System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining resource data; means for obtaining environment data; means for obtaining city layout data; means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environment data, and the city layout data; and means for improving construction and city planning of a target area based on the optimal design parameters.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditional construction and urban planning often neglects sustainability, resulting in environmental degradation and inefficient resource use. Furthermore, these plans are often done manually, making it difficult to optimally allocate resources and minimize environmental impact. The present invention aims to solve these problems and promote sustainable urban development. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a means for acquiring resource data, environmental data, and urban layout data, and a means for executing an artificial intelligence algorithm using the acquired data to generate optimal design parameters. The present invention also provides a means for improving the construction and urban planning of a target area based on the optimal design parameters, thereby achieving efficient use of resources and minimizing environmental impact. Furthermore, the present invention includes a means for displaying the optimization results of the artificial intelligence algorithm to a user and collecting user feedback, thereby enabling continuous improvement of the project.

[0006] "Resource data" refers to data on resources required for construction and urban planning, including building materials, labor, energy, etc.

[0007] "Environmental Data" is data relating to the environment, such as local climate, air quality, water quality, and soil conditions.

[0008] "City layout data" refers to data on the layout of existing city infrastructure, road networks, parks, public facilities, and the like.

[0009] "Design parameters" are design values ​​and indicators required to optimize construction and urban planning, and are standards for ensuring efficient resource use and minimizing environmental impact.

[0010] "Artificial intelligence algorithm" refers to a computational procedure or program that uses resource data, environmental data, and urban layout data to generate optimal design parameters.

[0011] "Optimization results" are proposals and design data regarding the efficient allocation of resources and minimization of environmental impacts, generated by the processing of artificial intelligence algorithms.

[0012] "Feedback" refers to opinions and improvement suggestions provided by users based on optimization results, which are used to improve future projects.

[0013] The term "system" refers to a combination of a series of means provided by the present invention, and is a configuration that includes everything from resource data acquisition to optimization and feedback collection. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

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

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data. The system of the present invention is used in collaboration between a server, terminals, and users.

[0036] Data collection and initialization

[0037] server

[0038] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[0039] Set of data

[0040] server

[0041] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[0042] Project Optimization

[0043] server

[0044] The server runs an artificial intelligence algorithm based on the configured data, which calculates the efficient allocation of resources, minimizes environmental impact, and optimizes living space, thereby generating optimal design parameters.

[0045] Viewing results and gathering feedback

[0046] Terminal

[0047] The terminal displays the optimization results generated by the server to the user in an easy-to-understand format consisting of graphs, diagrams, and text. The user can then enter feedback based on the results.

[0048] User

[0049] Users can check the optimization results through their devices and provide feedback on the results, which will be used for future project optimizations.

[0050] Specific examples

[0051] For example, if there is a construction project for a new office building, the specific steps would be as follows:

[0052] 1. Data collected:

[0053] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[0054] 2. Data set:

[0055] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[0056] 3. Optimization:

[0057] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[0058] 4. Displaying the results:

[0059] The terminal graphically displays the generated optimization results and provides them to the user.

[0060] 5. Gathering Feedback:

[0061] The user checks the results and enters feedback on improvements and opinions, which is then collected by the device and sent to the server.

[0062] In this way, the system of the present invention supports sustainable and efficient construction and urban planning.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] server

[0066] The server retrieves resource data, specifically information about building materials, labor, and energy required for construction, from a database using a data management module.

[0067] Step 2:

[0068] server

[0069] The server acquires environmental data, specifically regional climate data, air quality data, and water quality data, and compiles this information into an environmental dataset.

[0070] Step 3:

[0071] server

[0072] The server acquires city layout data, specifically information on the layout of existing city infrastructure, road networks, parks, and public facilities, from a database.

[0073] Step 4:

[0074] server

[0075] The server inputs the acquired resource data, environmental data, and city layout data into the artificial intelligence algorithm, specifically converting this data into an appropriate format and setting it as input parameters for the algorithm.

[0076] Step 5:

[0077] server

[0078] The server runs artificial intelligence algorithms to generate optimal design parameters for efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space.

[0079] Step 6:

[0080] server

[0081] The server stores the optimization results of the artificial intelligence algorithm, specifically, the generated design parameters and optimization results in a database.

[0082] Step 7:

[0083] Terminal

[0084] The terminal receives the optimization results from the server and displays them to the user, specifically graphically displaying the reduction in resource usage, the improvement in energy efficiency, and the reduction in environmental impact.

[0085] Step 8:

[0086] User

[0087] The user checks the optimization results and inputs feedback on them. Specifically, based on the displayed results, the user inputs suggestions and opinions on how to improve the project.

[0088] Step 9:

[0089] Terminal

[0090] The device sends the feedback entered by the user to the server. Specifically, it collects the user's opinions and points for improvement as data and transfers them to the server.

[0091] Step 10:

[0092] server

[0093] The server stores the feedback received from the devices, specifically storing the collected feedback data in a database and using it to optimize future projects.

[0094] In this way, a system is realized in which servers, terminals, and users work together to improve resource efficiency and reduce environmental impact.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] In modern urban planning and construction projects, it is difficult to quickly develop appropriate plans that provide optimal living spaces while efficiently and sustainably allocating resources and minimizing environmental impact. Traditional methods have the drawback of making it difficult to comprehensively evaluate these factors, which is time-consuming and costly. Furthermore, the lack of a mechanism for incorporating user feedback slows down the improvement cycle.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server

[0100] a means for collecting resource data;

[0101] a means for collecting environmental data;

[0102] a means for collecting city layout data;

[0103] means for pre-processing the resource data, the environmental data, and the city layout data;

[0104] means for feeding the pre-processed data into an artificial intelligence algorithm;

[0105] means for executing the artificial intelligence algorithm to calculate efficient allocation of resources, minimization of environmental impact, and optimization of living space;

[0106] means for generating the optimal design parameters;

[0107] means for improving construction and urban planning of a target area based on the generated design parameters;

[0108] means for displaying the optimization results of the artificial intelligence algorithm to a user;

[0109] a means for collecting feedback from users on the optimization results and reflecting the feedback in subsequent optimization processes;

[0110] Includes:

[0111] This allows urban planning and construction projects to quickly and effectively achieve efficient resource allocation, minimize environmental impact, and optimize living space. It also allows for rapid reflection of user feedback, improving the accuracy and adaptability of plans.

[0112] 1. "Resource Data" means information related to resources required for urban planning and construction projects, including, but not limited to, building materials, labor, and energy.

[0113] 2. "Environmental data" means information related to the environment that should be taken into account in construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[0114] 3. "Urban layout data" refers to data that includes information about the layout and structure of a city, such as existing infrastructure information, road networks, and public facilities.

[0115] 4. "Preprocessing" refers to the process of converting data into a format suitable for artificial intelligence algorithms, including missing value imputation, standardization, and normalization.

[0116] 5. "Artificial intelligence algorithm" means a computational procedure for achieving objectives such as efficient allocation of resources, minimizing environmental impact, or optimizing living space. Examples include linear programming or multi-objective optimization.

[0117] 6. "Optimal design parameters" refers to specific criteria and values ​​generated by artificial intelligence algorithms to ensure the efficient and sustainable development of urban planning and construction projects.

[0118] 7. "Server" means a computer system responsible for collecting data, pre-processing, executing artificial intelligence algorithms and transmitting the generated results to the terminal.

[0119] 8. "Terminal" means a device that displays the optimization results sent from the server to the user and collects feedback from the user.

[0120] 9. "Feedback" refers to opinions and suggestions for improvement provided by users regarding optimization results, and is information that will be reflected in subsequent optimization processes.

[0121] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used by a server, terminals, and users in cooperation with each other.

[0122] Data collection and initialization

[0123] server

[0124] The server first collects resource data, environmental data, and city layout data. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes information on existing infrastructure, road networks, public facilities, etc. The server obtains this data using APIs and database queries.

[0125] Data preprocessing and collection

[0126] server

[0127] The server preprocesses the collected data and feeds it into the AI ​​algorithm. Preprocessing involves imputing missing values, standardizing, normalizing, etc. For example, data is cleaned using Pandas or NumPy and formatted for TensorFlow or PyTorch.

[0128] Project optimization calculations

[0129] server

[0130] The server runs artificial intelligence algorithms to perform optimization calculations, such as efficient resource allocation, minimizing environmental impact, and optimizing living space. For example, it uses linear programming and multi-objective optimization to generate optimal design parameters.

[0131] Generating and displaying results

[0132] server

[0133] The server generates optimization results and sends them to the terminal. The results include the rate of energy consumption reduction, the rate of green space increase, construction costs, etc. This data is often sent in JSON or XML format.

[0134] Terminal

[0135] The device then displays the optimization results to the user. Using data visualization tools such as Tableau or Power BI, the results are presented in graph and text format. For example, a dashboard might show the rate of reduction in energy consumption or the rate of increase in green space.

[0136] Collecting and incorporating user feedback

[0137] User

[0138] The user checks the optimization results and provides feedback. Feedback is entered through the device's input form or comment function. For example, a user might enter an opinion such as "cost reduction should be prioritized over energy consumption reduction."

[0139] Terminal

[0140] The terminal sends the collected user feedback to the server, which stores it so that it can be reflected in the next optimization process. The data stored in the database is in the form of text and numerical data.

[0141] Specific examples

[0142] For example, here is a specific example from a new office building construction project:

[0143] 1. Data Collection:

[0144] The server obtains resource data such as building materials, labor, and energy through APIs, environmental data such as climate data, air quality data, and water quality data from the Japan Meteorological Agency API, and city layout data from local government databases.

[0145] 2. Data preprocessing and set:

[0146] The server uses Pandas to impute missing values ​​and standardize the data, then sends the preprocessed data to TensorFlow.

[0147] 3. Project optimization calculation:

[0148] The server uses TensorFlow to execute multi-objective optimization algorithms to calculate the optimal energy efficiency and maximize living space, generating optimal building material placement and energy consumption reduction plans.

[0149] 4. Generate and display results:

[0150] The server generates optimization results in JSON format and sends them to the terminal, including the rate of reduction in energy consumption and the rate of increase in green space area.

[0151] 5. Collect and incorporate user feedback:

[0152] The user enters feedback through an input form on the device. For example, they may enter their opinion that "reducing costs should be prioritized over reducing energy consumption." The device then sends the feedback to the server and stores it in a database. This feedback will be used in the next optimization process.

[0153] Example prompts for generative AI models

[0154] "Generate optimal design parameters for a proposed office building."

[0155] "Optimize urban layout to achieve reduced energy consumption and increased green space."

[0156] "Please propose a plan to minimize the environmental impact of the new residential area."

[0157] The above is a specific embodiment of the present invention, which aims to quickly achieve efficient allocation of resources, minimization of environmental impact, and optimization of living space.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] Data collection

[0161] The server collects resource data, environmental data, and city layout data. As input, it uses APIs and database queries to obtain resource data such as building materials, labor, and energy, environmental data such as climate data, air quality data, and water quality data, and city layout data such as existing infrastructure information, road networks, and public facilities. As output, it stores these data within the system.

[0162] Step 2:

[0163] Data Preprocessing

[0164] The server preprocesses the collected data. It receives collected resource data, environmental data, and city layout data as input. Specific operations include using Pandas and NumPy to fill in missing values ​​in the data, standardizing and normalizing it, and converting it into a format suitable for artificial intelligence algorithms. The output is the preprocessed data.

[0165] Step 3:

[0166] Set of data

[0167] The server feeds the preprocessed data into an artificial intelligence algorithm. As input, it receives preprocessed resource data, environmental data, and city layout data. Using a machine learning framework such as TensorFlow or PyTorch, it feeds this data into the algorithm. As output, it receives a dataset ready for the algorithm to run.

[0168] Step 4:

[0169] Project optimization calculations

[0170] The server executes artificial intelligence algorithms to perform optimization calculations. It receives set data as input. Specific operations include using linear programming and multi-objective optimization to calculate the efficient allocation of resources, minimize environmental impact, and optimize living space. Optimal design parameters are generated as output.

[0171] Step 5:

[0172] Generate and send results

[0173] The server generates optimization results and sends them to the terminal. It receives optimal design parameters as input. The results include information such as the rate of reduction in energy consumption, the rate of increase in green space, and construction costs. It generates the results in JSON or XML format and sends them to the terminal. The output is the optimization results that can be displayed on the terminal.

[0174] Step 6:

[0175] Displaying the results

[0176] The terminal displays the received optimization results to the user. As input, it receives the optimization results sent from the server. Specifically, it uses a data visualization tool such as Tableau or Power BI to display the results in graphs or text format. As output, it obtains the optimization results that the user can visually confirm.

[0177] Step 7:

[0178] Collecting feedback

[0179] The user checks the optimization results and provides feedback. The optimization results are received as input. Specifically, the user enters feedback through the device's input form or comment function. For example, the user may enter an opinion such as "prioritize cost reduction over energy consumption reduction." The user's feedback data is obtained as output.

[0180] Step 8:

[0181] Send and save feedback

[0182] The terminal sends the collected user feedback to the server. It receives the user feedback as input. Specifically, it sends the feedback in JSON or text format to the server, which stores it in a database. The output is the feedback data that will be reflected in the next optimization process.

[0183] The above is the specific processing flow in this system.

[0184] (Application example 1)

[0185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] Conventional urban planning and construction projects have faced the challenge of efficiently collecting and analyzing resource data, environmental data, and urban layout data to generate optimal design parameters. Furthermore, there was no system in place to collect data in real time at actual construction sites and optimize resource efficiency based on that data. This made it difficult to achieve sustainable urban planning and efficient resource management.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0188] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring city layout data, thereby executing an artificial intelligence algorithm for generating optimal design parameters using the resource data, the environmental data, and the city layout data, a means for collecting environmental data and resource data in real time using sensors mounted on industrial machines, a means for executing an artificial intelligence algorithm for optimizing resource efficiency based on the data collected by the industrial machines, and a means for displaying the results of the resource efficiency optimization, thereby enabling urban planning and construction projects to achieve sustainable and efficient resource utilization.

[0189] "Resource data" is information about the supplies and resources needed for urban planning and construction projects, such as building materials, labor, and energy.

[0190] "Environmental data" refers to information about the environmental conditions of proposed construction sites or industrial areas, such as climate, humidity, air quality, and water quality.

[0191] "City layout data" refers to information about the structure and layout of a city, including its existing infrastructure, road networks, and public facilities.

[0192] An "artificial intelligence algorithm" is a calculation procedure or model for generating optimal design parameters based on collected data.

[0193] A "sensor" is a device that is installed in industrial machinery and collects environmental and resource data in real time.

[0194] "Optimization results" are proposals and design parameters generated by artificial intelligence algorithms regarding efficient resource allocation and utilization.

[0195] "Resource efficiency" refers to the efficiency with which resources are used to maximize results and ensure that they are used without waste.

[0196] "Industrial machinery" refers to machinery and equipment that is placed in a factory and has the function of collecting and analyzing various data.

[0197] The present invention is a system that uses sensors mounted on industrial machines to collect environmental and resource data in real time and optimizes resource efficiency based on that data. The system of the present invention is used in collaboration with a server, industrial machines, terminals, and users.

[0198] Data collection and initialization

[0199] Industrial Machinery

[0200] Industrial machinery is equipped with various sensors that collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in the factory in real time, enabling accurate and timely acquisition of various data.

[0201] server

[0202] The server receives the collected environmental data and resource data, stores them in a database, and sets up the data required for analysis.

[0203] Project Optimization

[0204] server

[0205] The server uses artificial intelligence algorithms to analyze collected resource and environmental data. This analysis generates optimal design parameters, including efficient resource allocation, reduced energy consumption, and minimized environmental impact. The software used by the server includes Python, machine learning libraries (e.g., TensorFlow, Scikit-learn), and data processing libraries (e.g., Pandas, NumPy).

[0206] Viewing results and gathering feedback

[0207] Terminal

[0208] The terminals display the optimization results generated from the server to workers in the factory. The display format consists of graphs, diagrams, text, etc., and is provided in a way that is easy for users to understand. For example, the optimization results of energy consumption and the optimal placement of materials are visually displayed.

[0209] User

[0210] Users can view the optimization results through their devices and provide feedback on the results. This feedback is used in subsequent data analysis and optimization processes, thereby continuously improving the system's performance.

[0211] Specific examples

[0212] For example, when creating a plan to minimize energy consumption within a factory, industrial machines collect temperature data in each area and energy consumption data of the machines, and the data is analyzed on a server. As a result, the most energy-efficient layout is proposed and notified on the terminal. The user can check the results and provide more specific feedback if necessary.

[0213] Example prompts to input to the generative AI model

[0214] "Collect temperature, humidity, and machine energy consumption data for each area in the factory and generate the optimal resource allocation pattern. Send the collected data in JSON format and display the optimization results graphically."

[0215] In this way, the system of the present invention realizes efficient use of resources within the factory and a reduction in the burden on the environment.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] Sensors installed on industrial machinery collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in real time. The input of the sensors is environmental information and resource status for each area in the factory, and the output is digital information processed in the format of the collected data. Specifically, the sensors periodically acquire data and convert it into a specific format (e.g., JSON).

[0219] Step 2:

[0220] Industrial machines send collected data to a server. The input is environmental and resource data collected from sensors, and the output is data sent to the server. Specifically, data is sent to the server using an HTTP request.

[0221] Step 3:

[0222] The server stores the received environmental data and resource data in a database and sets it up in the format required for analysis. The input is the transmitted data, and the output is the set up data for analysis. Specific operations include saving the data in the database and organizing the data structure.

[0223] Step 4:

[0224] The server uses an artificial intelligence algorithm to analyze the collected data. This algorithm generates an optimal resource allocation pattern based on a variety of data. The input is the data set up for analysis, and the output is the results of optimizing resource efficiency. Specifically, data analysis is performed using Python and machine learning libraries (e.g., TensorFlow, Scikit-learn).

[0225] Step 5:

[0226] The server sends the generated optimization results to the terminal. The input is the resource efficiency optimization results, and the output is the data to be sent to the terminal. Specifically, the server sends the data to the terminal using an HTTP request.

[0227] Step 6:

[0228] The terminal displays the optimization results received from the server. The display format consists of graphs, diagrams, text, etc., and is provided in a form that is easy for the user to understand. The input is the data sent from the server, and the output is the visualized information displayed to the user. Specifically, the data is visualized on a web page using HTML and CSS.

[0229] Step 7:

[0230] The user checks the optimization results through the terminal and provides feedback on the results. The input is the displayed optimization results and the user's feedback, and the output is the collected feedback data. Specific operations include filling out a feedback form and submitting the data.

[0231] Step 8:

[0232] The terminal sends the collected feedback to the server. The input is the feedback data from the user, and the output is the data to be sent to the server. Specifically, the feedback data is sent to the server using an HTTP request.

[0233] This series of processing steps creates a system that collects and analyzes data in real time to optimize resource efficiency.

[0234] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0235] The present invention combines a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data with an emotion engine that recognizes user emotions. The system of the present invention is used in collaboration between a server, terminals, and users.

[0236] Data collection and initialization

[0237] server

[0238] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[0239] Set of data

[0240] server

[0241] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[0242] Project Optimization

[0243] server

[0244] The server runs artificial intelligence algorithms based on the configured data, which calculate how to use resources efficiently, reduce energy consumption, minimize environmental impact, and optimize the living space, thereby generating optimal design parameters.

[0245] Emotion Engine Operation

[0246] server

[0247] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. Based on the analysis results, it adjusts the way the optimization results are presented.

[0248] Terminal

[0249] When displaying the optimization results received from the server to the user, the device adjusts the display content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the device will display information succinctly and emphasize positive information.

[0250] Viewing results and gathering feedback

[0251] Terminal

[0252] The device displays the optimization results to the user and analyzes the user's emotional state through an emotion engine, thereby providing more personalized results and increasing user satisfaction.

[0253] User

[0254] Users can check the optimization results through their devices and provide feedback on the results. This feedback is analyzed by the emotion engine and the results are sent to the server. The feedback and the results of the emotion analysis are used to optimize future projects.

[0255] Specific examples

[0256] For example, in a construction project for a new office building, the system operates in the following specific steps:

[0257] 1. Data collected:

[0258] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[0259] 2. Data set:

[0260] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[0261] 3. Optimization:

[0262] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[0263] 4. Use of Emotion Engine:

[0264] The server analyzes the optimization results using an emotion engine and adjusts the results so that the user does not feel stressed.

[0265] 5. Displaying the results:

[0266] The device graphically displays the generated optimization results and provides them to the user in an adjusted form based on the analysis results of the emotion engine.

[0267] 6. Gathering Feedback:

[0268] The user checks the results and inputs feedback on improvements and opinions. The device collects this feedback and sentiment analysis results and sends them to the server.

[0269] In this way, the system of the present invention supports sustainable and efficient construction and urban planning, while providing advanced feedback functionality that also takes into account the user's emotions.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] server

[0273] The server retrieves resource data from a database, specifically, information on building materials, labor, and energy required for a construction project, using a data management module.

[0274] Step 2:

[0275] server

[0276] The server obtains environmental data, specifically, regional climate data, air quality data, and water quality data from an environmental information database.

[0277] Step 3:

[0278] server

[0279] The server collects city layout data, specifically information on existing infrastructure, road networks, and the location of public facilities, from a city database.

[0280] Step 4:

[0281] server

[0282] The server inputs this data (resource data, environmental data, and city layout data) into the artificial intelligence algorithm. Specifically, it standardizes the data format and sets it as an input parameter for the algorithm.

[0283] Step 5:

[0284] server

[0285] The server runs artificial intelligence algorithms that calculate and generate optimal design parameters for efficient use of resources, minimizing environmental impact, and optimizing living space.

[0286] Step 6:

[0287] server

[0288] The server stores the optimization results generated by the artificial intelligence algorithm, specifically, the design parameters and optimization results, which are saved in a database for later use.

[0289] Step 7:

[0290] server

[0291] The server uses the emotion engine when displaying the optimization results to the user. Specifically, the emotion engine analyzes the user's emotions and adjusts the display method of the optimization results based on the results.

[0292] Step 8:

[0293] Terminal

[0294] The device displays the optimization results sent from the server to the user. The display content is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be simplified and the positive aspects will be emphasized.

[0295] Step 9:

[0296] User

[0297] The user checks the displayed optimization results and inputs feedback on the results into the device. The user's reactions are also analyzed by the emotion engine.

[0298] Step 10:

[0299] Terminal

[0300] The terminal transmits the feedback from the user and the emotion analysis results from the emotion engine to the server.

[0301] Step 11:

[0302] server

[0303] The server stores the feedback received from the devices and the results of sentiment analysis in a database, which is used to optimize future projects.

[0304] For example, in the case of a new office building construction project, the server first collects the necessary data (building materials, energy, climate information, etc.) and inputs it into an AI algorithm for optimization. The generated optimization results are displayed to the user through an emotion engine, which analyzes the user's reactions and collects feedback. This feedback is then reflected in future projects.

[0305] In this way, the system of the present invention can optimize projects while taking into account the user's feelings, while achieving resource efficiency and reducing environmental impact.

[0306] Example 2

[0307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] Traditional construction and urban planning systems focus on generating design parameters that take into account the efficient use of resources and environmental impacts, but few systems consider user emotions or intuitive acceptability. While user feedback is sometimes collected, it is unclear how it will be reflected in the next project optimization. Therefore, achieving both an improved user experience and efficient project optimization is a challenge.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0310] In this invention, the server includes means for acquiring resource data, means for acquiring environmental data, means for acquiring city layout data, means for executing an artificial intelligence algorithm, means for analyzing the optimization results and recognizing the user's emotions, means for adjusting the information content presented to the user based on the emotion recognition results, and means for improving construction and urban planning based on the optimal design parameters, thereby enabling efficient and satisfactory project optimization while taking the user's emotions into consideration.

[0311] "Resource data" refers to resource information required for construction and urban planning, specifically including data on building materials, labor, energy, etc.

[0312] "Environmental data" refers to data about the natural and man-made environment that influences construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[0313] "City layout data" refers to information about the structure and layout of a city, and specifically includes information about road networks, public facilities, and existing infrastructure.

[0314] "Artificial intelligence algorithm" refers to a computational method that analyzes a variety of data and generates optimal design parameters, and includes machine learning models and data analysis techniques.

[0315] "Optimal design parameters" refer to design indicators aimed at efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[0316] "Emotion recognition means" refers to technology that analyzes a user's emotions and uses the results to adjust the system's behavior and information presentation.

[0317] "Feedback collection means" refers to a mechanism for obtaining opinions and impressions from users and reflecting that information in the system.

[0318] "Means for adjusting the content of information presented to the user" refers to a technology that appropriately changes the format and content of the displayed information based on the recognized user's emotions.

[0319] "Means for improving construction and urban planning" refers to technologies that streamline and optimize the design and execution of actual construction projects and urban planning projects based on the generated optimal design parameters.

[0320] The present invention provides a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used in collaboration between a server, a terminal, and a user, and which incorporates an emotion engine that recognizes the user's emotions.

[0321] Data collection and initialization

[0322] server

[0323] The server first obtains resource data, environmental data, and city layout data from external APIs and databases. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes existing infrastructure information, road networks, public facilities, etc. These data are temporarily stored in a database on the server.

[0324] Set of data

[0325] server

[0326] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server. This process involves preprocessing the data using Python-based scripts, such as standardizing numerical data and encoding categorical data. After data cleansing and normalization, the data is ready to be input into the AI ​​model.

[0327] Project Optimization

[0328] server

[0329] The server uses the prepared data to run artificial intelligence algorithms, utilizing machine learning libraries (e.g., TensorFlow and PyTorch) to perform calculations aimed at efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space. Specific design parameters are generated as a result of these algorithms.

[0330] Emotion Engine Operation

[0331] server

[0332] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. For example, Microsoft's Emotion API is used. Based on the results of this analysis, the way the optimization results are presented is adjusted.

[0333] Terminal

[0334] When displaying the optimization results received from the server to the user, the device adjusts the information based on the results of the emotion engine. For example, if the user is feeling stressed, the device displays information succinctly and emphasizes positive information.

[0335] Viewing results and gathering feedback

[0336] Terminal

[0337] The device displays the optimization results to the user and also analyzes the user's emotional state through an emotion engine to provide more personalized results. The results are displayed graphically and dynamically adjusted based on the emotion engine's analysis of the user's emotions.

[0338] User

[0339] Users can check the optimization results through their devices and provide feedback, such as specific opinions or requests for improvement. This feedback is sent from the device to the server and reanalyzed by the emotion engine. This feedback is then reflected in the next project optimization.

[0340] Specific examples

[0341] For example, in a construction project for a new office building, the system operates in the following specific steps:

[0342] Data collected

[0343] The server retrieves resource data (building materials, energy), environmental data (climate, air quality), and city layout data (infrastructure, road network) for the proposed construction site, which includes collecting environmental data from weather data APIs and retrieving city layout data from public databases.

[0344] Set of data

[0345] The server then feeds this data into the algorithm, performing preprocessing such as standardization and encoding. Once the data is properly formatted, it is fed into the AI ​​model and calculations are performed.

[0346] optimization

[0347] The server runs multiple simulations to generate optimal design plans to reduce energy consumption and increase green space.

[0348] Using the Emotion Engine

[0349] The server analyzes the optimization results using an emotion engine and adjusts the way the results are presented to avoid stress for the user. For example, complex information is summarized succinctly.

[0350] Displaying the results

[0351] The terminal graphically displays the adjusted optimization results and presents them in a way that is easy for the user to understand.

[0352] Collecting feedback

[0353] The user checks the presented results and provides feedback, which is then sent to the server along with the sentiment analysis results, which are then reflected in future project optimizations.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] Step 1:

[0356] Server Data Collection

[0357] The server obtains resource data, environmental data, and city layout data from external APIs and databases. For example, it collects climate data from weather data APIs and obtains city infrastructure information from public databases. It uses API keys and database connection information as input, and the resource data, environmental data, and city layout data are saved in the server's storage as output.

[0358] Step 2:

[0359] Server data preprocessing

[0360] The server preprocesses the collected data. This preprocessing includes data cleansing, standardization, and categorical encoding. Specifically, it normalizes numerical data to the 0-1 range and one-hot encodes categorical data. It uses the collected raw data as input and generates preprocessed data as output.

[0361] Step 3:

[0362] Server Dataset

[0363] The server then feeds the preprocessed data into an artificial intelligence algorithm, using a Python-based script to prepare the data for input into the AI ​​model. The preprocessed data is used as input, and the output is data that matches the AI ​​model.

[0364] Step 4:

[0365] Optimization algorithm execution on the server

[0366] The server uses the provided data to run artificial intelligence algorithms, using machine learning libraries such as TensorFlow and PyTorch to perform calculations that optimize energy efficiency and environmental impact. The AI ​​model uses the data as input and generates optimal design parameters as output.

[0367] Step 5:

[0368] Uses the server's emotion engine

[0369] The server analyzes the optimization results with an emotion engine, which recognizes emotions based on the user's past feedback and current input data and adjusts the presentation of the results. Using the optimization results and user feedback as input, the adjusted presentation is obtained as output.

[0370] Step 6:

[0371] Displaying results on a terminal

[0372] The device then displays the optimization results received from the server to the user. This display is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be displayed succinctly and positive information will be emphasized. Using the adjusted presentation data as input, the adjusted display content is provided to the user as output.

[0373] Step 7:

[0374] User feedback input

[0375] The user checks the optimization results presented through the terminal and provides feedback. The user's opinions and impressions are used as input, and the feedback data is saved on the terminal as output.

[0376] Step 8:

[0377] Sending feedback via device

[0378] The device sends the feedback collected from the user to the server, where it is reanalyzed by the emotion engine and reflected in the next project optimization. The feedback data is used as input, and the reanalysis results are saved as output on the server.

[0379] (Application example 2)

[0380] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] Optimizing the operation routes of autonomous vehicles has been an important challenge even with conventional technologies, but it has been difficult to efficiently handle environmental data, resource data, and urban layout data. Furthermore, there has been a lack of technology to improve the riding experience by taking into account the emotions of users. In particular, the way operation information is presented does not adapt to the user's emotional state, which often causes stress for users. Therefore, there is a need for a system that can simultaneously improve efficiency and user satisfaction in the operation of autonomous vehicles.

[0382] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0383] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring urban layout data. This allows it to execute an artificial intelligence algorithm to generate optimal design parameters. Based on this, it is possible to not only improve the construction and urban planning of the target area, but also optimize the operation routes of autonomous vehicles, analyze user emotions using an emotion engine, and adjust the presentation method of operation information based on the analysis results, thereby simultaneously improving efficiency and user satisfaction.

[0384] "Resource data" refers to information about various resources needed in urban planning and construction projects, such as building materials, energy, and labor.

[0385] "Environmental Data" refers to information about the environment in which the project is implemented, such as climate data, air quality data, and water quality data.

[0386] "City layout data" refers to information about the structure of a city, such as existing infrastructure information, road networks, and public facilities.

[0387] An "artificial intelligence algorithm" is a calculation method for generating optimal design parameters based on resource data, environmental data, and urban layout data.

[0388] "Optimal design parameters" are project settings that achieve efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[0389] "Emotion engine" is a general term for devices and software that analyze users' input data and reactions to recognize their emotional state.

[0390] An "autonomous vehicle" is a vehicle that uses artificial intelligence technology to drive itself without the assistance of a driver.

[0391] A "travel route" is the path an autonomous vehicle follows to reach its destination.

[0392] "Feedback" refers to opinions and impressions provided by users, and is information that is used to improve and adjust the system.

[0393] System Configuration

[0394] The system of the present invention operates in collaboration with a server, a terminal, and a user. The server collects resource data, environmental data, and city layout data, and uses this data to execute an artificial intelligence algorithm. It also has an emotion engine that analyzes the user's emotions and proposes an optimized route based on the results. Meanwhile, the terminal displays the optimized route and adjustment information based on the emotion analysis results to the user, and collects user feedback.

[0395] A detailed explanation of each system function

[0396] Data Collection and Optimization

[0397] The server collects environmental data (weather, road conditions), city layout data (existing infrastructure, traffic volume), and resource data (fuel levels, location of charging stations). This data is obtained using APIs (for example, weather forecast APIs or city open data APIs). The collected data is fed into an artificial intelligence algorithm to optimize the route. The algorithm calculates the optimal route, taking into account factors such as reducing energy consumption, shortening travel time, and minimizing environmental impact.

[0398] Sentiment analysis and service information adjustment

[0399] The server also uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's facial expressions and voice to recognize states such as stress and relaxation. Based on the recognized emotional state, the server adjusts the way it presents operational information. For example, if the user is stressed, the server may simplify the display content, while if the user is relaxed, the server may provide more detailed information.

[0400] Viewing results and gathering feedback

[0401] The device displays the optimized route and information adjusted based on the emotion engine's analysis results on smart glasses or a head-mounted display. The user can check the route based on the displayed information and provide feedback as needed. This feedback is sent to the server via the device and used for future optimization.

[0402] Hardware and software used

[0403] Hardware: Servers, smart glasses, head-mounted displays

[0404] Software: Weather forecast API, city open data API, artificial intelligence algorithms, emotion engines (e.g., EmotionEngine)

[0405] Specific examples

[0406] For example, when an autonomous vehicle is carrying passengers, the server collects environmental data in real time and optimizes the route. At the same time, the emotion engine analyzes the passenger's facial expressions. If the passenger is stressed, the amount of information displayed will be reduced and concise. If the passenger is relaxed, detailed information and route options will be displayed.

[0407] Prompt Sentence Examples

[0408] "Design optimal driving routes based on environmental data, city layout data, and resource data, and display results that are adjusted in real time according to the user's emotional state. If the user is stressed, show abbreviated and concise results, but if the user is relaxed, provide detailed information."

[0409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0410] Step 1:

[0411] The server collects environmental data

[0412] Input: Use the API to send a request to get environmental data such as weather, road conditions, etc.

[0413] Processing: Calls the weather forecast API and the city's open data API to obtain real-time weather information and road conditions.

[0414] Output: Save the acquired environmental data to the internal database.

[0415] Step 2:

[0416] The server collects city layout data

[0417] Input: Send a request to get city layout data, i.e. existing infrastructure information and traffic data.

[0418] Processing: Use the city's open data API to obtain road network information and location information for public facilities.

[0419] Output: Save the acquired city layout data to the internal database.

[0420] Step 3:

[0421] The server collects resource data

[0422] Input: Send a request to get resource data for the autonomous vehicle (e.g., fuel level, charging station locations, etc.).

[0423] Processing: Acquires resource data in real time from the vehicle's internal sensors and external data sources.

[0424] Output: Saves the retrieved resource data to an internal database.

[0425] Step 4:

[0426] The server collects data and feeds it into an artificial intelligence algorithm.

[0427] Input: Environmental data, city layout data, resource data

[0428] Processing: Converting this data into a suitable format and feeding it into artificial intelligence algorithms.

[0429] Output: Data passed to the algorithm

[0430] Step 5:

[0431] The server runs an artificial intelligence algorithm to calculate the optimal driving route.

[0432] Input: Environment data, city layout data, and resource data set

[0433] Processing: The data is used to calculate optimal routes that reduce energy consumption, shorten journey times, and minimize environmental impact.

[0434] Output: Optimal route information

[0435] Step 6:

[0436] The server uses an emotion engine to analyze the user's emotions.

[0437] Input: User facial and voice data

[0438] Processing: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state (stress, relaxation, etc.) in real time.

[0439] Output: Emotion analysis results

[0440] Step 7:

[0441] The server adjusts the presentation method based on the optimized route and sentiment analysis results.

[0442] Input: Optimized route information, sentiment analysis results

[0443] Processing: Adapt the content based on the sentiment analysis, for example, being brief if the user is stressed, or including more information if the user is relaxed.

[0444] Output: Adjusted route information display

[0445] Step 8:

[0446] The device displays the adjusted route information to the user.

[0447] Input: Adjusted route information

[0448] Processing: Display adjusted route information using smart glasses or a head-mounted display.

[0449] Output: A visual representation of the optimized driving route to the user.

[0450] Step 9:

[0451] The user provides feedback on the displayed route information

[0452] Input: User opinions and feedback

[0453] Processing: Feedback is obtained from input devices (smart glasses, head-mounted display) and sent to the server.

[0454] Output: Feedback data

[0455] Step 10:

[0456] The server collects feedback and uses it for future optimizations.

[0457] Input: Feedback data

[0458] Processing: Analyze the feedback data and reflect it in the next route optimization.

[0459] Output: An improved AI model

[0460] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0462] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0463] [Second embodiment]

[0464] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0465] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0466] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0467] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0468] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0469] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0470] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0471] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0472] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0473] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0474] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0475] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0476] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data. The system of the present invention is used in collaboration between a server, terminals, and users.

[0477] Data collection and initialization

[0478] server

[0479] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[0480] Set of data

[0481] server

[0482] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[0483] Project Optimization

[0484] server

[0485] The server runs an artificial intelligence algorithm based on the configured data, which calculates the efficient allocation of resources, minimizes environmental impact, and optimizes living space, thereby generating optimal design parameters.

[0486] Viewing results and gathering feedback

[0487] Terminal

[0488] The terminal displays the optimization results generated by the server to the user in an easy-to-understand format consisting of graphs, diagrams, and text. The user can then enter feedback based on the results.

[0489] User

[0490] Users can check the optimization results through their devices and provide feedback on the results, which will be used for future project optimizations.

[0491] Specific examples

[0492] For example, if there is a construction project for a new office building, the specific steps would be as follows:

[0493] 1. Data collected:

[0494] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[0495] 2. Data set:

[0496] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[0497] 3. Optimization:

[0498] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[0499] 4. Displaying the results:

[0500] The terminal graphically displays the generated optimization results and provides them to the user.

[0501] 5. Gathering Feedback:

[0502] The user checks the results and enters feedback on improvements and opinions, which is then collected by the device and sent to the server.

[0503] In this way, the system of the present invention supports sustainable and efficient construction and urban planning.

[0504] The processing flow will be explained below.

[0505] Step 1:

[0506] server

[0507] The server retrieves resource data, specifically information about building materials, labor, and energy required for construction, from a database using a data management module.

[0508] Step 2:

[0509] server

[0510] The server acquires environmental data, specifically regional climate data, air quality data, and water quality data, and compiles this information into an environmental dataset.

[0511] Step 3:

[0512] server

[0513] The server acquires city layout data, specifically information on the layout of existing city infrastructure, road networks, parks, and public facilities, from a database.

[0514] Step 4:

[0515] server

[0516] The server inputs the acquired resource data, environmental data, and city layout data into the artificial intelligence algorithm, specifically converting this data into an appropriate format and setting it as input parameters for the algorithm.

[0517] Step 5:

[0518] server

[0519] The server runs artificial intelligence algorithms to generate optimal design parameters for efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space.

[0520] Step 6:

[0521] server

[0522] The server stores the optimization results of the artificial intelligence algorithm, specifically, the generated design parameters and optimization results in a database.

[0523] Step 7:

[0524] Terminal

[0525] The terminal receives the optimization results from the server and displays them to the user, specifically graphically displaying the reduction in resource usage, the improvement in energy efficiency, and the reduction in environmental impact.

[0526] Step 8:

[0527] User

[0528] The user checks the optimization results and inputs feedback on them. Specifically, based on the displayed results, the user inputs suggestions and opinions on how to improve the project.

[0529] Step 9:

[0530] Terminal

[0531] The device sends the feedback entered by the user to the server. Specifically, it collects the user's opinions and points for improvement as data and transfers them to the server.

[0532] Step 10:

[0533] server

[0534] The server stores the feedback received from the devices, specifically storing the collected feedback data in a database and using it to optimize future projects.

[0535] In this way, a system is realized in which servers, terminals, and users work together to improve resource efficiency and reduce environmental impact.

[0536] Example 1

[0537] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0538] In modern urban planning and construction projects, it is difficult to quickly develop appropriate plans that provide optimal living spaces while efficiently and sustainably allocating resources and minimizing environmental impact. Traditional methods have the drawback of making it difficult to comprehensively evaluate these factors, which is time-consuming and costly. Furthermore, the lack of a mechanism for incorporating user feedback slows down the improvement cycle.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0540] In this invention, the server

[0541] a means for collecting resource data;

[0542] a means for collecting environmental data;

[0543] a means for collecting city layout data;

[0544] means for pre-processing the resource data, the environmental data, and the city layout data;

[0545] means for feeding the pre-processed data into an artificial intelligence algorithm;

[0546] means for executing the artificial intelligence algorithm to calculate efficient allocation of resources, minimization of environmental impact, and optimization of living space;

[0547] means for generating the optimal design parameters;

[0548] means for improving construction and urban planning of a target area based on the generated design parameters;

[0549] means for displaying the optimization results of the artificial intelligence algorithm to a user;

[0550] a means for collecting feedback from users on the optimization results and reflecting the feedback in subsequent optimization processes;

[0551] Includes:

[0552] This allows urban planning and construction projects to quickly and effectively achieve efficient resource allocation, minimize environmental impact, and optimize living space. It also allows for rapid reflection of user feedback, improving the accuracy and adaptability of plans.

[0553] 1. "Resource Data" means information related to resources required for urban planning and construction projects, including, but not limited to, building materials, labor, and energy.

[0554] 2. "Environmental data" means information related to the environment that should be taken into account in construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[0555] 3. "Urban layout data" refers to data that includes information about the layout and structure of a city, such as existing infrastructure information, road networks, and public facilities.

[0556] 4. "Preprocessing" refers to the process of converting data into a format suitable for artificial intelligence algorithms, including missing value imputation, standardization, and normalization.

[0557] 5. "Artificial intelligence algorithm" means a computational procedure for achieving objectives such as efficient allocation of resources, minimizing environmental impact, or optimizing living space. Examples include linear programming or multi-objective optimization.

[0558] 6. "Optimal design parameters" refers to specific criteria and values ​​generated by artificial intelligence algorithms to ensure the efficient and sustainable development of urban planning and construction projects.

[0559] 7. "Server" means a computer system responsible for collecting data, pre-processing, executing artificial intelligence algorithms and transmitting the generated results to the terminal.

[0560] 8. "Terminal" means a device that displays the optimization results sent from the server to the user and collects feedback from the user.

[0561] 9. "Feedback" refers to opinions and suggestions for improvement provided by users regarding optimization results, and is information that will be reflected in subsequent optimization processes.

[0562] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used by a server, terminals, and users in cooperation with each other.

[0563] Data collection and initialization

[0564] server

[0565] The server first collects resource data, environmental data, and city layout data. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes information on existing infrastructure, road networks, public facilities, etc. The server obtains this data using APIs and database queries.

[0566] Data preprocessing and collection

[0567] server

[0568] The server preprocesses the collected data and feeds it into the AI ​​algorithm. Preprocessing involves imputing missing values, standardizing, normalizing, etc. For example, data is cleaned using Pandas or NumPy and formatted for TensorFlow or PyTorch.

[0569] Project optimization calculations

[0570] server

[0571] The server runs artificial intelligence algorithms to perform optimization calculations, such as efficient resource allocation, minimizing environmental impact, and optimizing living space. For example, it uses linear programming and multi-objective optimization to generate optimal design parameters.

[0572] Generating and displaying results

[0573] server

[0574] The server generates optimization results and sends them to the device. The results include the rate of energy consumption reduction, the rate of green space increase, construction costs, etc. This data is often sent in JSON or XML format.

[0575] Terminal

[0576] The device then displays the optimization results to the user. Using data visualization tools such as Tableau or Power BI, the results are presented in graph and text format. For example, a dashboard might show the rate of reduction in energy consumption or the rate of increase in green space.

[0577] Collecting and incorporating user feedback

[0578] User

[0579] The user checks the optimization results and provides feedback. Feedback is entered through the device's input form or comment function. For example, a user might enter an opinion such as "cost reduction should be prioritized over energy consumption reduction."

[0580] Terminal

[0581] The terminal sends the collected user feedback to the server, which stores it so that it can be reflected in the next optimization process. The data stored in the database is in the form of text and numerical data.

[0582] Specific examples

[0583] For example, here is a specific example from a new office building construction project:

[0584] 1. Data Collection:

[0585] The server obtains resource data such as building materials, labor, and energy through APIs, environmental data such as climate data, air quality data, and water quality data from the Japan Meteorological Agency API, and city layout data from local government databases.

[0586] 2. Data preprocessing and set:

[0587] The server uses Pandas to impute missing values ​​and standardize the data, then sends the preprocessed data to TensorFlow.

[0588] 3. Project optimization calculation:

[0589] The server uses TensorFlow to execute multi-objective optimization algorithms to calculate the optimal energy efficiency and maximize living space, generating optimal building material placement and energy consumption reduction plans.

[0590] 4. Generate and display results:

[0591] The server generates optimization results in JSON format and sends them to the terminal, including the rate of reduction in energy consumption and the rate of increase in green space area.

[0592] 5. Collect and incorporate user feedback:

[0593] The user enters feedback through an input form on the device. For example, they may enter their opinion that "reducing costs should be prioritized over reducing energy consumption." The device then sends the feedback to the server and stores it in a database. This feedback will be used in the next optimization process.

[0594] Example prompts for generative AI models

[0595] "Generate optimal design parameters for a proposed office building."

[0596] "Optimize urban layout to achieve reduced energy consumption and increased green space."

[0597] "Please propose a plan to minimize the environmental impact of the new residential area."

[0598] The above is a specific embodiment of the present invention, which aims to quickly achieve efficient allocation of resources, minimization of environmental impact, and optimization of living space.

[0599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0600] Step 1:

[0601] Data collection

[0602] The server collects resource data, environmental data, and city layout data. As input, it uses APIs and database queries to obtain resource data such as building materials, labor, and energy, environmental data such as climate data, air quality data, and water quality data, and city layout data such as existing infrastructure information, road networks, and public facilities. As output, it stores these data within the system.

[0603] Step 2:

[0604] Data Preprocessing

[0605] The server preprocesses the collected data. It receives collected resource data, environmental data, and city layout data as input. Specific operations include using Pandas and NumPy to fill in missing values ​​in the data, standardizing and normalizing it, and converting it into a format suitable for artificial intelligence algorithms. The output is the preprocessed data.

[0606] Step 3:

[0607] Set of data

[0608] The server feeds the preprocessed data into an artificial intelligence algorithm. As input, it receives preprocessed resource data, environmental data, and city layout data. Using a machine learning framework such as TensorFlow or PyTorch, it feeds this data into the algorithm. As output, it receives a dataset ready for the algorithm to run.

[0609] Step 4:

[0610] Project optimization calculations

[0611] The server executes artificial intelligence algorithms to perform optimization calculations. It receives set data as input. Specific operations include using linear programming and multi-objective optimization to calculate the efficient allocation of resources, minimize environmental impact, and optimize living space. Optimal design parameters are generated as output.

[0612] Step 5:

[0613] Generate and send results

[0614] The server generates optimization results and sends them to the terminal. It receives optimal design parameters as input. The results include information such as the rate of reduction in energy consumption, the rate of increase in green space, and construction costs. It generates the results in JSON or XML format and sends them to the terminal. The output is the optimization results that can be displayed on the terminal.

[0615] Step 6:

[0616] Displaying the results

[0617] The terminal displays the received optimization results to the user. As input, it receives the optimization results sent from the server. Specifically, it uses a data visualization tool such as Tableau or Power BI to display the results in graphs or text format. As output, it obtains the optimization results that the user can visually confirm.

[0618] Step 7:

[0619] Collecting feedback

[0620] The user checks the optimization results and provides feedback. The optimization results are received as input. Specifically, the user enters feedback through the device's input form or comment function. For example, the user may enter an opinion such as "prioritize cost reduction over energy consumption reduction." The user's feedback data is obtained as output.

[0621] Step 8:

[0622] Send and save feedback

[0623] The terminal sends the collected user feedback to the server. It receives the user feedback as input. Specifically, it sends the feedback in JSON or text format to the server, which stores it in a database. The output is the feedback data that will be reflected in the next optimization process.

[0624] The above is the specific processing flow in this system.

[0625] (Application example 1)

[0626] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0627] Conventional urban planning and construction projects have faced the challenge of efficiently collecting and analyzing resource data, environmental data, and urban layout data to generate optimal design parameters. Furthermore, there was no system in place to collect data in real time at actual construction sites and optimize resource efficiency based on that data. This made it difficult to achieve sustainable urban planning and efficient resource management.

[0628] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0629] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring city layout data, thereby executing an artificial intelligence algorithm for generating optimal design parameters using the resource data, the environmental data, and the city layout data, a means for collecting environmental data and resource data in real time using sensors mounted on industrial machines, a means for executing an artificial intelligence algorithm for optimizing resource efficiency based on the data collected by the industrial machines, and a means for displaying the results of the resource efficiency optimization, thereby enabling urban planning and construction projects to achieve sustainable and efficient resource utilization.

[0630] "Resource data" is information about the supplies and resources needed for urban planning and construction projects, such as building materials, labor, and energy.

[0631] "Environmental data" refers to information about the environmental conditions of proposed construction sites or industrial areas, such as climate, humidity, air quality, and water quality.

[0632] "City layout data" refers to information about the structure and layout of a city, including its existing infrastructure, road networks, and public facilities.

[0633] An "artificial intelligence algorithm" is a calculation procedure or model for generating optimal design parameters based on collected data.

[0634] A "sensor" is a device that is installed in industrial machinery and collects environmental and resource data in real time.

[0635] "Optimization results" are proposals and design parameters generated by artificial intelligence algorithms regarding efficient resource allocation and utilization.

[0636] "Resource efficiency" refers to the efficiency with which resources are used to maximize results and ensure that they are used without waste.

[0637] "Industrial machinery" refers to machinery and equipment that is placed in a factory and has the function of collecting and analyzing various data.

[0638] The present invention is a system that uses sensors mounted on industrial machines to collect environmental and resource data in real time and optimizes resource efficiency based on that data. The system of the present invention is used in collaboration with a server, industrial machines, terminals, and users.

[0639] Data collection and initialization

[0640] Industrial Machinery

[0641] Industrial machinery is equipped with various sensors that collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in the factory in real time, enabling accurate and timely acquisition of various data.

[0642] server

[0643] The server receives the collected environmental data and resource data, stores them in a database, and sets up the data required for analysis.

[0644] Project Optimization

[0645] server

[0646] The server uses artificial intelligence algorithms to analyze collected resource and environmental data. This analysis generates optimal design parameters, including efficient resource allocation, reduced energy consumption, and minimized environmental impact. The software used by the server includes Python, machine learning libraries (e.g., TensorFlow, Scikit-learn), and data processing libraries (e.g., Pandas, NumPy).

[0647] Viewing results and gathering feedback

[0648] Terminal

[0649] The terminals display the optimization results generated from the server to workers in the factory. The display format consists of graphs, diagrams, text, etc., and is provided in a way that is easy for users to understand. For example, the optimization results of energy consumption and the optimal placement of materials are visually displayed.

[0650] User

[0651] Users can view the optimization results through their devices and provide feedback on the results. This feedback is used in subsequent data analysis and optimization processes, thereby continuously improving the system's performance.

[0652] Specific examples

[0653] For example, when creating a plan to minimize energy consumption within a factory, industrial machines collect temperature data in each area and energy consumption data of the machines, and the data is analyzed on a server. As a result, the most energy-efficient layout is proposed and notified on the terminal. The user can check the results and provide more specific feedback if necessary.

[0654] Example prompts to input to the generative AI model

[0655] "Collect temperature, humidity, and machine energy consumption data for each area in the factory and generate the optimal resource allocation pattern. Send the collected data in JSON format and display the optimization results graphically."

[0656] In this way, the system of the present invention realizes efficient use of resources within the factory and a reduction in the burden on the environment.

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] Sensors installed on industrial machinery collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in real time. The input of the sensors is environmental information and resource status for each area in the factory, and the output is digital information processed in the format of the collected data. Specifically, the sensors periodically acquire data and convert it into a specific format (e.g., JSON).

[0660] Step 2:

[0661] Industrial machines send collected data to a server. The input is environmental and resource data collected from sensors, and the output is data sent to the server. Specifically, data is sent to the server using an HTTP request.

[0662] Step 3:

[0663] The server stores the received environmental data and resource data in a database and sets it up in the format required for analysis. The input is the transmitted data, and the output is the set up data for analysis. Specific operations include saving the data in the database and organizing the data structure.

[0664] Step 4:

[0665] The server uses an artificial intelligence algorithm to analyze the collected data. This algorithm generates an optimal resource allocation pattern based on a variety of data. The input is the data set up for analysis, and the output is the results of optimizing resource efficiency. Specifically, data analysis is performed using Python and machine learning libraries (e.g., TensorFlow, Scikit-learn).

[0666] Step 5:

[0667] The server sends the generated optimization results to the terminal. The input is the resource efficiency optimization results, and the output is the data to be sent to the terminal. Specifically, the server sends the data to the terminal using an HTTP request.

[0668] Step 6:

[0669] The terminal displays the optimization results received from the server. The display format consists of graphs, diagrams, text, etc., and is provided in a form that is easy for the user to understand. The input is the data sent from the server, and the output is the visualized information displayed to the user. Specifically, the data is visualized on a web page using HTML and CSS.

[0670] Step 7:

[0671] The user checks the optimization results through the terminal and provides feedback on the results. The input is the displayed optimization results and the user's feedback, and the output is the collected feedback data. Specific operations include filling out a feedback form and submitting the data.

[0672] Step 8:

[0673] The terminal sends the collected feedback to the server. The input is the feedback data from the user, and the output is the data to be sent to the server. Specifically, the feedback data is sent to the server using an HTTP request.

[0674] This series of processing steps creates a system that collects and analyzes data in real time to optimize resource efficiency.

[0675] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0676] The present invention combines a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data with an emotion engine that recognizes user emotions. The system of the present invention is used in collaboration between a server, terminals, and users.

[0677] Data collection and initialization

[0678] server

[0679] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[0680] Set of data

[0681] server

[0682] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[0683] Project Optimization

[0684] server

[0685] The server runs artificial intelligence algorithms based on the configured data, which calculate how to use resources efficiently, reduce energy consumption, minimize environmental impact, and optimize the living space, thereby generating optimal design parameters.

[0686] Emotion Engine Operation

[0687] server

[0688] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. Based on the analysis results, it adjusts the way the optimization results are presented.

[0689] Terminal

[0690] When displaying the optimization results received from the server to the user, the device adjusts the display content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the device will display information succinctly and emphasize positive information.

[0691] Viewing results and gathering feedback

[0692] Terminal

[0693] The device displays the optimization results to the user and analyzes the user's emotional state through an emotion engine, thereby providing more personalized results and increasing user satisfaction.

[0694] User

[0695] Users can check the optimization results through their devices and provide feedback on the results. This feedback is analyzed by the emotion engine and the results are sent to the server. The feedback and the results of the emotion analysis are used to optimize future projects.

[0696] Specific examples

[0697] For example, in a construction project for a new office building, the system operates in the following specific steps:

[0698] 1. Data collected:

[0699] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[0700] 2. Data set:

[0701] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[0702] 3. Optimization:

[0703] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[0704] 4. Use of Emotion Engine:

[0705] The server analyzes the optimization results using an emotion engine and adjusts the results so that the user does not feel stressed.

[0706] 5. Displaying the results:

[0707] The device graphically displays the generated optimization results and provides them to the user in an adjusted form based on the analysis results of the emotion engine.

[0708] 6. Gathering Feedback:

[0709] The user checks the results and inputs feedback on improvements and opinions. The device collects this feedback and sentiment analysis results and sends them to the server.

[0710] In this way, the system of the present invention supports sustainable and efficient construction and urban planning, while providing advanced feedback functionality that also takes into account the user's emotions.

[0711] The processing flow will be explained below.

[0712] Step 1:

[0713] server

[0714] The server retrieves resource data from a database, specifically, information on building materials, labor, and energy required for a construction project, using a data management module.

[0715] Step 2:

[0716] server

[0717] The server obtains environmental data, specifically, regional climate data, air quality data, and water quality data from an environmental information database.

[0718] Step 3:

[0719] server

[0720] The server collects city layout data, specifically information on existing infrastructure, road networks, and the location of public facilities, from a city database.

[0721] Step 4:

[0722] server

[0723] The server inputs this data (resource data, environmental data, and city layout data) into the artificial intelligence algorithm. Specifically, it standardizes the data format and sets it as an input parameter for the algorithm.

[0724] Step 5:

[0725] server

[0726] The server runs artificial intelligence algorithms that calculate and generate optimal design parameters for efficient use of resources, minimizing environmental impact, and optimizing living space.

[0727] Step 6:

[0728] server

[0729] The server stores the optimization results generated by the artificial intelligence algorithm, specifically, the design parameters and optimization results, which are saved in a database for later use.

[0730] Step 7:

[0731] server

[0732] The server uses the emotion engine when displaying the optimization results to the user. Specifically, the emotion engine analyzes the user's emotions and adjusts the display method of the optimization results based on the results.

[0733] Step 8:

[0734] Terminal

[0735] The device displays the optimization results sent from the server to the user. The display content is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be simplified and the positive aspects will be emphasized.

[0736] Step 9:

[0737] User

[0738] The user checks the displayed optimization results and inputs feedback on the results into the device. The user's reactions are also analyzed by the emotion engine.

[0739] Step 10:

[0740] Terminal

[0741] The terminal transmits the feedback from the user and the emotion analysis results from the emotion engine to the server.

[0742] Step 11:

[0743] server

[0744] The server stores the feedback received from the devices and the results of sentiment analysis in a database, which is used to optimize future projects.

[0745] For example, in the case of a new office building construction project, the server first collects the necessary data (building materials, energy, climate information, etc.) and inputs it into an AI algorithm for optimization. The generated optimization results are displayed to the user through an emotion engine, which analyzes the user's reactions and collects feedback. This feedback is then reflected in future projects.

[0746] In this way, the system of the present invention can optimize projects while taking into account the user's feelings, while achieving resource efficiency and reducing environmental impact.

[0747] Example 2

[0748] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0749] Traditional construction and urban planning systems focus on generating design parameters that take into account the efficient use of resources and environmental impacts, but few systems consider user emotions or intuitive acceptability. While user feedback is sometimes collected, it is unclear how it will be reflected in the next project optimization. Therefore, achieving both an improved user experience and efficient project optimization is a challenge.

[0750] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0751] In this invention, the server includes means for acquiring resource data, means for acquiring environmental data, means for acquiring city layout data, means for executing an artificial intelligence algorithm, means for analyzing the optimization results and recognizing the user's emotions, means for adjusting the information content presented to the user based on the emotion recognition results, and means for improving construction and urban planning based on the optimal design parameters, thereby enabling efficient and satisfactory project optimization while taking the user's emotions into consideration.

[0752] "Resource data" refers to resource information required for construction and urban planning, specifically including data on building materials, labor, energy, etc.

[0753] "Environmental data" refers to data about the natural and man-made environment that influences construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[0754] "City layout data" refers to information about the structure and layout of a city, and specifically includes information about road networks, public facilities, and existing infrastructure.

[0755] "Artificial intelligence algorithm" refers to a computational method that analyzes a variety of data and generates optimal design parameters, and includes machine learning models and data analysis techniques.

[0756] "Optimal design parameters" refer to design indicators aimed at efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[0757] "Emotion recognition means" refers to technology that analyzes a user's emotions and uses the results to adjust the system's behavior and information presentation.

[0758] "Feedback collection means" refers to a mechanism for obtaining opinions and impressions from users and reflecting that information in the system.

[0759] "Means for adjusting the content of information presented to the user" refers to a technology that appropriately changes the format and content of the displayed information based on the recognized user's emotions.

[0760] "Means for improving construction and urban planning" refers to technologies that streamline and optimize the design and execution of actual construction projects and urban planning projects based on the generated optimal design parameters.

[0761] The present invention provides a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used in collaboration between a server, a terminal, and a user, and which incorporates an emotion engine that recognizes the user's emotions.

[0762] Data collection and initialization

[0763] server

[0764] The server first obtains resource data, environmental data, and city layout data from external APIs and databases. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes existing infrastructure information, road networks, public facilities, etc. These data are temporarily stored in a database on the server.

[0765] Set of data

[0766] server

[0767] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server. This process involves preprocessing the data using Python-based scripts, such as standardizing numerical data and encoding categorical data. After data cleansing and normalization, the data is ready to be input into the AI ​​model.

[0768] Project Optimization

[0769] server

[0770] The server uses the prepared data to run artificial intelligence algorithms, utilizing machine learning libraries (e.g., TensorFlow and PyTorch) to perform calculations aimed at efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space. Specific design parameters are generated as a result of these algorithms.

[0771] Emotion Engine Operation

[0772] server

[0773] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. For example, Microsoft's Emotion API is used. Based on the results of this analysis, the way the optimization results are presented is adjusted.

[0774] Terminal

[0775] When displaying the optimization results received from the server to the user, the device adjusts the information based on the results of the emotion engine. For example, if the user is feeling stressed, the device displays information succinctly and emphasizes positive information.

[0776] Viewing results and gathering feedback

[0777] Terminal

[0778] The device displays the optimization results to the user and also analyzes the user's emotional state through an emotion engine to provide more personalized results. The results are displayed graphically and dynamically adjusted based on the emotion engine's analysis of the user's emotions.

[0779] User

[0780] Users can check the optimization results through their devices and provide feedback, such as specific opinions or requests for improvement. This feedback is sent from the device to the server and reanalyzed by the emotion engine. This feedback is then reflected in the next project optimization.

[0781] Specific examples

[0782] For example, in a construction project for a new office building, the system operates in the following specific steps:

[0783] Data collected

[0784] The server retrieves resource data (building materials, energy), environmental data (climate, air quality), and city layout data (infrastructure, road network) for the proposed construction site, which includes collecting environmental data from weather data APIs and retrieving city layout data from public databases.

[0785] Set of data

[0786] The server then feeds this data into the algorithm, performing preprocessing such as standardization and encoding. Once the data is properly formatted, it is fed into the AI ​​model and calculations are performed.

[0787] optimization

[0788] The server runs multiple simulations to generate optimal design plans to reduce energy consumption and increase green space.

[0789] Using the Emotion Engine

[0790] The server analyzes the optimization results using an emotion engine and adjusts the way the results are presented to avoid stress for the user. For example, complex information is summarized succinctly.

[0791] Displaying the results

[0792] The terminal graphically displays the adjusted optimization results and presents them in a way that is easy for the user to understand.

[0793] Collecting feedback

[0794] The user checks the presented results and provides feedback, which is then sent to the server along with the sentiment analysis results, which are then reflected in future project optimizations.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Step 1:

[0797] Server Data Collection

[0798] The server obtains resource data, environmental data, and city layout data from external APIs and databases. For example, it collects climate data from weather data APIs and obtains city infrastructure information from public databases. It uses API keys and database connection information as input, and the resource data, environmental data, and city layout data are saved in the server's storage as output.

[0799] Step 2:

[0800] Server data preprocessing

[0801] The server preprocesses the collected data. This preprocessing includes data cleansing, standardization, and categorical encoding. Specifically, it normalizes numerical data to the 0-1 range and one-hot encodes categorical data. It uses the collected raw data as input and generates preprocessed data as output.

[0802] Step 3:

[0803] Server Dataset

[0804] The server then feeds the preprocessed data into an artificial intelligence algorithm, using a Python-based script to prepare the data for input into the AI ​​model. The preprocessed data is used as input, and the output is data that matches the AI ​​model.

[0805] Step 4:

[0806] Optimization algorithm execution on the server

[0807] The server uses the provided data to run artificial intelligence algorithms, using machine learning libraries such as TensorFlow and PyTorch to perform calculations that optimize energy efficiency and environmental impact. The AI ​​model uses the data as input and generates optimal design parameters as output.

[0808] Step 5:

[0809] Uses the server's emotion engine

[0810] The server analyzes the optimization results with an emotion engine, which recognizes emotions based on the user's past feedback and current input data and adjusts the presentation of the results. Using the optimization results and user feedback as input, the adjusted presentation is obtained as output.

[0811] Step 6:

[0812] Displaying results on a terminal

[0813] The device then displays the optimization results received from the server to the user. This display is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be displayed succinctly and positive information will be emphasized. Using the adjusted presentation data as input, the adjusted display content is provided to the user as output.

[0814] Step 7:

[0815] User feedback input

[0816] The user checks the optimization results presented through the terminal and provides feedback. The user's opinions and impressions are used as input, and the feedback data is saved on the terminal as output.

[0817] Step 8:

[0818] Sending feedback via device

[0819] The device sends the feedback collected from the user to the server, where it is reanalyzed by the emotion engine and reflected in the next project optimization. The feedback data is used as input, and the reanalysis results are saved as output on the server.

[0820] (Application example 2)

[0821] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0822] Optimizing the operation routes of autonomous vehicles has been an important challenge even with conventional technologies, but it has been difficult to efficiently handle environmental data, resource data, and urban layout data. Furthermore, there has been a lack of technology to improve the riding experience by taking into account the emotions of users. In particular, the way operation information is presented does not adapt to the user's emotional state, which often causes stress for users. Therefore, there is a need for a system that can simultaneously improve efficiency and user satisfaction in the operation of autonomous vehicles.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0824] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring urban layout data. This allows it to execute an artificial intelligence algorithm to generate optimal design parameters. Based on this, it is possible to not only improve the construction and urban planning of the target area, but also optimize the operation routes of autonomous vehicles, analyze user emotions using an emotion engine, and adjust the presentation method of operation information based on the analysis results, thereby simultaneously improving efficiency and user satisfaction.

[0825] "Resource data" refers to information about various resources needed in urban planning and construction projects, such as building materials, energy, and labor.

[0826] "Environmental Data" refers to information about the environment in which the project is implemented, such as climate data, air quality data, and water quality data.

[0827] "City layout data" refers to information about the structure of a city, such as existing infrastructure information, road networks, and public facilities.

[0828] An "artificial intelligence algorithm" is a calculation method for generating optimal design parameters based on resource data, environmental data, and urban layout data.

[0829] "Optimal design parameters" are project settings that achieve efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[0830] "Emotion engine" is a general term for devices and software that analyze users' input data and reactions to recognize their emotional state.

[0831] An "autonomous vehicle" is a vehicle that uses artificial intelligence technology to drive itself without the assistance of a driver.

[0832] A "travel route" is the path an autonomous vehicle follows to reach its destination.

[0833] "Feedback" refers to opinions and impressions provided by users, and is information that is used to improve and adjust the system.

[0834] System Configuration

[0835] The system of the present invention operates in collaboration with a server, a terminal, and a user. The server collects resource data, environmental data, and city layout data, and uses this data to execute an artificial intelligence algorithm. It also has an emotion engine that analyzes the user's emotions and proposes an optimized route based on the results. Meanwhile, the terminal displays the optimized route and adjustment information based on the emotion analysis results to the user, and collects user feedback.

[0836] A detailed explanation of each system function

[0837] Data Collection and Optimization

[0838] The server collects environmental data (weather, road conditions), city layout data (existing infrastructure, traffic volume), and resource data (fuel levels, location of charging stations). This data is obtained using APIs (for example, weather forecast APIs or city open data APIs). The collected data is fed into an artificial intelligence algorithm to optimize the route. The algorithm calculates the optimal route, taking into account factors such as reducing energy consumption, shortening travel time, and minimizing environmental impact.

[0839] Sentiment analysis and service information adjustment

[0840] The server also uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's facial expressions and voice to recognize states such as stress and relaxation. Based on the recognized emotional state, the server adjusts the way it presents operational information. For example, if the user is stressed, the server may simplify the display content, while if the user is relaxed, the server may provide more detailed information.

[0841] Viewing results and gathering feedback

[0842] The device displays the optimized route and information adjusted based on the emotion engine's analysis results on smart glasses or a head-mounted display. The user can check the route based on the displayed information and provide feedback as needed. This feedback is sent to the server via the device and used for future optimization.

[0843] Hardware and software used

[0844] Hardware: Servers, smart glasses, head-mounted displays

[0845] Software: Weather forecast API, city open data API, artificial intelligence algorithms, emotion engines (e.g., EmotionEngine)

[0846] Specific examples

[0847] For example, when an autonomous vehicle is carrying passengers, the server collects environmental data in real time and optimizes the route. At the same time, the emotion engine analyzes the passenger's facial expressions. If the passenger is stressed, the amount of information displayed will be reduced and concise. If the passenger is relaxed, detailed information and route options will be displayed.

[0848] Prompt Sentence Examples

[0849] "Design optimal driving routes based on environmental data, city layout data, and resource data, and display results that are adjusted in real time according to the user's emotional state. If the user is stressed, show abbreviated and concise results, but if the user is relaxed, provide detailed information."

[0850] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0851] Step 1:

[0852] The server collects environmental data

[0853] Input: Use the API to send a request to get environmental data such as weather, road conditions, etc.

[0854] Processing: Calls the weather forecast API and the city's open data API to obtain real-time weather information and road conditions.

[0855] Output: Save the acquired environmental data to the internal database.

[0856] Step 2:

[0857] The server collects city layout data

[0858] Input: Send a request to get city layout data, i.e. existing infrastructure information and traffic data.

[0859] Processing: Use the city's open data API to obtain road network information and location information for public facilities.

[0860] Output: Save the acquired city layout data to the internal database.

[0861] Step 3:

[0862] The server collects resource data

[0863] Input: Send a request to get resource data for the autonomous vehicle (e.g., fuel level, charging station locations, etc.).

[0864] Processing: Acquires resource data in real time from the vehicle's internal sensors and external data sources.

[0865] Output: Saves the retrieved resource data to an internal database.

[0866] Step 4:

[0867] The server collects data and feeds it into an artificial intelligence algorithm.

[0868] Input: Environmental data, city layout data, resource data

[0869] Processing: Converting this data into a suitable format and feeding it into artificial intelligence algorithms.

[0870] Output: Data passed to the algorithm

[0871] Step 5:

[0872] The server runs an artificial intelligence algorithm to calculate the optimal driving route.

[0873] Input: Environment data, city layout data, and resource data set

[0874] Processing: The data is used to calculate optimal routes that reduce energy consumption, shorten journey times, and minimize environmental impact.

[0875] Output: Optimal route information

[0876] Step 6:

[0877] The server uses an emotion engine to analyze the user's emotions.

[0878] Input: User facial and voice data

[0879] Processing: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state (stress, relaxation, etc.) in real time.

[0880] Output: Emotion analysis results

[0881] Step 7:

[0882] The server adjusts the presentation method based on the optimized route and sentiment analysis results.

[0883] Input: Optimized route information, sentiment analysis results

[0884] Processing: Adapt the content based on the sentiment analysis, for example, being brief if the user is stressed, or including more information if the user is relaxed.

[0885] Output: Adjusted route information display

[0886] Step 8:

[0887] The device displays the adjusted route information to the user.

[0888] Input: Adjusted route information

[0889] Processing: Display adjusted route information using smart glasses or a head-mounted display.

[0890] Output: A visual representation of the optimized driving route to the user.

[0891] Step 9:

[0892] The user provides feedback on the displayed route information

[0893] Input: User opinions and feedback

[0894] Processing: Feedback is obtained from input devices (smart glasses, head-mounted display) and sent to the server.

[0895] Output: Feedback data

[0896] Step 10:

[0897] The server collects feedback and uses it for future optimizations.

[0898] Input: Feedback data

[0899] Processing: Analyze the feedback data and reflect it in the next route optimization.

[0900] Output: An improved AI model

[0901] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0902] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0903] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0904] [Third embodiment]

[0905] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0906] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0907] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0908] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0909] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0910] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0911] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0912] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0913] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0914] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0915] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0916] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0917] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data. The system of the present invention is used in collaboration between a server, terminals, and users.

[0918] Data collection and initialization

[0919] server

[0920] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[0921] Set of data

[0922] server

[0923] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[0924] Project Optimization

[0925] server

[0926] The server runs an artificial intelligence algorithm based on the configured data, which calculates the efficient allocation of resources, minimizes environmental impact, and optimizes living space, thereby generating optimal design parameters.

[0927] Viewing results and gathering feedback

[0928] Terminal

[0929] The terminal displays the optimization results generated by the server to the user in an easy-to-understand format consisting of graphs, diagrams, and text. The user can then enter feedback based on the results.

[0930] User

[0931] Users can check the optimization results through their devices and provide feedback on the results, which will be used for future project optimizations.

[0932] Specific examples

[0933] For example, if there is a construction project for a new office building, the specific steps would be as follows:

[0934] 1. Data collected:

[0935] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[0936] 2. Data set:

[0937] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[0938] 3. Optimization:

[0939] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[0940] 4. Displaying the results:

[0941] The terminal graphically displays the generated optimization results and provides them to the user.

[0942] 5. Gathering Feedback:

[0943] The user checks the results and enters feedback on improvements and opinions, which is then collected by the device and sent to the server.

[0944] In this way, the system of the present invention supports sustainable and efficient construction and urban planning.

[0945] The processing flow will be explained below.

[0946] Step 1:

[0947] server

[0948] The server retrieves resource data, specifically information about building materials, labor, and energy required for construction, from a database using a data management module.

[0949] Step 2:

[0950] server

[0951] The server acquires environmental data, specifically regional climate data, air quality data, and water quality data, and compiles this information into an environmental dataset.

[0952] Step 3:

[0953] server

[0954] The server acquires city layout data, specifically information on the layout of existing city infrastructure, road networks, parks, and public facilities, from a database.

[0955] Step 4:

[0956] server

[0957] The server inputs the acquired resource data, environmental data, and city layout data into the artificial intelligence algorithm, specifically converting this data into an appropriate format and setting it as input parameters for the algorithm.

[0958] Step 5:

[0959] server

[0960] The server runs artificial intelligence algorithms to generate optimal design parameters for efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space.

[0961] Step 6:

[0962] server

[0963] The server stores the optimization results of the artificial intelligence algorithm, specifically, the generated design parameters and optimization results in a database.

[0964] Step 7:

[0965] Terminal

[0966] The terminal receives the optimization results from the server and displays them to the user, specifically graphically displaying the reduction in resource usage, the improvement in energy efficiency, and the reduction in environmental impact.

[0967] Step 8:

[0968] User

[0969] The user checks the optimization results and inputs feedback on them. Specifically, based on the displayed results, the user inputs suggestions and opinions on how to improve the project.

[0970] Step 9:

[0971] Terminal

[0972] The device sends the feedback entered by the user to the server. Specifically, it collects the user's opinions and points for improvement as data and transfers them to the server.

[0973] Step 10:

[0974] server

[0975] The server stores the feedback received from the devices, specifically storing the collected feedback data in a database and using it to optimize future projects.

[0976] In this way, a system is realized in which servers, terminals, and users work together to improve resource efficiency and reduce environmental impact.

[0977] Example 1

[0978] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0979] In modern urban planning and construction projects, it is difficult to quickly develop appropriate plans that provide optimal living spaces while efficiently and sustainably allocating resources and minimizing environmental impact. Traditional methods have the drawback of making it difficult to comprehensively evaluate these factors, which is time-consuming and costly. Furthermore, the lack of a mechanism for incorporating user feedback slows down the improvement cycle.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0981] In this invention, the server

[0982] a means for collecting resource data;

[0983] a means for collecting environmental data;

[0984] a means for collecting city layout data;

[0985] means for pre-processing the resource data, the environmental data, and the city layout data;

[0986] means for feeding the pre-processed data into an artificial intelligence algorithm;

[0987] means for executing the artificial intelligence algorithm to calculate efficient allocation of resources, minimization of environmental impact, and optimization of living space;

[0988] means for generating the optimal design parameters;

[0989] means for improving construction and urban planning of a target area based on the generated design parameters;

[0990] means for displaying the optimization results of the artificial intelligence algorithm to a user;

[0991] a means for collecting feedback from users on the optimization results and reflecting the feedback in subsequent optimization processes;

[0992] Includes:

[0993] This allows urban planning and construction projects to quickly and effectively achieve efficient resource allocation, minimize environmental impact, and optimize living space. It also allows for rapid reflection of user feedback, improving the accuracy and adaptability of plans.

[0994] 1. "Resource Data" means information related to resources required for urban planning and construction projects, including, but not limited to, building materials, labor, and energy.

[0995] 2. "Environmental data" means information related to the environment that should be taken into account in construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[0996] 3. "Urban layout data" refers to data that includes information about the layout and structure of a city, such as existing infrastructure information, road networks, and public facilities.

[0997] 4. "Preprocessing" refers to the process of converting data into a format suitable for artificial intelligence algorithms, including missing value imputation, standardization, and normalization.

[0998] 5. "Artificial intelligence algorithm" means a computational procedure for achieving objectives such as efficient allocation of resources, minimizing environmental impact, or optimizing living space. Examples include linear programming or multi-objective optimization.

[0999] 6. "Optimal design parameters" refers to specific criteria and values ​​generated by artificial intelligence algorithms to ensure the efficient and sustainable development of urban planning and construction projects.

[1000] 7. "Server" means a computer system responsible for collecting data, pre-processing, executing artificial intelligence algorithms and transmitting the generated results to the terminal.

[1001] 8. "Terminal" means a device that displays the optimization results sent from the server to the user and collects feedback from the user.

[1002] 9. "Feedback" refers to opinions and suggestions for improvement provided by users regarding optimization results, and is information that will be reflected in subsequent optimization processes.

[1003] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used by a server, terminals, and users in cooperation with each other.

[1004] Data collection and initialization

[1005] server

[1006] The server first collects resource data, environmental data, and city layout data. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes information on existing infrastructure, road networks, public facilities, etc. The server obtains this data using APIs and database queries.

[1007] Data preprocessing and collection

[1008] server

[1009] The server preprocesses the collected data and feeds it into the AI ​​algorithm. Preprocessing involves imputing missing values, standardizing, normalizing, etc. For example, data is cleaned using Pandas or NumPy and formatted for TensorFlow or PyTorch.

[1010] Project optimization calculations

[1011] server

[1012] The server runs artificial intelligence algorithms to perform optimization calculations, such as efficient resource allocation, minimizing environmental impact, and optimizing living space. For example, it uses linear programming and multi-objective optimization to generate optimal design parameters.

[1013] Generating and displaying results

[1014] server

[1015] The server generates optimization results and sends them to the device. The results include the rate of energy consumption reduction, the rate of green space increase, construction costs, etc. This data is often sent in JSON or XML format.

[1016] Terminal

[1017] The device then displays the optimization results to the user. Using data visualization tools such as Tableau or Power BI, the results are presented in graph and text format. For example, a dashboard might show the rate of reduction in energy consumption or the rate of increase in green space.

[1018] Collecting and incorporating user feedback

[1019] User

[1020] The user checks the optimization results and provides feedback. Feedback is entered through the device's input form or comment function. For example, a user might enter an opinion such as "cost reduction should be prioritized over energy consumption reduction."

[1021] Terminal

[1022] The terminal sends the collected user feedback to the server, which stores it so that it can be reflected in the next optimization process. The data stored in the database is in the form of text and numerical data.

[1023] Specific examples

[1024] For example, here is a specific example from a new office building construction project:

[1025] 1. Data Collection:

[1026] The server obtains resource data such as building materials, labor, and energy through APIs, environmental data such as climate data, air quality data, and water quality data from the Japan Meteorological Agency API, and city layout data from local government databases.

[1027] 2. Data preprocessing and set:

[1028] The server uses Pandas to impute missing values ​​and standardize the data, then sends the preprocessed data to TensorFlow.

[1029] 3. Project optimization calculation:

[1030] The server uses TensorFlow to execute multi-objective optimization algorithms to calculate the optimal energy efficiency and maximize living space, generating optimal building material placement and energy consumption reduction plans.

[1031] 4. Generate and display results:

[1032] The server generates optimization results in JSON format and sends them to the terminal, including the rate of reduction in energy consumption and the rate of increase in green space area.

[1033] 5. Collect and incorporate user feedback:

[1034] The user enters feedback through an input form on the device. For example, they may enter their opinion that "reducing costs should be prioritized over reducing energy consumption." The device then sends the feedback to the server and stores it in a database. This feedback will be used in the next optimization process.

[1035] Example prompts for generative AI models

[1036] "Generate optimal design parameters for a proposed office building."

[1037] "Optimize urban layout to achieve reduced energy consumption and increased green space."

[1038] "Please propose a plan to minimize the environmental impact of the new residential area."

[1039] The above is a specific embodiment of the present invention, which aims to quickly achieve efficient allocation of resources, minimization of environmental impact, and optimization of living space.

[1040] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1041] Step 1:

[1042] Data collection

[1043] The server collects resource data, environmental data, and city layout data. As input, it uses APIs and database queries to obtain resource data such as building materials, labor, and energy, environmental data such as climate data, air quality data, and water quality data, and city layout data such as existing infrastructure information, road networks, and public facilities. As output, it stores these data within the system.

[1044] Step 2:

[1045] Data Preprocessing

[1046] The server preprocesses the collected data. It receives collected resource data, environmental data, and city layout data as input. Specific operations include using Pandas and NumPy to fill in missing values ​​in the data, standardizing and normalizing it, and converting it into a format suitable for artificial intelligence algorithms. The output is the preprocessed data.

[1047] Step 3:

[1048] Set of data

[1049] The server feeds the preprocessed data into an artificial intelligence algorithm. As input, it receives preprocessed resource data, environmental data, and city layout data. Using a machine learning framework such as TensorFlow or PyTorch, it feeds this data into the algorithm. As output, it receives a dataset ready for the algorithm to run.

[1050] Step 4:

[1051] Project optimization calculations

[1052] The server executes artificial intelligence algorithms to perform optimization calculations. It receives set data as input. Specific operations include using linear programming and multi-objective optimization to calculate the efficient allocation of resources, minimize environmental impact, and optimize living space. Optimal design parameters are generated as output.

[1053] Step 5:

[1054] Generate and send results

[1055] The server generates optimization results and sends them to the terminal. It receives optimal design parameters as input. The results include information such as the rate of reduction in energy consumption, the rate of increase in green space, and construction costs. It generates the results in JSON or XML format and sends them to the terminal. The output is the optimization results that can be displayed on the terminal.

[1056] Step 6:

[1057] Displaying the results

[1058] The terminal displays the received optimization results to the user. As input, it receives the optimization results sent from the server. Specifically, it uses a data visualization tool such as Tableau or Power BI to display the results in graphs or text format. As output, it obtains the optimization results that the user can visually confirm.

[1059] Step 7:

[1060] Collecting feedback

[1061] The user checks the optimization results and provides feedback. The optimization results are received as input. Specifically, the user enters feedback through the device's input form or comment function. For example, the user may enter an opinion such as "prioritize cost reduction over energy consumption reduction." The user's feedback data is obtained as output.

[1062] Step 8:

[1063] Send and save feedback

[1064] The terminal sends the collected user feedback to the server. It receives the user feedback as input. Specifically, it sends the feedback in JSON or text format to the server, which stores it in a database. The output is the feedback data that will be reflected in the next optimization process.

[1065] The above is the specific processing flow in this system.

[1066] (Application example 1)

[1067] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1068] Conventional urban planning and construction projects have faced the challenge of efficiently collecting and analyzing resource data, environmental data, and urban layout data to generate optimal design parameters. Furthermore, there was no system in place to collect data in real time at actual construction sites and optimize resource efficiency based on that data. This made it difficult to achieve sustainable urban planning and efficient resource management.

[1069] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1070] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring city layout data, thereby executing an artificial intelligence algorithm for generating optimal design parameters using the resource data, the environmental data, and the city layout data, a means for collecting environmental data and resource data in real time using sensors mounted on industrial machines, a means for executing an artificial intelligence algorithm for optimizing resource efficiency based on the data collected by the industrial machines, and a means for displaying the results of the resource efficiency optimization, thereby enabling urban planning and construction projects to achieve sustainable and efficient resource utilization.

[1071] "Resource data" is information about the supplies and resources needed for urban planning and construction projects, such as building materials, labor, and energy.

[1072] "Environmental data" refers to information about the environmental conditions of proposed construction sites or industrial areas, such as climate, humidity, air quality, and water quality.

[1073] "City layout data" refers to information about the structure and layout of a city, including its existing infrastructure, road networks, and public facilities.

[1074] An "artificial intelligence algorithm" is a calculation procedure or model for generating optimal design parameters based on collected data.

[1075] A "sensor" is a device that is installed in industrial machinery and collects environmental and resource data in real time.

[1076] "Optimization results" are proposals and design parameters generated by artificial intelligence algorithms regarding efficient resource allocation and utilization.

[1077] "Resource efficiency" refers to the efficiency with which resources are used to maximize results and ensure that they are used without waste.

[1078] "Industrial machinery" refers to machinery and equipment that is placed in a factory and has the function of collecting and analyzing various data.

[1079] The present invention is a system that uses sensors mounted on industrial machines to collect environmental and resource data in real time and optimizes resource efficiency based on that data. The system of the present invention is used in collaboration with a server, industrial machines, terminals, and users.

[1080] Data collection and initialization

[1081] Industrial Machinery

[1082] Industrial machinery is equipped with various sensors that collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in the factory in real time, enabling accurate and timely acquisition of various data.

[1083] server

[1084] The server receives the collected environmental data and resource data, stores them in a database, and sets up the data required for analysis.

[1085] Project Optimization

[1086] server

[1087] The server uses artificial intelligence algorithms to analyze collected resource and environmental data. This analysis generates optimal design parameters, including efficient resource allocation, reduced energy consumption, and minimized environmental impact. The software used by the server includes Python, machine learning libraries (e.g., TensorFlow, Scikit-learn), and data processing libraries (e.g., Pandas, NumPy).

[1088] Viewing results and gathering feedback

[1089] Terminal

[1090] The terminals display the optimization results generated from the server to workers in the factory. The display format consists of graphs, diagrams, text, etc., and is provided in a way that is easy for users to understand. For example, the optimization results of energy consumption and the optimal placement of materials are visually displayed.

[1091] User

[1092] Users can view the optimization results through their devices and provide feedback on the results. This feedback is used in subsequent data analysis and optimization processes, thereby continuously improving the system's performance.

[1093] Specific examples

[1094] For example, when creating a plan to minimize energy consumption within a factory, industrial machines collect temperature data in each area and energy consumption data of the machines, and the data is analyzed on a server. As a result, the most energy-efficient layout is proposed and notified on the terminal. The user can check the results and provide more specific feedback if necessary.

[1095] Example prompts to input to the generative AI model

[1096] "Collect temperature, humidity, and machine energy consumption data for each area in the factory and generate the optimal resource allocation pattern. Send the collected data in JSON format and display the optimization results graphically."

[1097] In this way, the system of the present invention realizes efficient use of resources within the factory and a reduction in the burden on the environment.

[1098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1099] Step 1:

[1100] Sensors installed on industrial machinery collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in real time. The input of the sensors is environmental information and resource status for each area in the factory, and the output is digital information processed in the format of the collected data. Specifically, the sensors periodically acquire data and convert it into a specific format (e.g., JSON).

[1101] Step 2:

[1102] Industrial machines send collected data to a server. The input is environmental and resource data collected from sensors, and the output is data sent to the server. Specifically, data is sent to the server using an HTTP request.

[1103] Step 3:

[1104] The server stores the received environmental data and resource data in a database and sets it up in the format required for analysis. The input is the transmitted data, and the output is the set up data for analysis. Specific operations include saving the data in the database and organizing the data structure.

[1105] Step 4:

[1106] The server uses an artificial intelligence algorithm to analyze the collected data. This algorithm generates an optimal resource allocation pattern based on a variety of data. The input is the data set up for analysis, and the output is the results of optimizing resource efficiency. Specifically, data analysis is performed using Python and machine learning libraries (e.g., TensorFlow, Scikit-learn).

[1107] Step 5:

[1108] The server sends the generated optimization results to the terminal. The input is the resource efficiency optimization results, and the output is the data to be sent to the terminal. Specifically, the server sends the data to the terminal using an HTTP request.

[1109] Step 6:

[1110] The terminal displays the optimization results received from the server. The display format consists of graphs, diagrams, text, etc., and is provided in a form that is easy for the user to understand. The input is the data sent from the server, and the output is the visualized information displayed to the user. Specifically, the data is visualized on a web page using HTML and CSS.

[1111] Step 7:

[1112] The user checks the optimization results through the terminal and provides feedback on the results. The input is the displayed optimization results and the user's feedback, and the output is the collected feedback data. Specific operations include filling out a feedback form and submitting the data.

[1113] Step 8:

[1114] The terminal sends the collected feedback to the server. The input is the feedback data from the user, and the output is the data to be sent to the server. Specifically, the feedback data is sent to the server using an HTTP request.

[1115] This series of processing steps creates a system that collects and analyzes data in real time to optimize resource efficiency.

[1116] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1117] The present invention combines a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data with an emotion engine that recognizes user emotions. The system of the present invention is used in collaboration between a server, terminals, and users.

[1118] Data collection and initialization

[1119] server

[1120] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[1121] Set of data

[1122] server

[1123] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[1124] Project Optimization

[1125] server

[1126] The server runs artificial intelligence algorithms based on the configured data, which calculate how to use resources efficiently, reduce energy consumption, minimize environmental impact, and optimize the living space, thereby generating optimal design parameters.

[1127] Emotion Engine Operation

[1128] server

[1129] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. Based on the analysis results, it adjusts the way the optimization results are presented.

[1130] Terminal

[1131] When displaying the optimization results received from the server to the user, the device adjusts the display content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the device will display information succinctly and emphasize positive information.

[1132] Viewing results and gathering feedback

[1133] Terminal

[1134] The device displays the optimization results to the user and analyzes the user's emotional state through an emotion engine, thereby providing more personalized results and increasing user satisfaction.

[1135] User

[1136] Users can check the optimization results through their devices and provide feedback on the results. This feedback is analyzed by the emotion engine and the results are sent to the server. The feedback and the results of the emotion analysis are used to optimize future projects.

[1137] Specific examples

[1138] For example, in a construction project for a new office building, the system operates in the following specific steps:

[1139] 1. Data collected:

[1140] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[1141] 2. Data set:

[1142] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[1143] 3. Optimization:

[1144] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[1145] 4. Use of Emotion Engine:

[1146] The server analyzes the optimization results using an emotion engine and adjusts the results so that the user does not feel stressed.

[1147] 5. Displaying the results:

[1148] The device graphically displays the generated optimization results and provides them to the user in an adjusted form based on the analysis results of the emotion engine.

[1149] 6. Gathering Feedback:

[1150] The user checks the results and inputs feedback on improvements and opinions. The device collects this feedback and sentiment analysis results and sends them to the server.

[1151] In this way, the system of the present invention supports sustainable and efficient construction and urban planning, while providing advanced feedback functionality that also takes into account the user's emotions.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] server

[1155] The server retrieves resource data from a database, specifically, information on building materials, labor, and energy required for a construction project, using a data management module.

[1156] Step 2:

[1157] server

[1158] The server obtains environmental data, specifically, regional climate data, air quality data, and water quality data from an environmental information database.

[1159] Step 3:

[1160] server

[1161] The server collects city layout data, specifically information on existing infrastructure, road networks, and the location of public facilities, from a city database.

[1162] Step 4:

[1163] server

[1164] The server inputs this data (resource data, environmental data, and city layout data) into the artificial intelligence algorithm. Specifically, it standardizes the data format and sets it as an input parameter for the algorithm.

[1165] Step 5:

[1166] server

[1167] The server runs artificial intelligence algorithms that calculate and generate optimal design parameters for efficient use of resources, minimizing environmental impact, and optimizing living space.

[1168] Step 6:

[1169] server

[1170] The server stores the optimization results generated by the artificial intelligence algorithm, specifically, the design parameters and optimization results, which are saved in a database for later use.

[1171] Step 7:

[1172] server

[1173] The server uses the emotion engine when displaying the optimization results to the user. Specifically, the emotion engine analyzes the user's emotions and adjusts the display method of the optimization results based on the results.

[1174] Step 8:

[1175] Terminal

[1176] The device displays the optimization results sent from the server to the user. The display content is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be simplified and the positive aspects will be emphasized.

[1177] Step 9:

[1178] User

[1179] The user checks the displayed optimization results and inputs feedback on the results into the device. The user's reactions are also analyzed by the emotion engine.

[1180] Step 10:

[1181] Terminal

[1182] The terminal transmits the feedback from the user and the emotion analysis results from the emotion engine to the server.

[1183] Step 11:

[1184] server

[1185] The server stores the feedback received from the devices and the results of sentiment analysis in a database, which is used to optimize future projects.

[1186] For example, in the case of a new office building construction project, the server first collects the necessary data (building materials, energy, climate information, etc.) and inputs it into an AI algorithm for optimization. The generated optimization results are displayed to the user through an emotion engine, which analyzes the user's reactions and collects feedback. This feedback is then reflected in future projects.

[1187] In this way, the system of the present invention can optimize projects while taking into account the user's feelings, while achieving resource efficiency and reducing environmental impact.

[1188] Example 2

[1189] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1190] Traditional construction and urban planning systems focus on generating design parameters that take into account the efficient use of resources and environmental impacts, but few systems consider user emotions or intuitive acceptability. While user feedback is sometimes collected, it is unclear how it will be reflected in the next project optimization. Therefore, achieving both an improved user experience and efficient project optimization is a challenge.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1192] In this invention, the server includes means for acquiring resource data, means for acquiring environmental data, means for acquiring city layout data, means for executing an artificial intelligence algorithm, means for analyzing the optimization results and recognizing the user's emotions, means for adjusting the information content presented to the user based on the emotion recognition results, and means for improving construction and urban planning based on the optimal design parameters, thereby enabling efficient and satisfactory project optimization while taking the user's emotions into consideration.

[1193] "Resource data" refers to resource information required for construction and urban planning, specifically including data on building materials, labor, energy, etc.

[1194] "Environmental data" refers to data about the natural and man-made environment that influences construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[1195] "City layout data" refers to information about the structure and layout of a city, and specifically includes information about road networks, public facilities, and existing infrastructure.

[1196] "Artificial intelligence algorithm" refers to a computational method that analyzes a variety of data and generates optimal design parameters, and includes machine learning models and data analysis techniques.

[1197] "Optimal design parameters" refer to design indicators aimed at efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[1198] "Emotion recognition means" refers to technology that analyzes a user's emotions and uses the results to adjust the system's behavior and information presentation.

[1199] "Feedback collection means" refers to a mechanism for obtaining opinions and impressions from users and reflecting that information in the system.

[1200] "Means for adjusting the content of information presented to the user" refers to a technology that appropriately changes the format and content of the displayed information based on the recognized user's emotions.

[1201] "Means for improving construction and urban planning" refers to technologies that streamline and optimize the design and execution of actual construction projects and urban planning projects based on the generated optimal design parameters.

[1202] The present invention provides a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used in collaboration between a server, a terminal, and a user, and which incorporates an emotion engine that recognizes the user's emotions.

[1203] Data collection and initialization

[1204] server

[1205] The server first obtains resource data, environmental data, and city layout data from external APIs and databases. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes existing infrastructure information, road networks, public facilities, etc. These data are temporarily stored in a database on the server.

[1206] Set of data

[1207] server

[1208] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server. This process involves preprocessing the data using Python-based scripts, such as standardizing numerical data and encoding categorical data. After data cleansing and normalization, the data is ready to be input into the AI ​​model.

[1209] Project Optimization

[1210] server

[1211] The server uses the prepared data to run artificial intelligence algorithms, utilizing machine learning libraries (e.g., TensorFlow and PyTorch) to perform calculations aimed at efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space. Specific design parameters are generated as a result of these algorithms.

[1212] Emotion Engine Operation

[1213] server

[1214] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. For example, Microsoft's Emotion API is used. Based on the results of this analysis, the way the optimization results are presented is adjusted.

[1215] Terminal

[1216] When displaying the optimization results received from the server to the user, the device adjusts the information based on the results of the emotion engine. For example, if the user is feeling stressed, the device displays information succinctly and emphasizes positive information.

[1217] Viewing results and gathering feedback

[1218] Terminal

[1219] The device displays the optimization results to the user and also analyzes the user's emotional state through an emotion engine to provide more personalized results. The results are displayed graphically and dynamically adjusted based on the emotion engine's analysis of the user's emotions.

[1220] User

[1221] Users can check the optimization results through their devices and provide feedback, such as specific opinions or requests for improvement. This feedback is sent from the device to the server and reanalyzed by the emotion engine. This feedback is then reflected in the next project optimization.

[1222] Specific examples

[1223] For example, in a construction project for a new office building, the system operates in the following specific steps:

[1224] Data collected

[1225] The server retrieves resource data (building materials, energy), environmental data (climate, air quality), and city layout data (infrastructure, road network) for the proposed construction site, which includes collecting environmental data from weather data APIs and retrieving city layout data from public databases.

[1226] Set of data

[1227] The server then feeds this data into the algorithm, performing preprocessing such as standardization and encoding. Once the data is properly formatted, it is fed into the AI ​​model and calculations are performed.

[1228] optimization

[1229] The server runs multiple simulations to generate optimal design plans to reduce energy consumption and increase green space.

[1230] Using the Emotion Engine

[1231] The server analyzes the optimization results using an emotion engine and adjusts the way the results are presented to avoid stress for the user. For example, complex information is summarized succinctly.

[1232] Displaying the results

[1233] The terminal graphically displays the adjusted optimization results and presents them in a way that is easy for the user to understand.

[1234] Collecting feedback

[1235] The user checks the presented results and provides feedback, which is then sent to the server along with the sentiment analysis results, which are then reflected in future project optimizations.

[1236] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1237] Step 1:

[1238] Server Data Collection

[1239] The server obtains resource data, environmental data, and city layout data from external APIs and databases. For example, it collects climate data from weather data APIs and obtains city infrastructure information from public databases. It uses API keys and database connection information as input, and the resource data, environmental data, and city layout data are saved in the server's storage as output.

[1240] Step 2:

[1241] Server data preprocessing

[1242] The server preprocesses the collected data. This preprocessing includes data cleansing, standardization, and categorical encoding. Specifically, it normalizes numerical data to the 0-1 range and one-hot encodes categorical data. It uses the collected raw data as input and generates preprocessed data as output.

[1243] Step 3:

[1244] Server Dataset

[1245] The server then feeds the preprocessed data into an artificial intelligence algorithm, using a Python-based script to prepare the data for input into the AI ​​model. The preprocessed data is used as input, and the output is data that matches the AI ​​model.

[1246] Step 4:

[1247] Optimization algorithm execution on the server

[1248] The server uses the provided data to run artificial intelligence algorithms, using machine learning libraries such as TensorFlow and PyTorch to perform calculations that optimize energy efficiency and environmental impact. The AI ​​model uses the data as input and generates optimal design parameters as output.

[1249] Step 5:

[1250] Uses the server's emotion engine

[1251] The server analyzes the optimization results with an emotion engine, which recognizes emotions based on the user's past feedback and current input data and adjusts the presentation of the results. Using the optimization results and user feedback as input, the adjusted presentation is obtained as output.

[1252] Step 6:

[1253] Displaying results on a terminal

[1254] The device then displays the optimization results received from the server to the user. This display is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be displayed succinctly and positive information will be emphasized. Using the adjusted presentation data as input, the adjusted display content is provided to the user as output.

[1255] Step 7:

[1256] User feedback input

[1257] The user checks the optimization results presented through the terminal and provides feedback. The user's opinions and impressions are used as input, and the feedback data is saved on the terminal as output.

[1258] Step 8:

[1259] Sending feedback via device

[1260] The device sends the feedback collected from the user to the server, where it is reanalyzed by the emotion engine and reflected in the next project optimization. The feedback data is used as input, and the reanalysis results are saved as output on the server.

[1261] (Application example 2)

[1262] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1263] Optimizing the operation routes of autonomous vehicles has been an important challenge even with conventional technologies, but it has been difficult to efficiently handle environmental data, resource data, and urban layout data. Furthermore, there has been a lack of technology to improve the riding experience by taking into account the emotions of users. In particular, the way operation information is presented does not adapt to the user's emotional state, which often causes stress for users. Therefore, there is a need for a system that can simultaneously improve efficiency and user satisfaction in the operation of autonomous vehicles.

[1264] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1265] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring urban layout data. This allows it to execute an artificial intelligence algorithm to generate optimal design parameters. Based on this, it is possible to not only improve the construction and urban planning of the target area, but also optimize the operation routes of autonomous vehicles, analyze user emotions using an emotion engine, and adjust the presentation method of operation information based on the analysis results, thereby simultaneously improving efficiency and user satisfaction.

[1266] "Resource data" refers to information about various resources needed in urban planning and construction projects, such as building materials, energy, and labor.

[1267] "Environmental Data" refers to information about the environment in which the project is implemented, such as climate data, air quality data, and water quality data.

[1268] "City layout data" refers to information about the structure of a city, such as existing infrastructure information, road networks, and public facilities.

[1269] An "artificial intelligence algorithm" is a calculation method for generating optimal design parameters based on resource data, environmental data, and urban layout data.

[1270] "Optimal design parameters" are project settings that achieve efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[1271] "Emotion engine" is a general term for devices and software that analyze users' input data and reactions to recognize their emotional state.

[1272] An "autonomous vehicle" is a vehicle that uses artificial intelligence technology to drive itself without the assistance of a driver.

[1273] A "travel route" is the path an autonomous vehicle follows to reach its destination.

[1274] "Feedback" refers to opinions and impressions provided by users, and is information that is used to improve and adjust the system.

[1275] System Configuration

[1276] The system of the present invention operates in collaboration with a server, a terminal, and a user. The server collects resource data, environmental data, and city layout data, and uses this data to execute an artificial intelligence algorithm. It also has an emotion engine that analyzes the user's emotions and proposes an optimized route based on the results. Meanwhile, the terminal displays the optimized route and adjustment information based on the emotion analysis results to the user, and collects user feedback.

[1277] A detailed explanation of each system function

[1278] Data Collection and Optimization

[1279] The server collects environmental data (weather, road conditions), city layout data (existing infrastructure, traffic volume), and resource data (fuel levels, location of charging stations). This data is obtained using APIs (for example, weather forecast APIs or city open data APIs). The collected data is fed into an artificial intelligence algorithm to optimize the route. The algorithm calculates the optimal route, taking into account factors such as reducing energy consumption, shortening travel time, and minimizing environmental impact.

[1280] Sentiment analysis and service information adjustment

[1281] The server also uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's facial expressions and voice to recognize states such as stress and relaxation. Based on the recognized emotional state, the server adjusts the way it presents operational information. For example, if the user is stressed, the server may simplify the display content, while if the user is relaxed, the server may provide more detailed information.

[1282] Viewing results and gathering feedback

[1283] The device displays the optimized route and information adjusted based on the emotion engine's analysis results on smart glasses or a head-mounted display. The user can check the route based on the displayed information and provide feedback as needed. This feedback is sent to the server via the device and used for future optimization.

[1284] Hardware and software used

[1285] Hardware: Servers, smart glasses, head-mounted displays

[1286] Software: Weather forecast API, city open data API, artificial intelligence algorithms, emotion engines (e.g., EmotionEngine)

[1287] Specific examples

[1288] For example, when an autonomous vehicle is carrying passengers, the server collects environmental data in real time and optimizes the route. At the same time, the emotion engine analyzes the passenger's facial expressions. If the passenger is stressed, the amount of information displayed will be reduced and concise. If the passenger is relaxed, detailed information and route options will be displayed.

[1289] Prompt Sentence Examples

[1290] "Design optimal driving routes based on environmental data, city layout data, and resource data, and display results that are adjusted in real time according to the user's emotional state. If the user is stressed, show abbreviated and concise results, but if the user is relaxed, provide detailed information."

[1291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1292] Step 1:

[1293] The server collects environmental data

[1294] Input: Use the API to send a request to get environmental data such as weather, road conditions, etc.

[1295] Processing: Calls the weather forecast API and the city's open data API to obtain real-time weather information and road conditions.

[1296] Output: Save the acquired environmental data to the internal database.

[1297] Step 2:

[1298] The server collects city layout data

[1299] Input: Send a request to get city layout data, i.e. existing infrastructure information and traffic data.

[1300] Processing: Use the city's open data API to obtain road network information and location information for public facilities.

[1301] Output: Save the acquired city layout data to the internal database.

[1302] Step 3:

[1303] The server collects resource data

[1304] Input: Send a request to get resource data for the autonomous vehicle (e.g., fuel level, charging station locations, etc.).

[1305] Processing: Acquires resource data in real time from the vehicle's internal sensors and external data sources.

[1306] Output: Saves the retrieved resource data to an internal database.

[1307] Step 4:

[1308] The server collects data and feeds it into an artificial intelligence algorithm.

[1309] Input: Environmental data, city layout data, resource data

[1310] Processing: Converting this data into a suitable format and feeding it into artificial intelligence algorithms.

[1311] Output: Data passed to the algorithm

[1312] Step 5:

[1313] The server runs an artificial intelligence algorithm to calculate the optimal driving route.

[1314] Input: Environment data, city layout data, and resource data set

[1315] Processing: The data is used to calculate optimal routes that reduce energy consumption, shorten journey times, and minimize environmental impact.

[1316] Output: Optimal route information

[1317] Step 6:

[1318] The server uses an emotion engine to analyze the user's emotions.

[1319] Input: User facial and voice data

[1320] Processing: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state (stress, relaxation, etc.) in real time.

[1321] Output: Emotion analysis results

[1322] Step 7:

[1323] The server adjusts the presentation method based on the optimized route and sentiment analysis results.

[1324] Input: Optimized route information, sentiment analysis results

[1325] Processing: Adapt the content based on the sentiment analysis, for example, being brief if the user is stressed, or including more information if the user is relaxed.

[1326] Output: Adjusted route information display

[1327] Step 8:

[1328] The device displays the adjusted route information to the user.

[1329] Input: Adjusted route information

[1330] Processing: Display adjusted route information using smart glasses or a head-mounted display.

[1331] Output: A visual representation of the optimized driving route to the user.

[1332] Step 9:

[1333] The user provides feedback on the displayed route information

[1334] Input: User opinions and feedback

[1335] Processing: Feedback is obtained from input devices (smart glasses, head-mounted display) and sent to the server.

[1336] Output: Feedback data

[1337] Step 10:

[1338] The server collects feedback and uses it for future optimizations.

[1339] Input: Feedback data

[1340] Processing: Analyze the feedback data and reflect it in the next route optimization.

[1341] Output: An improved AI model

[1342] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1343] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1344] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1345] [Fourth embodiment]

[1346] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1347] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1348] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1349] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1350] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1351] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1352] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1353] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1354] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1355] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1356] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1357] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1358] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1359] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data. The system of the present invention is used in collaboration between a server, terminals, and users.

[1360] Data collection and initialization

[1361] server

[1362] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[1363] Set of data

[1364] server

[1365] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[1366] Project Optimization

[1367] server

[1368] The server runs an artificial intelligence algorithm based on the configured data, which calculates the efficient allocation of resources, minimizes environmental impact, and optimizes living space, thereby generating optimal design parameters.

[1369] Viewing results and gathering feedback

[1370] Terminal

[1371] The terminal displays the optimization results generated by the server to the user in an easy-to-understand format consisting of graphs, diagrams, and text. The user can then enter feedback based on the results.

[1372] User

[1373] Users can check the optimization results through their devices and provide feedback on the results, which will be used for future project optimizations.

[1374] Specific examples

[1375] For example, if there is a construction project for a new office building, the specific steps would be as follows:

[1376] 1. Data collected:

[1377] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[1378] 2. Data set:

[1379] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[1380] 3. Optimization:

[1381] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[1382] 4. Displaying the results:

[1383] The terminal graphically displays the generated optimization results and provides them to the user.

[1384] 5. Gathering Feedback:

[1385] The user checks the results and enters feedback on improvements and opinions, which is then collected by the device and sent to the server.

[1386] In this way, the system of the present invention supports sustainable and efficient construction and urban planning.

[1387] The processing flow will be explained below.

[1388] Step 1:

[1389] server

[1390] The server retrieves resource data, specifically information about building materials, labor, and energy required for construction, from a database using a data management module.

[1391] Step 2:

[1392] server

[1393] The server acquires environmental data, specifically regional climate data, air quality data, and water quality data, and compiles this information into an environmental dataset.

[1394] Step 3:

[1395] server

[1396] The server acquires city layout data, specifically information on the layout of existing city infrastructure, road networks, parks, and public facilities, from a database.

[1397] Step 4:

[1398] server

[1399] The server inputs the acquired resource data, environmental data, and city layout data into the artificial intelligence algorithm, specifically converting this data into an appropriate format and setting it as input parameters for the algorithm.

[1400] Step 5:

[1401] server

[1402] The server runs artificial intelligence algorithms to generate optimal design parameters for efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space.

[1403] Step 6:

[1404] server

[1405] The server stores the optimization results of the artificial intelligence algorithm, specifically, the generated design parameters and optimization results in a database.

[1406] Step 7:

[1407] Terminal

[1408] The terminal receives the optimization results from the server and displays them to the user, specifically graphically displaying the reduction in resource usage, the improvement in energy efficiency, and the reduction in environmental impact.

[1409] Step 8:

[1410] User

[1411] The user checks the optimization results and inputs feedback on them. Specifically, based on the displayed results, the user inputs suggestions and opinions on how to improve the project.

[1412] Step 9:

[1413] Terminal

[1414] The device sends the feedback entered by the user to the server. Specifically, it collects the user's opinions and points for improvement as data and transfers them to the server.

[1415] Step 10:

[1416] server

[1417] The server stores the feedback received from the devices, specifically storing the collected feedback data in a database and using it to optimize future projects.

[1418] In this way, a system is realized in which servers, terminals, and users work together to improve resource efficiency and reduce environmental impact.

[1419] Example 1

[1420] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1421] In modern urban planning and construction projects, it is difficult to quickly develop appropriate plans that provide optimal living spaces while efficiently and sustainably allocating resources and minimizing environmental impact. Traditional methods have the drawback of making it difficult to comprehensively evaluate these factors, which is time-consuming and costly. Furthermore, the lack of a mechanism for incorporating user feedback slows down the improvement cycle.

[1422] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1423] In this invention, the server

[1424] a means for collecting resource data;

[1425] a means for collecting environmental data;

[1426] a means for collecting city layout data;

[1427] means for pre-processing the resource data, the environmental data, and the city layout data;

[1428] means for feeding the pre-processed data into an artificial intelligence algorithm;

[1429] means for executing the artificial intelligence algorithm to calculate efficient allocation of resources, minimization of environmental impact, and optimization of living space;

[1430] means for generating the optimal design parameters;

[1431] means for improving construction and urban planning of a target area based on the generated design parameters;

[1432] means for displaying the optimization results of the artificial intelligence algorithm to a user;

[1433] a means for collecting feedback from users on the optimization results and reflecting the feedback in subsequent optimization processes;

[1434] Includes:

[1435] This allows urban planning and construction projects to quickly and effectively achieve efficient resource allocation, minimize environmental impact, and optimize living space. It also allows for rapid reflection of user feedback, improving the accuracy and adaptability of plans.

[1436] 1. "Resource Data" means information related to resources required for urban planning and construction projects, including, but not limited to, building materials, labor, and energy.

[1437] 2. "Environmental data" means information related to the environment that should be taken into account in construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[1438] 3. "Urban layout data" refers to data that includes information about the layout and structure of a city, such as existing infrastructure information, road networks, and public facilities.

[1439] 4. "Preprocessing" refers to the process of converting data into a format suitable for artificial intelligence algorithms, including missing value imputation, standardization, and normalization.

[1440] 5. "Artificial intelligence algorithm" means a computational procedure for achieving objectives such as efficient allocation of resources, minimizing environmental impact, or optimizing living space. Examples include linear programming or multi-objective optimization.

[1441] 6. "Optimal design parameters" refers to specific criteria and values ​​generated by artificial intelligence algorithms to ensure the efficient and sustainable development of urban planning and construction projects.

[1442] 7. "Server" means a computer system responsible for collecting data, pre-processing, executing artificial intelligence algorithms and transmitting the generated results to the terminal.

[1443] 8. "Terminal" means a device that displays the optimization results sent from the server to the user and collects feedback from the user.

[1444] 9. "Feedback" refers to opinions and suggestions for improvement provided by users regarding optimization results, and is information that will be reflected in subsequent optimization processes.

[1445] The present invention is a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used by a server, terminals, and users in cooperation with each other.

[1446] Data collection and initialization

[1447] server

[1448] The server first collects resource data, environmental data, and city layout data. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes information on existing infrastructure, road networks, public facilities, etc. The server obtains this data using APIs and database queries.

[1449] Data preprocessing and collection

[1450] server

[1451] The server preprocesses the collected data and feeds it into the AI ​​algorithm. Preprocessing involves imputing missing values, standardizing, normalizing, etc. For example, data is cleaned using Pandas or NumPy and formatted for TensorFlow or PyTorch.

[1452] Project optimization calculations

[1453] server

[1454] The server runs artificial intelligence algorithms to perform optimization calculations, such as efficient resource allocation, minimizing environmental impact, and optimizing living space. For example, it uses linear programming and multi-objective optimization to generate optimal design parameters.

[1455] Generating and displaying results

[1456] server

[1457] The server generates optimization results and sends them to the device. The results include the rate of energy consumption reduction, the rate of green space increase, construction costs, etc. This data is often sent in JSON or XML format.

[1458] Terminal

[1459] The device then displays the optimization results to the user. Using data visualization tools such as Tableau or Power BI, the results are presented in graph and text format. For example, a dashboard might show the rate of reduction in energy consumption or the rate of increase in green space.

[1460] Collecting and incorporating user feedback

[1461] User

[1462] The user checks the optimization results and provides feedback. Feedback is entered through the device's input form or comment function. For example, a user might enter an opinion such as "cost reduction should be prioritized over energy consumption reduction."

[1463] Terminal

[1464] The terminal sends the collected user feedback to the server, which stores it so that it can be reflected in the next optimization process. The data stored in the database is in the form of text and numerical data.

[1465] Specific examples

[1466] For example, here is a specific example from a new office building construction project:

[1467] 1. Data Collection:

[1468] The server obtains resource data such as building materials, labor, and energy through APIs, environmental data such as climate data, air quality data, and water quality data from the Japan Meteorological Agency API, and city layout data from local government databases.

[1469] 2. Data preprocessing and set:

[1470] The server uses Pandas to impute missing values ​​and standardize the data, then sends the preprocessed data to TensorFlow.

[1471] 3. Project optimization calculation:

[1472] The server uses TensorFlow to execute multi-objective optimization algorithms to calculate the optimal energy efficiency and maximize living space, generating optimal building material placement and energy consumption reduction plans.

[1473] 4. Generate and display results:

[1474] The server generates optimization results in JSON format and sends them to the terminal, including the rate of reduction in energy consumption and the rate of increase in green space area.

[1475] 5. Collect and incorporate user feedback:

[1476] The user enters feedback through an input form on the device. For example, they may enter their opinion that "reducing costs should be prioritized over reducing energy consumption." The device then sends the feedback to the server and stores it in a database. This feedback will be used in the next optimization process.

[1477] Example prompts for generative AI models

[1478] "Generate optimal design parameters for a proposed office building."

[1479] "Optimize urban layout to achieve reduced energy consumption and increased green space."

[1480] "Please propose a plan to minimize the environmental impact of the new residential area."

[1481] The above is a specific embodiment of the present invention, which aims to quickly achieve efficient allocation of resources, minimization of environmental impact, and optimization of living space.

[1482] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1483] Step 1:

[1484] Data collection

[1485] The server collects resource data, environmental data, and city layout data. As input, it uses APIs and database queries to obtain resource data such as building materials, labor, and energy, environmental data such as climate data, air quality data, and water quality data, and city layout data such as existing infrastructure information, road networks, and public facilities. As output, it stores these data within the system.

[1486] Step 2:

[1487] Data Preprocessing

[1488] The server preprocesses the collected data. It receives collected resource data, environmental data, and city layout data as input. Specific operations include using Pandas and NumPy to fill in missing values ​​in the data, standardizing and normalizing it, and converting it into a format suitable for artificial intelligence algorithms. The output is the preprocessed data.

[1489] Step 3:

[1490] Set of data

[1491] The server feeds the preprocessed data into an artificial intelligence algorithm. As input, it receives preprocessed resource data, environmental data, and city layout data. Using a machine learning framework such as TensorFlow or PyTorch, it feeds this data into the algorithm. As output, it receives a dataset ready for the algorithm to run.

[1492] Step 4:

[1493] Project optimization calculations

[1494] The server executes artificial intelligence algorithms to perform optimization calculations. It receives set data as input. Specific operations include using linear programming and multi-objective optimization to calculate the efficient allocation of resources, minimize environmental impact, and optimize living space. Optimal design parameters are generated as output.

[1495] Step 5:

[1496] Generate and send results

[1497] The server generates optimization results and sends them to the terminal. It receives optimal design parameters as input. The results include information such as the rate of reduction in energy consumption, the rate of increase in green space, and construction costs. It generates the results in JSON or XML format and sends them to the terminal. The output is the optimization results that can be displayed on the terminal.

[1498] Step 6:

[1499] Displaying the results

[1500] The terminal displays the received optimization results to the user. As input, it receives the optimization results sent from the server. Specifically, it uses a data visualization tool such as Tableau or Power BI to display the results in graphs or text format. As output, it obtains the optimization results that the user can visually confirm.

[1501] Step 7:

[1502] Collecting feedback

[1503] The user checks the optimization results and provides feedback. The optimization results are received as input. Specifically, the user enters feedback through the device's input form or comment function. For example, the user may enter an opinion such as "prioritize cost reduction over energy consumption reduction." The user's feedback data is obtained as output.

[1504] Step 8:

[1505] Send and save feedback

[1506] The terminal sends the collected user feedback to the server. It receives the user feedback as input. Specifically, it sends the feedback in JSON or text format to the server, which stores it in a database. The output is the feedback data that will be reflected in the next optimization process.

[1507] The above is the specific processing flow in this system.

[1508] (Application example 1)

[1509] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1510] Conventional urban planning and construction projects have faced the challenge of efficiently collecting and analyzing resource data, environmental data, and urban layout data to generate optimal design parameters. Furthermore, there was no system in place to collect data in real time at actual construction sites and optimize resource efficiency based on that data. This made it difficult to achieve sustainable urban planning and efficient resource management.

[1511] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1512] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring city layout data, thereby executing an artificial intelligence algorithm for generating optimal design parameters using the resource data, the environmental data, and the city layout data, a means for collecting environmental data and resource data in real time using sensors mounted on industrial machines, a means for executing an artificial intelligence algorithm for optimizing resource efficiency based on the data collected by the industrial machines, and a means for displaying the results of the resource efficiency optimization, thereby enabling urban planning and construction projects to achieve sustainable and efficient resource utilization.

[1513] "Resource data" is information about the supplies and resources needed for urban planning and construction projects, such as building materials, labor, and energy.

[1514] "Environmental data" refers to information about the environmental conditions of proposed construction sites or industrial areas, such as climate, humidity, air quality, and water quality.

[1515] "City layout data" refers to information about the structure and layout of a city, including its existing infrastructure, road networks, and public facilities.

[1516] An "artificial intelligence algorithm" is a calculation procedure or model for generating optimal design parameters based on collected data.

[1517] A "sensor" is a device that is installed in industrial machinery and collects environmental and resource data in real time.

[1518] "Optimization results" are proposals and design parameters generated by artificial intelligence algorithms regarding efficient resource allocation and utilization.

[1519] "Resource efficiency" refers to the efficiency with which resources are used to maximize results and ensure that they are used without waste.

[1520] "Industrial machinery" refers to machinery and equipment that is placed in a factory and has the function of collecting and analyzing various data.

[1521] The present invention is a system that uses sensors mounted on industrial machines to collect environmental and resource data in real time and optimizes resource efficiency based on that data. The system of the present invention is used in collaboration with a server, industrial machines, terminals, and users.

[1522] Data collection and initialization

[1523] Industrial Machinery

[1524] Industrial machinery is equipped with various sensors that collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in the factory in real time, enabling accurate and timely acquisition of various data.

[1525] server

[1526] The server receives the collected environmental data and resource data, stores them in a database, and sets up the data required for analysis.

[1527] Project Optimization

[1528] server

[1529] The server uses artificial intelligence algorithms to analyze collected resource and environmental data. This analysis generates optimal design parameters, including efficient resource allocation, reduced energy consumption, and minimized environmental impact. The software used by the server includes Python, machine learning libraries (e.g., TensorFlow, Scikit-learn), and data processing libraries (e.g., Pandas, NumPy).

[1530] Viewing results and gathering feedback

[1531] Terminal

[1532] The terminals display the optimization results generated from the server to workers in the factory. The display format consists of graphs, diagrams, text, etc., and is provided in a way that is easy for users to understand. For example, the optimization results of energy consumption and the optimal placement of materials are visually displayed.

[1533] User

[1534] Users can view the optimization results through their devices and provide feedback on the results. This feedback is used in subsequent data analysis and optimization processes, thereby continuously improving the system's performance.

[1535] Specific examples

[1536] For example, when creating a plan to minimize energy consumption within a factory, industrial machines collect temperature data in each area and energy consumption data of the machines, and the data is analyzed on a server. As a result, the most energy-efficient layout is proposed and notified on the terminal. The user can check the results and provide more specific feedback if necessary.

[1537] Example prompts to input to the generative AI model

[1538] "Collect temperature, humidity, and machine energy consumption data for each area in the factory and generate the optimal resource allocation pattern. Send the collected data in JSON format and display the optimization results graphically."

[1539] In this way, the system of the present invention realizes efficient use of resources within the factory and a reduction in the burden on the environment.

[1540] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1541] Step 1:

[1542] Sensors installed on industrial machinery collect environmental data (temperature, humidity, air quality) and resource data (material inventory, energy usage) in real time. The input of the sensors is environmental information and resource status for each area in the factory, and the output is digital information processed in the format of the collected data. Specifically, the sensors periodically acquire data and convert it into a specific format (e.g., JSON).

[1543] Step 2:

[1544] Industrial machines send collected data to a server. The input is environmental and resource data collected from sensors, and the output is data sent to the server. Specifically, data is sent to the server using an HTTP request.

[1545] Step 3:

[1546] The server stores the received environmental data and resource data in a database and sets it up in the format required for analysis. The input is the transmitted data, and the output is the set up data for analysis. Specific operations include saving the data in the database and organizing the data structure.

[1547] Step 4:

[1548] The server uses an artificial intelligence algorithm to analyze the collected data. This algorithm generates an optimal resource allocation pattern based on a variety of data. The input is the data set up for analysis, and the output is the results of optimizing resource efficiency. Specifically, data analysis is performed using Python and machine learning libraries (e.g., TensorFlow, Scikit-learn).

[1549] Step 5:

[1550] The server sends the generated optimization results to the terminal. The input is the resource efficiency optimization results, and the output is the data to be sent to the terminal. Specifically, the server sends the data to the terminal using an HTTP request.

[1551] Step 6:

[1552] The terminal displays the optimization results received from the server. The display format consists of graphs, diagrams, text, etc., and is provided in a form that is easy for the user to understand. The input is the data sent from the server, and the output is the visualized information displayed to the user. Specifically, the data is visualized on a web page using HTML and CSS.

[1553] Step 7:

[1554] The user checks the optimization results through the terminal and provides feedback on the results. The input is the displayed optimization results and the user's feedback, and the output is the collected feedback data. Specific operations include filling out a feedback form and submitting the data.

[1555] Step 8:

[1556] The terminal sends the collected feedback to the server. The input is the feedback data from the user, and the output is the data to be sent to the server. Specifically, the feedback data is sent to the server using an HTTP request.

[1557] This series of processing steps creates a system that collects and analyzes data in real time to optimize resource efficiency.

[1558] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1559] The present invention combines a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data with an emotion engine that recognizes user emotions. The system of the present invention is used in collaboration between a server, terminals, and users.

[1560] Data collection and initialization

[1561] server

[1562] The server first obtains resource data, environmental data, and city layout data. These data contain detailed information about various elements required for construction and urban planning. Resource data includes building materials, labor, and energy, while environmental data includes climate data, air quality data, and water quality data. City layout data includes information about existing infrastructure, road networks, and public facilities.

[1563] Set of data

[1564] server

[1565] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server, and in this process each data set is passed to the algorithm in an appropriate format.

[1566] Project Optimization

[1567] server

[1568] The server runs artificial intelligence algorithms based on the configured data, which calculate how to use resources efficiently, reduce energy consumption, minimize environmental impact, and optimize the living space, thereby generating optimal design parameters.

[1569] Emotion Engine Operation

[1570] server

[1571] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. Based on the analysis results, it adjusts the way the optimization results are presented.

[1572] Terminal

[1573] When displaying the optimization results received from the server to the user, the device adjusts the display content based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the device will display information succinctly and emphasize positive information.

[1574] Viewing results and gathering feedback

[1575] Terminal

[1576] The device displays the optimization results to the user and analyzes the user's emotional state through an emotion engine, thereby providing more personalized results and increasing user satisfaction.

[1577] User

[1578] Users can check the optimization results through their devices and provide feedback on the results. This feedback is analyzed by the emotion engine and the results are sent to the server. The feedback and the results of the emotion analysis are used to optimize future projects.

[1579] Specific examples

[1580] For example, in a construction project for a new office building, the system operates in the following specific steps:

[1581] 1. Data collected:

[1582] The server obtains resource data (building materials, energy), environmental data (climate, air quality), and urban layout data (infrastructure, road network) of the proposed construction site.

[1583] 2. Data set:

[1584] The server inputs this data into an artificial intelligence algorithm, preparing to generate optimal design parameters.

[1585] 3. Optimization:

[1586] The server runs algorithms to generate specific design plans, such as reducing energy consumption and increasing green space.

[1587] 4. Use of Emotion Engine:

[1588] The server analyzes the optimization results using an emotion engine and adjusts the results so that the user does not feel stressed.

[1589] 5. Displaying the results:

[1590] The device graphically displays the generated optimization results and provides them to the user in an adjusted form based on the analysis results of the emotion engine.

[1591] 6. Gathering Feedback:

[1592] The user checks the results and inputs feedback on improvements and opinions. The device collects this feedback and sentiment analysis results and sends them to the server.

[1593] In this way, the system of the present invention supports sustainable and efficient construction and urban planning, while providing advanced feedback functionality that also takes into account the user's emotions.

[1594] The processing flow will be explained below.

[1595] Step 1:

[1596] server

[1597] The server retrieves resource data from a database, specifically, information on building materials, labor, and energy required for a construction project, using a data management module.

[1598] Step 2:

[1599] server

[1600] The server obtains environmental data, specifically, regional climate data, air quality data, and water quality data from an environmental information database.

[1601] Step 3:

[1602] server

[1603] The server collects city layout data, specifically information on existing infrastructure, road networks, and the location of public facilities, from a city database.

[1604] Step 4:

[1605] server

[1606] The server inputs this data (resource data, environmental data, and city layout data) into the artificial intelligence algorithm. Specifically, it standardizes the data format and sets it as an input parameter for the algorithm.

[1607] Step 5:

[1608] server

[1609] The server runs artificial intelligence algorithms that calculate and generate optimal design parameters for efficient use of resources, minimizing environmental impact, and optimizing living space.

[1610] Step 6:

[1611] server

[1612] The server stores the optimization results generated by the artificial intelligence algorithm, specifically, the design parameters and optimization results, which are saved in a database for later use.

[1613] Step 7:

[1614] server

[1615] The server uses the emotion engine when displaying the optimization results to the user. Specifically, the emotion engine analyzes the user's emotions and adjusts the display method of the optimization results based on the results.

[1616] Step 8:

[1617] Terminal

[1618] The device displays the optimization results sent from the server to the user. The display content is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be simplified and the positive aspects will be emphasized.

[1619] Step 9:

[1620] User

[1621] The user checks the displayed optimization results and inputs feedback on the results into the device. The user's reactions are also analyzed by the emotion engine.

[1622] Step 10:

[1623] Terminal

[1624] The terminal transmits the feedback from the user and the emotion analysis results from the emotion engine to the server.

[1625] Step 11:

[1626] server

[1627] The server stores the feedback received from the devices and the results of sentiment analysis in a database, which is used to optimize future projects.

[1628] For example, in the case of a new office building construction project, the server first collects the necessary data (building materials, energy, climate information, etc.) and inputs it into an AI algorithm for optimization. The generated optimization results are displayed to the user through an emotion engine, which analyzes the user's reactions and collects feedback. This feedback is then reflected in future projects.

[1629] In this way, the system of the present invention can optimize projects while taking into account the user's feelings, while achieving resource efficiency and reducing environmental impact.

[1630] Example 2

[1631] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1632] Traditional construction and urban planning systems focus on generating design parameters that take into account the efficient use of resources and environmental impacts, but few systems consider user emotions or intuitive acceptability. While user feedback is sometimes collected, it is unclear how it will be reflected in the next project optimization. Therefore, achieving both an improved user experience and efficient project optimization is a challenge.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1634] In this invention, the server includes means for acquiring resource data, means for acquiring environmental data, means for acquiring city layout data, means for executing an artificial intelligence algorithm, means for analyzing the optimization results and recognizing the user's emotions, means for adjusting the information content presented to the user based on the emotion recognition results, and means for improving construction and urban planning based on the optimal design parameters, thereby enabling efficient and satisfactory project optimization while taking the user's emotions into consideration.

[1635] "Resource data" refers to resource information required for construction and urban planning, specifically including data on building materials, labor, energy, etc.

[1636] "Environmental data" refers to data about the natural and man-made environment that influences construction and urban planning, including, for example, climate data, air quality data, and water quality data.

[1637] "City layout data" refers to information about the structure and layout of a city, and specifically includes information about road networks, public facilities, and existing infrastructure.

[1638] "Artificial intelligence algorithm" refers to a computational method that analyzes a variety of data and generates optimal design parameters, and includes machine learning models and data analysis techniques.

[1639] "Optimal design parameters" refer to design indicators aimed at efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[1640] "Emotion recognition means" refers to technology that analyzes a user's emotions and uses the results to adjust the system's behavior and information presentation.

[1641] "Feedback collection means" refers to a mechanism for obtaining opinions and impressions from users and reflecting that information in the system.

[1642] "Means for adjusting the content of information presented to the user" refers to a technology that appropriately changes the format and content of the displayed information based on the recognized user's emotions.

[1643] "Means for improving construction and urban planning" refers to technologies that streamline and optimize the design and execution of actual construction projects and urban planning projects based on the generated optimal design parameters.

[1644] The present invention provides a system for optimizing urban planning and construction projects using resource data, environmental data, and urban layout data, which is used in collaboration between a server, a terminal, and a user, and which incorporates an emotion engine that recognizes the user's emotions.

[1645] Data collection and initialization

[1646] server

[1647] The server first obtains resource data, environmental data, and city layout data from external APIs and databases. Resource data includes building materials, labor, energy, etc., while environmental data includes climate data, air quality data, water quality data, etc. City layout data includes existing infrastructure information, road networks, public facilities, etc. These data are temporarily stored in a database on the server.

[1648] Set of data

[1649] server

[1650] The collected resource data, environmental data, and city layout data are fed into artificial intelligence algorithms by the server. This process involves preprocessing the data using Python-based scripts, such as standardizing numerical data and encoding categorical data. After data cleansing and normalization, the data is ready to be input into the AI ​​model.

[1651] Project Optimization

[1652] server

[1653] The server uses the prepared data to run artificial intelligence algorithms, utilizing machine learning libraries (e.g., TensorFlow and PyTorch) to perform calculations aimed at efficient resource use, reduced energy consumption, minimized environmental impact, and optimized living space. Specific design parameters are generated as a result of these algorithms.

[1654] Emotion Engine Operation

[1655] server

[1656] The server analyzes the optimization results of the AI ​​algorithm and uses an emotion engine to present them to the user. The emotion engine analyzes the user's reactions and input data to recognize the user's emotions. For example, Microsoft's Emotion API is used. Based on the results of this analysis, the way the optimization results are presented is adjusted.

[1657] Terminal

[1658] When displaying the optimization results received from the server to the user, the device adjusts the information based on the results of the emotion engine. For example, if the user is feeling stressed, the device displays information succinctly and emphasizes positive information.

[1659] Viewing results and gathering feedback

[1660] Terminal

[1661] The device displays the optimization results to the user and also analyzes the user's emotional state through an emotion engine to provide more personalized results. The results are displayed graphically and dynamically adjusted based on the emotion engine's analysis of the user's emotions.

[1662] User

[1663] Users can check the optimization results through their devices and provide feedback, such as specific opinions or requests for improvement. This feedback is sent from the device to the server and reanalyzed by the emotion engine. This feedback is then reflected in the next project optimization.

[1664] Specific examples

[1665] For example, in a construction project for a new office building, the system operates in the following specific steps:

[1666] Data collected

[1667] The server retrieves resource data (building materials, energy), environmental data (climate, air quality), and city layout data (infrastructure, road network) for the proposed construction site, which includes collecting environmental data from weather data APIs and retrieving city layout data from public databases.

[1668] Set of data

[1669] The server then feeds this data into the algorithm, performing preprocessing such as standardization and encoding. Once the data is properly formatted, it is fed into the AI ​​model and calculations are performed.

[1670] optimization

[1671] The server runs multiple simulations to generate optimal design plans to reduce energy consumption and increase green space.

[1672] Using the Emotion Engine

[1673] The server analyzes the optimization results using an emotion engine and adjusts the way the results are presented to avoid stress for the user. For example, complex information is summarized succinctly.

[1674] Displaying the results

[1675] The terminal graphically displays the adjusted optimization results and presents them in a way that is easy for the user to understand.

[1676] Collecting feedback

[1677] The user checks the presented results and provides feedback, which is then sent to the server along with the sentiment analysis results, which are then reflected in future project optimizations.

[1678] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1679] Step 1:

[1680] Server Data Collection

[1681] The server obtains resource data, environmental data, and city layout data from external APIs and databases. For example, it collects climate data from weather data APIs and obtains city infrastructure information from public databases. It uses API keys and database connection information as input, and the resource data, environmental data, and city layout data are saved in the server's storage as output.

[1682] Step 2:

[1683] Server data preprocessing

[1684] The server preprocesses the collected data. This preprocessing includes data cleansing, standardization, and categorical encoding. Specifically, it normalizes numerical data to the 0-1 range and one-hot encodes categorical data. It uses the collected raw data as input and generates preprocessed data as output.

[1685] Step 3:

[1686] Server Dataset

[1687] The server then feeds the preprocessed data into an artificial intelligence algorithm, using a Python-based script to prepare the data for input into the AI ​​model. The preprocessed data is used as input, and the output is data that matches the AI ​​model.

[1688] Step 4:

[1689] Optimization algorithm execution on the server

[1690] The server uses the provided data to run artificial intelligence algorithms, using machine learning libraries such as TensorFlow and PyTorch to perform calculations that optimize energy efficiency and environmental impact. The AI ​​model uses the data as input and generates optimal design parameters as output.

[1691] Step 5:

[1692] Uses the server's emotion engine

[1693] The server analyzes the optimization results with an emotion engine, which recognizes emotions based on the user's past feedback and current input data and adjusts the presentation of the results. Using the optimization results and user feedback as input, the adjusted presentation is obtained as output.

[1694] Step 6:

[1695] Displaying results on a terminal

[1696] The device then displays the optimization results received from the server to the user. This display is adjusted based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the information will be displayed succinctly and positive information will be emphasized. Using the adjusted presentation data as input, the adjusted display content is provided to the user as output.

[1697] Step 7:

[1698] User feedback input

[1699] The user checks the optimization results presented through the terminal and provides feedback. The user's opinions and impressions are used as input, and the feedback data is saved on the terminal as output.

[1700] Step 8:

[1701] Sending feedback via device

[1702] The device sends the feedback collected from the user to the server, where it is reanalyzed by the emotion engine and reflected in the next project optimization. The feedback data is used as input, and the reanalysis results are saved as output on the server.

[1703] (Application example 2)

[1704] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1705] Optimizing the operation routes of autonomous vehicles has been an important challenge even with conventional technologies, but it has been difficult to efficiently handle environmental data, resource data, and urban layout data. Furthermore, there has been a lack of technology to improve the riding experience by taking into account the emotions of users. In particular, the way operation information is presented does not adapt to the user's emotional state, which often causes stress for users. Therefore, there is a need for a system that can simultaneously improve efficiency and user satisfaction in the operation of autonomous vehicles.

[1706] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1707] In this invention, the server includes a means for acquiring resource data, a means for acquiring environmental data, and a means for acquiring urban layout data. This allows it to execute an artificial intelligence algorithm to generate optimal design parameters. Based on this, it is possible to not only improve the construction and urban planning of the target area, but also optimize the operation routes of autonomous vehicles, analyze user emotions using an emotion engine, and adjust the presentation method of operation information based on the analysis results, thereby simultaneously improving efficiency and user satisfaction.

[1708] "Resource data" refers to information about various resources needed in urban planning and construction projects, such as building materials, energy, and labor.

[1709] "Environmental Data" refers to information about the environment in which the project is implemented, such as climate data, air quality data, and water quality data.

[1710] "City layout data" refers to information about the structure of a city, such as existing infrastructure information, road networks, and public facilities.

[1711] An "artificial intelligence algorithm" is a calculation method for generating optimal design parameters based on resource data, environmental data, and urban layout data.

[1712] "Optimal design parameters" are project settings that achieve efficient use of resources, reduced energy consumption, minimized environmental impact, and optimized living space.

[1713] "Emotion engine" is a general term for devices and software that analyze users' input data and reactions to recognize their emotional state.

[1714] An "autonomous vehicle" is a vehicle that uses artificial intelligence technology to drive itself without the assistance of a driver.

[1715] A "travel route" is the path an autonomous vehicle follows to reach its destination.

[1716] "Feedback" refers to opinions and impressions provided by users, and is information that is used to improve and adjust the system.

[1717] System Configuration

[1718] The system of the present invention operates in collaboration with a server, a terminal, and a user. The server collects resource data, environmental data, and city layout data, and uses this data to execute an artificial intelligence algorithm. It also has an emotion engine that analyzes the user's emotions and proposes an optimized route based on the results. Meanwhile, the terminal displays the optimized route and adjustment information based on the emotion analysis results to the user, and collects user feedback.

[1719] A detailed explanation of each system function

[1720] Data Collection and Optimization

[1721] The server collects environmental data (weather, road conditions), city layout data (existing infrastructure, traffic volume), and resource data (fuel levels, location of charging stations). This data is obtained using APIs (for example, weather forecast APIs or city open data APIs). The collected data is fed into an artificial intelligence algorithm to optimize the route. The algorithm calculates the optimal route, taking into account factors such as reducing energy consumption, shortening travel time, and minimizing environmental impact.

[1722] Sentiment analysis and service information adjustment

[1723] The server also uses an emotion engine to analyze the user's emotions. The emotion engine analyzes the user's facial expressions and voice to recognize states such as stress and relaxation. Based on the recognized emotional state, the server adjusts the way it presents operational information. For example, if the user is stressed, the server may simplify the display content, while if the user is relaxed, the server may provide more detailed information.

[1724] Viewing results and gathering feedback

[1725] The device displays the optimized route and information adjusted based on the emotion engine's analysis results on smart glasses or a head-mounted display. The user can check the route based on the displayed information and provide feedback as needed. This feedback is sent to the server via the device and used for future optimization.

[1726] Hardware and software used

[1727] Hardware: Servers, smart glasses, head-mounted displays

[1728] Software: Weather forecast API, city open data API, artificial intelligence algorithms, emotion engines (e.g., EmotionEngine)

[1729] Specific examples

[1730] For example, when an autonomous vehicle is carrying passengers, the server collects environmental data in real time and optimizes the route. At the same time, the emotion engine analyzes the passenger's facial expressions. If the passenger is stressed, the amount of information displayed will be reduced and concise. If the passenger is relaxed, detailed information and route options will be displayed.

[1731] Prompt Sentence Examples

[1732] "Design optimal driving routes based on environmental data, city layout data, and resource data, and display results that are adjusted in real time according to the user's emotional state. If the user is stressed, show abbreviated and concise results, but if the user is relaxed, provide detailed information."

[1733] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1734] Step 1:

[1735] The server collects environmental data

[1736] Input: Use the API to send a request to get environmental data such as weather, road conditions, etc.

[1737] Processing: Calls the weather forecast API and the city's open data API to obtain real-time weather information and road conditions.

[1738] Output: Save the acquired environmental data to the internal database.

[1739] Step 2:

[1740] The server collects city layout data

[1741] Input: Send a request to get city layout data, i.e. existing infrastructure information and traffic data.

[1742] Processing: Use the city's open data API to obtain road network information and location information for public facilities.

[1743] Output: Save the acquired city layout data to the internal database.

[1744] Step 3:

[1745] The server collects resource data

[1746] Input: Send a request to get resource data for the autonomous vehicle (e.g., fuel level, charging station locations, etc.).

[1747] Processing: Acquires resource data in real time from the vehicle's internal sensors and external data sources.

[1748] Output: Saves the retrieved resource data to an internal database.

[1749] Step 4:

[1750] The server collects data and feeds it into an artificial intelligence algorithm.

[1751] Input: Environmental data, city layout data, resource data

[1752] Processing: Converting this data into a suitable format and feeding it into artificial intelligence algorithms.

[1753] Output: Data passed to the algorithm

[1754] Step 5:

[1755] The server runs an artificial intelligence algorithm to calculate the optimal driving route.

[1756] Input: Environment data, city layout data, and resource data set

[1757] Processing: The data is used to calculate optimal routes that reduce energy consumption, shorten journey times, and minimize environmental impact.

[1758] Output: Optimal route information

[1759] Step 6:

[1760] The server uses an emotion engine to analyze the user's emotions.

[1761] Input: User facial and voice data

[1762] Processing: The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state (stress, relaxation, etc.) in real time.

[1763] Output: Emotion analysis results

[1764] Step 7:

[1765] The server adjusts the presentation method based on the optimized route and sentiment analysis results.

[1766] Input: Optimized route information, sentiment analysis results

[1767] Processing: Adapt the content based on the sentiment analysis, for example, being brief if the user is stressed, or including more information if the user is relaxed.

[1768] Output: Adjusted route information display

[1769] Step 8:

[1770] The device displays the adjusted route information to the user.

[1771] Input: Adjusted route information

[1772] Processing: Display adjusted route information using smart glasses or a head-mounted display.

[1773] Output: A visual representation of the optimized driving route to the user.

[1774] Step 9:

[1775] The user provides feedback on the displayed route information

[1776] Input: User opinions and feedback

[1777] Processing: Feedback is obtained from input devices (smart glasses, head-mounted display) and sent to the server.

[1778] Output: Feedback data

[1779] Step 10:

[1780] The server collects feedback and uses it for future optimizations.

[1781] Input: Feedback data

[1782] Processing: Analyze the feedback data and reflect it in the next route optimization.

[1783] Output: An improved AI model

[1784] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1785] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1786] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1787] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1788] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1789] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1790] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1791] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1792] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1793] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1794] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1795] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1796] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1797] 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.

[1798] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1799] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1800] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1801] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1802] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1803] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1804] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1805] The following is further disclosed regarding the above embodiment.

[1806] (Claim 1)

[1807] a means for obtaining resource data;

[1808] a means for acquiring environmental data;

[1809] a means for obtaining city layout data;

[1810] means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environmental data, and the city layout data;

[1811] means for improving construction and urban planning of a target area based on said optimal design parameters;

[1812] A system including:

[1813] (Claim 2)

[1814] 2. The system of claim 1, further comprising: means for inputting the resource data, the environmental data, and the city layout data into an artificial intelligence algorithm; and means for executing the artificial intelligence algorithm.

[1815] (Claim 3)

[1816] 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; and means for collecting feedback from the user regarding the optimization results.

[1817] "Example 1"

[1818] (Claim 1)

[1819] a means for collecting resource data;

[1820] a means for collecting environmental data;

[1821] a means for collecting city layout data;

[1822] means for pre-processing the resource data, the environmental data, and the city layout data;

[1823] means for feeding the pre-processed data into an artificial intelligence algorithm;

[1824] means for executing the artificial intelligence algorithm to calculate efficient allocation of resources, minimization of environmental impact, and optimization of living space;

[1825] means for generating the optimal design parameters;

[1826] means for improving construction and urban planning of a target area based on the generated design parameters;

[1827] A system including:

[1828] (Claim 2)

[1829] 2. The system of claim 1, further comprising: means for inputting the resource data, the environmental data, and the city layout data into an artificial intelligence algorithm; and means for executing the artificial intelligence algorithm.

[1830] (Claim 3)

[1831] 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; and means for collecting feedback from the user on the optimization results and reflecting the feedback in subsequent optimization processes.

[1832] "Application Example 1"

[1833] (Claim 1)

[1834] a means for obtaining resource data;

[1835] a means for acquiring environmental data;

[1836] a means for obtaining city layout data;

[1837] means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environmental data, and the city layout data;

[1838] means for improving construction and urban planning of a target area based on said optimal design parameters;

[1839] means for collecting environmental and resource data in real time using sensors mounted on industrial machines;

[1840] means for executing artificial intelligence algorithms to optimize resource efficiency based on data collected by said industrial machine;

[1841] means for displaying the resource efficiency optimization results;

[1842] A system including:

[1843] (Claim 2)

[1844] 2. The system of claim 1, further comprising: means for inputting the resource data, the environmental data, and the city layout data into an artificial intelligence algorithm; and means for executing the artificial intelligence algorithm.

[1845] (Claim 3)

[1846] 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; and means for collecting feedback from the user regarding the optimization results.

[1847] "Example 2: Combining Emotion Engines"

[1848] (Claim 1)

[1849] a means for obtaining resource data;

[1850] a means for acquiring environmental data;

[1851] a means for obtaining city layout data;

[1852] means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environmental data, and the city layout data;

[1853] A means for analyzing the optimization result and recognizing the user's emotions;

[1854] a means for adjusting information content to be presented to a user based on the emotion recognition result;

[1855] means for improving construction and urban planning of a target area based on said optimal design parameters;

[1856] A system including:

[1857] (Claim 2)

[1858] 2. The system of claim 1, further comprising: means for inputting the resource data, the environmental data, and the city layout data into an artificial intelligence algorithm; and means for executing the artificial intelligence algorithm.

[1859] (Claim 3)

[1860] 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; means for adjusting the display content based on the emotion recognition results; and means for collecting feedback from the user regarding the optimization results.

[1861] "Application example 2 when combining emotion engines"

[1862] (Claim 1)

[1863] a means for obtaining resource data;

[1864] a means for acquiring environmental data;

[1865] a means for obtaining city layout data;

[1866] means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environmental data, and the city layout data;

[1867] means for improving construction and urban planning of a target area based on said optimal design parameters;

[1868] A method for optimizing the route of an autonomous vehicle;

[1869] A means for analyzing the user's emotions using an emotion engine;

[1870] a means for adjusting a method for presenting operation information based on an analysis result of the emotion engine;

[1871] A system including:

[1872] (Claim 2)

[1873] 2. The system of claim 1, further comprising: means for inputting the resource data, the environmental data, and the city layout data into an artificial intelligence algorithm; and means for executing the artificial intelligence algorithm.

[1874] (Claim 3)

[1875] 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; and means for collecting feedback from the user regarding the optimization results. [Explanation of symbols]

[1876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for obtaining resource data; a means for acquiring environmental data; a means for obtaining city layout data; means for executing an artificial intelligence algorithm to generate optimal design parameters using the resource data, the environmental data, and the city layout data; means for improving construction and urban planning of a target area based on said optimal design parameters; A system including:

2. 2. The system of claim 1, further comprising: means for inputting said resource data, said environmental data, and said city layout data into an artificial intelligence algorithm; and means for executing said artificial intelligence algorithm.

3. 2. The system of claim 1, further comprising: means for displaying the optimization results of the artificial intelligence algorithm to a user; and means for collecting feedback from the user regarding the optimization results.

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