system

Abstract

We provide the system. [Solution] A means of constructing a virtual model of a physical system using a generative model, A means of performing calculations based on a virtual model and obtaining data about the physical system, A means of formulating an operational strategy for the physical system by analyzing the acquired data, A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data, A means of updating the virtual model using the acquired operational results, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern energy management systems, efficient utilization of renewable energy and optimization of energy costs are required. However, conventional methods have problems in that it is difficult to quickly respond to changes in physical systems, resulting in a decrease in operation efficiency and waste of resources. By solving this problem, it is necessary to achieve sustainable energy utilization and economical operation.

Means for Solving the Problems

[0005] This invention provides a system that constructs a virtual model of a physical system using a generative model, performs calculations based on this virtual model, and acquires data related to the physical system. The acquired data is then analyzed to formulate an operational strategy, and the physical system is operated based on this strategy. Furthermore, the operational results are acquired again as data, and the virtual model is updated to achieve cyclical optimization. Specific means to achieve this include generating a flexible digital twin model according to the requirements of the physical system and optimizing the use of energy resources using energy storage means.

[0006] A "generative model" is an algorithm or mathematical method for creating a virtual physical model based on digital information.

[0007] A "physical system" is a real-world system consisting of specific equipment and facilities used for energy management, etc.

[0008] A "virtual model" is a digital simulation built on a computer to mimic the behavior and characteristics of a physical system.

[0009] "Calculation" refers to performing computational processes using a virtual model for a specific purpose.

[0010] "Data" is a collection of information obtained through calculations and measurements.

[0011] An "operational strategy" is a plan or guideline formulated to achieve the efficient and effective operation of a physical system.

[0012] "Energy storage means" refers to devices or systems for storing energy and supplying it as needed.

[0013] "Updating" is the process of improving a virtual model based on the latest data and information. [Brief explanation of the drawing]

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

MODE FOR CARRYING OUT THE INVENTION

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

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

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

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

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

[0020] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

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

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

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

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0035] This invention relates to a system that utilizes generative models and digital twin technology to perform cyclical simulations and develop operational strategies to support the optimal operation of physical systems. The embodiments for carrying out this invention are described below.

[0036] First, the server collects information such as historical operational data and environmental conditions, and uses a generative model to construct a detailed virtual model of the physical system. This virtual model accurately mimics the operating characteristics of the actual physical system, making it possible to predict various operational scenarios.

[0037] Next, the terminal runs a simulation using a virtual model. In this simulation, for example, in the case of an energy management system, fluctuations in power generation and charging / discharging of storage batteries are simulated. The data obtained from the simulation is used to evaluate the overall operational efficiency and bottlenecks of the system.

[0038] Subsequently, the user develops an operational strategy based on the simulation results. This process proposes specific measures to optimize the balance between energy supply and demand and to reduce economic costs. Examples include battery discharge schedules that avoid peak hours and optimal utilization methods for surplus energy.

[0039] Based on the established operational strategy, users operate the actual system and record the results. New data obtained during operation is transferred to the server and used to update the virtual model. This updated virtual model serves as the starting point for the next simulation, enabling further optimization based on the latest information.

[0040] This enables continuous system improvement and efficient energy management through the interconnectedness of servers, terminals, and users. Specific examples include optimizing the use of renewable energy, reducing energy costs, and minimizing the carbon footprint. These are concrete implementations for improving the overall sustainability and economic efficiency of the system.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server collects historical operational data, environmental conditions data, and other relevant information from the physical system. Based on this information, it uses a generative model to construct a virtual model that accurately mimics the real-world physical system. This creates an environment where system operation can be simulated.

[0044] Step 2:

[0045] The terminal runs a simulation using a virtual model built on the server. This simulation simulates different operational scenarios and conditions, generating data on system performance and efficiency. Once the simulation completes successfully, the obtained data is saved and prepared for analysis.

[0046] Step 3:

[0047] Users analyze simulation data acquired from their terminals to formulate operational strategies for the physical system. These strategies are tailored to specific goals, such as reducing energy consumption, lowering costs, and improving availability. The formulated operational strategies are then implemented in the actual system's operations.

[0048] Step 4:

[0049] Users operate the actual physical system according to the established operational strategy and record the operational results daily. The data accumulated during system operation provides insights necessary for continuous optimization of operations.

[0050] Step 5:

[0051] The server receives operational results and the latest data sent from the actual system and uses them to update the virtual model. By utilizing generative AI and updating the model based on this constantly updated information, the next simulation will be more realistic.

[0052] Step 6:

[0053] The terminal runs a new simulation using the updated virtual model and analyzes the data again. This process proceeds cyclically, enabling continuous improvement and optimization of the system.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In modern, complex physical systems, efficient operation and management present challenges. In particular, there is a need to optimize energy resources, improve operational efficiency, and reduce environmental impact. Conventional methods struggle to adequately achieve these objectives.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for creating a detailed virtual model of the physical mechanism using a generative model, means for performing computer simulations based on the virtual model and collecting information about the physical mechanism, and means for analyzing the collected information and formulating an operational strategy for the physical mechanism. This enables improved operational efficiency of the physical mechanism and optimal use of energy resources.

[0059] A "generative model" is a mathematical model created to virtually reproduce a physical mechanism and simulate the operation of a system.

[0060] A "virtual model" is a virtualized system that faithfully reproduces the characteristics and operation of physical mechanisms based on a model generated on a computer.

[0061] A "physical mechanism" refers to a specific structure, device, or collection of such structures or devices that operate in the real world.

[0062] "Computer simulation" is a method of simulating and predicting the operation of a system under various conditions on a computer using a virtual model.

[0063] An "operational strategy" is a specific plan or policy formulated to achieve efficient and effective management and control of a physical mechanism.

[0064] A "energy storage device" is a device that temporarily stores energy resources in the form of electricity and supplies power as needed.

[0065] This invention is a system that utilizes generative models and digital twin technology to support the optimal operation of physical mechanisms. Specific embodiments are described below.

[0066] The server first collects historical operational data and various environmental condition information from a database. This includes weather information, power consumption, and equipment operating status. Based on the collected data, the server uses a generative AI model to create a detailed virtual model of the physical mechanism. Data analysis tools utilizing Python and R are used here.

[0067] The terminal receives a virtual model generated by the server and performs a computer simulation. MATLAB® is often used as the simulation software. This simulation allows for detailed analysis of, for example, fluctuations in power generation and the charge-discharge cycles of energy storage devices, and evaluates the operational efficiency of the system.

[0068] Subsequently, the user formulates an operational strategy for the physical mechanism based on the simulation results provided by the terminal. This process involves developing specific plans aimed at optimizing energy efficiency and reducing costs. Users can develop strategies using Excel spreadsheets or dedicated applications. For example, one might design a schedule to charge energy storage devices at night and discharge them during the day to shift peak demand.

[0069] Furthermore, as an application of the generative AI model, an example of a prompt message is shown below: "Predict the energy demand for the next 24 hours and suggest the optimal battery charging and discharging schedule."

[0070] This system enables servers, terminals, and users to work together, effectively achieving optimal use of energy resources and efficient operation of physical mechanisms.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects historical operational data and environmental condition information from a database. This input data includes weather information, power consumption, and equipment operating status. The collected data is analyzed using Python or R to extract operating patterns of physical mechanisms. As a result, an initial dataset for generative AI models can be created.

[0074] Step 2:

[0075] Based on the data collected and analyzed in Step 1, the server uses a generated AI model to construct a virtual model of the physical mechanism. This virtual model replicates the characteristics of the physical mechanism and serves as the foundation for simulating actual system operation. The output includes system operating characteristics in various simulated environments.

[0076] Step 3:

[0077] The terminal performs computer simulations based on virtual models received from the server. Inputs include virtual models and various operational scenarios. MATLAB is used to perform the simulations, analyzing in detail fluctuations in power generation and charge / discharge cycles of energy storage devices to evaluate operational efficiency. As a result, output information regarding reductions in operating costs and improvements in energy efficiency is obtained.

[0078] Step 4:

[0079] The user formulates an operational strategy for the physical mechanism based on the simulation results obtained from the terminal. The input here is the evaluation result of operational efficiency from the simulation. The user formulates the strategy using Excel or a dedicated application and plans to achieve an optimal balance between energy supply and demand. A specific action is the design of a charge / discharge schedule for peak shifting.

[0080] Step 5:

[0081] The user operates the actual physical mechanism based on the operational strategy they have devised. The input here is the formulated operational strategy. The user monitors sensor information generated in real time and feeds the results back to the server. This feedback data is used for the subsequent virtual simulation update process.

[0082] Step 6:

[0083] The server updates the virtual model based on operational data provided by the user. This input data consists of operational results based on actual performance. The server adjusts the parameters of the generated AI model as needed, preparing a foundation for more accurate simulations and strategy formulation for the next time. As output, a virtual model reflecting the latest operational status is created.

[0084] (Application Example 1)

[0085] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0086] Managing and optimizing energy consumption in logistics facilities is a crucial challenge for improving operational efficiency and reducing costs. However, there is a lack of effective systems to cope with fluctuations in energy demand and complex operational schedules. Therefore, there is a need for systems that can optimize energy consumption while increasing the operational efficiency of logistics facilities.

[0087] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0088] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data on energy consumption in the logistics facility, and means for updating the virtual model using the acquired operational results. This enables real-time optimization of energy management in the logistics facility and efficient operation.

[0089] A "generative model" is an algorithm that learns patterns from data and generates new data.

[0090] A "physical system" is a structure that refers to the entirety of hardware and processes in the real world.

[0091] A "virtual model" is a digital representation used to simulate the characteristics and behavior of a physical system.

[0092] An "operational strategy" is a set of plans and policies formulated to maximize system efficiency.

[0093] A "logistics facility" refers to a comprehensive infrastructure and group of buildings used for storing, collecting, and distributing goods.

[0094] "Energy consumption" refers to the total amount of energy, such as electricity and fuel, used in the operation of a logistics facility.

[0095] "Optimization" refers to the actions or methods used to maximize the performance of a system or process.

[0096] To realize this invention, the server, terminal, and user elements must work together in coordination. The server first collects historical energy usage data and operational records of the logistics facility. This involves obtaining information from sensors installed in the logistics facility and from existing databases. Data cleaning and preprocessing using Python are used to create a dataset for building a generative model. This generative model is designed using TENSORFLOW® to construct a virtual model of the energy consumption of the logistics facility.

[0097] The terminal runs various simulations based on a virtual model and evaluates the results in real time. It then presents the user with an optimized energy management plan. This uses a mobile app built with React Native, which visualizes operating schedules and energy usage. Users can then improve actual facility operations according to the presented plan.

[0098] For example, when a logistics facility operator plans the optimal charging time for trucks based on the next day's delivery schedule, they can plan to charge during off-peak hours when energy costs are low. In this embodiment, a prompt such as "Please create an optimal energy consumption plan considering the next day's truck departure schedule" can be input to the generating AI model to obtain an appropriate operational strategy.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The server collects energy data and historical operational data from within the logistics facility. Real-time data from sensors installed at the facility, as well as historical records, are used as input. Python is used for data cleaning and preprocessing, and the results are output and stored in a database.

[0102] Step 2:

[0103] The server constructs a generative model using TensorFlow based on the collected data. Cleaned energy data is provided as input, and the generative model creates a virtual model of the energy consumption of the logistics facility. This virtual model becomes the output.

[0104] Step 3:

[0105] The terminal runs a simulation using a virtual model provided by the server. The input consists of the virtual model and user prompts. Specifically, it evaluates various operational scenarios based on the model and outputs an optimized energy management plan.

[0106] Step 4:

[0107] The user receives the energy usage plan presented by the terminal and works to improve the operation of the logistics facility according to its contents. The input is the energy usage plan from the terminal, and the user makes specific adjustments to the operational schedule and changes to equipment settings, and then implements the results.

[0108] Step 5:

[0109] The user's operational results are retrieved as data and transferred to the server. The server then updates the virtual model using this new operational data as input, creating the basis for the next simulation. This updated virtual model is the output.

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention is a system that optimizes operational efficiency by constructing a virtual model of a physical system using a generative model and linking the physical system with a virtual world. Furthermore, it incorporates an emotion engine that recognizes user emotions to improve operational flexibility and user experience. The embodiments for carrying out this invention are described below.

[0112] First, the server aggregates data about the physical system and uses generative models to build a detailed virtual model. This creates an environment where various operational scenarios of the actual system can be simulated in detail.

[0113] Next, the terminal runs a simulation based on a virtual model to evaluate the system's operational performance. This process analyzes energy consumption trends and the efficiency of various resource usage to find the optimal operational strategy.

[0114] In addition, the present invention recognizes the user's emotional state in real time using an emotion engine and incorporates this information into the operational model. Emotional data is used to adjust the system's notification methods and operational schedule. For example, if a user is feeling stressed, the system reduces the notification frequency and manages energy levels to take into account calmer periods.

[0115] Furthermore, users can receive feedback on energy consumption and system operation through the emotion engine. This allows for operational adjustments that take into account the comfort and satisfaction of individual users. In addition, the use of energy storage methods is optimized according to emotion recognition, thus achieving efficient energy use while enhancing user convenience.

[0116] In this way, an integrated system of servers, terminals, and users improves the operational efficiency of the actual system while simultaneously enabling sophisticated energy management that takes user emotions into consideration. Specific examples include its application in home energy management systems and minimizing power consumption in office environments. This comprehensively improves both system sustainability and user experience.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The server uses a generative model to construct a virtual model based on operational data collected from the physical system and external environment data. In this process, the system's operating state and parameters are input into the generative model, accurately recreating the virtual environment.

[0120] Step 2:

[0121] The terminal runs simulations using a virtual model provided by the server. These simulations examine possible operational scenarios and evaluate energy consumption, supply balance, and energy storage effectiveness. The results provide insights into the timing of charging and discharging of stored resources and system optimization.

[0122] Step 3:

[0123] Users formulate operational strategies based on simulation results obtained from their devices. This includes specific details such as the optimal timing for seasonal energy storage and measures to reduce peak energy consumption. Furthermore, adjustments are made based on the user's stress and satisfaction levels through an emotional engine.

[0124] Step 4:

[0125] The user operates the physical system based on the established operational strategy. During operation, the user's emotional state is monitored by an emotion engine, and adjustments are automatically made, for example, by reducing notifications if the user is feeling stressed.

[0126] Step 5:

[0127] The server continuously collects result data obtained from actual operation and updates the virtual model. This update process reflects the latest real-world data and changes in user sentiment, improving the accuracy of the next simulation.

[0128] Step 6:

[0129] The terminal runs new simulations based on the updated virtual model, providing data to develop even more refined operational strategies. This cycle continuously improves system efficiency and enhances the user experience.

[0130] (Example 2)

[0131] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0132] Modern physical systems require optimizing operational efficiency while also considering user emotions and comfort. However, conventional systems, while capable of operational optimization based on physical parameters, have struggled to flexibly adjust operations while considering the emotional state of users. Furthermore, efficient resource utilization through the optimal use of energy storage methods remains a challenge.

[0133] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0134] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state in real time and adjusting notification settings and operational schedules based on that emotional state. This enables flexible operation that considers the user's emotions while pursuing physical efficiency, and resource-efficient system operation that utilizes energy storage means.

[0135] A "generative model" is a mathematical and statistical method used to generate new information based on existing data.

[0136] A "virtual model" is a digital representation used to simulate the actual operation and state of a physical system on a computer.

[0137] "Calculation" refers to the mathematical computation process used for data processing and analysis.

[0138] "Data" refers to a collection of information about the operation and function of a system, including numerical values ​​and records used for analysis.

[0139] An "operational strategy" is a plan or policy for the effective and efficient use of a physical system.

[0140] "Emotional state" refers to the user's psychological state or mood, and is recognized by the system.

[0141] "Notification settings" refer to the criteria and adjustments used to determine the frequency and method of providing information to users.

[0142] An "operation schedule" is a time-based plan for the operation and management of a system.

[0143] "Energy storage means" refers to devices and technologies for storing electricity, which are used in system operation.

[0144] This invention optimizes the operational efficiency of a physical system and enables flexible operation that takes into account the user's feelings. The embodiments for carrying out this invention are described below.

[0145] First, the server acquires and aggregates various data related to the physical system. This data is transmitted in real time from input devices such as sensors and includes temperature, humidity, and energy consumption. A database management system such as "PostgreSQL" is used to manage this data, and "Apache Spark" is used for big data processing.

[0146] Next, the server utilizes the generated AI model to build a virtual model of the physical system based on the collected data. By training the AI ​​model using machine learning frameworks such as "TensorFlow" and "PyTorch" and generating the virtual model, it simulates information useful for actual operation.

[0147] The terminal uses a virtual model built on the server to perform calculations and evaluate the system's operational performance. This evaluation involves dynamic simulations using MATLAB and Simulink, which are then used to formulate operational strategies.

[0148] Furthermore, users interact with the system using an emotion engine. This emotion engine utilizes "EmotionAPI" and other tools to recognize the user's psychological state in real time and reflect this in the system's operation. Notifications received by the user and the system's operating schedule are automatically adjusted based on the user's emotion data.

[0149] As a concrete example, a home energy management system minimizes energy consumption when the user is away from home and begins adjusting the room temperature before they return. This automated operation enhances user comfort and improves energy efficiency.

[0150] An example of a prompt to input into the generating AI model might be, "Create an optimal schedule to maximize energy efficiency in a home energy management system." This prompt allows the system to dynamically generate the optimal operational strategy.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The server collects data from sensors connected to a physical system. This collected data includes a wide range of physical parameters such as temperature, humidity, and energy consumption. The input data is stored in the database management system "PostgreSQL," and data cleansing and formatting are performed using the big data processing framework "Apache Spark." The output is a cleaned dataset.

[0154] Step 2:

[0155] The server constructs a virtual model using the generated AI model. This process uses the dataset obtained in step 1 as input. The server trains the AI ​​model using the machine learning framework "TensorFlow" or "PyTorch" to create the virtual model. This model is intended to simulate the operation of a physical system. A detailed virtual model is generated as output.

[0156] Step 3:

[0157] The terminal performs simulations to evaluate the system's operational performance based on a virtual model provided by the server. At this stage, dynamic simulations are performed using MATLAB or Simulink. The system evaluates energy consumption and resource utilization efficiency for the virtual model as input, and derives the optimal operational strategy. The output is a recommended operational strategy.

[0158] Step 4:

[0159] The server receives the operational strategy provided by the terminal and reflects it in the control of the physical system. Here, it applies instructions to the real system and collects the operational results as data again. Again, the server saves this new data to the database, preparing for updating the virtual model in the next step. Operational result data is generated as output.

[0160] Step 5:

[0161] Users utilize the emotion engine to provide feedback to the system based on their emotional state. The system recognizes the user's psychological state in real time using "EmotionAPI" and other means, and this data is reflected in the system's notification settings and operational schedule. The output provides the user with notification frequencies and operational schedules tailored to their needs.

[0162] Step 6:

[0163] The server updates the virtual model based on sentiment data and operational results data. In this step, the newly acquired information is used to retrain the generative AI model. Based on operational and sentiment data as inputs, a virtual model adapted to the latest scenario is deployed, and the updated virtual model is generated as output. By continuously repeating this process, the system can perform sequentially optimized operations.

[0164] (Application Example 2)

[0165] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0166] Existing systems have a problem in that they have difficulty considering user emotions when optimizing the operation of physical systems, resulting in a lack of improvement in the user experience. Furthermore, this may prevent the system's operational efficiency from being sufficiently increased, potentially hindering the effective use of energy resources.

[0167] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0168] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state via an information terminal and dynamically adjusting the environment settings of the physical system based on that emotional state. This makes it possible to improve the user experience and the effective use of energy resources while increasing the operational efficiency of the physical system.

[0169] A "generative model" is an algorithm used to construct a virtual model of a physical system, recreating a virtual environment based on various data.

[0170] A "physical system" is a collection of actual machines and devices through which energy and information flow, and its operation is the subject of optimization.

[0171] A "virtual model" is a model that mimics a physical system and is reproduced on a computer. It is used to explore operational strategies through simulation.

[0172] "Calculation" refers to the process of numerical or logical calculations performed based on a virtual model, and is necessary for formulating operational strategies for a system.

[0173] "Data acquisition" is the process of collecting necessary information from physical systems, and this information is used in formulating operational strategies.

[0174] An "operational strategy" is a plan or policy designed to maximize the utilization efficiency and operational effectiveness of a physical system.

[0175] "Operational results" refer to data on the activities and effects that the physical system actually performed based on the formulated operational strategy.

[0176] "Dynamic adjustment of environment settings" is a process that changes external factors of the physical system in real time based on the user's emotional state.

[0177] "Emotional state" refers to data that indicates the psychological and emotional responses of users, and is used to improve operational efficiency and enhance the user experience.

[0178] To implement this invention, the server first collects various data related to the physical system, and then uses this data to construct a sophisticated virtual model by utilizing a generative model. Based on this virtual model, the server simulates various operational scenarios. This provides information for formulating the optimal operational strategy for the entire system.

[0179] The terminal executes operational strategies formulated through simulations and makes necessary adjustments to improve operational efficiency. In particular, the terminal is equipped with an emotion engine to recognize the user's emotional state in real time, and dynamically changes the environment settings of the physical system based on this information. This optimizes the user experience and provides a comfortable environment.

[0180] Based on the emotional state recognized by the emotion engine and feedback from the system, users can adjust their energy usage and environmental settings in their daily lives. For example, if a user feels stressed, the system can automatically change the music in the store to something calming and adjust the lighting. Through this process, users can enjoy a more comfortable and efficient living environment.

[0181] As a concrete example, cameras and sensors within the store collect facial expression data from customers, and AI analyzes this data to recognize their emotions. Based on this information, smartphones and the store's control system adjust the music and lighting. In this way, the system provides an optimal environment tailored to the customer's emotions.

[0182] An example of a prompt for a generating AI model is the text, "Design an algorithm that determines stress levels in real time from customer facial images and optimizes the in-store environment settings." Based on this prompt, the AI ​​model can generate an algorithm and help optimize the entire system.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The server collects various sensor data from the physical system. This data includes environmental information such as temperature, lighting, and sound. This data is preprocessed and converted into an input dataset for building a virtual model. Specifically, outliers are removed and the data is normalized to prepare it for subsequent processing.

[0186] Step 2:

[0187] The server uses a generative model to construct a virtual model of the physical system from collected sensor data. This virtual model serves as the foundation for simulating the actual physical system. This model enables simulation of various operational scenarios for the system. The generative model analyzes the input data and generates an appropriate virtual environment through computational processing.

[0188] Step 3:

[0189] The device activates an emotion engine to recognize the user's emotional state in real time, taking data on the user's facial expressions and voice acquired from the camera and microphone as input. By analyzing this input data, it identifies the user's emotions and outputs them as an emotional state. This output data plays an important role in the system's environment settings.

[0190] Step 4:

[0191] The device dynamically adjusts the physical system's environmental settings based on the user's emotional state, as determined by the emotion engine. Specifically, it changes settings such as music, lighting, and temperature to suit the user's emotional state. It receives the emotional state and current environmental settings as input, selects the appropriate setting options, and outputs them.

[0192] Step 5:

[0193] Users enjoy a new experience under optimized environment settings. User feedback is re-entered into the system as input, leading to continuous operational improvements. Specifically, feedback on user satisfaction and comfort is collected and used to evaluate operational strategies.

[0194] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0195] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0201] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0205] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0207] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] This invention relates to a system that utilizes generative models and digital twin technology to perform cyclical simulations and develop operational strategies to support the optimal operation of physical systems. The embodiments for carrying out this invention are described below.

[0211] First, the server collects information such as historical operational data and environmental conditions, and uses a generative model to construct a detailed virtual model of the physical system. This virtual model accurately mimics the operating characteristics of the actual physical system, making it possible to predict various operational scenarios.

[0212] Next, the terminal runs a simulation using a virtual model. In this simulation, for example, in the case of an energy management system, fluctuations in power generation and charging / discharging of storage batteries are simulated. The data obtained from the simulation is used to evaluate the overall operational efficiency and bottlenecks of the system.

[0213] Subsequently, the user develops an operational strategy based on the simulation results. This process proposes specific measures to optimize the balance between energy supply and demand and to reduce economic costs. Examples include battery discharge schedules that avoid peak hours and optimal utilization methods for surplus energy.

[0214] Based on the established operational strategy, users operate the actual system and record the results. New data obtained during operation is transferred to the server and used to update the virtual model. This updated virtual model serves as the starting point for the next simulation, enabling further optimization based on the latest information.

[0215] This enables continuous system improvement and efficient energy management through the interconnectedness of servers, terminals, and users. Specific examples include optimizing the use of renewable energy, reducing energy costs, and minimizing the carbon footprint. These are concrete implementations for improving the overall sustainability and economic efficiency of the system.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The server collects historical operational data, environmental conditions data, and other relevant information from the physical system. Based on this information, it uses a generative model to construct a virtual model that accurately mimics the real-world physical system. This creates an environment where system operation can be simulated.

[0219] Step 2:

[0220] The terminal runs a simulation using a virtual model built on the server. This simulation simulates different operational scenarios and conditions, generating data on system performance and efficiency. Once the simulation completes successfully, the obtained data is saved and prepared for analysis.

[0221] Step 3:

[0222] Users analyze simulation data acquired from their terminals to formulate operational strategies for the physical system. These strategies are tailored to specific goals, such as reducing energy consumption, lowering costs, and improving availability. The formulated operational strategies are then implemented in the actual system's operations.

[0223] Step 4:

[0224] Users operate the actual physical system according to the established operational strategy and record the operational results daily. The data accumulated during system operation provides insights necessary for continuous optimization of operations.

[0225] Step 5:

[0226] The server receives operational results and the latest data sent from the actual system and uses them to update the virtual model. By utilizing generative AI and updating the model based on this constantly updated information, the next simulation will be more realistic.

[0227] Step 6:

[0228] The terminal runs a new simulation using the updated virtual model and analyzes the data again. This process proceeds cyclically, enabling continuous improvement and optimization of the system.

[0229] (Example 1)

[0230] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0231] In modern, complex physical systems, efficient operation and management present challenges. In particular, there is a need to optimize energy resources, improve operational efficiency, and reduce environmental impact. Conventional methods struggle to adequately achieve these objectives.

[0232] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0233] In this invention, the server includes means for creating a detailed virtual model of the physical mechanism using a generative model, means for performing computer simulations based on the virtual model and collecting information about the physical mechanism, and means for analyzing the collected information and formulating an operational strategy for the physical mechanism. This enables improved operational efficiency of the physical mechanism and optimal use of energy resources.

[0234] A "generative model" is a mathematical model created to virtually reproduce a physical mechanism and simulate the operation of a system.

[0235] A "virtual model" is a virtualized system that faithfully reproduces the characteristics and operation of physical mechanisms based on a model generated on a computer.

[0236] A "physical mechanism" refers to a specific structure, device, or collection of such structures or devices that operate in the real world.

[0237] "Computer simulation" is a method of simulating and predicting the operation of a system under various conditions on a computer using a virtual model.

[0238] An "operational strategy" is a specific plan or policy formulated to achieve efficient and effective management and control of a physical mechanism.

[0239] A "energy storage device" is a device that temporarily stores energy resources in the form of electricity and supplies power as needed.

[0240] This invention is a system that utilizes generative models and digital twin technology to support the optimal operation of physical mechanisms. Specific embodiments are described below.

[0241] The server first collects historical operational data and various environmental condition information from a database. This includes weather information, power consumption, and equipment operating status. Based on the collected data, the server uses a generative AI model to create a detailed virtual model of the physical mechanism. Data analysis tools utilizing Python and R are used here.

[0242] The terminal receives a virtual model generated by the server and performs a computer simulation. MATLAB is often used as the simulation software. This simulation allows for detailed analysis of, for example, fluctuations in power generation and the charge-discharge cycles of energy storage devices, and evaluates the operational efficiency of the system.

[0243] Subsequently, the user formulates an operational strategy for the physical mechanism based on the simulation results provided by the terminal. This process involves developing specific plans aimed at optimizing energy efficiency and reducing costs. Users can develop strategies using Excel spreadsheets or dedicated applications. For example, one might design a schedule to charge energy storage devices at night and discharge them during the day to shift peak demand.

[0244] Furthermore, as an application of the generative AI model, an example of a prompt message is shown below: "Predict the energy demand for the next 24 hours and suggest the optimal battery charging and discharging schedule."

[0245] This system enables servers, terminals, and users to work together, effectively achieving optimal use of energy resources and efficient operation of physical mechanisms.

[0246] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0247] Step 1:

[0248] The server collects historical operational data and environmental condition information from a database. This input data includes weather information, power consumption, and equipment operating status. The collected data is analyzed using Python or R to extract operating patterns of physical mechanisms. As a result, an initial dataset for generative AI models can be created.

[0249] Step 2:

[0250] Based on the data collected and analyzed in Step 1, the server uses a generated AI model to construct a virtual model of the physical mechanism. This virtual model replicates the characteristics of the physical mechanism and serves as the foundation for simulating actual system operation. The output includes system operating characteristics in various simulated environments.

[0251] Step 3:

[0252] The terminal performs computer simulations based on virtual models received from the server. Inputs include virtual models and various operational scenarios. MATLAB is used to perform the simulations, analyzing in detail fluctuations in power generation and charge / discharge cycles of energy storage devices to evaluate operational efficiency. As a result, output information regarding reductions in operating costs and improvements in energy efficiency is obtained.

[0253] Step 4:

[0254] The user formulates an operational strategy for the physical mechanism based on the simulation results obtained from the terminal. The input here is the evaluation result of operational efficiency from the simulation. The user formulates the strategy using Excel or a dedicated application and plans to achieve an optimal balance between energy supply and demand. A specific action is the design of a charge / discharge schedule for peak shifting.

[0255] Step 5:

[0256] The user operates the actual physical mechanism based on the operational strategy they have devised. The input here is the formulated operational strategy. The user monitors sensor information generated in real time and feeds the results back to the server. This feedback data is used for the subsequent virtual simulation update process.

[0257] Step 6:

[0258] The server updates the virtual model based on operational data provided by the user. This input data consists of operational results based on actual performance. The server adjusts the parameters of the generated AI model as needed, preparing a foundation for more accurate simulations and strategy formulation for the next time. As output, a virtual model reflecting the latest operational status is created.

[0259] (Application Example 1)

[0260] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0261] Managing and optimizing energy consumption in logistics facilities is a crucial challenge for improving operational efficiency and reducing costs. However, there is a lack of effective systems to cope with fluctuations in energy demand and complex operational schedules. Therefore, there is a need for systems that can optimize energy consumption while increasing the operational efficiency of logistics facilities.

[0262] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0263] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data on energy consumption in the logistics facility, and means for updating the virtual model using the acquired operational results. This enables real-time optimization of energy management in the logistics facility and efficient operation.

[0264] A "generative model" is an algorithm that learns patterns from data and generates new data.

[0265] A "physical system" is a structure that refers to the entirety of hardware and processes in the real world.

[0266] A "virtual model" is a digital representation used to simulate the characteristics and behavior of a physical system.

[0267] An "operational strategy" is a set of plans and policies formulated to maximize system efficiency.

[0268] A "logistics facility" refers to a comprehensive infrastructure and group of buildings used for storing, collecting, and distributing goods.

[0269] "Energy consumption" refers to the total amount of energy, such as electricity and fuel, used in the operation of a logistics facility.

[0270] "Optimization" refers to the actions or methods used to maximize the performance of a system or process.

[0271] To realize this invention, the server, terminal, and user elements must work together in coordination. The server first collects historical energy usage data and operational records of the logistics facility. This involves obtaining information from sensors installed in the logistics facility and from existing databases. Data cleaning and preprocessing using Python are used to create a dataset for building a generative model. This generative model is designed using TensorFlow to build a virtual model of the energy consumption of the logistics facility.

[0272] The terminal runs various simulations based on a virtual model and evaluates the results in real time. It then presents the user with an optimized energy management plan. This uses a mobile app built with React Native, which visualizes operating schedules and energy usage. Users can then improve actual facility operations according to the presented plan.

[0273] For example, when a logistics facility operator plans the optimal charging time for trucks based on the next day's delivery schedule, they can plan to charge during off-peak hours when energy costs are low. In this embodiment, a prompt such as "Please create an optimal energy consumption plan considering the next day's truck departure schedule" can be input to the generating AI model to obtain an appropriate operational strategy.

[0274] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0275] Step 1:

[0276] The server collects energy data and historical operational data from within the logistics facility. Real-time data from sensors installed at the facility, as well as historical records, are used as input. Python is used for data cleaning and preprocessing, and the results are output and stored in a database.

[0277] Step 2:

[0278] Based on the collected data, the server constructs a generation model using TensorFlow. As input, the cleaned energy data is provided, and the generation model creates a virtual model of the energy consumption of the logistics facility. This virtual model is the output.

[0279] Step 3:

[0280] The terminal executes a simulation using the virtual model provided by the server. The inputs are the virtual model and a prompt sentence from the user. Specifically, various operation scenarios are evaluated based on the model, and an optimized energy management plan is output.

[0281] Step 4:

[0282] The user receives the energy usage plan presented by the terminal and works on improving the operation of the logistics facility according to its content. The input is the energy usage plan from the terminal, and specific operation schedule adjustments and equipment setting changes are made and the results are implemented.

[0283] Step 5:

[0284] The operation results obtained by the user are acquired again as data and transferred to the server. Using the new operation data as input, the server updates the virtual model, which serves as the basic data for the next simulation. This updated virtual model is the output.

[0285] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0286] This invention is a system that constructs a virtual model of a physical system using a generation model and optimizes operation efficiency by linking the physical system and the virtual world. Furthermore, it incorporates an emotion engine that recognizes the user's emotions to enhance operation flexibility and the user experience. The embodiments for implementing this invention will be described below.

[0287] First, the server aggregates data related to the physical system and constructs a detailed virtual model by leveraging the generation model. This creates an environment where various operation scenarios of the actual system can be precisely simulated.

[0288] Next, the terminal executes a simulation based on the virtual model and evaluates the operation performance of the system. In this process, an optimal operation strategy is found by analyzing the trends in energy consumption and the utilization efficiency of various resources.

[0289] In addition, this invention recognizes the user's emotional state in real time through the emotion engine and incorporates this information into the operation model. The emotion data is reflected in the adjustment of the system's notification method and operation schedule. For example, when the user is feeling stressed, the system reduces the notification frequency and performs energy management considering calm time periods.

[0290] Furthermore, the user can obtain feedback regarding energy consumption and system operations through the emotion engine. This enables operation adjustments considering the comfort and satisfaction of individual users. Also, since the use of power storage means is optimized according to emotion recognition, effective utilization of energy is achieved while enhancing user convenience.

[0291] In this way, an integrated system of servers, terminals, and users improves the operational efficiency of the actual system while simultaneously enabling sophisticated energy management that takes user emotions into consideration. Specific examples include its application in home energy management systems and minimizing power consumption in office environments. This comprehensively improves both system sustainability and user experience.

[0292] The following describes the processing flow.

[0293] Step 1:

[0294] The server uses a generative model to construct a virtual model based on operational data collected from the physical system and external environment data. In this process, the system's operating state and parameters are input into the generative model, accurately recreating the virtual environment.

[0295] Step 2:

[0296] The terminal runs simulations using a virtual model provided by the server. These simulations examine possible operational scenarios and evaluate energy consumption, supply balance, and energy storage effectiveness. The results provide insights into the timing of charging and discharging of stored resources and system optimization.

[0297] Step 3:

[0298] Users formulate operational strategies based on simulation results obtained from their devices. This includes specific details such as the optimal timing for seasonal energy storage and measures to reduce peak energy consumption. Furthermore, adjustments are made based on the user's stress and satisfaction levels through an emotional engine.

[0299] Step 4:

[0300] The user actually operates the physical system based on the formulated operation strategy. During operation, the user's emotional state is monitored by the emotion engine, and adjustments such as reducing notifications are automatically made when, for example, the user is feeling stressed.

[0301] Step 5:

[0302] The server continuously collects the result data obtained from actual operation and updates the virtual model. This update process reflects the latest real-world data and the user's emotional transition, improving the accuracy of the next simulation.

[0303] Step 6:

[0304] The terminal executes a new simulation based on the updated virtual model and supplies data for formulating a more refined operation strategy. Through this cycle, the system efficiency and user experience are continuously improved.

[0305] (Example 2)

[0306] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] In modern physical systems, while optimizing operation efficiency, it is also required to consider the emotions and comfort of users. However, conventional systems have the problem that although they can handle operation optimization based on physical parameters, it is difficult to make flexible operation adjustments considering the emotional state of users. Furthermore, efficient operation of resources by optimally using power storage means is also an issue.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0309] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state in real time and adjusting notification settings and operational schedules based on that emotional state. This enables flexible operation that considers the user's emotions while pursuing physical efficiency, and resource-efficient system operation that utilizes energy storage means.

[0310] A "generative model" is a mathematical and statistical method used to generate new information based on existing data.

[0311] A "virtual model" is a digital representation used to simulate the actual operation and state of a physical system on a computer.

[0312] "Calculation" refers to the mathematical computation process used for data processing and analysis.

[0313] "Data" refers to a collection of information about the operation and function of a system, including numerical values ​​and records used for analysis.

[0314] An "operational strategy" is a plan or policy for the effective and efficient use of a physical system.

[0315] "Emotional state" refers to the user's psychological state or mood, and is recognized by the system.

[0316] "Notification settings" refer to the criteria and adjustments used to determine the frequency and method of providing information to users.

[0317] An "operation schedule" is a time-based plan for the operation and management of a system.

[0318] "Energy storage means" refers to devices and technologies for storing electricity, which are used in system operation.

[0319] This invention optimizes the operational efficiency of a physical system and enables flexible operation that takes into account the user's feelings. The embodiments for carrying out this invention are described below.

[0320] First, the server acquires and aggregates various data related to the physical system. This data is transmitted in real time from input devices such as sensors and includes temperature, humidity, and energy consumption. A database management system such as "PostgreSQL" is used to manage this data, and "Apache Spark" is used for big data processing.

[0321] Next, the server utilizes the generated AI model to build a virtual model of the physical system based on the collected data. By training the AI ​​model using machine learning frameworks such as "TensorFlow" and "PyTorch" and generating the virtual model, it simulates information useful for actual operation.

[0322] The terminal uses a virtual model built on the server to perform calculations and evaluate the system's operational performance. This evaluation involves dynamic simulations using MATLAB and Simulink, which are then used to formulate operational strategies.

[0323] Furthermore, users interact with the system using an emotion engine. This emotion engine utilizes "EmotionAPI" and other tools to recognize the user's psychological state in real time and reflect this in the system's operation. Notifications received by the user and the system's operating schedule are automatically adjusted based on the user's emotion data.

[0324] As a concrete example, a home energy management system minimizes energy consumption when the user is away from home and begins adjusting the room temperature before they return. This automated operation enhances user comfort and improves energy efficiency.

[0325] An example of a prompt to input into the generating AI model might be, "Create an optimal schedule to maximize energy efficiency in a home energy management system." This prompt allows the system to dynamically generate the optimal operational strategy.

[0326] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0327] Step 1:

[0328] The server collects data from sensors connected to a physical system. This collected data includes a wide range of physical parameters such as temperature, humidity, and energy consumption. The input data is stored in the database management system "PostgreSQL," and data cleansing and formatting are performed using the big data processing framework "Apache Spark." The output is a cleaned dataset.

[0329] Step 2:

[0330] The server constructs a virtual model using the generated AI model. This process uses the dataset obtained in step 1 as input. The server trains the AI ​​model using the machine learning framework "TensorFlow" or "PyTorch" to create the virtual model. This model is intended to simulate the operation of a physical system. A detailed virtual model is generated as output.

[0331] Step 3:

[0332] The terminal performs simulations to evaluate the system's operational performance based on a virtual model provided by the server. At this stage, dynamic simulations are performed using MATLAB or Simulink. The system evaluates energy consumption and resource utilization efficiency for the virtual model as input, and derives the optimal operational strategy. The output is a recommended operational strategy.

[0333] Step 4:

[0334] The server receives the operational strategy provided by the terminal and reflects it in the control of the physical system. Here, it applies instructions to the real system and collects the operational results as data again. Again, the server saves this new data to the database, preparing for updating the virtual model in the next step. Operational result data is generated as output.

[0335] Step 5:

[0336] Users utilize the emotion engine to provide feedback to the system based on their emotional state. The system recognizes the user's psychological state in real time using "EmotionAPI" and other means, and this data is reflected in the system's notification settings and operational schedule. The output provides the user with notification frequencies and operational schedules tailored to their needs.

[0337] Step 6:

[0338] The server updates the virtual model based on sentiment data and operational results data. In this step, the newly acquired information is used to retrain the generative AI model. Based on operational and sentiment data as inputs, a virtual model adapted to the latest scenario is deployed, and the updated virtual model is generated as output. By continuously repeating this process, the system can perform sequentially optimized operations.

[0339] (Application Example 2)

[0340] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0341] Existing systems have a problem in that they have difficulty considering user emotions when optimizing the operation of physical systems, resulting in a lack of improvement in the user experience. Furthermore, this may prevent the system's operational efficiency from being sufficiently increased, potentially hindering the effective use of energy resources.

[0342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0343] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state via an information terminal and dynamically adjusting the environment settings of the physical system based on that emotional state. This makes it possible to improve the user experience and the effective use of energy resources while increasing the operational efficiency of the physical system.

[0344] A "generative model" is an algorithm used to construct a virtual model of a physical system, recreating a virtual environment based on various data.

[0345] A "physical system" is a collection of actual machines and devices through which energy and information flow, and its operation is the subject of optimization.

[0346] A "virtual model" is a model that mimics a physical system and is reproduced on a computer. It is used to explore operational strategies through simulation.

[0347] "Calculation" refers to the process of numerical or logical calculations performed based on a virtual model, and is necessary for formulating operational strategies for a system.

[0348] "Data acquisition" is the process of collecting necessary information from physical systems, and this information is used in formulating operational strategies.

[0349] An "operational strategy" is a plan or policy designed to maximize the utilization efficiency and operational effectiveness of a physical system.

[0350] "Operational results" refer to data on the activities and effects that the physical system actually performed based on the formulated operational strategy.

[0351] "Dynamic adjustment of environment settings" is a process that changes external factors of the physical system in real time based on the user's emotional state.

[0352] "Emotional state" refers to data that indicates the psychological and emotional responses of users, and is used to improve operational efficiency and enhance the user experience.

[0353] To implement this invention, the server first collects various data related to the physical system, and then uses this data to construct a sophisticated virtual model by utilizing a generative model. Based on this virtual model, the server simulates various operational scenarios. This provides information for formulating the optimal operational strategy for the entire system.

[0354] The terminal executes operational strategies formulated through simulations and makes necessary adjustments to improve operational efficiency. In particular, the terminal is equipped with an emotion engine to recognize the user's emotional state in real time, and dynamically changes the environment settings of the physical system based on this information. This optimizes the user experience and provides a comfortable environment.

[0355] Based on the emotional state recognized by the emotion engine and feedback from the system, users can adjust their energy usage and environmental settings in their daily lives. For example, if a user feels stressed, the system can automatically change the music in the store to something calming and adjust the lighting. Through this process, users can enjoy a more comfortable and efficient living environment.

[0356] As a concrete example, cameras and sensors within the store collect facial expression data from customers, and AI analyzes this data to recognize their emotions. Based on this information, smartphones and the store's control system adjust the music and lighting. In this way, the system provides an optimal environment tailored to the customer's emotions.

[0357] An example of a prompt for a generating AI model is the text, "Design an algorithm that determines stress levels in real time from customer facial images and optimizes the in-store environment settings." Based on this prompt, the AI ​​model can generate an algorithm and help optimize the entire system.

[0358] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0359] Step 1:

[0360] The server collects various sensor data from the physical system. This data includes environmental information such as temperature, lighting, and sound. This data is preprocessed and converted into an input dataset for building a virtual model. Specifically, outliers are removed and the data is normalized to prepare it for subsequent processing.

[0361] Step 2:

[0362] The server uses a generative model to construct a virtual model of the physical system from collected sensor data. This virtual model serves as the foundation for simulating the actual physical system. This model enables simulation of various operational scenarios for the system. The generative model analyzes the input data and generates an appropriate virtual environment through computational processing.

[0363] Step 3:

[0364] The device activates an emotion engine to recognize the user's emotional state in real time, taking data on the user's facial expressions and voice acquired from the camera and microphone as input. By analyzing this input data, it identifies the user's emotions and outputs them as an emotional state. This output data plays an important role in the system's environment settings.

[0365] Step 4:

[0366] The device dynamically adjusts the physical system's environmental settings based on the user's emotional state, as determined by the emotion engine. Specifically, it changes settings such as music, lighting, and temperature to suit the user's emotional state. It receives the emotional state and current environmental settings as input, selects the appropriate setting options, and outputs them.

[0367] Step 5:

[0368] Users enjoy a new experience under optimized environment settings. User feedback is re-entered into the system as input, leading to continuous operational improvements. Specifically, feedback on user satisfaction and comfort is collected and used to evaluate operational strategies.

[0369] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0370] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0371] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0375] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0376] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0377] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0378] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0379] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0380] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0381] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0382] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0383] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0384] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0385] This invention relates to a system that utilizes generative models and digital twin technology to perform cyclical simulations and develop operational strategies to support the optimal operation of physical systems. The embodiments for carrying out this invention are described below.

[0386] First, the server collects information such as historical operational data and environmental conditions, and uses a generative model to construct a detailed virtual model of the physical system. This virtual model accurately mimics the operating characteristics of the actual physical system, making it possible to predict various operational scenarios.

[0387] Next, the terminal runs a simulation using a virtual model. In this simulation, for example, in the case of an energy management system, fluctuations in power generation and charging / discharging of storage batteries are simulated. The data obtained from the simulation is used to evaluate the overall operational efficiency and bottlenecks of the system.

[0388] Subsequently, the user develops an operational strategy based on the simulation results. This process proposes specific measures to optimize the balance between energy supply and demand and to reduce economic costs. Examples include battery discharge schedules that avoid peak hours and optimal utilization methods for surplus energy.

[0389] Based on the established operational strategy, users operate the actual system and record the results. New data obtained during operation is transferred to the server and used to update the virtual model. This updated virtual model serves as the starting point for the next simulation, enabling further optimization based on the latest information.

[0390] This enables continuous system improvement and efficient energy management through the interconnectedness of servers, terminals, and users. Specific examples include optimizing the use of renewable energy, reducing energy costs, and minimizing the carbon footprint. These are concrete implementations for improving the overall sustainability and economic efficiency of the system.

[0391] The following describes the processing flow.

[0392] Step 1:

[0393] The server collects historical operational data, environmental conditions data, and other relevant information from the physical system. Based on this information, it uses a generative model to construct a virtual model that accurately mimics the real-world physical system. This creates an environment where system operation can be simulated.

[0394] Step 2:

[0395] The terminal runs a simulation using a virtual model built on the server. This simulation simulates different operational scenarios and conditions, generating data on system performance and efficiency. Once the simulation completes successfully, the obtained data is saved and prepared for analysis.

[0396] Step 3:

[0397] Users analyze simulation data acquired from their terminals to formulate operational strategies for the physical system. These strategies are tailored to specific goals, such as reducing energy consumption, lowering costs, and improving availability. The formulated operational strategies are then implemented in the actual system's operations.

[0398] Step 4:

[0399] Users operate the actual physical system according to the established operational strategy and record the operational results daily. The data accumulated during system operation provides insights necessary for continuous optimization of operations.

[0400] Step 5:

[0401] The server receives operational results and the latest data sent from the actual system and uses them to update the virtual model. By utilizing generative AI and updating the model based on this constantly updated information, the next simulation will be more realistic.

[0402] Step 6:

[0403] The terminal runs a new simulation using the updated virtual model and analyzes the data again. This process proceeds cyclically, enabling continuous improvement and optimization of the system.

[0404] (Example 1)

[0405] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0406] In modern, complex physical systems, efficient operation and management present challenges. In particular, there is a need to optimize energy resources, improve operational efficiency, and reduce environmental impact. Conventional methods struggle to adequately achieve these objectives.

[0407] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0408] In this invention, the server includes means for creating a detailed virtual model of the physical mechanism using a generative model, means for performing computer simulations based on the virtual model and collecting information about the physical mechanism, and means for analyzing the collected information and formulating an operational strategy for the physical mechanism. This enables improved operational efficiency of the physical mechanism and optimal use of energy resources.

[0409] A "generative model" is a mathematical model created to virtually reproduce a physical mechanism and simulate the operation of a system.

[0410] A "virtual model" is a virtualized system that faithfully reproduces the characteristics and operation of physical mechanisms based on a model generated on a computer.

[0411] A "physical mechanism" refers to a specific structure, device, or collection of such structures or devices that operate in the real world.

[0412] "Computer simulation" is a method of simulating and predicting the operation of a system under various conditions on a computer using a virtual model.

[0413] An "operational strategy" is a specific plan or policy formulated to achieve efficient and effective management and control of a physical mechanism.

[0414] A "energy storage device" is a device that temporarily stores energy resources in the form of electricity and supplies power as needed.

[0415] This invention is a system that utilizes generative models and digital twin technology to support the optimal operation of physical mechanisms. Specific embodiments are described below.

[0416] The server first collects historical operational data and various environmental condition information from a database. This includes weather information, power consumption, and equipment operating status. Based on the collected data, the server uses a generative AI model to create a detailed virtual model of the physical mechanism. Data analysis tools utilizing Python and R are used here.

[0417] The terminal receives a virtual model generated by the server and performs a computer simulation. MATLAB is often used as the simulation software. This simulation allows for detailed analysis of, for example, fluctuations in power generation and the charge-discharge cycles of energy storage devices, and evaluates the operational efficiency of the system.

[0418] Subsequently, the user formulates an operational strategy for the physical mechanism based on the simulation results provided by the terminal. This process involves developing specific plans aimed at optimizing energy efficiency and reducing costs. Users can develop strategies using Excel spreadsheets or dedicated applications. For example, one might design a schedule to charge energy storage devices at night and discharge them during the day to shift peak demand.

[0419] Furthermore, as an application of the generative AI model, an example of a prompt message is shown below: "Predict the energy demand for the next 24 hours and suggest the optimal battery charging and discharging schedule."

[0420] This system enables servers, terminals, and users to work together, effectively achieving optimal use of energy resources and efficient operation of physical mechanisms.

[0421] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0422] Step 1:

[0423] The server collects historical operational data and environmental condition information from a database. This input data includes weather information, power consumption, and equipment operating status. The collected data is analyzed using Python or R to extract operating patterns of physical mechanisms. As a result, an initial dataset for generative AI models can be created.

[0424] Step 2:

[0425] Based on the data collected and analyzed in Step 1, the server uses a generated AI model to construct a virtual model of the physical mechanism. This virtual model replicates the characteristics of the physical mechanism and serves as the foundation for simulating actual system operation. The output includes system operating characteristics in various simulated environments.

[0426] Step 3:

[0427] The terminal performs computer simulations based on virtual models received from the server. Inputs include virtual models and various operational scenarios. MATLAB is used to perform the simulations, analyzing in detail fluctuations in power generation and charge / discharge cycles of energy storage devices to evaluate operational efficiency. As a result, output information regarding reductions in operating costs and improvements in energy efficiency is obtained.

[0428] Step 4:

[0429] The user formulates an operational strategy for the physical mechanism based on the simulation results obtained from the terminal. The input here is the evaluation result of operational efficiency from the simulation. The user formulates the strategy using Excel or a dedicated application and plans to achieve an optimal balance between energy supply and demand. A specific action is the design of a charge / discharge schedule for peak shifting.

[0430] Step 5:

[0431] The user operates the actual physical mechanism based on the operational strategy they have devised. The input here is the formulated operational strategy. The user monitors sensor information generated in real time and feeds the results back to the server. This feedback data is used for the subsequent virtual simulation update process.

[0432] Step 6:

[0433] The server updates the virtual model based on operational data provided by the user. This input data consists of operational results based on actual performance. The server adjusts the parameters of the generated AI model as needed, preparing a foundation for more accurate simulations and strategy formulation for the next time. As output, a virtual model reflecting the latest operational status is created.

[0434] (Application Example 1)

[0435] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0436] Managing and optimizing energy consumption in logistics facilities is a crucial challenge for improving operational efficiency and reducing costs. However, there is a lack of effective systems to cope with fluctuations in energy demand and complex operational schedules. Therefore, there is a need for systems that can optimize energy consumption while increasing the operational efficiency of logistics facilities.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0438] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data on energy consumption in the logistics facility, and means for updating the virtual model using the acquired operational results. This enables real-time optimization of energy management in the logistics facility and efficient operation.

[0439] A "generative model" is an algorithm that learns patterns from data and generates new data.

[0440] A "physical system" is a structure that refers to the entirety of hardware and processes in the real world.

[0441] A "virtual model" is a digital representation used to simulate the characteristics and behavior of a physical system.

[0442] An "operational strategy" is a set of plans and policies formulated to maximize system efficiency.

[0443] A "logistics facility" refers to a comprehensive infrastructure and group of buildings used for storing, collecting, and distributing goods.

[0444] "Energy consumption" refers to the total amount of energy, such as electricity and fuel, used in the operation of a logistics facility.

[0445] "Optimization" refers to the actions or methods used to maximize the performance of a system or process.

[0446] To realize this invention, the server, terminal, and user elements must work together in coordination. The server first collects historical energy usage data and operational records of the logistics facility. This involves obtaining information from sensors installed in the logistics facility and from existing databases. Data cleaning and preprocessing using Python are used to create a dataset for building a generative model. This generative model is designed using TensorFlow to build a virtual model of the energy consumption of the logistics facility.

[0447] The terminal runs various simulations based on a virtual model and evaluates the results in real time. It then presents the user with an optimized energy management plan. This uses a mobile app built with React Native, which visualizes operating schedules and energy usage. Users can then improve actual facility operations according to the presented plan.

[0448] For example, when a logistics facility operator plans the optimal charging time for trucks based on the next day's delivery schedule, they can plan to charge during off-peak hours when energy costs are low. In this embodiment, a prompt such as "Please create an optimal energy consumption plan considering the next day's truck departure schedule" can be input to the generating AI model to obtain an appropriate operational strategy.

[0449] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0450] Step 1:

[0451] The server collects energy data and historical operational data from within the logistics facility. Real-time data from sensors installed at the facility, as well as historical records, are used as input. Python is used for data cleaning and preprocessing, and the results are output and stored in a database.

[0452] Step 2:

[0453] The server constructs a generative model using TensorFlow based on the collected data. Cleaned energy data is provided as input, and the generative model creates a virtual model of the energy consumption of the logistics facility. This virtual model becomes the output.

[0454] Step 3:

[0455] The terminal runs a simulation using a virtual model provided by the server. The input consists of the virtual model and user prompts. Specifically, it evaluates various operational scenarios based on the model and outputs an optimized energy management plan.

[0456] Step 4:

[0457] The user receives the energy usage plan presented by the terminal and works to improve the operation of the logistics facility according to its contents. The input is the energy usage plan from the terminal, and the user makes specific adjustments to the operational schedule and changes to equipment settings, and then implements the results.

[0458] Step 5:

[0459] The user's operational results are retrieved as data and transferred to the server. The server then updates the virtual model using this new operational data as input, creating the basis for the next simulation. This updated virtual model is the output.

[0460] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0461] This invention is a system that optimizes operational efficiency by constructing a virtual model of a physical system using a generative model and linking the physical system with a virtual world. Furthermore, it incorporates an emotion engine that recognizes user emotions to improve operational flexibility and user experience. The embodiments for carrying out this invention are described below.

[0462] First, the server aggregates data about the physical system and uses generative models to build a detailed virtual model. This creates an environment where various operational scenarios of the actual system can be simulated in detail.

[0463] Next, the terminal runs a simulation based on a virtual model to evaluate the system's operational performance. This process analyzes energy consumption trends and the efficiency of various resource usage to find the optimal operational strategy.

[0464] In addition, the present invention recognizes the user's emotional state in real time using an emotion engine and incorporates this information into the operational model. Emotional data is used to adjust the system's notification methods and operational schedule. For example, if a user is feeling stressed, the system reduces the notification frequency and manages energy levels to take into account calmer periods.

[0465] Furthermore, users can receive feedback on energy consumption and system operation through the emotion engine. This allows for operational adjustments that take into account the comfort and satisfaction of individual users. In addition, the use of energy storage methods is optimized according to emotion recognition, thus achieving efficient energy use while enhancing user convenience.

[0466] In this way, an integrated system of servers, terminals, and users improves the operational efficiency of the actual system while simultaneously enabling sophisticated energy management that takes user emotions into consideration. Specific examples include its application in home energy management systems and minimizing power consumption in office environments. This comprehensively improves both system sustainability and user experience.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The server uses a generative model to construct a virtual model based on operational data collected from the physical system and external environment data. In this process, the system's operating state and parameters are input into the generative model, accurately recreating the virtual environment.

[0470] Step 2:

[0471] The terminal runs simulations using a virtual model provided by the server. These simulations examine possible operational scenarios and evaluate energy consumption, supply balance, and energy storage effectiveness. The results provide insights into the timing of charging and discharging of stored resources and system optimization.

[0472] Step 3:

[0473] Users formulate operational strategies based on simulation results obtained from their devices. This includes specific details such as the optimal timing for seasonal energy storage and measures to reduce peak energy consumption. Furthermore, adjustments are made based on the user's stress and satisfaction levels through an emotional engine.

[0474] Step 4:

[0475] The user operates the physical system based on the established operational strategy. During operation, the user's emotional state is monitored by an emotion engine, and adjustments are automatically made, for example, by reducing notifications if the user is feeling stressed.

[0476] Step 5:

[0477] The server continuously collects result data obtained from actual operation and updates the virtual model. This update process reflects the latest real-world data and changes in user sentiment, improving the accuracy of the next simulation.

[0478] Step 6:

[0479] The terminal runs new simulations based on the updated virtual model, providing data to develop even more refined operational strategies. This cycle continuously improves system efficiency and enhances the user experience.

[0480] (Example 2)

[0481] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0482] Modern physical systems require optimizing operational efficiency while also considering user emotions and comfort. However, conventional systems, while capable of operational optimization based on physical parameters, have struggled to flexibly adjust operations while considering the emotional state of users. Furthermore, efficient resource utilization through the optimal use of energy storage methods remains a challenge.

[0483] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0484] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state in real time and adjusting notification settings and operational schedules based on that emotional state. This enables flexible operation that considers the user's emotions while pursuing physical efficiency, and resource-efficient system operation that utilizes energy storage means.

[0485] A "generative model" is a mathematical and statistical method used to generate new information based on existing data.

[0486] A "virtual model" is a digital representation used to simulate the actual operation and state of a physical system on a computer.

[0487] "Calculation" refers to the mathematical computation process used for data processing and analysis.

[0488] "Data" refers to a collection of information about the operation and function of a system, including numerical values ​​and records used for analysis.

[0489] An "operational strategy" is a plan or policy for the effective and efficient use of a physical system.

[0490] "Emotional state" refers to the user's psychological state or mood, and is recognized by the system.

[0491] "Notification settings" refer to the criteria and adjustments used to determine the frequency and method of providing information to users.

[0492] An "operation schedule" is a time-based plan for the operation and management of a system.

[0493] "Energy storage means" refers to devices and technologies for storing electricity, which are used in system operation.

[0494] This invention optimizes the operational efficiency of a physical system and enables flexible operation that takes into account the user's feelings. The embodiments for carrying out this invention are described below.

[0495] First, the server acquires and aggregates various data related to the physical system. This data is transmitted in real time from input devices such as sensors and includes temperature, humidity, and energy consumption. A database management system such as "PostgreSQL" is used to manage this data, and "Apache Spark" is used for big data processing.

[0496] Next, the server utilizes the generated AI model to build a virtual model of the physical system based on the collected data. By training the AI ​​model using machine learning frameworks such as "TensorFlow" and "PyTorch" and generating the virtual model, it simulates information useful for actual operation.

[0497] The terminal uses a virtual model built on the server to perform calculations and evaluate the system's operational performance. This evaluation involves dynamic simulations using MATLAB and Simulink, which are then used to formulate operational strategies.

[0498] Furthermore, users interact with the system using an emotion engine. This emotion engine utilizes "EmotionAPI" and other tools to recognize the user's psychological state in real time and reflect this in the system's operation. Notifications received by the user and the system's operating schedule are automatically adjusted based on the user's emotion data.

[0499] As a concrete example, a home energy management system minimizes energy consumption when the user is away from home and begins adjusting the room temperature before they return. This automated operation enhances user comfort and improves energy efficiency.

[0500] An example of a prompt to input into the generating AI model might be, "Create an optimal schedule to maximize energy efficiency in a home energy management system." This prompt allows the system to dynamically generate the optimal operational strategy.

[0501] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0502] Step 1:

[0503] The server collects data from sensors connected to a physical system. This collected data includes a wide range of physical parameters such as temperature, humidity, and energy consumption. The input data is stored in the database management system "PostgreSQL," and data cleansing and formatting are performed using the big data processing framework "Apache Spark." The output is a cleaned dataset.

[0504] Step 2:

[0505] The server constructs a virtual model using the generated AI model. This process uses the dataset obtained in step 1 as input. The server trains the AI ​​model using the machine learning framework "TensorFlow" or "PyTorch" to create the virtual model. This model is intended to simulate the operation of a physical system. A detailed virtual model is generated as output.

[0506] Step 3:

[0507] The terminal performs simulations to evaluate the system's operational performance based on a virtual model provided by the server. At this stage, dynamic simulations are performed using MATLAB or Simulink. The system evaluates energy consumption and resource utilization efficiency for the virtual model as input, and derives the optimal operational strategy. The output is a recommended operational strategy.

[0508] Step 4:

[0509] The server receives the operational strategy provided by the terminal and reflects it in the control of the physical system. Here, it applies instructions to the real system and collects the operational results as data again. Again, the server saves this new data to the database, preparing for updating the virtual model in the next step. Operational result data is generated as output.

[0510] Step 5:

[0511] Users utilize the emotion engine to provide feedback to the system based on their emotional state. The system recognizes the user's psychological state in real time using "EmotionAPI" and other means, and this data is reflected in the system's notification settings and operational schedule. The output provides the user with notification frequencies and operational schedules tailored to their needs.

[0512] Step 6:

[0513] The server updates the virtual model based on sentiment data and operational results data. In this step, the newly acquired information is used to retrain the generative AI model. Based on operational and sentiment data as inputs, a virtual model adapted to the latest scenario is deployed, and the updated virtual model is generated as output. By continuously repeating this process, the system can perform sequentially optimized operations.

[0514] (Application Example 2)

[0515] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] Existing systems have a problem in that they have difficulty considering user emotions when optimizing the operation of physical systems, resulting in a lack of improvement in the user experience. Furthermore, this may prevent the system's operational efficiency from being sufficiently increased, potentially hindering the effective use of energy resources.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0518] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state via an information terminal and dynamically adjusting the environment settings of the physical system based on that emotional state. This makes it possible to improve the user experience and the effective use of energy resources while increasing the operational efficiency of the physical system.

[0519] A "generative model" is an algorithm used to construct a virtual model of a physical system, recreating a virtual environment based on various data.

[0520] A "physical system" is a collection of actual machines and devices through which energy and information flow, and its operation is the subject of optimization.

[0521] A "virtual model" is a model that mimics a physical system and is reproduced on a computer. It is used to explore operational strategies through simulation.

[0522] "Calculation" refers to the process of numerical or logical calculations performed based on a virtual model, and is necessary for formulating operational strategies for a system.

[0523] "Data acquisition" is the process of collecting necessary information from physical systems, and this information is used in formulating operational strategies.

[0524] An "operational strategy" is a plan or policy designed to maximize the utilization efficiency and operational effectiveness of a physical system.

[0525] "Operational results" refer to data on the activities and effects that the physical system actually performed based on the formulated operational strategy.

[0526] "Dynamic adjustment of environment settings" is a process that changes external factors of the physical system in real time based on the user's emotional state.

[0527] "Emotional state" refers to data that indicates the psychological and emotional responses of users, and is used to improve operational efficiency and enhance the user experience.

[0528] To implement this invention, the server first collects various data related to the physical system, and then uses this data to construct a sophisticated virtual model by utilizing a generative model. Based on this virtual model, the server simulates various operational scenarios. This provides information for formulating the optimal operational strategy for the entire system.

[0529] The terminal executes operational strategies formulated through simulations and makes necessary adjustments to improve operational efficiency. In particular, the terminal is equipped with an emotion engine to recognize the user's emotional state in real time, and dynamically changes the environment settings of the physical system based on this information. This optimizes the user experience and provides a comfortable environment.

[0530] Based on the emotional state recognized by the emotion engine and feedback from the system, users can adjust their energy usage and environmental settings in their daily lives. For example, if a user feels stressed, the system can automatically change the music in the store to something calming and adjust the lighting. Through this process, users can enjoy a more comfortable and efficient living environment.

[0531] As a concrete example, cameras and sensors within the store collect facial expression data from customers, and AI analyzes this data to recognize their emotions. Based on this information, smartphones and the store's control system adjust the music and lighting. In this way, the system provides an optimal environment tailored to the customer's emotions.

[0532] An example of a prompt for a generating AI model is the text, "Design an algorithm that determines stress levels in real time from customer facial images and optimizes the in-store environment settings." Based on this prompt, the AI ​​model can generate an algorithm and help optimize the entire system.

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] The server collects various sensor data from the physical system. This data includes environmental information such as temperature, lighting, and sound. This data is preprocessed and converted into an input dataset for building a virtual model. Specifically, outliers are removed and the data is normalized to prepare it for subsequent processing.

[0536] Step 2:

[0537] The server uses a generative model to construct a virtual model of the physical system from collected sensor data. This virtual model serves as the foundation for simulating the actual physical system. This model enables simulation of various operational scenarios for the system. The generative model analyzes the input data and generates an appropriate virtual environment through computational processing.

[0538] Step 3:

[0539] The device activates an emotion engine to recognize the user's emotional state in real time, taking data on the user's facial expressions and voice acquired from the camera and microphone as input. By analyzing this input data, it identifies the user's emotions and outputs them as an emotional state. This output data plays an important role in the system's environment settings.

[0540] Step 4:

[0541] The device dynamically adjusts the physical system's environmental settings based on the user's emotional state, as determined by the emotion engine. Specifically, it changes settings such as music, lighting, and temperature to suit the user's emotional state. It receives the emotional state and current environmental settings as input, selects the appropriate setting options, and outputs them.

[0542] Step 5:

[0543] Users enjoy a new experience under optimized environment settings. User feedback is re-entered into the system as input, leading to continuous operational improvements. Specifically, feedback on user satisfaction and comfort is collected and used to evaluate operational strategies.

[0544] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0545] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0546] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0547] [Fourth Embodiment]

[0548] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0549] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0550] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0551] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0552] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0553] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0554] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0555] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0556] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0557] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0558] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0559] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0560] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0561] This invention relates to a system that utilizes generative models and digital twin technology to perform cyclical simulations and develop operational strategies to support the optimal operation of physical systems. The embodiments for carrying out this invention are described below.

[0562] First, the server collects information such as historical operational data and environmental conditions, and uses a generative model to construct a detailed virtual model of the physical system. This virtual model accurately mimics the operating characteristics of the actual physical system, making it possible to predict various operational scenarios.

[0563] Next, the terminal runs a simulation using a virtual model. In this simulation, for example, in the case of an energy management system, fluctuations in power generation and charging / discharging of storage batteries are simulated. The data obtained from the simulation is used to evaluate the overall operational efficiency and bottlenecks of the system.

[0564] Subsequently, the user develops an operational strategy based on the simulation results. This process proposes specific measures to optimize the balance between energy supply and demand and to reduce economic costs. Examples include battery discharge schedules that avoid peak hours and optimal utilization methods for surplus energy.

[0565] Based on the established operational strategy, users operate the actual system and record the results. New data obtained during operation is transferred to the server and used to update the virtual model. This updated virtual model serves as the starting point for the next simulation, enabling further optimization based on the latest information.

[0566] This enables continuous system improvement and efficient energy management through the interconnectedness of servers, terminals, and users. Specific examples include optimizing the use of renewable energy, reducing energy costs, and minimizing the carbon footprint. These are concrete implementations for improving the overall sustainability and economic efficiency of the system.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The server collects historical operational data, environmental conditions data, and other relevant information from the physical system. Based on this information, it uses a generative model to construct a virtual model that accurately mimics the real-world physical system. This creates an environment where system operation can be simulated.

[0570] Step 2:

[0571] The terminal runs a simulation using a virtual model built on the server. This simulation simulates different operational scenarios and conditions, generating data on system performance and efficiency. Once the simulation completes successfully, the obtained data is saved and prepared for analysis.

[0572] Step 3:

[0573] Users analyze simulation data acquired from their terminals to formulate operational strategies for the physical system. These strategies are tailored to specific goals, such as reducing energy consumption, lowering costs, and improving availability. The formulated operational strategies are then implemented in the actual system's operations.

[0574] Step 4:

[0575] Users operate the actual physical system according to the established operational strategy and record the operational results daily. The data accumulated during system operation provides insights necessary for continuous optimization of operations.

[0576] Step 5:

[0577] The server receives operational results and the latest data sent from the actual system and uses them to update the virtual model. By utilizing generative AI and updating the model based on this constantly updated information, the next simulation will be more realistic.

[0578] Step 6:

[0579] The terminal runs a new simulation using the updated virtual model and analyzes the data again. This process proceeds cyclically, enabling continuous improvement and optimization of the system.

[0580] (Example 1)

[0581] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] In modern, complex physical systems, efficient operation and management present challenges. In particular, there is a need to optimize energy resources, improve operational efficiency, and reduce environmental impact. Conventional methods struggle to adequately achieve these objectives.

[0583] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0584] In this invention, the server includes means for creating a detailed virtual model of the physical mechanism using a generative model, means for performing computer simulations based on the virtual model and collecting information about the physical mechanism, and means for analyzing the collected information and formulating an operational strategy for the physical mechanism. This enables improved operational efficiency of the physical mechanism and optimal use of energy resources.

[0585] A "generative model" is a mathematical model created to virtually reproduce a physical mechanism and simulate the operation of a system.

[0586] A "virtual model" is a virtualized system that faithfully reproduces the characteristics and operation of physical mechanisms based on a model generated on a computer.

[0587] A "physical mechanism" refers to a specific structure, device, or collection of such structures or devices that operate in the real world.

[0588] "Computer simulation" is a method of simulating and predicting the operation of a system under various conditions on a computer using a virtual model.

[0589] An "operational strategy" is a specific plan or policy formulated to achieve efficient and effective management and control of a physical mechanism.

[0590] A "energy storage device" is a device that temporarily stores energy resources in the form of electricity and supplies power as needed.

[0591] This invention is a system that utilizes generative models and digital twin technology to support the optimal operation of physical mechanisms. Specific embodiments are described below.

[0592] The server first collects historical operational data and various environmental condition information from a database. This includes weather information, power consumption, and equipment operating status. Based on the collected data, the server uses a generative AI model to create a detailed virtual model of the physical mechanism. Data analysis tools utilizing Python and R are used here.

[0593] The terminal receives a virtual model generated by the server and performs a computer simulation. MATLAB is often used as the simulation software. This simulation allows for detailed analysis of, for example, fluctuations in power generation and the charge-discharge cycles of energy storage devices, and evaluates the operational efficiency of the system.

[0594] Subsequently, the user formulates an operational strategy for the physical mechanism based on the simulation results provided by the terminal. This process involves developing specific plans aimed at optimizing energy efficiency and reducing costs. Users can develop strategies using Excel spreadsheets or dedicated applications. For example, one might design a schedule to charge energy storage devices at night and discharge them during the day to shift peak demand.

[0595] Furthermore, as an application of the generative AI model, an example of a prompt message is shown below: "Predict the energy demand for the next 24 hours and suggest the optimal battery charging and discharging schedule."

[0596] This system enables servers, terminals, and users to work together, effectively achieving optimal use of energy resources and efficient operation of physical mechanisms.

[0597] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0598] Step 1:

[0599] The server collects historical operational data and environmental condition information from a database. This input data includes weather information, power consumption, and equipment operating status. The collected data is analyzed using Python or R to extract operating patterns of physical mechanisms. As a result, an initial dataset for generative AI models can be created.

[0600] Step 2:

[0601] Based on the data collected and analyzed in Step 1, the server uses a generated AI model to construct a virtual model of the physical mechanism. This virtual model replicates the characteristics of the physical mechanism and serves as the foundation for simulating actual system operation. The output includes system operating characteristics in various simulated environments.

[0602] Step 3:

[0603] The terminal performs computer simulations based on virtual models received from the server. Inputs include virtual models and various operational scenarios. MATLAB is used to perform the simulations, analyzing in detail fluctuations in power generation and charge / discharge cycles of energy storage devices to evaluate operational efficiency. As a result, output information regarding reductions in operating costs and improvements in energy efficiency is obtained.

[0604] Step 4:

[0605] The user formulates an operational strategy for the physical mechanism based on the simulation results obtained from the terminal. The input here is the evaluation result of operational efficiency from the simulation. The user formulates the strategy using Excel or a dedicated application and plans to achieve an optimal balance between energy supply and demand. A specific action is the design of a charge / discharge schedule for peak shifting.

[0606] Step 5:

[0607] The user operates the actual physical mechanism based on the operational strategy they have devised. The input here is the formulated operational strategy. The user monitors sensor information generated in real time and feeds the results back to the server. This feedback data is used for the subsequent virtual simulation update process.

[0608] Step 6:

[0609] The server updates the virtual model based on operational data provided by the user. This input data consists of operational results based on actual performance. The server adjusts the parameters of the generated AI model as needed, preparing a foundation for more accurate simulations and strategy formulation for the next time. As output, a virtual model reflecting the latest operational status is created.

[0610] (Application Example 1)

[0611] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] Managing and optimizing energy consumption in logistics facilities is a crucial challenge for improving operational efficiency and reducing costs. However, there is a lack of effective systems to cope with fluctuations in energy demand and complex operational schedules. Therefore, there is a need for systems that can optimize energy consumption while increasing the operational efficiency of logistics facilities.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0614] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data on energy consumption in the logistics facility, and means for updating the virtual model using the acquired operational results. This enables real-time optimization of energy management in the logistics facility and efficient operation.

[0615] A "generative model" is an algorithm that learns patterns from data and generates new data.

[0616] A "physical system" is a structure that refers to the entirety of hardware and processes in the real world.

[0617] A "virtual model" is a digital representation used to simulate the characteristics and behavior of a physical system.

[0618] An "operational strategy" is a set of plans and policies formulated to maximize system efficiency.

[0619] A "logistics facility" refers to a comprehensive infrastructure and group of buildings used for storing, collecting, and distributing goods.

[0620] "Energy consumption" refers to the total amount of energy, such as electricity and fuel, used in the operation of a logistics facility.

[0621] "Optimization" refers to the actions or methods used to maximize the performance of a system or process.

[0622] To realize this invention, the server, terminal, and user elements must work together in coordination. The server first collects historical energy usage data and operational records of the logistics facility. This involves obtaining information from sensors installed in the logistics facility and from existing databases. Data cleaning and preprocessing using Python are used to create a dataset for building a generative model. This generative model is designed using TensorFlow to build a virtual model of the energy consumption of the logistics facility.

[0623] The terminal runs various simulations based on a virtual model and evaluates the results in real time. It then presents the user with an optimized energy management plan. This uses a mobile app built with React Native, which visualizes operating schedules and energy usage. Users can then improve actual facility operations according to the presented plan.

[0624] For example, when a logistics facility operator plans the optimal charging time for trucks based on the next day's delivery schedule, they can plan to charge during off-peak hours when energy costs are low. In this embodiment, a prompt such as "Please create an optimal energy consumption plan considering the next day's truck departure schedule" can be input to the generating AI model to obtain an appropriate operational strategy.

[0625] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0626] Step 1:

[0627] The server collects energy data and historical operational data from within the logistics facility. Real-time data from sensors installed at the facility, as well as historical records, are used as input. Python is used for data cleaning and preprocessing, and the results are output and stored in a database.

[0628] Step 2:

[0629] The server constructs a generative model using TensorFlow based on the collected data. Cleaned energy data is provided as input, and the generative model creates a virtual model of the energy consumption of the logistics facility. This virtual model becomes the output.

[0630] Step 3:

[0631] The terminal runs a simulation using a virtual model provided by the server. The input consists of the virtual model and user prompts. Specifically, it evaluates various operational scenarios based on the model and outputs an optimized energy management plan.

[0632] Step 4:

[0633] The user receives the energy usage plan presented by the terminal and works to improve the operation of the logistics facility according to its contents. The input is the energy usage plan from the terminal, and the user makes specific adjustments to the operational schedule and changes to equipment settings, and then implements the results.

[0634] Step 5:

[0635] The user's operational results are retrieved as data and transferred to the server. The server then updates the virtual model using this new operational data as input, creating the basis for the next simulation. This updated virtual model is the output.

[0636] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0637] This invention is a system that optimizes operational efficiency by constructing a virtual model of a physical system using a generative model and linking the physical system with a virtual world. Furthermore, it incorporates an emotion engine that recognizes user emotions to improve operational flexibility and user experience. The embodiments for carrying out this invention are described below.

[0638] First, the server aggregates data about the physical system and uses generative models to build a detailed virtual model. This creates an environment where various operational scenarios of the actual system can be simulated in detail.

[0639] Next, the terminal runs a simulation based on a virtual model to evaluate the system's operational performance. This process analyzes energy consumption trends and the efficiency of various resource usage to find the optimal operational strategy.

[0640] In addition, the present invention recognizes the user's emotional state in real time using an emotion engine and incorporates this information into the operational model. Emotional data is used to adjust the system's notification methods and operational schedule. For example, if a user is feeling stressed, the system reduces the notification frequency and manages energy levels to take into account calmer periods.

[0641] Furthermore, users can receive feedback on energy consumption and system operation through the emotion engine. This allows for operational adjustments that take into account the comfort and satisfaction of individual users. In addition, the use of energy storage methods is optimized according to emotion recognition, thus achieving efficient energy use while enhancing user convenience.

[0642] In this way, an integrated system of servers, terminals, and users improves the operational efficiency of the actual system while simultaneously enabling sophisticated energy management that takes user emotions into consideration. Specific examples include its application in home energy management systems and minimizing power consumption in office environments. This comprehensively improves both system sustainability and user experience.

[0643] The following describes the processing flow.

[0644] Step 1:

[0645] The server uses a generative model to construct a virtual model based on operational data collected from the physical system and external environment data. In this process, the system's operating state and parameters are input into the generative model, accurately recreating the virtual environment.

[0646] Step 2:

[0647] The terminal runs simulations using a virtual model provided by the server. These simulations examine possible operational scenarios and evaluate energy consumption, supply balance, and energy storage effectiveness. The results provide insights into the timing of charging and discharging of stored resources and system optimization.

[0648] Step 3:

[0649] Users formulate operational strategies based on simulation results obtained from their devices. This includes specific details such as the optimal timing for seasonal energy storage and measures to reduce peak energy consumption. Furthermore, adjustments are made based on the user's stress and satisfaction levels through an emotional engine.

[0650] Step 4:

[0651] The user operates the physical system based on the established operational strategy. During operation, the user's emotional state is monitored by an emotion engine, and adjustments are automatically made, for example, by reducing notifications if the user is feeling stressed.

[0652] Step 5:

[0653] The server continuously collects result data obtained from actual operation and updates the virtual model. This update process reflects the latest real-world data and changes in user sentiment, improving the accuracy of the next simulation.

[0654] Step 6:

[0655] The terminal runs new simulations based on the updated virtual model, providing data to develop even more refined operational strategies. This cycle continuously improves system efficiency and enhances the user experience.

[0656] (Example 2)

[0657] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0658] Modern physical systems require optimizing operational efficiency while also considering user emotions and comfort. However, conventional systems, while capable of operational optimization based on physical parameters, have struggled to flexibly adjust operations while considering the emotional state of users. Furthermore, efficient resource utilization through the optimal use of energy storage methods remains a challenge.

[0659] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0660] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state in real time and adjusting notification settings and operational schedules based on that emotional state. This enables flexible operation that considers the user's emotions while pursuing physical efficiency, and resource-efficient system operation that utilizes energy storage means.

[0661] A "generative model" is a mathematical and statistical method used to generate new information based on existing data.

[0662] A "virtual model" is a digital representation used to simulate the actual operation and state of a physical system on a computer.

[0663] "Calculation" refers to the mathematical computation process used for data processing and analysis.

[0664] "Data" refers to a collection of information about the operation and function of a system, including numerical values ​​and records used for analysis.

[0665] An "operational strategy" is a plan or policy for the effective and efficient use of a physical system.

[0666] "Emotional state" refers to the user's psychological state or mood, and is recognized by the system.

[0667] "Notification settings" refer to the criteria and adjustments used to determine the frequency and method of providing information to users.

[0668] An "operation schedule" is a time-based plan for the operation and management of a system.

[0669] "Energy storage means" refers to devices and technologies for storing electricity, which are used in system operation.

[0670] This invention optimizes the operational efficiency of a physical system and enables flexible operation that takes into account the user's feelings. The embodiments for carrying out this invention are described below.

[0671] First, the server acquires and aggregates various data related to the physical system. This data is transmitted in real time from input devices such as sensors and includes temperature, humidity, and energy consumption. A database management system such as "PostgreSQL" is used to manage this data, and "Apache Spark" is used for big data processing.

[0672] Next, the server utilizes the generated AI model to build a virtual model of the physical system based on the collected data. By training the AI ​​model using machine learning frameworks such as "TensorFlow" and "PyTorch" and generating the virtual model, it simulates information useful for actual operation.

[0673] The terminal uses a virtual model built on the server to perform calculations and evaluate the system's operational performance. This evaluation involves dynamic simulations using MATLAB and Simulink, which are then used to formulate operational strategies.

[0674] Furthermore, users interact with the system using an emotion engine. This emotion engine utilizes "EmotionAPI" and other tools to recognize the user's psychological state in real time and reflect this in the system's operation. Notifications received by the user and the system's operating schedule are automatically adjusted based on the user's emotion data.

[0675] As a concrete example, a home energy management system minimizes energy consumption when the user is away from home and begins adjusting the room temperature before they return. This automated operation enhances user comfort and improves energy efficiency.

[0676] An example of a prompt to input into the generating AI model might be, "Create an optimal schedule to maximize energy efficiency in a home energy management system." This prompt allows the system to dynamically generate the optimal operational strategy.

[0677] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0678] Step 1:

[0679] The server collects data from sensors connected to a physical system. This collected data includes a wide range of physical parameters such as temperature, humidity, and energy consumption. The input data is stored in the database management system "PostgreSQL," and data cleansing and formatting are performed using the big data processing framework "Apache Spark." The output is a cleaned dataset.

[0680] Step 2:

[0681] The server constructs a virtual model using the generated AI model. This process uses the dataset obtained in step 1 as input. The server trains the AI ​​model using the machine learning framework "TensorFlow" or "PyTorch" to create the virtual model. This model is intended to simulate the operation of a physical system. A detailed virtual model is generated as output.

[0682] Step 3:

[0683] The terminal performs simulations to evaluate the system's operational performance based on a virtual model provided by the server. At this stage, dynamic simulations are performed using MATLAB or Simulink. The system evaluates energy consumption and resource utilization efficiency for the virtual model as input, and derives the optimal operational strategy. The output is a recommended operational strategy.

[0684] Step 4:

[0685] The server receives the operational strategy provided by the terminal and reflects it in the control of the physical system. Here, it applies instructions to the real system and collects the operational results as data again. Again, the server saves this new data to the database, preparing for updating the virtual model in the next step. Operational result data is generated as output.

[0686] Step 5:

[0687] Users utilize the emotion engine to provide feedback to the system based on their emotional state. The system recognizes the user's psychological state in real time using "EmotionAPI" and other means, and this data is reflected in the system's notification settings and operational schedule. The output provides the user with notification frequencies and operational schedules tailored to their needs.

[0688] Step 6:

[0689] The server updates the virtual model based on sentiment data and operational results data. In this step, the newly acquired information is used to retrain the generative AI model. Based on operational and sentiment data as inputs, a virtual model adapted to the latest scenario is deployed, and the updated virtual model is generated as output. By continuously repeating this process, the system can perform sequentially optimized operations.

[0690] (Application Example 2)

[0691] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0692] Existing systems have a problem in that they have difficulty considering user emotions when optimizing the operation of physical systems, resulting in a lack of improvement in the user experience. Furthermore, this may prevent the system's operational efficiency from being sufficiently increased, potentially hindering the effective use of energy resources.

[0693] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0694] In this invention, the server includes means for constructing a virtual model of a physical system using a generative model, means for performing calculations based on the virtual model and acquiring data related to the physical system, and means for recognizing the user's emotional state via an information terminal and dynamically adjusting the environment settings of the physical system based on that emotional state. This makes it possible to improve the user experience and the effective use of energy resources while increasing the operational efficiency of the physical system.

[0695] A "generative model" is an algorithm used to construct a virtual model of a physical system, recreating a virtual environment based on various data.

[0696] A "physical system" is a collection of actual machines and devices through which energy and information flow, and its operation is the subject of optimization.

[0697] A "virtual model" is a model that mimics a physical system and is reproduced on a computer. It is used to explore operational strategies through simulation.

[0698] "Calculation" refers to the process of numerical or logical calculations performed based on a virtual model, and is necessary for formulating operational strategies for a system.

[0699] "Data acquisition" is the process of collecting necessary information from physical systems, and this information is used in formulating operational strategies.

[0700] An "operational strategy" is a plan or policy designed to maximize the utilization efficiency and operational effectiveness of a physical system.

[0701] "Operational results" refer to data on the activities and effects that the physical system actually performed based on the formulated operational strategy.

[0702] "Dynamic adjustment of environment settings" is a process that changes external factors of the physical system in real time based on the user's emotional state.

[0703] "Emotional state" refers to data that indicates the psychological and emotional responses of users, and is used to improve operational efficiency and enhance the user experience.

[0704] To implement this invention, the server first collects various data related to the physical system, and then uses this data to construct a sophisticated virtual model by utilizing a generative model. Based on this virtual model, the server simulates various operational scenarios. This provides information for formulating the optimal operational strategy for the entire system.

[0705] The terminal executes operational strategies formulated through simulations and makes necessary adjustments to improve operational efficiency. In particular, the terminal is equipped with an emotion engine to recognize the user's emotional state in real time, and dynamically changes the environment settings of the physical system based on this information. This optimizes the user experience and provides a comfortable environment.

[0706] Based on the emotional state recognized by the emotion engine and feedback from the system, users can adjust their energy usage and environmental settings in their daily lives. For example, if a user feels stressed, the system can automatically change the music in the store to something calming and adjust the lighting. Through this process, users can enjoy a more comfortable and efficient living environment.

[0707] As a concrete example, cameras and sensors within the store collect facial expression data from customers, and AI analyzes this data to recognize their emotions. Based on this information, smartphones and the store's control system adjust the music and lighting. In this way, the system provides an optimal environment tailored to the customer's emotions.

[0708] An example of a prompt for a generating AI model is the text, "Design an algorithm that determines stress levels in real time from customer facial images and optimizes the in-store environment settings." Based on this prompt, the AI ​​model can generate an algorithm and help optimize the entire system.

[0709] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0710] Step 1:

[0711] The server collects various sensor data from the physical system. This data includes environmental information such as temperature, lighting, and sound. This data is preprocessed and converted into an input dataset for building a virtual model. Specifically, outliers are removed and the data is normalized to prepare it for subsequent processing.

[0712] Step 2:

[0713] The server uses a generative model to construct a virtual model of the physical system from collected sensor data. This virtual model serves as the foundation for simulating the actual physical system. This model enables simulation of various operational scenarios for the system. The generative model analyzes the input data and generates an appropriate virtual environment through computational processing.

[0714] Step 3:

[0715] The device activates an emotion engine to recognize the user's emotional state in real time, taking data on the user's facial expressions and voice acquired from the camera and microphone as input. By analyzing this input data, it identifies the user's emotions and outputs them as an emotional state. This output data plays an important role in the system's environment settings.

[0716] Step 4:

[0717] The device dynamically adjusts the physical system's environmental settings based on the user's emotional state, as determined by the emotion engine. Specifically, it changes settings such as music, lighting, and temperature to suit the user's emotional state. It receives the emotional state and current environmental settings as input, selects the appropriate setting options, and outputs them.

[0718] Step 5:

[0719] Users enjoy a new experience under optimized environment settings. User feedback is re-entered into the system as input, leading to continuous operational improvements. Specifically, feedback on user satisfaction and comfort is collected and used to evaluate operational strategies.

[0720] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0721] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0722] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0723] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0724] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0725] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0726] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0727] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0728] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0729] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0730] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0731] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0732] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0733] 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.

[0734] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0735] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0736] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0737] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0738] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0739] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0740] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0741] The following is further disclosed regarding the embodiments described above.

[0742] (Claim 1)

[0743] A means of constructing a virtual model of a physical system using a generative model,

[0744] A means of performing calculations based on a virtual model and obtaining data about the physical system,

[0745] A means of formulating an operational strategy for the physical system by analyzing the acquired data,

[0746] A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data,

[0747] A means of updating the virtual model using the acquired operational results,

[0748] A system that includes this.

[0749] (Claim 2)

[0750] The system according to claim 1, comprising energy storage means for optimizing the utilization efficiency of energy resources in a physical system.

[0751] (Claim 3)

[0752] The system according to claim 1, which performs calculations to adjust the charging and discharging of stored resources by an energy storage means.

[0753] "Example 1"

[0754] (Claim 1)

[0755] A means for creating a detailed virtual model of a physical mechanism using a generative model,

[0756] A means for performing computer simulations based on a virtual model and collecting information about the physical mechanism,

[0757] A means of analyzing the collected information to formulate an operational strategy for the physical mechanism,

[0758] A means of controlling physical mechanisms based on a devised operational strategy and collecting the control results again as information,

[0759] A means of updating the virtual simulated object using the collected control results and reflecting them in the next simulation,

[0760] A system that includes this.

[0761] (Claim 2)

[0762] The system according to claim 1, comprising an energy storage device for optimizing the utilization efficiency of energy resources in a physical mechanism.

[0763] (Claim 3)

[0764] The system according to claim 1, which performs calculations to optimally control the charging and discharging of resources stored by an energy storage device.

[0765] "Application Example 1"

[0766] (Claim 1)

[0767] A means of constructing a virtual model of a physical system using a generative model,

[0768] A means of performing calculations based on a virtual model and obtaining data about the physical system,

[0769] A means of formulating an operational strategy for the physical system by analyzing the acquired data,

[0770] A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data,

[0771] A means of updating the virtual model using the acquired operational results,

[0772] A means of presenting an operational plan to optimize energy consumption in logistics facilities,

[0773] A system that includes this.

[0774] (Claim 2)

[0775] The system according to claim 1, comprising energy storage means for optimizing the utilization efficiency of energy resources in a physical system.

[0776] (Claim 3)

[0777] The system according to claim 1, which performs calculations to adjust the charging and discharging of stored resources by an energy storage means, and manages energy consumption in the operation of a logistics facility.

[0778] "Example 2 of combining an emotion engine"

[0779] (Claim 1)

[0780] A means of constructing a virtual model of a physical system using a generative model,

[0781] A means of performing calculations based on a virtual model and obtaining data about the physical system,

[0782] A means of formulating an operational strategy for the physical system by analyzing the acquired data,

[0783] A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data,

[0784] A means of updating the virtual model using the acquired operational results,

[0785] A means of recognizing the user's emotional state in real time and adjusting notification settings and operational schedules based on that emotional state,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, comprising energy storage means for optimizing the resource utilization efficiency in a physical system.

[0789] (Claim 3)

[0790] The system according to claim 1, which performs calculations to adjust the charging and discharging of stored resources by an energy storage means.

[0791] "Application example 2 when combining with an emotional engine"

[0792] (Claim 1)

[0793] A means of constructing a virtual model of a physical system using a generative model,

[0794] A means of performing calculations based on a virtual model and obtaining data about the physical system,

[0795] A means of formulating an operational strategy for the physical system by analyzing the acquired data,

[0796] A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data,

[0797] A means of updating the virtual model using the acquired operational results,

[0798] A means of recognizing the user's emotional state using an information terminal and dynamically adjusting the environment settings of the physical system based on that emotional state,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, comprising energy storage means for optimizing the utilization efficiency of energy resources in a physical system.

[0802] (Claim 3)

[0803] The system according to claim 1, which performs calculations to adjust the charging and discharging of stored resources by an energy storage means and adjusts the environment based on the emotional state of the user. [Explanation of symbols]

[0804] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of constructing a virtual model of a physical system using a generative model, A means of performing calculations based on a virtual model and obtaining data about the physical system, A means of formulating an operational strategy for the physical system by analyzing the acquired data, A means of operating a physical system based on a formulated operational strategy and acquiring the operational results again as data, A means of updating the virtual model using the acquired operational results, A system that includes this.

2. The system according to claim 1, comprising energy storage means for optimizing the utilization efficiency of energy resources in a physical system.

3. The system according to claim 1, which performs calculations to adjust the charging and discharging of stored resources by an energy storage means.

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