to local government officials for use in decision-making.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] The challenge lies in accurately predicting the impact of policy changes on the urban environment and more appropriately reflecting the diverse opinions of residents in urban planning. Traditional urban planning has struggled to accurately reflect residents' feelings and opinions, often resulting in dissatisfaction and misunderstandings due to policy changes. Furthermore, a lack of rapid data processing and optimized proposal development has hindered the realization of effective urban planning.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes means for acquiring data from a measuring device for collecting urban information, means for constructing a virtual model based on the acquired information, means for applying policy changes to the virtual model and conducting a simulated experiment to estimate the impact, means for processing residents' opinions using sentiment analysis technology and deriving improved urban planning proposals, and means for presenting the improved proposals on a visual information display device. This makes it possible to realize urban planning that reflects the feelings and opinions of residents, resulting in more effective and acceptable policies.

[0356] "Urban information" refers to data on environmental conditions and human activity within a city, including traffic volume, temperature, and noise levels.

[0357] "Measuring devices" refer to sensors and various measuring instruments placed to collect urban information.

[0358] "Means of acquiring data" refers to the process and technology of collecting urban information from measuring devices and transferring it to servers or other locations.

[0359] A "virtual model" refers to a digital twin or computer model used to recreate the physical and social conditions of a city in a digital space.

[0360] "Methods for conducting simulated experiments" refer to techniques and processes that involve modifying a virtual model and evaluating the results through simulation.

[0361] "Opinions" refer to feedback that expresses the thoughts and feelings that residents have regarding urban policies and plans.

[0362] "Emotional analysis technology" refers to a method that uses natural language processing to extract and analyze emotional and opinion tendencies from text data.

[0363] A "visual information display device" refers to an electronic device or software used to visually display analysis results or proposed plans.

[0364] "Methods for deriving improvement proposals" refers to the process of analyzing residents' opinions using sentiment analysis technology and proposing necessary modifications and improvements to urban planning.

[0365] The system of the present invention makes it possible to predict the impact of policy changes in urban planning on the urban environment and to accurately reflect the diverse opinions of residents. Specific embodiments of the present invention are described below.

[0366] First, the server acquires data from measuring devices that collect urban information. This includes traffic sensors, weather sensors, noise meters, and so on. The collected data is then incorporated into a virtual model built on the server. To intuitively reproduce various elements of the city, the virtual model typically utilizes "Unity" or "Azure Digital Twins."

[0367] Next, municipal employees using the terminals input new policy information and infrastructure proposals through a dedicated interface. Based on this information, the server applies the policy changes to a virtual model and estimates their impact through simulated experiments. The simulation uses Python and its analysis libraries, Pandas and NumPy.

[0368] Furthermore, to gather residents' opinions, users submit feedback through a participatory platform. This platform is built using "Microsoft Forms" and "Google Forms." The server utilizes sentiment analysis technology and analyzes the feedback using natural language processing tools such as "NLTK" and "Hugging Face Transformers." This extracts the tendencies of residents' thoughts and emotions, which are then used to create improvement proposals for urban planning.

[0369] Finally, improvement proposals are displayed on a visual information display device and provided to staff in a dashboard format. This uses tools such as Tableau and Power BI. This makes it easier for staff to make quick and informed decisions.

[0370] For example, when considering the establishment of a new park, if residents frequently express concerns about public safety, it becomes possible to derive concrete improvement measures such as "installing surveillance cameras" or "restricting usage hours."

[0371] Furthermore, one possible prompt for the generating AI model could be: "Based on resident feedback data regarding the construction of a new park, analyze the positive and negative elements and generate improvement suggestions." This would allow for a more precise reflection of residents' voices in urban planning.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The server acquires necessary urban information from measuring devices placed throughout the city. This input data includes traffic volume, ambient noise, and weather conditions. The server receives this data and performs data preprocessing, such as imputing missing values ​​and detecting and correcting anomalies. The processed data is then stored in a database. The final output is a basic dataset for a digital twin.

[0375] Step 2:

[0376] The server builds a virtual model based on the data collected in Step 1. The server uses Unity or Azure Digital Twins to create a 3D model that replicates the city's conditions. Because this virtual model is updated in real time, it always reflects the latest city situation. The output is a digital twin model of the specific city.

[0377] Step 3:

[0378] Local government officials using terminals input information about new policies into the system. This input includes policy details, objectives, and expected changes. The server receives this information and applies it to a virtual model to simulate the policy change. Specifically, this process uses Python and its libraries, Pandas and NumPy, to perform data analysis. The output is predicted data on the impact of the policy change.

[0379] Step 4:

[0380] The server evaluates the simulation results obtained in step 3 and generates an optimized urban planning proposal. The server utilizes optimization techniques such as "Scikit-learn" and "TensorFlow" to evaluate numerous scenarios. The output is the urban planning proposal best suited to a specific objective.

[0381] Step 5:

[0382] Users provide feedback on urban planning through a participatory platform. This feedback is entered as opinions in text format. The server analyzes these opinions using natural language processing and sentiment analysis techniques, such as "NLTK" and "Hugging Face Transformers." The output after analysis extracts the emotional state and opinion trends of the residents.

[0383] Step 6:

[0384] Based on the analysis results obtained in Step 5, the server creates proposed improvements to the urban plan. The resulting improvement proposals can be viewed on a dashboard displayed on the terminal via visual information display devices such as "Tableau" and "Power BI." This allows local government officials to make decisions quickly.

[0385] Through these steps, this system aims to more effectively incorporate residents' opinions into urban planning.

[0386] (Application Example 2)

[0387] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0388] In modern urban planning and development processes, it is difficult to effectively incorporate the diverse opinions and feelings of residents. Conventional methods fail to accurately analyze resident feedback and reflect it in urban design, resulting in the failure to realize optimal urban planning that meets residents' needs. This invention aims to solve these problems and realize efficient and highly acceptable urban planning that reflects the feelings of residents.

[0389] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0390] In this invention, the server includes means for acquiring detector information for collecting urban information, means for generating a virtual space model based on the acquired detector information, means for performing simulated calculations to reflect policy changes in the virtual space model and predict their impact, means for analyzing opinions using an emotion analysis system that identifies the emotional state of residents' opinions, and means for generating optimized urban design proposals and displaying the proposals on a visual display device. This makes it possible to accurately analyze the diverse opinions of residents along with their emotions and reflect them in urban planning.

[0391] "Urban information" refers to various types of data related to cities, including data on the environment, infrastructure, transportation, and resident activities.

[0392] "Detector information" refers to data generated from various sensors and devices placed within a city, and is used to understand the state of the urban environment in real time.

[0393] A "virtual space model" is a model used to digitally reproduce real-world urban environments and serves as a foundation for conducting simulations of policy changes and other changes.

[0394] "Simulated calculation" refers to a simulation process in which assumptions such as policy changes are set in a virtual space model, and the effects of those changes are predicted.

[0395] "Resident opinions" refer to feedback and comments from residents regarding urban planning and policies, and are collected in order to reflect them in urban design from various perspectives.

[0396] A "sentiment analysis system" is a system that analyzes the emotional states contained in residents' opinions using natural language processing and machine learning, and processes the data based on the results.

[0397] A "visual display device" is a device that visually displays digital information and analysis results, and serves as a means of providing urban design proposals and other information to government officials.

[0398] This invention provides a system for urban planning that utilizes a digital twin model of a city. A server collects urban information in real time and constructs it as a virtual space model. This model is created using various detector information and can accurately reproduce the urban environment. The server uses this virtual space model to perform simulation calculations of policy changes and infrastructure plans, and predicts their impact.

[0399] Furthermore, the server collects opinions from residents and analyzes them using a combination of natural language processing technology and sentiment analysis systems. During this process, it identifies the emotional states contained in the residents' opinions and uses this information as crucial data for urban planning. Based on this analysis, it generates optimized urban design proposals and provides them to administrative staff using visual display devices.

[0400] As a concrete example, consider a case where residents send feedback on urban planning via their smartphones. If the feedback is, for example, "I'm looking forward to the new park plan, but I'm worried about transportation," the server receives this and uses an emotion analysis system to extract the positive emotion of "excitement" and the negative emotion of "worry." Based on these analysis results, the urban design proposal is adjusted to address the residents' concerns and is visually displayed on the terminals of administrative staff.

[0401] The program is implemented using Python, and libraries such as TensorFlow and BERT are used for natural language processing. Unity and Blender are used for simulating the virtual space model, and a dashboard-style web application is used to visualize the simulation results.

[0402] An example of a prompt would be: "We would like to conduct a sentiment analysis of resident feedback regarding a new urban planning project. Based on the feedback, 'I'm really happy about the new park plan, but I'm a little worried about traffic congestion,' analyze the residents' sentiments and generate suggestions for revising the plan based on that analysis."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] Users input feedback via their smartphones. This feedback consists of text data containing opinions and feelings about urban planning. This text is then sent to the server.

[0406] Step 2:

[0407] The server passes the string data received from the user to the sentiment analysis system. The sentiment analysis system uses natural language processing techniques to analyze the string and identify emotional states such as positive or negative. In this process, emotional attributes are extracted from the input data and output as structured data.

[0408] Step 3:

[0409] The server receives the results of the sentiment analysis and merges them into a virtual space model. Here, simulations of policy changes and infrastructure plans are performed. In this process, the simulation in the virtual space is re-run, taking into account the sentiment feedback of the residents. The output is predictive data regarding the impact on the urban environment.

[0410] Step 4:

[0411] The server generates optimized urban design proposals based on the predicted data from the simulation. In this generation process, various parameters are adjusted based on the predicted data, and urban planning proposals that match the needs of residents are output.

[0412] Step 5:

[0413] The generated urban design proposals are transmitted to terminals via visual display devices and displayed. Government officials, who are users of these terminals, can then make policy decisions based on this information. In this final stage, the proposals are presented in a visual dashboard format.

[0414] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0415] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0420] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0422] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0424] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0425] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0426] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0427] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0429] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0430] This invention is a system that utilizes urban data to recreate real cities in digital space and perform simulations and optimizations of urban planning. The implementation of this invention consists of several main modules, each of which works in conjunction with the others.

[0431] First, the server collects data in real time from various sensors. This includes data from traffic volume sensors and air pollution sensors, for example. This forms the basis for a digital twin model in which the latest urban conditions are constantly updated.

[0432] Next, the server integrates the collected data to build a digital twin model of the city. This visually reproduces elements such as the city's infrastructure and demographics in a virtual space. This model plays a crucial role in subsequent simulations.

[0433] Local government officials can use terminals to input new policies and infrastructure plans. For example, various plans such as the construction of new roads or changes to public transport routes can be entered. This information is used as a condition for simulations.

[0434] Subsequently, the server performs simulations based on the digital twin model. It predicts the impact of the input plan on the urban environment and performs calculations for each scenario. This makes it possible to visually and quantitatively evaluate the strengths and weaknesses of the plan.

[0435] Furthermore, using optimization algorithms, the system proposes optimal urban planning in light of goals such as mitigating traffic congestion and reducing environmental impact. The server automatically generates this proposal in report format, making it viewable on a dashboard on the user's device.

[0436] Furthermore, residents, as users, can directly submit feedback on urban planning and policies through a resident participation platform. This information is analyzed on the server using natural language processing technology and used to create more suitable proposals that reflect residents' opinions.

[0437] For example, suppose a local government has devised a new road plan to alleviate traffic congestion. The server conducts simulations based on the plan and provides the local government with an optimized proposal. At the same time, it can also incorporate the opinions of residents (the users) and present alternative plans, including the placement of public spaces preferred by residents.

[0438] Thus, the present invention supports the efficiency of urban planning and provides an innovative tool for evaluating feasible ideas from multiple perspectives.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The server collects data in real time from various sensors placed throughout the city. This includes traffic volume sensors, air pollution sensors, and demographic data, which are retrieved via APIs. The collected data is stored in a database, which serves as the foundation for building a digital twin model.

[0442] Step 2:

[0443] The server uses the collected data to generate a digital twin model of the city. The data is cleansed, synchronized, and missing values ​​are imputed. Next, 3D modeling software is used to recreate the city's infrastructure, buildings, and roads in the virtual space.

[0444] Step 3:

[0445] Local government officials input new policies and infrastructure plans using terminals. Specifically, they use a dedicated GUI (Graphical User Interface) to register details such as new road construction and changes to traffic regulations in a policy input form. This data is stored in the system and used as conditions for simulations.

[0446] Step 4:

[0447] The server runs simulations predicting the impact of policy changes based on a digital twin model. Using a dedicated simulation engine, it runs various scenarios in parallel, collecting and analyzing the results. Parameters such as traffic congestion, environmental impact, and disaster risk are evaluated.

[0448] Step 5:

[0449] The server applies an optimization algorithm based on the simulation results to generate the most effective urban planning proposal for achieving the goal. This process utilizes machine learning models to find the optimal solution based on historical data. The results are generated as a visual report and provided to municipal officials.

[0450] Step 6:

[0451] Residents, as users, input their opinions and feedback on the plan through a resident participation platform. The platform provides an intuitive interface and allows users to input information in text or survey formats.

[0452] Step 7:

[0453] The server analyzes feedback from residents using natural language processing technology and integrates it into urban planning proposals. The opinion data is subjected to text analysis, classifying positive and negative opinions and extracting keywords. This allows for the generation of more effective proposals that take residents' feedback into account.

[0454] (Example 1)

[0455] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0456] In today's urban environment, there is a need for urban planning that quickly incorporates traffic congestion, increasing environmental burden, and diverse opinions from residents. However, conventional methods have the challenge of delays in optimal planning due to the time required for data collection, analysis, simulation, and feedback.

[0457] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0458] In this invention, the server includes an information gathering device for acquiring data, a device for generating a virtual environment based on the acquired information, and a device for evaluating the analysis results and generating a plan proposal optimized for a specific purpose. This makes it possible to analyze urban data in real time, quickly generate an optimized urban planning proposal, and effectively reflect the opinions of participants.

[0459] An "information gathering device" is a collection of hardware and software, such as sensors, databases, and network devices, used to acquire various data from the real world.

[0460] A "virtual environment" is a digital space that can change in real time, such as a digital twin model built based on collected data.

[0461] An "analysis system" is a software and hardware system used to perform simulations using collected data and evaluate the results.

[0462] A "plan proposal" refers to specific measures and concepts for traffic management and environmental improvement that are generated based on the analysis results.

[0463] "Natural language processing technology" is a technology used to understand and analyze feedback expressed in human language.

[0464] A "visualization device" is a device such as a display or projector that displays digital information in a way that is easy for humans to understand.

[0465] The system of the present invention reproduces urban environments in digital space and supports more efficient urban planning. Specific embodiments are shown below.

[0466] First, a server takes the lead in collecting data in real time from various sensors installed throughout the city. This data includes traffic volume, air pollution, and water quality. This data is then collected by the server via information gathering devices. The server stores this data in a database and prepares it for analysis.

[0467] Next, the server uses the collected data to generate a virtual environment, or digital twin model. GIS (Geographic Information System) and 3D modeling software are used to visually reproduce the city's infrastructure and other elements. This allows users to view the overall picture of the city in a virtual space.

[0468] Following this, the server utilizes the generated AI model and functions as an analysis device. This analysis device reflects new policy and infrastructure information in the virtual environment and performs complex simulations to predict their impact. For example, it runs a simulation to predict how a plan to establish a new bus route will alleviate traffic congestion. An example of a prompt statement might be, "What infrastructure improvements can be considered to alleviate traffic congestion?"

[0469] Subsequently, a plan proposal is generated based on the analysis results and displayed on the terminal's dashboard via a visualization device. Through this interface, local government officials can review the proposal and make necessary decisions.

[0470] Furthermore, feedback provided by residents through the resident participation platform is analyzed on the server using natural language processing technology. This analysis is then used to further refine the plan. For example, if a resident requests more parks, this is reflected in the simulation, leading to the development of urban planning proposals that better align with residents' wishes.

[0471] This system will expedite the urban planning process and enable the creation of plans that meet diverse needs.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server collects data in real time from various sensors installed throughout the city. The input consists of numerical data transmitted from traffic volume sensors and air pollution sensors. The server stores this data in a database, preparing it for future analysis. Specific operations include normalizing the data and saving it in a consistent format.

[0475] Step 2:

[0476] The server generates a virtual environment based on the collected data. The data input at this stage is the sensor data normalized in Step 1. The server uses GIS and 3D modeling tools to construct a digital twin model and visually recreate the city. The output is a 3D model of the city, representing infrastructure and land use. Specifically, this process involves rendering a combination of geographic information and 3D building data.

[0477] Step 3:

[0478] Through a terminal, local government officials input new policies and infrastructure plans. This input data includes information on the placement of planned roads and public facilities. The terminal sends this information to a server, which is then used as a simulation condition. Specifically, officials use a GUI to place the planned infrastructure on a map and save the information to the database.

[0479] Step 4:

[0480] The server performs simulations based on the digital twin model and input from staff. The input consists of the 3D model from Step 2 and the policy data from Step 3. The server uses a generative AI model to predict the impact on the city and generates analysis results as output. Specific data processing includes traffic flow simulations and environmental impact assessments.

[0481] Step 5:

[0482] The server analyzes the simulation results and generates optimized plan proposals. The input data is the analysis results obtained in the previous step. The generated proposals are displayed on the terminal's dashboard via a visualization device. The specific output is a report that includes optimized transportation route plans and environmental improvement measures.

[0483] Step 6:

[0484] Users provide feedback using a community participation platform. The input consists of opinions and requests posted by residents. The server analyzes this feedback using natural language processing technology and incorporates it into urban planning. Specifically, the opinions are categorized by topic and statistically analyzed, and these results are used as conditions for re-simulation.

[0485] Step 7:

[0486] Ultimately, the server generates an updated draft plan and provides it to local government officials. The input consists of analyzed feedback and updated simulation results. The output is a final urban planning proposal that reflects residents' opinions and can be viewed on a terminal. Specific actions include creating a plan document that reflects residents' requests and preparing materials for a reporting meeting based on that plan.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] Modern cities face a wide variety of problems, including traffic congestion, environmental pollution, and urban planning that fails to adequately reflect residents' opinions. Furthermore, addressing these issues requires accurately predicting the impact of new policies and infrastructure and presenting it in a visually clear and understandable way. However, previous methods have struggled to respond to real-time changes and effectively incorporate resident feedback.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for acquiring data by sensors for collecting urban information, means for creating a virtual space model based on the acquired data, and means for performing simulations to reflect policy changes in the virtual space model and predict their impact. This makes it possible to visualize complex urban problems in real time and provide optimal urban planning that reflects the opinions of residents.

[0492] "Urban information" refers to various types of data necessary for urban management and planning, such as traffic volume, air pollution, and population dynamics.

[0493] A "sensor" is a device that measures physical phenomena and records them as data, and it forms the foundation of data collection.

[0494] "Means of acquiring data" refers to the process of collecting information about the city through sensors and incorporating it into the system.

[0495] A "virtual space model" is a digital 3D representation of a real city, used for urban planning simulations.

[0496] "Means of running simulations" refer to methods for reflecting policy and infrastructure changes in a virtual space model and calculating and predicting their impact.

[0497] "Optimized urban planning" refers to urban design proposed to implement the most effective plan for achieving specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0498] "Residents' opinions" refer to the thoughts and feedback of local residents regarding urban planning and policies.

[0499] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and converts it into information, and is used in the analysis of resident feedback.

[0500] "3D visualization technology" is a technology that displays virtual space models and simulation results in three dimensions, enabling real-time display on user devices.

[0501] A "policymaker" is an administrator or local government official who is responsible for formulating and implementing urban planning and public policies.

[0502] To implement this invention, a system is needed that acquires information from cities in real time, creates a digital twin model based on that information, and performs simulations.

[0503] First, the server acquires data in real time from various sensors (such as traffic sensors and air pollution sensors) to collect information about the city. This data is used to comprehensively understand the detailed current state of the entire city. The data collected by the sensors is stored in a database and forms the basis for all subsequent processing.

[0504] Next, the server uses the acquired data to create a virtual space model, or digital twin. The digital twin model is visually constructed using 3D rendering technologies such as Three.js. This makes it possible to faithfully reproduce the current state of the city in a digital environment.

[0505] The system also allows users to input new policies and infrastructure plans. The server receives this input and simulates the impact of these changes on a virtual space model. Using Python and Pandas, the system analyzes the simulation results. Based on this analysis, it proposes optimized urban plans for specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0506] Residents, as users, can provide opinions and feedback through a smartphone app. The server analyzes this feedback using natural language processing technology (e.g., NLTK) and incorporates it into urban planning. This process leads to improvements in the plan that meet the needs of the residents.

[0507] Ultimately, the device visualizes the optimized urban planning and simulation results on an application using React Native, for example, and provides them to policymakers and residents. This enables visual and intuitive urban planning proposals, supporting efficient decision-making.

[0508] For example, when proposing a plan for a new bypass road to alleviate traffic congestion in a small city, feedback from residents regarding the addition of commercial facilities is incorporated and presented as an optimized plan.

[0509] The following prompt could be used as input to the generative AI model: "Given an infrastructure plan to alleviate urban traffic congestion, what optimization proposals can you suggest? How will you incorporate resident feedback?"

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The server acquires real-time urban information from various sensors. Input data comes from traffic and air pollution sensors. The server converts this data into a specific format and stores it in a database. This makes it possible to provide the fundamental data necessary for future urban planning.

[0513] Step 2:

[0514] The server generates a virtual space model using data stored in a database. The input is city data acquired from sensors. Based on the acquired data, the server creates a virtual space model using 3D rendering technology with Three.js. The output is a digital twin model of a real city.

[0515] Step 3:

[0516] Users input new policies and infrastructure plans through their terminals. This input is data about policies and plans that will be added to the virtual space model. The server reflects this in the virtual space model and prepares it for the next simulation step.

[0517] Step 4:

[0518] The server runs the simulation. The inputs are a digital twin model and policy data entered by the user. The server uses Python and Pandas to calculate the impact of policy changes on the model. The output is the simulation's predicted impact data.

[0519] Step 5:

[0520] Residents, acting as users, provide feedback on the simulation results via a smartphone app. The input is resident feedback data. The server analyzes this feedback using natural language processing technology with NLTK and utilizes it to improve urban planning as needed. The output is the improved feedback analysis results.

[0521] Step 6:

[0522] The terminal displays the simulation results and optimized urban plan on a visualization device. The input is the final urban planning data sent from the server. Using React Native and other tools, it is presented directly to policymakers and residents as 3D models and graphs. The output is a visualized urban planning proposal.

[0523] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0524] This invention combines a system that utilizes a digital twin model of a city to predict the impact of policy changes on the urban environment and propose optimal urban planning solutions with an emotion engine that recognizes user emotions. This makes it possible to analyze feedback from residents more precisely and reflect it in urban planning.

[0525] First, the server collects sensor information from within the city and builds a digital twin model of the city. This model is updated in real time, recreating the real-world urban conditions in a virtual space.

[0526] Next, local government officials use terminals to input new policies and infrastructure plans into the system. Based on this information, the server uses a digital twin model to perform simulations and predict the impact of policy changes on the urban environment.

[0527] Furthermore, an optimization algorithm is used to generate urban planning proposals based on the simulation results. The server creates this in report format and displays it on a dashboard on the terminal, providing visual support to local government officials.

[0528] This is where the emotion engine, a key feature of the present invention, comes into play. Residents, acting as users, provide feedback on urban planning through a dedicated participatory platform. The server analyzes the collected feedback using a combination of natural language processing technology and the emotion engine. This extracts emotional states from residents' text data and utilizes them as important elements for influencing urban planning.

[0529] For example, if a new infrastructure plan is supported by residents but also raises many concerns, the emotion engine will extract both positive and negative sentiments. Based on these results, the server can adjust the proposal and derive revised versions to address residents' concerns. The revised urban planning proposal will then be reflected on a dashboard, allowing municipal officials to make decisions based on it immediately.

[0530] Thus, this invention, which incorporates an emotion engine, enables the realization of effective and highly acceptable urban planning by more accurately reflecting the diverse voices of residents.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] The server receives real-time data acquired from city sensors via a remote API and stores it in a database. This data includes traffic volume, weather information, and demographic data, and is used for subsequent processing.

[0534] Step 2:

[0535] The server constructs a digital twin model based on the collected data. It organizes and cleanses the data, and then uses 3D modeling tools to recreate the urban environment in a virtual space. This generates a model that accurately represents the current state of the city.

[0536] Step 3:

[0537] Using a terminal, local government officials input details of new infrastructure plans and policy changes into a user interface. This input includes information such as the location of new roads and plans for expanding public facilities. This information is then registered in the system as simulation parameters.

[0538] Step 4:

[0539] The server applies the input policy changes to a digital twin model and runs the scenario using a simulation engine. It comprehensively analyzes the impact of policy changes on the urban environment and predicts traffic flow, energy consumption, and environmental impact.

[0540] Step 5:

[0541] The server analyzes the simulation results and generates optimal urban planning proposals. These proposals aim to alleviate traffic congestion and reduce environmental impact, and are compiled using algorithms to create balanced measures addressing diverse objectives.

[0542] Step 6:

[0543] Residents, as users, provide feedback on the proposed plan through a resident participation platform. Here, they can freely write their opinions on their impressions and suggestions, and submit them through a user-friendly interface.

[0544] Step 7:

[0545] The server analyzes residents' feedback using natural language processing technology and an emotion engine. It quantitatively evaluates emotional states from text data, distinguishing between positive and negative emotions. This makes it possible to consider residents' emotional responses in urban planning.

[0546] Step 8:

[0547] Based on the sentiment analysis results, the server revises the urban planning proposals and generates new proposals. Proposals that reflect residents' sentiments are automatically displayed on a dashboard, making them easily accessible to local government officials for use in decision-making.

[0548] (Example 2)

[0549] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] The challenge lies in accurately predicting the impact of policy changes on the urban environment and more appropriately reflecting the diverse opinions of residents in urban planning. Traditional urban planning has struggled to accurately reflect residents' feelings and opinions, often resulting in dissatisfaction and misunderstandings due to policy changes. Furthermore, a lack of rapid data processing and optimized proposal development has hindered the realization of effective urban planning.

[0551] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0552] In this invention, the server includes means for acquiring data from a measuring device for collecting urban information, means for constructing a virtual model based on the acquired information, means for applying policy changes to the virtual model and conducting a simulated experiment to estimate the impact, means for processing residents' opinions using sentiment analysis technology and deriving improved urban planning proposals, and means for presenting the improved proposals on a visual information display device. This makes it possible to realize urban planning that reflects the feelings and opinions of residents, resulting in more effective and acceptable policies.

[0553] "Urban information" refers to data on environmental conditions and human activity within a city, including traffic volume, temperature, and noise levels.

[0554] "Measuring devices" refer to sensors and various measuring instruments placed to collect urban information.

[0555] "Means of acquiring data" refers to the process and technology of collecting urban information from measuring devices and transferring it to servers or other locations.

[0556] A "virtual model" refers to a digital twin or computer model used to recreate the physical and social conditions of a city in a digital space.

[0557] "Methods for conducting simulated experiments" refer to techniques and processes that involve modifying a virtual model and evaluating the results through simulation.

[0558] "Opinions" refer to feedback that expresses the thoughts and feelings that residents have regarding urban policies and plans.

[0559] "Emotional analysis technology" refers to a method that uses natural language processing to extract and analyze emotional and opinion tendencies from text data.

[0560] A "visual information display device" refers to an electronic device or software used to visually display analysis results or proposed plans.

[0561] "Methods for deriving improvement proposals" refers to the process of analyzing residents' opinions using sentiment analysis technology and proposing necessary modifications and improvements to urban planning.

[0562] The system of the present invention makes it possible to predict the impact of policy changes in urban planning on the urban environment and to accurately reflect the diverse opinions of residents. Specific embodiments of the present invention are described below.

[0563] First, the server acquires data from measuring devices that collect urban information. This includes traffic sensors, weather sensors, noise meters, and so on. The collected data is then incorporated into a virtual model built on the server. To intuitively reproduce various elements of the city, the virtual model typically utilizes "Unity" or "Azure Digital Twins."

[0564] Next, municipal employees using the terminals input new policy information and infrastructure proposals through a dedicated interface. Based on this information, the server applies the policy changes to a virtual model and estimates their impact through simulated experiments. The simulation uses Python and its analysis libraries, Pandas and NumPy.

[0565] Furthermore, to gather residents' opinions, users submit feedback through a participatory platform. This platform is built using "Microsoft Forms" and "Google Forms." The server utilizes sentiment analysis technology and analyzes the feedback using natural language processing tools such as "NLTK" and "Hugging Face Transformers." This extracts the tendencies of residents' thoughts and emotions, which are then used to create improvement proposals for urban planning.

[0566] Finally, improvement proposals are displayed on a visual information display device and provided to staff in a dashboard format. This uses tools such as Tableau and Power BI. This makes it easier for staff to make quick and informed decisions.

[0567] For example, when considering the establishment of a new park, if residents frequently express concerns about public safety, it becomes possible to derive concrete improvement measures such as "installing surveillance cameras" or "restricting usage hours."

[0568] Furthermore, one possible prompt for the generating AI model could be: "Based on resident feedback data regarding the construction of a new park, analyze the positive and negative elements and generate improvement suggestions." This would allow for a more precise reflection of residents' voices in urban planning.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The server acquires necessary urban information from measuring devices placed throughout the city. This input data includes traffic volume, ambient noise, and weather conditions. The server receives this data and performs data preprocessing, such as imputing missing values ​​and detecting and correcting anomalies. The processed data is then stored in a database. The final output is a basic dataset for a digital twin.

[0572] Step 2:

[0573] The server builds a virtual model based on the data collected in Step 1. The server uses Unity or Azure Digital Twins to create a 3D model that replicates the city's conditions. Because this virtual model is updated in real time, it always reflects the latest city situation. The output is a digital twin model of the specific city.

[0574] Step 3:

[0575] Local government officials using terminals input information about new policies into the system. This input includes policy details, objectives, and expected changes. The server receives this information and applies it to a virtual model to simulate the policy change. Specifically, this process uses Python and its libraries, Pandas and NumPy, to perform data analysis. The output is predicted data on the impact of the policy change.

[0576] Step 4:

[0577] The server evaluates the simulation results obtained in step 3 and generates an optimized urban planning proposal. The server utilizes optimization techniques such as "Scikit-learn" and "TensorFlow" to evaluate numerous scenarios. The output is the urban planning proposal best suited to a specific objective.

[0578] Step 5:

[0579] Users provide feedback on urban planning through a participatory platform. This feedback is entered as opinions in text format. The server analyzes these opinions using natural language processing and sentiment analysis techniques, such as "NLTK" and "Hugging Face Transformers." The output after analysis extracts the emotional state and opinion trends of the residents.

[0580] Step 6:

[0581] Based on the analysis results obtained in Step 5, the server creates proposed improvements to the urban plan. The resulting improvement proposals can be viewed on a dashboard displayed on the terminal via visual information display devices such as "Tableau" and "Power BI." This allows local government officials to make decisions quickly.

[0582] Through these steps, this system aims to more effectively incorporate residents' opinions into urban planning.

[0583] (Application Example 2)

[0584] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0585] In modern urban planning and development processes, it is difficult to effectively incorporate the diverse opinions and feelings of residents. Conventional methods fail to accurately analyze resident feedback and reflect it in urban design, resulting in the failure to realize optimal urban planning that meets residents' needs. This invention aims to solve these problems and realize efficient and highly acceptable urban planning that reflects the feelings of residents.

[0586] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0587] In this invention, the server includes means for acquiring detector information for collecting urban information, means for generating a virtual space model based on the acquired detector information, means for performing simulated calculations to reflect policy changes in the virtual space model and predict their impact, means for analyzing opinions using an emotion analysis system that identifies the emotional state of residents' opinions, and means for generating optimized urban design proposals and displaying the proposals on a visual display device. This makes it possible to accurately analyze the diverse opinions of residents along with their emotions and reflect them in urban planning.

[0588] "Urban information" refers to various types of data related to cities, including data on the environment, infrastructure, transportation, and resident activities.

[0589] "Detector information" refers to data generated from various sensors and devices placed within a city, and is used to understand the state of the urban environment in real time.

[0590] A "virtual space model" is a model used to digitally reproduce real-world urban environments and serves as a foundation for conducting simulations of policy changes and other changes.

[0591] "Simulated calculation" refers to a simulation process in which assumptions such as policy changes are set in a virtual space model, and the effects of those changes are predicted.

[0592] "Resident opinions" refer to feedback and comments from residents regarding urban planning and policies, and are collected in order to reflect them in urban design from various perspectives.

[0593] A "sentiment analysis system" is a system that analyzes the emotional states contained in residents' opinions using natural language processing and machine learning, and processes the data based on the results.

[0594] A "visual display device" is a device that visually displays digital information and analysis results, and serves as a means of providing urban design proposals and other information to government officials.

[0595] This invention provides a system for urban planning that utilizes a digital twin model of a city. A server collects urban information in real time and constructs it as a virtual space model. This model is created using various detector information and can accurately reproduce the urban environment. The server uses this virtual space model to perform simulation calculations of policy changes and infrastructure plans, and predicts their impact.

[0596] Furthermore, the server collects opinions from residents and analyzes them using a combination of natural language processing technology and sentiment analysis systems. During this process, it identifies the emotional states contained in the residents' opinions and uses this information as crucial data for urban planning. Based on this analysis, it generates optimized urban design proposals and provides them to administrative staff using visual display devices.

[0597] As a concrete example, consider a case where residents send feedback on urban planning via their smartphones. If the feedback is, for example, "I'm looking forward to the new park plan, but I'm worried about transportation," the server receives this and uses an emotion analysis system to extract the positive emotion of "excitement" and the negative emotion of "worry." Based on these analysis results, the urban design proposal is adjusted to address the residents' concerns and is visually displayed on the terminals of administrative staff.

[0598] The program is implemented using Python, and libraries such as TensorFlow and BERT are used for natural language processing. Unity and Blender are used for simulating the virtual space model, and a dashboard-style web application is used to visualize the simulation results.

[0599] An example of a prompt would be: "We would like to conduct a sentiment analysis of resident feedback regarding a new urban planning project. Based on the feedback, 'I'm really happy about the new park plan, but I'm a little worried about traffic congestion,' analyze the residents' sentiments and generate suggestions for revising the plan based on that analysis."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] Users input feedback via their smartphones. This feedback consists of text data containing opinions and feelings about urban planning. This text is then sent to the server.

[0603] Step 2:

[0604] The server passes the string data received from the user to the sentiment analysis system. The sentiment analysis system uses natural language processing techniques to analyze the string and identify emotional states such as positive or negative. In this process, emotional attributes are extracted from the input data and output as structured data.

[0605] Step 3:

[0606] The server receives the results of the sentiment analysis and merges them into a virtual space model. Here, simulations of policy changes and infrastructure plans are performed. In this process, the simulation in the virtual space is re-run, taking into account the sentiment feedback of the residents. The output is predictive data regarding the impact on the urban environment.

[0607] Step 4:

[0608] The server generates optimized urban design proposals based on the predicted data from the simulation. In this generation process, various parameters are adjusted based on the predicted data, and urban planning proposals that match the needs of residents are output.

[0609] Step 5:

[0610] The generated urban design proposals are transmitted to terminals via visual display devices and displayed. Government officials, who are users of these terminals, can then make policy decisions based on this information. In this final stage, the proposals are presented in a visual dashboard format.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] This invention is a system that utilizes urban data to recreate real cities in digital space and perform simulations and optimizations of urban planning. The implementation of this invention consists of several main modules, each of which works in conjunction with the others.

[0629] First, the server collects data in real time from various sensors. This includes data from traffic volume sensors and air pollution sensors, for example. This forms the basis for a digital twin model in which the latest urban conditions are constantly updated.

[0630] Next, the server integrates the collected data to build a digital twin model of the city. This visually reproduces elements such as the city's infrastructure and demographics in a virtual space. This model plays a crucial role in subsequent simulations.

[0631] Local government officials can use terminals to input new policies and infrastructure plans. For example, various plans such as the construction of new roads or changes to public transport routes can be entered. This information is used as a condition for simulations.

[0632] Subsequently, the server performs simulations based on the digital twin model. It predicts the impact of the input plan on the urban environment and performs calculations for each scenario. This makes it possible to visually and quantitatively evaluate the strengths and weaknesses of the plan.

[0633] Furthermore, using optimization algorithms, the system proposes optimal urban planning in light of goals such as mitigating traffic congestion and reducing environmental impact. The server automatically generates this proposal in report format, making it viewable on a dashboard on the user's device.

[0634] Furthermore, residents, as users, can directly submit feedback on urban planning and policies through a resident participation platform. This information is analyzed on the server using natural language processing technology and used to create more suitable proposals that reflect residents' opinions.

[0635] For example, suppose a local government has devised a new road plan to alleviate traffic congestion. The server conducts simulations based on the plan and provides the local government with an optimized proposal. At the same time, it can also incorporate the opinions of residents (the users) and present alternative plans, including the placement of public spaces preferred by residents.

[0636] Thus, the present invention supports the efficiency of urban planning and provides an innovative tool for evaluating feasible ideas from multiple perspectives.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The server collects data in real time from various sensors placed throughout the city. This includes traffic volume sensors, air pollution sensors, and demographic data, which are retrieved via APIs. The collected data is stored in a database, which serves as the foundation for building a digital twin model.

[0640] Step 2:

[0641] The server uses the collected data to generate a digital twin model of the city. The data is cleansed, synchronized, and missing values ​​are imputed. Next, 3D modeling software is used to recreate the city's infrastructure, buildings, and roads in the virtual space.

[0642] Step 3:

[0643] Local government officials input new policies and infrastructure plans using terminals. Specifically, they use a dedicated GUI (Graphical User Interface) to register details such as new road construction and changes to traffic regulations in a policy input form. This data is stored in the system and used as conditions for simulations.

[0644] Step 4:

[0645] The server runs simulations predicting the impact of policy changes based on a digital twin model. Using a dedicated simulation engine, it runs various scenarios in parallel, collecting and analyzing the results. Parameters such as traffic congestion, environmental impact, and disaster risk are evaluated.

[0646] Step 5:

[0647] The server applies an optimization algorithm based on the simulation results to generate the most effective urban planning proposal for achieving the goal. This process utilizes machine learning models to find the optimal solution based on historical data. The results are generated as a visual report and provided to municipal officials.

[0648] Step 6:

[0649] Residents, as users, input their opinions and feedback on the plan through a resident participation platform. The platform provides an intuitive interface and allows users to input information in text or survey formats.

[0650] Step 7:

[0651] The server analyzes feedback from residents using natural language processing technology and integrates it into urban planning proposals. The opinion data is subjected to text analysis, classifying positive and negative opinions and extracting keywords. This allows for the generation of more effective proposals that take residents' feedback into account.

[0652] (Example 1)

[0653] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] In today's urban environment, there is a need for urban planning that quickly incorporates traffic congestion, increasing environmental burden, and diverse opinions from residents. However, conventional methods have the challenge of delays in optimal planning due to the time required for data collection, analysis, simulation, and feedback.

[0655] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0656] In this invention, the server includes an information gathering device for acquiring data, a device for generating a virtual environment based on the acquired information, and a device for evaluating the analysis results and generating a plan proposal optimized for a specific purpose. This makes it possible to analyze urban data in real time, quickly generate an optimized urban planning proposal, and effectively reflect the opinions of participants.

[0657] An "information gathering device" is a collection of hardware and software, such as sensors, databases, and network devices, used to acquire various data from the real world.

[0658] A "virtual environment" is a digital space that can change in real time, such as a digital twin model built based on collected data.

[0659] An "analysis system" is a software and hardware system used to perform simulations using collected data and evaluate the results.

[0660] A "plan proposal" refers to specific measures and concepts for traffic management and environmental improvement that are generated based on the analysis results.

[0661] "Natural language processing technology" is a technology used to understand and analyze feedback expressed in human language.

[0662] A "visualization device" is a device such as a display or projector that displays digital information in a way that is easy for humans to understand.

[0663] The system of the present invention reproduces urban environments in digital space and supports more efficient urban planning. Specific embodiments are shown below.

[0664] First, a server takes the lead in collecting data in real time from various sensors installed throughout the city. This data includes traffic volume, air pollution, and water quality. This data is then collected by the server via information gathering devices. The server stores this data in a database and prepares it for analysis.

[0665] Next, the server uses the collected data to generate a virtual environment, or digital twin model. GIS (Geographic Information System) and 3D modeling software are used to visually reproduce the city's infrastructure and other elements. This allows users to view the overall picture of the city in a virtual space.

[0666] Following this, the server utilizes the generated AI model and functions as an analysis device. This analysis device reflects new policy and infrastructure information in the virtual environment and performs complex simulations to predict their impact. For example, it runs a simulation to predict how a plan to establish a new bus route will alleviate traffic congestion. An example of a prompt statement might be, "What infrastructure improvements can be considered to alleviate traffic congestion?"

[0667] Subsequently, a plan proposal is generated based on the analysis results and displayed on the terminal's dashboard via a visualization device. Through this interface, local government officials can review the proposal and make necessary decisions.

[0668] Furthermore, feedback provided by residents through the resident participation platform is analyzed on the server using natural language processing technology. This analysis is then used to further refine the plan. For example, if a resident requests more parks, this is reflected in the simulation, leading to the development of urban planning proposals that better align with residents' wishes.

[0669] This system will expedite the urban planning process and enable the creation of plans that meet diverse needs.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The server collects data in real time from various sensors installed throughout the city. The input consists of numerical data transmitted from traffic volume sensors and air pollution sensors. The server stores this data in a database, preparing it for future analysis. Specific operations include normalizing the data and saving it in a consistent format.

[0673] Step 2:

[0674] The server generates a virtual environment based on the collected data. The data input at this stage is the sensor data normalized in Step 1. The server uses GIS and 3D modeling tools to construct a digital twin model and visually recreate the city. The output is a 3D model of the city, representing infrastructure and land use. Specifically, this process involves rendering a combination of geographic information and 3D building data.

[0675] Step 3:

[0676] Through a terminal, local government officials input new policies and infrastructure plans. This input data includes information on the placement of planned roads and public facilities. The terminal sends this information to a server, which is then used as a simulation condition. Specifically, officials use a GUI to place the planned infrastructure on a map and save the information to the database.

[0677] Step 4:

[0678] The server performs simulations based on the digital twin model and input from staff. The input consists of the 3D model from Step 2 and the policy data from Step 3. The server uses a generative AI model to predict the impact on the city and generates analysis results as output. Specific data processing includes traffic flow simulations and environmental impact assessments.

[0679] Step 5:

[0680] The server analyzes the simulation results and generates optimized plan proposals. The input data is the analysis results obtained in the previous step. The generated proposals are displayed on the terminal's dashboard via a visualization device. The specific output is a report that includes optimized transportation route plans and environmental improvement measures.

[0681] Step 6:

[0682] Users provide feedback using a community participation platform. The input consists of opinions and requests posted by residents. The server analyzes this feedback using natural language processing technology and incorporates it into urban planning. Specifically, the opinions are categorized by topic and statistically analyzed, and these results are used as conditions for re-simulation.

[0683] Step 7:

[0684] Ultimately, the server generates an updated draft plan and provides it to local government officials. The input consists of analyzed feedback and updated simulation results. The output is a final urban planning proposal that reflects residents' opinions and can be viewed on a terminal. Specific actions include creating a plan document that reflects residents' requests and preparing materials for a reporting meeting based on that plan.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] Modern cities face a wide variety of problems, including traffic congestion, environmental pollution, and urban planning that fails to adequately reflect residents' opinions. Furthermore, addressing these issues requires accurately predicting the impact of new policies and infrastructure and presenting it in a visually clear and understandable way. However, previous methods have struggled to respond to real-time changes and effectively incorporate resident feedback.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for acquiring data by sensors for collecting urban information, means for creating a virtual space model based on the acquired data, and means for performing simulations to reflect policy changes in the virtual space model and predict their impact. This makes it possible to visualize complex urban problems in real time and provide optimal urban planning that reflects the opinions of residents.

[0690] "Urban information" refers to various types of data necessary for urban management and planning, such as traffic volume, air pollution, and population dynamics.

[0691] A "sensor" is a device that measures physical phenomena and records them as data, and it forms the foundation of data collection.

[0692] "Means of acquiring data" refers to the process of collecting information about the city through sensors and incorporating it into the system.

[0693] A "virtual space model" is a digital 3D representation of a real city, used for urban planning simulations.

[0694] "Means of running simulations" refer to methods for reflecting policy and infrastructure changes in a virtual space model and calculating and predicting their impact.

[0695] "Optimized urban planning" refers to urban design proposed to implement the most effective plan for achieving specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0696] "Residents' opinions" refer to the thoughts and feedback of local residents regarding urban planning and policies.

[0697] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and converts it into information, and is used in the analysis of resident feedback.

[0698] "3D visualization technology" is a technology that displays virtual space models and simulation results in three dimensions, enabling real-time display on user devices.

[0699] A "policymaker" is an administrator or local government official who is responsible for formulating and implementing urban planning and public policies.

[0700] To implement this invention, a system is needed that acquires information from cities in real time, creates a digital twin model based on that information, and performs simulations.

[0701] First, the server acquires data in real time from various sensors (such as traffic sensors and air pollution sensors) to collect information about the city. This data is used to comprehensively understand the detailed current state of the entire city. The data collected by the sensors is stored in a database and forms the basis for all subsequent processing.

[0702] Next, the server uses the acquired data to create a virtual space model, or digital twin. The digital twin model is visually constructed using 3D rendering technologies such as Three.js. This makes it possible to faithfully reproduce the current state of the city in a digital environment.

[0703] The system also allows users to input new policies and infrastructure plans. The server receives this input and simulates the impact of these changes on a virtual space model. Using Python and Pandas, the system analyzes the simulation results. Based on this analysis, it proposes optimized urban plans for specific goals, such as mitigating traffic congestion or reducing environmental impact.

[0704] Residents, as users, can provide opinions and feedback through a smartphone app. The server analyzes this feedback using natural language processing technology (e.g., NLTK) and incorporates it into urban planning. This process leads to improvements in the plan that meet the needs of the residents.

[0705] Ultimately, the device visualizes the optimized urban planning and simulation results on an application using React Native, for example, and provides them to policymakers and residents. This enables visual and intuitive urban planning proposals, supporting efficient decision-making.

[0706] For example, when proposing a plan for a new bypass road to alleviate traffic congestion in a small city, feedback from residents regarding the addition of commercial facilities is incorporated and presented as an optimized plan.

[0707] The following prompt could be used as input to the generative AI model: "Given an infrastructure plan to alleviate urban traffic congestion, what optimization proposals can you suggest? How will you incorporate resident feedback?"

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The server acquires real-time urban information from various sensors. Input data comes from traffic and air pollution sensors. The server converts this data into a specific format and stores it in a database. This makes it possible to provide the fundamental data necessary for future urban planning.

[0711] Step 2:

[0712] The server generates a virtual space model using data stored in a database. The input is city data acquired from sensors. Based on the acquired data, the server creates a virtual space model using 3D rendering technology with Three.js. The output is a digital twin model of a real city.

[0713] Step 3:

[0714] Users input new policies and infrastructure plans through their terminals. This input is data about policies and plans that will be added to the virtual space model. The server reflects this in the virtual space model and prepares it for the next simulation step.

[0715] Step 4:

[0716] The server runs the simulation. The inputs are a digital twin model and policy data entered by the user. The server uses Python and Pandas to calculate the impact of policy changes on the model. The output is the simulation's predicted impact data.

[0717] Step 5:

[0718] Residents, acting as users, provide feedback on the simulation results via a smartphone app. The input is resident feedback data. The server analyzes this feedback using natural language processing technology with NLTK and utilizes it to improve urban planning as needed. The output is the improved feedback analysis results.

[0719] Step 6:

[0720] The terminal displays the simulation results and optimized urban plan on a visualization device. The input is the final urban planning data sent from the server. Using React Native and other tools, it is presented directly to policymakers and residents as 3D models and graphs. The output is a visualized urban planning proposal.

[0721] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0722] This invention combines a system that utilizes a digital twin model of a city to predict the impact of policy changes on the urban environment and propose optimal urban planning solutions with an emotion engine that recognizes user emotions. This makes it possible to analyze feedback from residents more precisely and reflect it in urban planning.

[0723] First, the server collects sensor information from within the city and builds a digital twin model of the city. This model is updated in real time, recreating the real-world urban conditions in a virtual space.

[0724] Next, local government officials use terminals to input new policies and infrastructure plans into the system. Based on this information, the server uses a digital twin model to perform simulations and predict the impact of policy changes on the urban environment.

[0725] Furthermore, an optimization algorithm is used to generate urban planning proposals based on the simulation results. The server creates this in report format and displays it on a dashboard on the terminal, providing visual support to local government officials.

[0726] This is where the emotion engine, a key feature of the present invention, comes into play. Residents, acting as users, provide feedback on urban planning through a dedicated participatory platform. The server analyzes the collected feedback using a combination of natural language processing technology and the emotion engine. This extracts emotional states from residents' text data and utilizes them as important elements for influencing urban planning.

[0727] For example, if a new infrastructure plan is supported by residents but also raises many concerns, the emotion engine will extract both positive and negative sentiments. Based on these results, the server can adjust the proposal and derive revised versions to address residents' concerns. The revised urban planning proposal will then be reflected on a dashboard, allowing municipal officials to make decisions based on it immediately.

[0728] Thus, this invention, which incorporates an emotion engine, enables the realization of effective and highly acceptable urban planning by more accurately reflecting the diverse voices of residents.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The server receives real-time data acquired from city sensors via a remote API and stores it in a database. This data includes traffic volume, weather information, and demographic data, and is used for subsequent processing.

[0732] Step 2:

[0733] The server constructs a digital twin model based on the collected data. It organizes and cleanses the data, and then uses 3D modeling tools to recreate the urban environment in a virtual space. This generates a model that accurately represents the current state of the city.

[0734] Step 3:

[0735] Using a terminal, local government officials input details of new infrastructure plans and policy changes into a user interface. This input includes information such as the location of new roads and plans for expanding public facilities. This information is then registered in the system as simulation parameters.

[0736] Step 4:

[0737] The server applies the input policy changes to a digital twin model and runs the scenario using a simulation engine. It comprehensively analyzes the impact of policy changes on the urban environment and predicts traffic flow, energy consumption, and environmental impact.

[0738] Step 5:

[0739] The server analyzes the simulation results and generates optimal urban planning proposals. These proposals aim to alleviate traffic congestion and reduce environmental impact, and are compiled using algorithms to create balanced measures addressing diverse objectives.

[0740] Step 6:

[0741] Residents, as users, provide feedback on the proposed plan through a resident participation platform. Here, they can freely write their opinions on their impressions and suggestions, and submit them through a user-friendly interface.

[0742] Step 7:

[0743] The server analyzes residents' feedback using natural language processing technology and an emotion engine. It quantitatively evaluates emotional states from text data, distinguishing between positive and negative emotions. This makes it possible to consider residents' emotional responses in urban planning.

[0744] Step 8:

[0745] Based on the sentiment analysis results, the server revises the urban planning proposals and generates new proposals. Proposals that reflect residents' sentiments are automatically displayed on a dashboard, making them easily accessible to local government officials for use in decision-making.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] The challenge lies in accurately predicting the impact of policy changes on the urban environment and more appropriately reflecting the diverse opinions of residents in urban planning. Traditional urban planning has struggled to accurately reflect residents' feelings and opinions, often resulting in dissatisfaction and misunderstandings due to policy changes. Furthermore, a lack of rapid data processing and optimized proposal development has hindered the realization of effective urban planning.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0750] In this invention, the server includes means for acquiring data from a measuring device for collecting urban information, means for constructing a virtual model based on the acquired information, means for applying policy changes to the virtual model and conducting a simulated experiment to estimate the impact, means for processing residents' opinions using sentiment analysis technology and deriving improved urban planning proposals, and means for presenting the improved proposals on a visual information display device. This makes it possible to realize urban planning that reflects the feelings and opinions of residents, resulting in more effective and acceptable policies.

[0751] "Urban information" refers to data on environmental conditions and human activity within a city, including traffic volume, temperature, and noise levels.

[0752] "Measuring devices" refer to sensors and various measuring instruments placed to collect urban information.

[0753] "Means of acquiring data" refers to the process and technology of collecting urban information from measuring devices and transferring it to servers or other locations.

[0754] A "virtual model" refers to a digital twin or computer model used to recreate the physical and social conditions of a city in a digital space.

[0755] "Methods for conducting simulated experiments" refer to techniques and processes that involve modifying a virtual model and evaluating the results through simulation.

[0756] "Opinions" refer to feedback that expresses the thoughts and feelings that residents have regarding urban policies and plans.

[0757] "Emotional analysis technology" refers to a method that uses natural language processing to extract and analyze emotional and opinion tendencies from text data.

[0758] A "visual information display device" refers to an electronic device or software used to visually display analysis results or proposed plans.

[0759] "Methods for deriving improvement proposals" refers to the process of analyzing residents' opinions using sentiment analysis technology and proposing necessary modifications and improvements to urban planning.

[0760] The system of the present invention makes it possible to predict the impact of policy changes in urban planning on the urban environment and to accurately reflect the diverse opinions of residents. Specific embodiments of the present invention are described below.

[0761] First, the server acquires data from measuring devices that collect urban information. This includes traffic sensors, weather sensors, noise meters, and so on. The collected data is then incorporated into a virtual model built on the server. To intuitively reproduce various elements of the city, the virtual model typically utilizes "Unity" or "Azure Digital Twins."

[0762] Next, municipal employees using the terminals input new policy information and infrastructure proposals through a dedicated interface. Based on this information, the server applies the policy changes to a virtual model and estimates their impact through simulated experiments. The simulation uses Python and its analysis libraries, Pandas and NumPy.

[0763] Furthermore, to gather residents' opinions, users submit feedback through a participatory platform. This platform is built using "Microsoft Forms" and "Google Forms." The server utilizes sentiment analysis technology and analyzes the feedback using natural language processing tools such as "NLTK" and "Hugging Face Transformers." This extracts the tendencies of residents' thoughts and emotions, which are then used to create improvement proposals for urban planning.

[0764] Finally, improvement proposals are displayed on a visual information display device and provided to staff in a dashboard format. This uses tools such as Tableau and Power BI. This makes it easier for staff to make quick and informed decisions.

[0765] For example, when considering the establishment of a new park, if residents frequently express concerns about public safety, it becomes possible to derive concrete improvement measures such as "installing surveillance cameras" or "restricting usage hours."

[0766] Furthermore, one possible prompt for the generating AI model could be: "Based on resident feedback data regarding the construction of a new park, analyze the positive and negative elements and generate improvement suggestions." This would allow for a more precise reflection of residents' voices in urban planning.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] The server acquires necessary urban information from measuring devices placed throughout the city. This input data includes traffic volume, ambient noise, and weather conditions. The server receives this data and performs data preprocessing, such as imputing missing values ​​and detecting and correcting anomalies. The processed data is then stored in a database. The final output is a basic dataset for a digital twin.

[0770] Step 2:

[0771] The server builds a virtual model based on the data collected in Step 1. The server uses Unity or Azure Digital Twins to create a 3D model that replicates the city's conditions. Because this virtual model is updated in real time, it always reflects the latest city situation. The output is a digital twin model of the specific city.

[0772] Step 3:

[0773] Local government officials using terminals input information about new policies into the system. This input includes policy details, objectives, and expected changes. The server receives this information and applies it to a virtual model to simulate the policy change. Specifically, this process uses Python and its libraries, Pandas and NumPy, to perform data analysis. The output is predicted data on the impact of the policy change.

[0774] Step 4:

[0775] The server evaluates the simulation results obtained in step 3 and generates an optimized urban planning proposal. The server utilizes optimization techniques such as "Scikit-learn" and "TensorFlow" to evaluate numerous scenarios. The output is the urban planning proposal best suited to a specific objective.

[0776] Step 5:

[0777] Users provide feedback on urban planning through a participatory platform. This feedback is entered as opinions in text format. The server analyzes these opinions using natural language processing and sentiment analysis techniques, such as "NLTK" and "Hugging Face Transformers." The output after analysis extracts the emotional state and opinion trends of the residents.

[0778] Step 6:

[0779] Based on the analysis results obtained in Step 5, the server creates proposed improvements to the urban plan. The resulting improvement proposals can be viewed on a dashboard displayed on the terminal via visual information display devices such as "Tableau" and "Power BI." This allows local government officials to make decisions quickly.

[0780] Through these steps, this system aims to more effectively incorporate residents' opinions into urban planning.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] In modern urban planning and development processes, it is difficult to effectively incorporate the diverse opinions and feelings of residents. Conventional methods fail to accurately analyze resident feedback and reflect it in urban design, resulting in the failure to realize optimal urban planning that meets residents' needs. This invention aims to solve these problems and realize efficient and highly acceptable urban planning that reflects the feelings of residents.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes means for acquiring detector information for collecting urban information, means for generating a virtual space model based on the acquired detector information, means for performing simulated calculations to reflect policy changes in the virtual space model and predict their impact, means for analyzing opinions using an emotion analysis system that identifies the emotional state of residents' opinions, and means for generating optimized urban design proposals and displaying the proposals on a visual display device. This makes it possible to accurately analyze the diverse opinions of residents along with their emotions and reflect them in urban planning.

[0786] "Urban information" refers to various types of data related to cities, including data on the environment, infrastructure, transportation, and resident activities.

[0787] "Detector information" refers to data generated from various sensors and devices placed within a city, and is used to understand the state of the urban environment in real time.

[0788] A "virtual space model" is a model used to digitally reproduce real-world urban environments and serves as a foundation for conducting simulations of policy changes and other changes.

[0789] "Simulated calculation" refers to a simulation process in which assumptions such as policy changes are set in a virtual space model, and the effects of those changes are predicted.

[0790] "Resident opinions" refer to feedback and comments from residents regarding urban planning and policies, and are collected in order to reflect them in urban design from various perspectives.

[0791] A "sentiment analysis system" is a system that analyzes the emotional states contained in residents' opinions using natural language processing and machine learning, and processes the data based on the results.

[0792] A "visual display device" is a device that visually displays digital information and analysis results, and serves as a means of providing urban design proposals and other information to government officials.

[0793] This invention provides a system for urban planning that utilizes a digital twin model of a city. A server collects urban information in real time and constructs it as a virtual space model. This model is created using various detector information and can accurately reproduce the urban environment. The server uses this virtual space model to perform simulation calculations of policy changes and infrastructure plans, and predicts their impact.

[0794] Furthermore, the server collects opinions from residents and analyzes them using a combination of natural language processing technology and sentiment analysis systems. During this process, it identifies the emotional states contained in the residents' opinions and uses this information as crucial data for urban planning. Based on this analysis, it generates optimized urban design proposals and provides them to administrative staff using visual display devices.

[0795] As a concrete example, consider a case where residents send feedback on urban planning via their smartphones. If the feedback is, for example, "I'm looking forward to the new park plan, but I'm worried about transportation," the server receives this and uses an emotion analysis system to extract the positive emotion of "excitement" and the negative emotion of "worry." Based on these analysis results, the urban design proposal is adjusted to address the residents' concerns and is visually displayed on the terminals of administrative staff.

[0796] The program is implemented using Python, and libraries such as TensorFlow and BERT are used for natural language processing. Unity and Blender are used for simulating the virtual space model, and a dashboard-style web application is used to visualize the simulation results.

[0797] An example of a prompt would be: "We would like to conduct a sentiment analysis of resident feedback regarding a new urban planning project. Based on the feedback, 'I'm really happy about the new park plan, but I'm a little worried about traffic congestion,' analyze the residents' sentiments and generate suggestions for revising the plan based on that analysis."

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] Users input feedback via their smartphones. This feedback consists of text data containing opinions and feelings about urban planning. This text is then sent to the server.

[0801] Step 2:

[0802] The server passes the string data received from the user to the sentiment analysis system. The sentiment analysis system uses natural language processing techniques to analyze the string and identify emotional states such as positive or negative. In this process, emotional attributes are extracted from the input data and output as structured data.

[0803] Step 3:

[0804] The server receives the results of the sentiment analysis and merges them into a virtual space model. Here, simulations of policy changes and infrastructure plans are performed. In this process, the simulation in the virtual space is re-run, taking into account the sentiment feedback of the residents. The output is predictive data regarding the impact on the urban environment.

[0805] Step 4:

[0806] The server generates optimized urban design proposals based on the predicted data from the simulation. In this generation process, various parameters are adjusted based on the predicted data, and urban planning proposals that match the needs of residents are output.

[0807] Step 5:

[0808] The generated urban design proposals are transmitted to terminals via visual display devices and displayed. Government officials, who are users of these terminals, can then make policy decisions based on this information. In this final stage, the proposals are presented in a visual dashboard format.

[0809] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0810] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0811] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0812] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0813] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0814] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0815] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0816] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0817] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0818] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0819] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0820] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0821] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0822] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0823] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0824] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0825] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0826] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0827] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0828] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0829] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means of acquiring sensor information for collecting urban data,

[0833] A means for generating a digital twin model based on acquired sensor information,

[0834] A means of reflecting policy changes in a digital twin model and performing simulations to predict their impact,

[0835] A means for analyzing simulation results and generating an optimized urban planning proposal for a specific objective,

[0836] A means of collecting and analyzing feedback from residents and reflecting it in urban planning,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, which analyzes collected feedback using natural language processing technology and uses it to improve urban planning proposals.

[0840] (Claim 3)

[0841] The system according to claim 1, which generates optimized urban planning proposals and provides them to local government officials by displaying the proposals on a dashboard.

[0842] "Example 1"

[0843] (Claim 1)

[0844] An information gathering device for acquiring data,

[0845] A device that generates a virtual environment based on acquired information,

[0846] An analysis device for reflecting policy changes in a virtual environment and predicting their impact,

[0847] A device that evaluates analysis results and generates optimized plan proposals for specific purposes,

[0848] A device that collects and analyzes opinions from participants and incorporates them into the plan,

[0849] A device that provides proposals generated using a special algorithm in report format,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which analyzes opinions collected using natural language processing technology and uses them to improve plan proposals.

[0853] (Claim 3)

[0854] The system according to claim 1, which provides the generated optimized plan proposal to staff by displaying it on a visualization device.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] A means of acquiring data using sensors to collect information about the city,

[0858] A means for creating a virtual space model based on acquired data,

[0859] A means of reflecting policy changes in a virtual space model and performing simulations to predict their impact,

[0860] A means of analyzing simulation results and providing an optimized urban plan for a specific objective,

[0861] A means of incorporating and analyzing residents' opinions and reflecting them in urban planning,

[0862] A means of displaying results on a user device in real time using 3D visualization technology,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, which uses natural language processing technology to analyze collected opinions from residents and uses them to improve urban planning proposals.

[0866] (Claim 3)

[0867] The system according to claim 1, which provides an optimized urban plan and presents the proposal to policymakers by displaying it on a visualization device.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A means of acquiring data from measuring devices for collecting urban information,

[0871] A means of constructing a virtual model based on the acquired information,

[0872] A means of applying policy changes to a virtual model and conducting simulated experiments to estimate their impact,

[0873] A means for evaluating the results of a simulated experiment and generating an optimized urban planning proposal for a specific objective,

[0874] A means of collecting and analyzing opinions from residents and reflecting them in urban planning,

[0875] A means of processing residents' opinions using sentiment analysis technology and deriving improvement proposals for urban planning,

[0876] A means for presenting the improved proposal on a visual information display device,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, which analyzes collected opinions using natural language processing and sentiment analysis technologies and uses them to improve urban planning proposals.

[0880] (Claim 3)

[0881] The system according to claim 1, which generates an optimized urban planning proposal and provides the proposal to a visual information display device.

[0882] "Application example 2 when combining with an emotional engine"

[0883] (Claim 1)

[0884] A means of acquiring detector information for collecting urban information,

[0885] Means for generating a virtual space model based on acquired detector information,

[0886] A means of reflecting policy changes in a virtual space model and performing simulated calculations to predict their impact,

[0887] A means for analyzing simulation results and generating an optimized urban design proposal for a specific objective,

[0888] A means of collecting and analyzing opinions from residents and reflecting them in urban planning,

[0889] A means of analyzing opinions using an emotion analysis system that identifies the emotional state of residents' opinions,

[0890] A system that includes this.

[0891] (Claim 2)

[0892] The system according to claim 1, which analyzes collected opinions using natural language processing technology and sentiment analysis systems and uses them to revise urban design proposals.

[0893] (Claim 3)

[0894] The system according to claim 1, which generates an optimized urban design proposal and provides the proposal to administrative officials by displaying it on a visual display device. [Explanation of Symbols]

[0895] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of acquiring data using sensors to collect information about the city, A means for creating a virtual space model based on acquired data, A means of reflecting policy changes in a virtual space model and performing simulations to predict their impact, A means of analyzing simulation results and providing an optimized urban plan for a specific objective, A means of incorporating and analyzing residents' opinions and reflecting them in urban planning, A means of displaying results on a user device in real time using 3D visualization technology, A system that includes this.

2. The system according to claim 1, which uses natural language processing technology to analyze collected opinions from residents and uses them to improve urban planning proposals.

3. The system according to claim 1, which provides an optimized urban plan and presents the proposal to policymakers by displaying it on a visualization device.

Images